Apache HBase Logo

Preface

This is the official reference guide for the HBase version it ships with.

Herein you will find either the definitive documentation on an HBase topic as of its standing when the referenced HBase version shipped, or it will point to the location in Javadoc or JIRA where the pertinent information can be found.

About This Guide

This reference guide is a work in progress. The source for this guide can be found in the _src/main/asciidoc directory of the HBase source. This reference guide is marked up using AsciiDoc from which the finished guide is generated as part of the 'site' build target. Run

mvn site

to generate this documentation. Amendments and improvements to the documentation are welcomed. Click this link to file a new documentation bug against Apache HBase with some values pre-selected.

Contributing to the Documentation

For an overview of AsciiDoc and suggestions to get started contributing to the documentation, see the relevant section later in this documentation.

Heads-up if this is your first foray into the world of distributed computing…​

If this is your first foray into the wonderful world of Distributed Computing, then you are in for some interesting times. First off, distributed systems are hard; making a distributed system hum requires a disparate skillset that spans systems (hardware and software) and networking.

Your cluster’s operation can hiccup because of any of a myriad set of reasons from bugs in HBase itself through misconfigurations — misconfiguration of HBase but also operating system misconfigurations — through to hardware problems whether it be a bug in your network card drivers or an underprovisioned RAM bus (to mention two recent examples of hardware issues that manifested as "HBase is slow"). You will also need to do a recalibration if up to this your computing has been bound to a single box. Here is one good starting point: Fallacies of Distributed Computing.

That said, you are welcome.
It’s a fun place to be.
Yours, the HBase Community.

Reporting Bugs

Please use JIRA to report non-security-related bugs.

To protect existing HBase installations from new vulnerabilities, please do not use JIRA to report security-related bugs. Instead, send your report to the mailing list private@apache.org, which allows anyone to send messages, but restricts who can read them. Someone on that list will contact you to follow up on your report.

Support and Testing Expectations

The phrases /supported/, /not supported/, /tested/, and /not tested/ occur several places throughout this guide. In the interest of clarity, here is a brief explanation of what is generally meant by these phrases, in the context of HBase.

Commercial technical support for Apache HBase is provided by many Hadoop vendors. This is not the sense in which the term /support/ is used in the context of the Apache HBase project. The Apache HBase team assumes no responsibility for your HBase clusters, your configuration, or your data.
Supported

In the context of Apache HBase, /supported/ means that HBase is designed to work in the way described, and deviation from the defined behavior or functionality should be reported as a bug.

Not Supported

In the context of Apache HBase, /not supported/ means that a use case or use pattern is not expected to work and should be considered an antipattern. If you think this designation should be reconsidered for a given feature or use pattern, file a JIRA or start a discussion on one of the mailing lists.

Tested

In the context of Apache HBase, /tested/ means that a feature is covered by unit or integration tests, and has been proven to work as expected.

Not Tested

In the context of Apache HBase, /not tested/ means that a feature or use pattern may or may notwork in a given way, and may or may not corrupt your data or cause operational issues. It is an unknown, and there are no guarantees. If you can provide proof that a feature designated as /not tested/ does work in a given way, please submit the tests and/or the metrics so that other users can gain certainty about such features or use patterns.

Getting Started

1. Introduction

Quickstart will get you up and running on a single-node, standalone instance of HBase, followed by a pseudo-distributed single-machine instance, and finally a fully-distributed cluster.

2. Quick Start - Standalone HBase

This guide describes the setup of a standalone HBase instance running against the local filesystem. This is not an appropriate configuration for a production instance of HBase, but will allow you to experiment with HBase. This section shows you how to create a table in HBase using the hbase shell CLI, insert rows into the table, perform put and scan operations against the table, enable or disable the table, and start and stop HBase. Apart from downloading HBase, this procedure should take less than 10 minutes.

Local Filesystem and Durability
The following is fixed in HBase 0.98.3 and beyond. See HBASE-11272 and HBASE-11218.

Using HBase with a local filesystem does not guarantee durability. The HDFS local filesystem implementation will lose edits if files are not properly closed. This is very likely to happen when you are experimenting with new software, starting and stopping the daemons often and not always cleanly. You need to run HBase on HDFS to ensure all writes are preserved. Running against the local filesystem is intended as a shortcut to get you familiar with how the general system works, as the very first phase of evaluation. See HBASE-3696 and its associated issues for more details about the issues of running on the local filesystem.

Loopback IP - HBase 0.94.x and earlier
The below advice is for hbase-0.94.x and older versions only. This is fixed in hbase-0.96.0 and beyond.

Prior to HBase 0.94.x, HBase expected the loopback IP address to be 127.0.0.1. Ubuntu and some other distributions default to 127.0.1.1 and this will cause problems for you. See Why does HBase care about /etc/hosts? for detail

Example 1. Example /etc/hosts File for Ubuntu

The following /etc/hosts file works correctly for HBase 0.94.x and earlier, on Ubuntu. Use this as a template if you run into trouble.

127.0.0.1 localhost
127.0.0.1 ubuntu.ubuntu-domain ubuntu

2.1. JDK Version Requirements

HBase requires that a JDK be installed. See Java for information about supported JDK versions.

2.2. Get Started with HBase

Procedure: Download, Configure, and Start HBase

  1. Choose a download site from this list of Apache Download Mirrors. Click on the suggested top link. This will take you to a mirror of HBase Releases. Click on the folder named stable and then download the binary file that ends in .tar.gz to your local filesystem. Prior to 1.x version, be sure to choose the version that corresponds with the version of Hadoop you are likely to use later (in most cases, you should choose the file for Hadoop 2, which will be called something like hbase-0.98.13-hadoop2-bin.tar.gz). Do not download the file ending in src.tar.gz for now.

  2. Extract the downloaded file, and change to the newly-created directory.

    $ tar xzvf hbase-1.1.7-bin.tar.gz
$ cd hbase-1.1.7/

  3. For HBase 0.98.5 and later, you are required to set the JAVA_HOME environment variable before starting HBase. Prior to 0.98.5, HBase attempted to detect the location of Java if the variables was not set. You can set the variable via your operating system’s usual mechanism, but HBase provides a central mechanism, conf/hbase-env.sh. Edit this file, uncomment the line starting with JAVA_HOME, and set it to the appropriate location for your operating system. The JAVA_HOME variable should be set to a directory which contains the executable file bin/java. Most modern Linux operating systems provide a mechanism, such as /usr/bin/alternatives on RHEL or CentOS, for transparently switching between versions of executables such as Java. In this case, you can set JAVA_HOME to the directory containing the symbolic link to bin/java, which is usually /usr.

    JAVA_HOME=/usr
    These instructions assume that each node of your cluster uses the same configuration. If this is not the case, you may need to set JAVA_HOME separately for each node.

  4. Edit conf/hbase-site.xml, which is the main HBase configuration file. At this time, you only need to specify the directory on the local filesystem where HBase and ZooKeeper write data. By default, a new directory is created under /tmp. Many servers are configured to delete the contents of /tmp upon reboot, so you should store the data elsewhere. The following configuration will store HBase’s data in the hbase directory, in the home directory of the user called testuser. Paste the <property> tags beneath the <configuration> tags, which should be empty in a new HBase install.

    Example 2. Example hbase-site.xml for Standalone HBase
    <configuration>
  <property>
    <name>hbase.rootdir</name>
    <value>file:///home/testuser/hbase</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/home/testuser/zookeeper</value>
  </property>
</configuration>

    You do not need to create the HBase data directory. HBase will do this for you. If you create the directory, HBase will attempt to do a migration, which is not what you want.

  5. The bin/start-hbase.sh script is provided as a convenient way to start HBase. Issue the command, and if all goes well, a message is logged to standard output showing that HBase started successfully. You can use the jps command to verify that you have one running process called HMaster. In standalone mode HBase runs all daemons within this single JVM, i.e. the HMaster, a single HRegionServer, and the ZooKeeper daemon.

    Java needs to be installed and available. If you get an error indicating that Java is not installed, but it is on your system, perhaps in a non-standard location, edit the conf/hbase-env.sh file and modify the JAVA_HOME setting to point to the directory that contains bin/java your system.
Procedure: Use HBase For the First Time

  1. Connect to HBase.

    Connect to your running instance of HBase using the hbase shell command, located in the bin/ directory of your HBase install. In this example, some usage and version information that is printed when you start HBase Shell has been omitted. The HBase Shell prompt ends with a > character.

    $ ./bin/hbase shell
hbase(main):001:0>

  2. Display HBase Shell Help Text.

    Type help and press Enter, to display some basic usage information for HBase Shell, as well as several example commands. Notice that table names, rows, columns all must be enclosed in quote characters.

  3. Create a table.

    Use the create command to create a new table. You must specify the table name and the ColumnFamily name.

    hbase(main):001:0> create 'test', 'cf'
0 row(s) in 0.4170 seconds

=> Hbase::Table - test

  4. List Information About your Table

    Use the list command to

    hbase(main):002:0> list 'test'
TABLE
test
1 row(s) in 0.0180 seconds

=> ["test"]

  5. Put data into your table.

    To put data into your table, use the put command.

    hbase(main):003:0> put 'test', 'row1', 'cf:a', 'value1'
0 row(s) in 0.0850 seconds

hbase(main):004:0> put 'test', 'row2', 'cf:b', 'value2'
0 row(s) in 0.0110 seconds

hbase(main):005:0> put 'test', 'row3', 'cf:c', 'value3'
0 row(s) in 0.0100 seconds

    Here, we insert three values, one at a time. The first insert is at row1, column cf:a, with a value of value1. Columns in HBase are comprised of a column family prefix, cf in this example, followed by a colon and then a column qualifier suffix, a in this case.

  6. Scan the table for all data at once.

    One of the ways to get data from HBase is to scan. Use the scan command to scan the table for data. You can limit your scan, but for now, all data is fetched.

    hbase(main):006:0> scan 'test'
ROW                                      COLUMN+CELL
 row1                                    column=cf:a, timestamp=1421762485768, value=value1
 row2                                    column=cf:b, timestamp=1421762491785, value=value2
 row3                                    column=cf:c, timestamp=1421762496210, value=value3
3 row(s) in 0.0230 seconds

  7. Get a single row of data.

    To get a single row of data at a time, use the get command.

    hbase(main):007:0> get 'test', 'row1'
COLUMN                                   CELL
 cf:a                                    timestamp=1421762485768, value=value1
1 row(s) in 0.0350 seconds

  8. Disable a table.

    If you want to delete a table or change its settings, as well as in some other situations, you need to disable the table first, using the disable command. You can re-enable it using the enable command.

    hbase(main):008:0> disable 'test'
0 row(s) in 1.1820 seconds

hbase(main):009:0> enable 'test'
0 row(s) in 0.1770 seconds

    Disable the table again if you tested the enable command above:

    hbase(main):010:0> disable 'test'
0 row(s) in 1.1820 seconds

  9. Drop the table.

    To drop (delete) a table, use the drop command.

    hbase(main):011:0> drop 'test'
0 row(s) in 0.1370 seconds

  10. Exit the HBase Shell.

    To exit the HBase Shell and disconnect from your cluster, use the quit command. HBase is still running in the background.

Procedure: Stop HBase

  1. In the same way that the bin/start-hbase.sh script is provided to conveniently start all HBase daemons, the bin/stop-hbase.sh script stops them.

    $ ./bin/stop-hbase.sh
stopping hbase....................
$

  2. After issuing the command, it can take several minutes for the processes to shut down. Use the jps to be sure that the HMaster and HRegionServer processes are shut down.

2.3. Intermediate - Pseudo-Distributed Local Install

After working your way through quickstart, you can re-configure HBase to run in pseudo-distributed mode. Pseudo-distributed mode means that HBase still runs completely on a single host, but each HBase daemon (HMaster, HRegionServer, and ZooKeeper) runs as a separate process. By default, unless you configure the hbase.rootdir property as described in quickstart, your data is still stored in /tmp/. In this walk-through, we store your data in HDFS instead, assuming you have HDFS available. You can skip the HDFS configuration to continue storing your data in the local filesystem.

Hadoop Configuration

This procedure assumes that you have configured Hadoop and HDFS on your local system and/or a remote system, and that they are running and available. It also assumes you are using Hadoop 2. The guide on Setting up a Single Node Cluster in the Hadoop documentation is a good starting point.

  1. Stop HBase if it is running.

    If you have just finished quickstart and HBase is still running, stop it. This procedure will create a totally new directory where HBase will store its data, so any databases you created before will be lost.

  2. Configure HBase.

    Edit the hbase-site.xml configuration. First, add the following property. which directs HBase to run in distributed mode, with one JVM instance per daemon.

    <property>
  <name>hbase.cluster.distributed</name>
  <value>true</value>
</property>

    Next, change the hbase.rootdir from the local filesystem to the address of your HDFS instance, using the hdfs://// URI syntax. In this example, HDFS is running on the localhost at port 8020.

    <property>
  <name>hbase.rootdir</name>
  <value>hdfs://localhost:8020/hbase</value>
</property>

    You do not need to create the directory in HDFS. HBase will do this for you. If you create the directory, HBase will attempt to do a migration, which is not what you want.

  3. Start HBase.

    Use the bin/start-hbase.sh command to start HBase. If your system is configured correctly, the jps command should show the HMaster and HRegionServer processes running.

  4. Check the HBase directory in HDFS.

    If everything worked correctly, HBase created its directory in HDFS. In the configuration above, it is stored in /hbase/ on HDFS. You can use the hadoop fs command in Hadoop’s bin/ directory to list this directory.

    $ ./bin/hadoop fs -ls /hbase
Found 7 items
drwxr-xr-x   - hbase users          0 2014-06-25 18:58 /hbase/.tmp
drwxr-xr-x   - hbase users          0 2014-06-25 21:49 /hbase/WALs
drwxr-xr-x   - hbase users          0 2014-06-25 18:48 /hbase/corrupt
drwxr-xr-x   - hbase users          0 2014-06-25 18:58 /hbase/data
-rw-r--r--   3 hbase users         42 2014-06-25 18:41 /hbase/hbase.id
-rw-r--r--   3 hbase users          7 2014-06-25 18:41 /hbase/hbase.version
drwxr-xr-x   - hbase users          0 2014-06-25 21:49 /hbase/oldWALs

  5. Create a table and populate it with data.

    You can use the HBase Shell to create a table, populate it with data, scan and get values from it, using the same procedure as in shell exercises.

  6. Start and stop a backup HBase Master (HMaster) server.

    Running multiple HMaster instances on the same hardware does not make sense in a production environment, in the same way that running a pseudo-distributed cluster does not make sense for production. This step is offered for testing and learning purposes only.

    The HMaster server controls the HBase cluster. You can start up to 9 backup HMaster servers, which makes 10 total HMasters, counting the primary. To start a backup HMaster, use the local-master-backup.sh. For each backup master you want to start, add a parameter representing the port offset for that master. Each HMaster uses three ports (16010, 16020, and 16030 by default). The port offset is added to these ports, so using an offset of 2, the backup HMaster would use ports 16012, 16022, and 16032. The following command starts 3 backup servers using ports 16012/16022/16032, 16013/16023/16033, and 16015/16025/16035.

    $ ./bin/local-master-backup.sh 2 3 5

    To kill a backup master without killing the entire cluster, you need to find its process ID (PID). The PID is stored in a file with a name like /tmp/hbase-USER-X-master.pid. The only contents of the file is the PID. You can use the kill -9 command to kill that PID. The following command will kill the master with port offset 1, but leave the cluster running:

    $ cat /tmp/hbase-testuser-1-master.pid |xargs kill -9

  7. Start and stop additional RegionServers

    The HRegionServer manages the data in its StoreFiles as directed by the HMaster. Generally, one HRegionServer runs per node in the cluster. Running multiple HRegionServers on the same system can be useful for testing in pseudo-distributed mode. The local-regionservers.sh command allows you to run multiple RegionServers. It works in a similar way to the local-master-backup.sh command, in that each parameter you provide represents the port offset for an instance. Each RegionServer requires two ports, and the default ports are 16020 and 16030. However, the base ports for additional RegionServers are not the default ports since the default ports are used by the HMaster, which is also a RegionServer since HBase version 1.0.0. The base ports are 16200 and 16300 instead. You can run 99 additional RegionServers that are not a HMaster or backup HMaster, on a server. The following command starts four additional RegionServers, running on sequential ports starting at 16202/16302 (base ports 16200/16300 plus 2).

    $ .bin/local-regionservers.sh start 2 3 4 5

    To stop a RegionServer manually, use the local-regionservers.sh command with the stop parameter and the offset of the server to stop.

    $ .bin/local-regionservers.sh stop 3

  8. Stop HBase.

    You can stop HBase the same way as in the quickstart procedure, using the bin/stop-hbase.sh command.

2.4. Advanced - Fully Distributed

In reality, you need a fully-distributed configuration to fully test HBase and to use it in real-world scenarios. In a distributed configuration, the cluster contains multiple nodes, each of which runs one or more HBase daemon. These include primary and backup Master instances, multiple ZooKeeper nodes, and multiple RegionServer nodes.

This advanced quickstart adds two more nodes to your cluster. The architecture will be as follows:

Table 1. Distributed Cluster Demo Architecture
Node Name Master ZooKeeper RegionServer

node-a.example.com

yes

yes

no

node-b.example.com

backup

yes

yes

node-c.example.com

no

yes

yes

This quickstart assumes that each node is a virtual machine and that they are all on the same network. It builds upon the previous quickstart, Intermediate - Pseudo-Distributed Local Install, assuming that the system you configured in that procedure is now node-a. Stop HBase on node-a before continuing.

Be sure that all the nodes have full access to communicate, and that no firewall rules are in place which could prevent them from talking to each other. If you see any errors like no route to host, check your firewall.
Procedure: Configure Passwordless SSH Access

node-a needs to be able to log into node-b and node-c (and to itself) in order to start the daemons. The easiest way to accomplish this is to use the same username on all hosts, and configure password-less SSH login from node-a to each of the others.

  1. On node-a, generate a key pair.

    While logged in as the user who will run HBase, generate a SSH key pair, using the following command:

    $ ssh-keygen -t rsa

    If the command succeeds, the location of the key pair is printed to standard output. The default name of the public key is id_rsa.pub.

  2. Create the directory that will hold the shared keys on the other nodes.

    On node-b and node-c, log in as the HBase user and create a .ssh/ directory in the user’s home directory, if it does not already exist. If it already exists, be aware that it may already contain other keys.

  3. Copy the public key to the other nodes.

    Securely copy the public key from node-a to each of the nodes, by using the scp or some other secure means. On each of the other nodes, create a new file called .ssh/authorized_keys if it does not already exist, and append the contents of the id_rsa.pub file to the end of it. Note that you also need to do this for node-a itself.

    $ cat id_rsa.pub >> ~/.ssh/authorized_keys

  4. Test password-less login.

    If you performed the procedure correctly, if you SSH from node-a to either of the other nodes, using the same username, you should not be prompted for a password.

  5. Since node-b will run a backup Master, repeat the procedure above, substituting node-b everywhere you see node-a. Be sure not to overwrite your existing .ssh/authorized_keys files, but concatenate the new key onto the existing file using the >> operator rather than the > operator.

Procedure: Prepare node-a

node-a will run your primary master and ZooKeeper processes, but no RegionServers. . Stop the RegionServer from starting on node-a.

  1. Edit conf/regionservers and remove the line which contains localhost. Add lines with the hostnames or IP addresses for node-b and node-c.

    Even if you did want to run a RegionServer on node-a, you should refer to it by the hostname the other servers would use to communicate with it. In this case, that would be node-a.example.com. This enables you to distribute the configuration to each node of your cluster any hostname conflicts. Save the file.

  2. Configure HBase to use node-b as a backup master.

    Create a new file in conf/ called backup-masters, and add a new line to it with the hostname for node-b. In this demonstration, the hostname is node-b.example.com.

  3. Configure ZooKeeper

    In reality, you should carefully consider your ZooKeeper configuration. You can find out more about configuring ZooKeeper in zookeeper. This configuration will direct HBase to start and manage a ZooKeeper instance on each node of the cluster.

    On node-a, edit conf/hbase-site.xml and add the following properties.

    <property>
  <name>hbase.zookeeper.quorum</name>
  <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
</property>
<property>
  <name>hbase.zookeeper.property.dataDir</name>
  <value>/usr/local/zookeeper</value>
</property>

  4. Everywhere in your configuration that you have referred to node-a as localhost, change the reference to point to the hostname that the other nodes will use to refer to node-a. In these examples, the hostname is node-a.example.com.

Procedure: Prepare node-b and node-c

node-b will run a backup master server and a ZooKeeper instance.

  1. Download and unpack HBase.

    Download and unpack HBase to node-b, just as you did for the standalone and pseudo-distributed quickstarts.

  2. Copy the configuration files from node-a to node-b.and node-c.

    Each node of your cluster needs to have the same configuration information. Copy the contents of the conf/ directory to the conf/ directory on node-b and node-c.

Procedure: Start and Test Your Cluster

  1. Be sure HBase is not running on any node.

    If you forgot to stop HBase from previous testing, you will have errors. Check to see whether HBase is running on any of your nodes by using the jps command. Look for the processes HMaster, HRegionServer, and HQuorumPeer. If they exist, kill them.

  2. Start the cluster.

    On node-a, issue the start-hbase.sh command. Your output will be similar to that below.

    $ bin/start-hbase.sh
node-c.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-c.example.com.out
node-a.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-a.example.com.out
node-b.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-b.example.com.out
starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-node-a.example.com.out
node-c.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-c.example.com.out
node-b.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-b.example.com.out
node-b.example.com: starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-nodeb.example.com.out

    ZooKeeper starts first, followed by the master, then the RegionServers, and finally the backup masters.

  3. Verify that the processes are running.

    On each node of the cluster, run the jps command and verify that the correct processes are running on each server. You may see additional Java processes running on your servers as well, if they are used for other purposes.

    Example 3. node-a jps Output
    $ jps
20355 Jps
20071 HQuorumPeer
20137 HMaster
    Example 4. node-b jps Output
    $ jps
15930 HRegionServer
16194 Jps
15838 HQuorumPeer
16010 HMaster
    Example 5. node-a jps Output
    $ jps
13901 Jps
13639 HQuorumPeer
13737 HRegionServer
    ZooKeeper Process Name

    The HQuorumPeer process is a ZooKeeper instance which is controlled and started by HBase. If you use ZooKeeper this way, it is limited to one instance per cluster node, , and is appropriate for testing only. If ZooKeeper is run outside of HBase, the process is called QuorumPeer. For more about ZooKeeper configuration, including using an external ZooKeeper instance with HBase, see zookeeper.

  4. Browse to the Web UI.

    Web UI Port Changes
    Web UI Port Changes

    In HBase newer than 0.98.x, the HTTP ports used by the HBase Web UI changed from 60010 for the Master and 60030 for each RegionServer to 16010 for the Master and 16030 for the RegionServer.

    If everything is set up correctly, you should be able to connect to the UI for the Master http://node-a.example.com:16010/ or the secondary master at http://node-b.example.com:16010/ for the secondary master, using a web browser. If you can connect via localhost but not from another host, check your firewall rules. You can see the web UI for each of the RegionServers at port 16030 of their IP addresses, or by clicking their links in the web UI for the Master.

  5. Test what happens when nodes or services disappear.

    With a three-node cluster like you have configured, things will not be very resilient. Still, you can test what happens when the primary Master or a RegionServer disappears, by killing the processes and watching the logs.

2.5. Where to go next

The next chapter, configuration, gives more information about the different HBase run modes, system requirements for running HBase, and critical configuration areas for setting up a distributed HBase cluster.

Apache HBase Configuration

This chapter expands upon the Getting Started chapter to further explain configuration of Apache HBase. Please read this chapter carefully, especially the Basic Prerequisites to ensure that your HBase testing and deployment goes smoothly, and prevent data loss. Familiarize yourself with Support and Testing Expectations as well.

3. Configuration Files

Apache HBase uses the same configuration system as Apache Hadoop. All configuration files are located in the conf/ directory, which needs to be kept in sync for each node on your cluster.

HBase Configuration File Descriptions
backup-masters

Not present by default. A plain-text file which lists hosts on which the Master should start a backup Master process, one host per line.

hadoop-metrics2-hbase.properties

Used to connect HBase Hadoop’s Metrics2 framework. See the Hadoop Wiki entry for more information on Metrics2. Contains only commented-out examples by default.

hbase-env.cmd and hbase-env.sh

Script for Windows and Linux / Unix environments to set up the working environment for HBase, including the location of Java, Java options, and other environment variables. The file contains many commented-out examples to provide guidance.

hbase-policy.xml

The default policy configuration file used by RPC servers to make authorization decisions on client requests. Only used if HBase security is enabled.

hbase-site.xml

The main HBase configuration file. This file specifies configuration options which override HBase’s default configuration. You can view (but do not edit) the default configuration file at docs/hbase-default.xml. You can also view the entire effective configuration for your cluster (defaults and overrides) in the HBase Configuration tab of the HBase Web UI.

log4j.properties

Configuration file for HBase logging via log4j.

regionservers

A plain-text file containing a list of hosts which should run a RegionServer in your HBase cluster. By default this file contains the single entry localhost. It should contain a list of hostnames or IP addresses, one per line, and should only contain localhost if each node in your cluster will run a RegionServer on its localhost interface.

Checking XML Validity

When you edit XML, it is a good idea to use an XML-aware editor to be sure that your syntax is correct and your XML is well-formed. You can also use the xmllint utility to check that your XML is well-formed. By default, xmllint re-flows and prints the XML to standard output. To check for well-formedness and only print output if errors exist, use the command xmllint -noout filename.xml.
Keep Configuration In Sync Across the Cluster

When running in distributed mode, after you make an edit to an HBase configuration, make sure you copy the content of the conf/ directory to all nodes of the cluster. HBase will not do this for you. Use rsync, scp, or another secure mechanism for copying the configuration files to your nodes. For most configuration, a restart is needed for servers to pick up changes An exception is dynamic configuration. to be described later below.

4. Basic Prerequisites

This section lists required services and some required system configuration.

Table 2. Java
HBase Version JDK 6 JDK 7 JDK 8

2.0

Not Supported

Not Supported

yes

1.3

Not Supported

yes

yes

1.2

Not Supported

yes

yes

1.1

Not Supported

yes

Running with JDK 8 will work but is not well tested.

1.0

Not Supported

yes

Running with JDK 8 will work but is not well tested.

0.98

yes

yes

Running with JDK 8 works but is not well tested. Building with JDK 8 would require removal of the deprecated remove() method of the PoolMap class and is under consideration. See HBASE-7608 for more information about JDK 8 support.

0.94

yes

yes

N/A
In HBase 0.98.5 and newer, you must set JAVA_HOME on each node of your cluster. hbase-env.sh provides a handy mechanism to do this.
Operating System Utilities
ssh

HBase uses the Secure Shell (ssh) command and utilities extensively to communicate between cluster nodes. Each server in the cluster must be running ssh so that the Hadoop and HBase daemons can be managed. You must be able to connect to all nodes via SSH, including the local node, from the Master as well as any backup Master, using a shared key rather than a password. You can see the basic methodology for such a set-up in Linux or Unix systems at "Procedure: Configure Passwordless SSH Access". If your cluster nodes use OS X, see the section, SSH: Setting up Remote Desktop and Enabling Self-Login on the Hadoop wiki.

DNS

HBase uses the local hostname to self-report its IP address. Both forward and reverse DNS resolving must work in versions of HBase previous to 0.92.0. The hadoop-dns-checker tool can be used to verify DNS is working correctly on the cluster. The project README file provides detailed instructions on usage.

Loopback IP

Prior to hbase-0.96.0, HBase only used the IP address 127.0.0.1 to refer to localhost, and this could not be configured. See Loopback IP for more details.

NTP

The clocks on cluster nodes should be synchronized. A small amount of variation is acceptable, but larger amounts of skew can cause erratic and unexpected behavior. Time synchronization is one of the first things to check if you see unexplained problems in your cluster. It is recommended that you run a Network Time Protocol (NTP) service, or another time-synchronization mechanism, on your cluster, and that all nodes look to the same service for time synchronization. See the Basic NTP Configuration at The Linux Documentation Project (TLDP) to set up NTP.

Limits on Number of Files and Processes (ulimit)

Apache HBase is a database. It requires the ability to open a large number of files at once. Many Linux distributions limit the number of files a single user is allowed to open to 1024 (or 256 on older versions of OS X). You can check this limit on your servers by running the command ulimit -n when logged in as the user which runs HBase. See the Troubleshooting section for some of the problems you may experience if the limit is too low. You may also notice errors such as the following:

2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException
2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901

It is recommended to raise the ulimit to at least 10,000, but more likely 10,240, because the value is usually expressed in multiples of 1024. Each ColumnFamily has at least one StoreFile, and possibly more than six StoreFiles if the region is under load. The number of open files required depends upon the number of ColumnFamilies and the number of regions. The following is a rough formula for calculating the potential number of open files on a RegionServer.

Calculate the Potential Number of Open Files
(StoreFiles per ColumnFamily) x (regions per RegionServer)

For example, assuming that a schema had 3 ColumnFamilies per region with an average of 3 StoreFiles per ColumnFamily, and there are 100 regions per RegionServer, the JVM will open 3 * 3 * 100 = 900 file descriptors, not counting open JAR files, configuration files, and others. Opening a file does not take many resources, and the risk of allowing a user to open too many files is minimal.

Another related setting is the number of processes a user is allowed to run at once. In Linux and Unix, the number of processes is set using the ulimit -u command. This should not be confused with the nproc command, which controls the number of CPUs available to a given user. Under load, a ulimit -u that is too low can cause OutOfMemoryError exceptions. See Jack Levin’s major HDFS issues thread on the hbase-users mailing list, from 2011.

Configuring the maximum number of file descriptors and processes for the user who is running the HBase process is an operating system configuration, rather than an HBase configuration. It is also important to be sure that the settings are changed for the user that actually runs HBase. To see which user started HBase, and that user’s ulimit configuration, look at the first line of the HBase log for that instance. A useful read setting config on your hadoop cluster is Aaron Kimball’s Configuration Parameters: What can you just ignore?

Example 6. ulimit Settings on Ubuntu

To configure ulimit settings on Ubuntu, edit /etc/security/limits.conf, which is a space-delimited file with four columns. Refer to the man page for limits.conf for details about the format of this file. In the following example, the first line sets both soft and hard limits for the number of open files (nofile) to 32768 for the operating system user with the username hadoop. The second line sets the number of processes to 32000 for the same user.

hadoop  -       nofile  32768
hadoop  -       nproc   32000

The settings are only applied if the Pluggable Authentication Module (PAM) environment is directed to use them. To configure PAM to use these limits, be sure that the /etc/pam.d/common-session file contains the following line:

session required  pam_limits.so
Linux Shell

All of the shell scripts that come with HBase rely on the GNU Bash shell.

Windows

Prior to HBase 0.96, testing for running HBase on Microsoft Windows was limited. Running a on Windows nodes is not recommended for production systems.

4.1. Hadoop

The following table summarizes the versions of Hadoop supported with each version of HBase. Based on the version of HBase, you should select the most appropriate version of Hadoop. You can use Apache Hadoop, or a vendor’s distribution of Hadoop. No distinction is made here. See the Hadoop wiki for information about vendors of Hadoop.

Hadoop 2.x is recommended.

Hadoop 2.x is faster and includes features, such as short-circuit reads, which will help improve your HBase random read profile. Hadoop 2.x also includes important bug fixes that will improve your overall HBase experience. HBase 0.98 drops support for Hadoop 1.0, deprecates use of Hadoop 1.1+, and HBase 1.0 will not support Hadoop 1.x.

Use the following legend to interpret this table:

Hadoop version support matrix

  • "S" = supported

  • "X" = not supported

  • "NT" = Not tested

HBase-0.94.x HBase-0.98.x (Support for Hadoop 1.1+ is deprecated.) HBase-1.0.x (Hadoop 1.x is NOT supported) HBase-1.1.x HBase-1.2.x HBase-1.3.x

Hadoop-1.0.x

X

X

X

X

X

X

Hadoop-1.1.x

S

NT

X

X

X

X

Hadoop-0.23.x

S

X

X

X

X

X

Hadoop-2.0.x-alpha

NT

X

X

X

X

X

Hadoop-2.1.0-beta

NT

X

X

X

X

X

Hadoop-2.2.0

NT

S

NT

NT

X

X

Hadoop-2.3.x

NT

S

NT

NT

X

X

Hadoop-2.4.x

NT

S

S

S

S

S

Hadoop-2.5.x

NT

S

S

S

S

S

Hadoop-2.6.0

X

X

X

X

X

X

Hadoop-2.6.1+

NT

NT

NT

NT

S

S

Hadoop-2.7.0

X

X

X

X

X

X

Hadoop-2.7.1+

NT

NT

NT

NT

S

S
Hadoop 2.6.x

Hadoop distributions based on the 2.6.x line must have HADOOP-11710 applied if you plan to run HBase on top of an HDFS Encryption Zone. Failure to do so will result in cluster failure and data loss. This patch is present in Apache Hadoop releases 2.6.1+.
Hadoop 2.7.x

Hadoop version 2.7.0 is not tested or supported as the Hadoop PMC has explicitly labeled that release as not being stable.
Replace the Hadoop Bundled With HBase!

Because HBase depends on Hadoop, it bundles an instance of the Hadoop jar under its lib directory. The bundled jar is ONLY for use in standalone mode. In distributed mode, it is critical that the version of Hadoop that is out on your cluster match what is under HBase. Replace the hadoop jar found in the HBase lib directory with the hadoop jar you are running on your cluster to avoid version mismatch issues. Make sure you replace the jar in HBase everywhere on your cluster. Hadoop version mismatch issues have various manifestations but often all looks like its hung up.

4.1.1. Apache HBase 0.94 with Hadoop 2

To get 0.94.x to run on Hadoop 2.2.0, you need to change the hadoop 2 and protobuf versions in the pom.xml: Here is a diff with pom.xml changes:

$ svn diff pom.xml
Index: pom.xml
===================================================================
--- pom.xml     (revision 1545157)
+++ pom.xml     (working copy)
@@ -1034,7 +1034,7 @@
     <slf4j.version>1.4.3</slf4j.version>
     <log4j.version>1.2.16</log4j.version>
     <mockito-all.version>1.8.5</mockito-all.version>
-    <protobuf.version>2.4.0a</protobuf.version>
+    <protobuf.version>2.5.0</protobuf.version>
     <stax-api.version>1.0.1</stax-api.version>
     <thrift.version>0.8.0</thrift.version>
     <zookeeper.version>3.4.5</zookeeper.version>
@@ -2241,7 +2241,7 @@
         </property>
       </activation>
       <properties>
-        <hadoop.version>2.0.0-alpha</hadoop.version>
+        <hadoop.version>2.2.0</hadoop.version>
         <slf4j.version>1.6.1</slf4j.version>
       </properties>
       <dependencies>

The next step is to regenerate Protobuf files and assuming that the Protobuf has been installed:

  • Go to the HBase root folder, using the command line;

  • Type the following commands:

    $ protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/hbase.proto
    $ protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/ErrorHandling.proto

Building against the hadoop 2 profile by running something like the following command:

$  mvn clean install assembly:single -Dhadoop.profile=2.0 -DskipTests

4.1.2. Apache HBase 0.92 and 0.94

HBase 0.92 and 0.94 versions can work with Hadoop versions, 0.20.205, 0.22.x, 1.0.x, and 1.1.x. HBase-0.94 can additionally work with Hadoop-0.23.x and 2.x, but you may have to recompile the code using the specific maven profile (see top level pom.xml)

4.1.3. Apache HBase 0.96

As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required. Hadoop 2 is strongly encouraged (faster but also has fixes that help MTTR). We will no longer run properly on older Hadoops such as 0.20.205 or branch-0.20-append. Do not move to Apache HBase 0.96.x if you cannot upgrade your Hadoop. See HBase, mail # dev - DISCUSS: Have hbase require at least hadoop 1.0.0 in hbase 0.96.0?

4.1.4. Hadoop versions 0.20.x - 1.x

DO NOT use Hadoop versions older than 2.2.0 for HBase versions greater than 1.0. Check release documentation if you are using an older version of HBase for Hadoop related information.

4.1.5. Apache HBase on Secure Hadoop

Apache HBase will run on any Hadoop 0.20.x that incorporates Hadoop security features as long as you do as suggested above and replace the Hadoop jar that ships with HBase with the secure version. If you want to read more about how to setup Secure HBase, see hbase.secure.configuration.

4.1.6. dfs.datanode.max.transfer.threads

An HDFS DataNode has an upper bound on the number of files that it will serve at any one time. Before doing any loading, make sure you have configured Hadoop’s conf/hdfs-site.xml, setting the dfs.datanode.max.transfer.threads value to at least the following:

<property>
  <name>dfs.datanode.max.transfer.threads</name>
  <value>4096</value>
</property>

Be sure to restart your HDFS after making the above configuration.

Not having this configuration in place makes for strange-looking failures. One manifestation is a complaint about missing blocks. For example:

10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block
          blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No live nodes
          contain current block. Will get new block locations from namenode and retry...

See also casestudies.max.transfer.threads and note that this property was previously known as dfs.datanode.max.xcievers (e.g. Hadoop HDFS: Deceived by Xciever).

4.2. ZooKeeper Requirements

ZooKeeper 3.4.x is required as of HBase 1.0.0. HBase makes use of the multi functionality that is only available since Zookeeper 3.4.0. The hbase.zookeeper.useMulti configuration property defaults to true in HBase 1.0.0. Refer to HBASE-12241 (The crash of regionServer when taking deadserver’s replication queue breaks replication) and HBASE-6775 (Use ZK.multi when available for HBASE-6710 0.92/0.94 compatibility fix) for background. The property is deprecated and useMulti is always enabled in HBase 2.0.

5. HBase run modes: Standalone and Distributed

HBase has two run modes: standalone and distributed. Out of the box, HBase runs in standalone mode. Whatever your mode, you will need to configure HBase by editing files in the HBase conf directory. At a minimum, you must edit conf/hbase-env.sh to tell HBase which java to use. In this file you set HBase environment variables such as the heapsize and other options for the JVM, the preferred location for log files, etc. Set JAVA_HOME to point at the root of your java install.

5.1. Standalone HBase

This is the default mode. Standalone mode is what is described in the quickstart section. In standalone mode, HBase does not use HDFS — it uses the local filesystem instead — and it runs all HBase daemons and a local ZooKeeper all up in the same JVM. ZooKeeper binds to a well known port so clients may talk to HBase.

5.2. Distributed

Distributed mode can be subdivided into distributed but all daemons run on a single node — a.k.a. pseudo-distributed — and fully-distributed where the daemons are spread across all nodes in the cluster. The pseudo-distributed vs. fully-distributed nomenclature comes from Hadoop.

Pseudo-distributed mode can run against the local filesystem or it can run against an instance of the Hadoop Distributed File System (HDFS). Fully-distributed mode can ONLY run on HDFS. See the Hadoop documentation for how to set up HDFS. A good walk-through for setting up HDFS on Hadoop 2 can be found at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide.

5.2.1. Pseudo-distributed

Pseudo-Distributed Quickstart

A quickstart has been added to the quickstart chapter. See quickstart-pseudo. Some of the information that was originally in this section has been moved there.

A pseudo-distributed mode is simply a fully-distributed mode run on a single host. Use this configuration testing and prototyping on HBase. Do not use this configuration for production nor for evaluating HBase performance.

5.3. Fully-distributed

By default, HBase runs in standalone mode. Both standalone mode and pseudo-distributed mode are provided for the purposes of small-scale testing. For a production environment, distributed mode is appropriate. In distributed mode, multiple instances of HBase daemons run on multiple servers in the cluster.

Just as in pseudo-distributed mode, a fully distributed configuration requires that you set the hbase-cluster.distributed property to true. Typically, the hbase.rootdir is configured to point to a highly-available HDFS filesystem.

In addition, the cluster is configured so that multiple cluster nodes enlist as RegionServers, ZooKeeper QuorumPeers, and backup HMaster servers. These configuration basics are all demonstrated in quickstart-fully-distributed.

Distributed RegionServers

Typically, your cluster will contain multiple RegionServers all running on different servers, as well as primary and backup Master and ZooKeeper daemons. The conf/regionservers file on the master server contains a list of hosts whose RegionServers are associated with this cluster. Each host is on a separate line. All hosts listed in this file will have their RegionServer processes started and stopped when the master server starts or stops.

ZooKeeper and HBase

See the ZooKeeper section for ZooKeeper setup instructions for HBase.

Example 7. Example Distributed HBase Cluster

This is a bare-bones conf/hbase-site.xml for a distributed HBase cluster. A cluster that is used for real-world work would contain more custom configuration parameters. Most HBase configuration directives have default values, which are used unless the value is overridden in the hbase-site.xml. See "Configuration Files" for more information.

<configuration>
  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://namenode.example.org:8020/hbase</value>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
  </property>
</configuration>

This is an example conf/regionservers file, which contains a list of nodes that should run a RegionServer in the cluster. These nodes need HBase installed and they need to use the same contents of the conf/ directory as the Master server

node-a.example.com
node-b.example.com
node-c.example.com

This is an example conf/backup-masters file, which contains a list of each node that should run a backup Master instance. The backup Master instances will sit idle unless the main Master becomes unavailable.

node-b.example.com
node-c.example.com
Distributed HBase Quickstart

See quickstart-fully-distributed for a walk-through of a simple three-node cluster configuration with multiple ZooKeeper, backup HMaster, and RegionServer instances.

Procedure: HDFS Client Configuration

  1. Of note, if you have made HDFS client configuration changes on your Hadoop cluster, such as configuration directives for HDFS clients, as opposed to server-side configurations, you must use one of the following methods to enable HBase to see and use these configuration changes:

    1. Add a pointer to your HADOOP_CONF_DIR to the HBASE_CLASSPATH environment variable in hbase-env.sh.

    2. Add a copy of hdfs-site.xml (or hadoop-site.xml) or, better, symlinks, under ${HBASE_HOME}/conf, or

    3. if only a small set of HDFS client configurations, add them to hbase-site.xml.

An example of such an HDFS client configuration is dfs.replication. If for example, you want to run with a replication factor of 5, HBase will create files with the default of 3 unless you do the above to make the configuration available to HBase.

6. Running and Confirming Your Installation

Make sure HDFS is running first. Start and stop the Hadoop HDFS daemons by running bin/start-hdfs.sh over in the HADOOP_HOME directory. You can ensure it started properly by testing the put and get of files into the Hadoop filesystem. HBase does not normally use the MapReduce or YARN daemons. These do not need to be started.

If you are managing your own ZooKeeper, start it and confirm it’s running, else HBase will start up ZooKeeper for you as part of its start process.

Start HBase with the following command:

bin/start-hbase.sh

Run the above from the HBASE_HOME directory.

You should now have a running HBase instance. HBase logs can be found in the logs subdirectory. Check them out especially if HBase had trouble starting.

HBase also puts up a UI listing vital attributes. By default it’s deployed on the Master host at port 16010 (HBase RegionServers listen on port 16020 by default and put up an informational HTTP server at port 16030). If the Master is running on a host named master.example.org on the default port, point your browser at http://master.example.org:16010 to see the web interface.

Prior to HBase 0.98 the master UI was deployed on port 60010, and the HBase RegionServers UI on port 60030.

Once HBase has started, see the shell exercises section for how to create tables, add data, scan your insertions, and finally disable and drop your tables.

To stop HBase after exiting the HBase shell enter

$ ./bin/stop-hbase.sh
stopping hbase...............

Shutdown can take a moment to complete. It can take longer if your cluster is comprised of many machines. If you are running a distributed operation, be sure to wait until HBase has shut down completely before stopping the Hadoop daemons.

7. Default Configuration

7.1. hbase-site.xml and hbase-default.xml

Just as in Hadoop where you add site-specific HDFS configuration to the hdfs-site.xml file, for HBase, site specific customizations go into the file conf/hbase-site.xml. For the list of configurable properties, see hbase default configurations below or view the raw hbase-default.xml source file in the HBase source code at src/main/resources.

Not all configuration options make it out to hbase-default.xml. Configuration that it is thought rare anyone would change can exist only in code; the only way to turn up such configurations is via a reading of the source code itself.

Currently, changes here will require a cluster restart for HBase to notice the change.

7.2. HBase Default Configuration

The documentation below is generated using the default hbase configuration file, hbase-default.xml, as source.

hbase.tmp.dir
Description

Temporary directory on the local filesystem. Change this setting to point to a location more permanent than '/tmp', the usual resolve for java.io.tmpdir, as the '/tmp' directory is cleared on machine restart.

Default

${java.io.tmpdir}/hbase-${user.name}

hbase.rootdir
Description

The directory shared by region servers and into which HBase persists. The URL should be 'fully-qualified' to include the filesystem scheme. For example, to specify the HDFS directory '/hbase' where the HDFS instance’s namenode is running at namenode.example.org on port 9000, set this value to: hdfs://namenode.example.org:9000/hbase. By default, we write to whatever ${hbase.tmp.dir} is set too — usually /tmp — so change this configuration or else all data will be lost on machine restart.

Default

${hbase.tmp.dir}/hbase

hbase.fs.tmp.dir
Description

A staging directory in default file system (HDFS) for keeping temporary data.

Default

/user/${user.name}/hbase-staging

hbase.bulkload.staging.dir
Description

A staging directory in default file system (HDFS) for bulk loading.

Default

${hbase.fs.tmp.dir}

hbase.cluster.distributed
Description

The mode the cluster will be in. Possible values are false for standalone mode and true for distributed mode. If false, startup will run all HBase and ZooKeeper daemons together in the one JVM.

Default

false

hbase.zookeeper.quorum
Description

Comma separated list of servers in the ZooKeeper ensemble (This config. should have been named hbase.zookeeper.ensemble). For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com". By default this is set to localhost for local and pseudo-distributed modes of operation. For a fully-distributed setup, this should be set to a full list of ZooKeeper ensemble servers. If HBASE_MANAGES_ZK is set in hbase-env.sh this is the list of servers which hbase will start/stop ZooKeeper on as part of cluster start/stop. Client-side, we will take this list of ensemble members and put it together with the hbase.zookeeper.clientPort config. and pass it into zookeeper constructor as the connectString parameter.

Default

localhost

hbase.local.dir
Description

Directory on the local filesystem to be used as a local storage.

Default

${hbase.tmp.dir}/local/

hbase.master.port
Description

The port the HBase Master should bind to.

Default

16000

hbase.master.info.port
Description

The port for the HBase Master web UI. Set to -1 if you do not want a UI instance run.

Default

16010

hbase.master.info.bindAddress
Description

The bind address for the HBase Master web UI

Default

0.0.0.0

hbase.master.logcleaner.plugins
Description

A comma-separated list of BaseLogCleanerDelegate invoked by the LogsCleaner service. These WAL cleaners are called in order, so put the cleaner that prunes the most files in front. To implement your own BaseLogCleanerDelegate, just put it in HBase’s classpath and add the fully qualified class name here. Always add the above default log cleaners in the list.

Default

org.apache.hadoop.hbase.master.cleaner.TimeToLiveLogCleaner

hbase.master.logcleaner.ttl
Description

Maximum time a WAL can stay in the .oldlogdir directory, after which it will be cleaned by a Master thread.

Default

600000

hbase.master.hfilecleaner.plugins
Description

A comma-separated list of BaseHFileCleanerDelegate invoked by the HFileCleaner service. These HFiles cleaners are called in order, so put the cleaner that prunes the most files in front. To implement your own BaseHFileCleanerDelegate, just put it in HBase’s classpath and add the fully qualified class name here. Always add the above default log cleaners in the list as they will be overwritten in hbase-site.xml.

Default

org.apache.hadoop.hbase.master.cleaner.TimeToLiveHFileCleaner

hbase.master.catalog.timeout
Description

Timeout value for the Catalog Janitor from the master to META.

Default

600000

hbase.master.infoserver.redirect
Description

Whether or not the Master listens to the Master web UI port (hbase.master.info.port) and redirects requests to the web UI server shared by the Master and RegionServer.

Default

true

hbase.regionserver.port
Description

The port the HBase RegionServer binds to.

Default

16020

hbase.regionserver.info.port
Description

The port for the HBase RegionServer web UI Set to -1 if you do not want the RegionServer UI to run.

Default

16030

hbase.regionserver.info.bindAddress
Description

The address for the HBase RegionServer web UI

Default

0.0.0.0

hbase.regionserver.info.port.auto
Description

Whether or not the Master or RegionServer UI should search for a port to bind to. Enables automatic port search if hbase.regionserver.info.port is already in use. Useful for testing, turned off by default.

Default

false

hbase.regionserver.handler.count
Description

Count of RPC Listener instances spun up on RegionServers. Same property is used by the Master for count of master handlers.

Default

30

hbase.ipc.server.callqueue.handler.factor
Description

Factor to determine the number of call queues. A value of 0 means a single queue shared between all the handlers. A value of 1 means that each handler has its own queue.

Default

0.1

hbase.ipc.server.callqueue.read.ratio
Description

Split the call queues into read and write queues. The specified interval (which should be between 0.0 and 1.0) will be multiplied by the number of call queues. A value of 0 indicate to not split the call queues, meaning that both read and write requests will be pushed to the same set of queues. A value lower than 0.5 means that there will be less read queues than write queues. A value of 0.5 means there will be the same number of read and write queues. A value greater than 0.5 means that there will be more read queues than write queues. A value of 1.0 means that all the queues except one are used to dispatch read requests. Example: Given the total number of call queues being 10 a read.ratio of 0 means that: the 10 queues will contain both read/write requests. a read.ratio of 0.3 means that: 3 queues will contain only read requests and 7 queues will contain only write requests. a read.ratio of 0.5 means that: 5 queues will contain only read requests and 5 queues will contain only write requests. a read.ratio of 0.8 means that: 8 queues will contain only read requests and 2 queues will contain only write requests. a read.ratio of 1 means that: 9 queues will contain only read requests and 1 queues will contain only write requests.

Default

0

hbase.ipc.server.callqueue.scan.ratio
Description

Given the number of read call queues, calculated from the total number of call queues multiplied by the callqueue.read.ratio, the scan.ratio property will split the read call queues into small-read and long-read queues. A value lower than 0.5 means that there will be less long-read queues than short-read queues. A value of 0.5 means that there will be the same number of short-read and long-read queues. A value greater than 0.5 means that there will be more long-read queues than short-read queues A value of 0 or 1 indicate to use the same set of queues for gets and scans. Example: Given the total number of read call queues being 8 a scan.ratio of 0 or 1 means that: 8 queues will contain both long and short read requests. a scan.ratio of 0.3 means that: 2 queues will contain only long-read requests and 6 queues will contain only short-read requests. a scan.ratio of 0.5 means that: 4 queues will contain only long-read requests and 4 queues will contain only short-read requests. a scan.ratio of 0.8 means that: 6 queues will contain only long-read requests and 2 queues will contain only short-read requests.

Default

0

hbase.regionserver.msginterval
Description

Interval between messages from the RegionServer to Master in milliseconds.

Default

3000

hbase.regionserver.logroll.period
Description

Period at which we will roll the commit log regardless of how many edits it has.

Default

3600000

hbase.regionserver.logroll.errors.tolerated
Description

The number of consecutive WAL close errors we will allow before triggering a server abort. A setting of 0 will cause the region server to abort if closing the current WAL writer fails during log rolling. Even a small value (2 or 3) will allow a region server to ride over transient HDFS errors.

Default

2

hbase.regionserver.hlog.reader.impl
Description

The WAL file reader implementation.

Default

org.apache.hadoop.hbase.regionserver.wal.ProtobufLogReader

hbase.regionserver.hlog.writer.impl
Description

The WAL file writer implementation.

Default

org.apache.hadoop.hbase.regionserver.wal.ProtobufLogWriter

hbase.master.distributed.log.replay
Description

Enable 'distributed log replay' as default engine splitting WAL files on server crash. Turn this to true to try out this experimental feature, replacing the old mode 'distributed log splitter'. 'Distributed log replay' improves MTTR because it does not write intermediate files. 'DLR' required that 'hfile.format.version' be set to version 3 or higher.

Default

false

hbase.regionserver.global.memstore.size
Description

Maximum size of all memstores in a region server before new updates are blocked and flushes are forced. Defaults to 40% of heap (0.4). Updates are blocked and flushes are forced until size of all memstores in a region server hits hbase.regionserver.global.memstore.size.lower.limit. The default value in this configuration has been intentionally left emtpy in order to honor the old hbase.regionserver.global.memstore.upperLimit property if present.

Default

none

hbase.regionserver.global.memstore.size.lower.limit
Description

Maximum size of all memstores in a region server before flushes are forced. Defaults to 95% of hbase.regionserver.global.memstore.size (0.95). A 100% value for this value causes the minimum possible flushing to occur when updates are blocked due to memstore limiting. The default value in this configuration has been intentionally left emtpy in order to honor the old hbase.regionserver.global.memstore.lowerLimit property if present.

Default

none

hbase.regionserver.optionalcacheflushinterval
Description

Maximum amount of time an edit lives in memory before being automatically flushed. Default 1 hour. Set it to 0 to disable automatic flushing.

Default

3600000

hbase.regionserver.catalog.timeout
Description

Timeout value for the Catalog Janitor from the regionserver to META.

Default

600000

hbase.regionserver.dns.interface
Description

The name of the Network Interface from which a region server should report its IP address.

Default

default

hbase.regionserver.dns.nameserver
Description

The host name or IP address of the name server (DNS) which a region server should use to determine the host name used by the master for communication and display purposes.

Default

default

hbase.regionserver.region.split.policy
Description

A split policy determines when a region should be split. The various other split policies that are available currently are ConstantSizeRegionSplitPolicy, DisabledRegionSplitPolicy, DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy etc.

Default

org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy

hbase.regionserver.regionSplitLimit
Description

Limit for the number of regions after which no more region splitting should take place. This is not hard limit for the number of regions but acts as a guideline for the regionserver to stop splitting after a certain limit. Default is set to 1000.

Default

1000

zookeeper.session.timeout
Description

ZooKeeper session timeout in milliseconds. It is used in two different ways. First, this value is used in the ZK client that HBase uses to connect to the ensemble. It is also used by HBase when it starts a ZK server and it is passed as the 'maxSessionTimeout'. See http://hadoop.apache.org/zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions. For example, if a HBase region server connects to a ZK ensemble that’s also managed by HBase, then the session timeout will be the one specified by this configuration. But, a region server that connects to an ensemble managed with a different configuration will be subjected that ensemble’s maxSessionTimeout. So, even though HBase might propose using 90 seconds, the ensemble can have a max timeout lower than this and it will take precedence. The current default that ZK ships with is 40 seconds, which is lower than HBase’s.

Default

90000

zookeeper.znode.parent
Description

Root ZNode for HBase in ZooKeeper. All of HBase’s ZooKeeper files that are configured with a relative path will go under this node. By default, all of HBase’s ZooKeeper file path are configured with a relative path, so they will all go under this directory unless changed.

Default

/hbase

zookeeper.znode.rootserver
Description

Path to ZNode holding root region location. This is written by the master and read by clients and region servers. If a relative path is given, the parent folder will be ${zookeeper.znode.parent}. By default, this means the root location is stored at /hbase/root-region-server.

Default

root-region-server

zookeeper.znode.acl.parent
Description

Root ZNode for access control lists.

Default

acl

hbase.zookeeper.dns.interface
Description

The name of the Network Interface from which a ZooKeeper server should report its IP address.

Default

default

hbase.zookeeper.dns.nameserver
Description

The host name or IP address of the name server (DNS) which a ZooKeeper server should use to determine the host name used by the master for communication and display purposes.

Default

default

hbase.zookeeper.peerport
Description

Port used by ZooKeeper peers to talk to each other. See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

2888

hbase.zookeeper.leaderport
Description

Port used by ZooKeeper for leader election. See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

3888

hbase.zookeeper.useMulti
Description

Instructs HBase to make use of ZooKeeper’s multi-update functionality. This allows certain ZooKeeper operations to complete more quickly and prevents some issues with rare Replication failure scenarios (see the release note of HBASE-2611 for an example). IMPORTANT: only set this to true if all ZooKeeper servers in the cluster are on version 3.4+ and will not be downgraded. ZooKeeper versions before 3.4 do not support multi-update and will not fail gracefully if multi-update is invoked (see ZOOKEEPER-1495).

Default

true

hbase.config.read.zookeeper.config
Description

Set to true to allow HBaseConfiguration to read the zoo.cfg file for ZooKeeper properties. Switching this to true is not recommended, since the functionality of reading ZK properties from a zoo.cfg file has been deprecated.

Default

false

hbase.zookeeper.property.initLimit
Description

Property from ZooKeeper’s config zoo.cfg. The number of ticks that the initial synchronization phase can take.

Default

10

hbase.zookeeper.property.syncLimit
Description

Property from ZooKeeper’s config zoo.cfg. The number of ticks that can pass between sending a request and getting an acknowledgment.

Default

5

hbase.zookeeper.property.dataDir
Description

Property from ZooKeeper’s config zoo.cfg. The directory where the snapshot is stored.

Default

${hbase.tmp.dir}/zookeeper

hbase.zookeeper.property.clientPort
Description

Property from ZooKeeper’s config zoo.cfg. The port at which the clients will connect.

Default

2181

hbase.zookeeper.property.maxClientCnxns
Description

Property from ZooKeeper’s config zoo.cfg. Limit on number of concurrent connections (at the socket level) that a single client, identified by IP address, may make to a single member of the ZooKeeper ensemble. Set high to avoid zk connection issues running standalone and pseudo-distributed.

Default

300

hbase.client.write.buffer
Description

Default size of the HTable client write buffer in bytes. A bigger buffer takes more memory — on both the client and server side since server instantiates the passed write buffer to process it — but a larger buffer size reduces the number of RPCs made. For an estimate of server-side memory-used, evaluate hbase.client.write.buffer * hbase.regionserver.handler.count

Default

2097152

hbase.client.pause
Description

General client pause value. Used mostly as value to wait before running a retry of a failed get, region lookup, etc. See hbase.client.retries.number for description of how we backoff from this initial pause amount and how this pause works w/ retries.

Default

100

hbase.client.retries.number
Description

Maximum retries. Used as maximum for all retryable operations such as the getting of a cell’s value, starting a row update, etc. Retry interval is a rough function based on hbase.client.pause. At first we retry at this interval but then with backoff, we pretty quickly reach retrying every ten seconds. See HConstants#RETRY_BACKOFF for how the backup ramps up. Change this setting and hbase.client.pause to suit your workload.

Default

35

hbase.client.max.total.tasks
Description

The maximum number of concurrent tasks a single HTable instance will send to the cluster.

Default

100

hbase.client.max.perserver.tasks
Description

The maximum number of concurrent tasks a single HTable instance will send to a single region server.

Default

5

hbase.client.max.perregion.tasks
Description

The maximum number of concurrent connections the client will maintain to a single Region. That is, if there is already hbase.client.max.perregion.tasks writes in progress for this region, new puts won’t be sent to this region until some writes finishes.

Default

1

hbase.client.scanner.caching
Description

Number of rows that we try to fetch when calling next on a scanner if it is not served from (local, client) memory. This configuration works together with hbase.client.scanner.max.result.size to try and use the network efficiently. The default value is Integer.MAX_VALUE by default so that the network will fill the chunk size defined by hbase.client.scanner.max.result.size rather than be limited by a particular number of rows since the size of rows varies table to table. If you know ahead of time that you will not require more than a certain number of rows from a scan, this configuration should be set to that row limit via Scan#setCaching. Higher caching values will enable faster scanners but will eat up more memory and some calls of next may take longer and longer times when the cache is empty. Do not set this value such that the time between invocations is greater than the scanner timeout; i.e. hbase.client.scanner.timeout.period

Default

2147483647

hbase.client.keyvalue.maxsize
Description

Specifies the combined maximum allowed size of a KeyValue instance. This is to set an upper boundary for a single entry saved in a storage file. Since they cannot be split it helps avoiding that a region cannot be split any further because the data is too large. It seems wise to set this to a fraction of the maximum region size. Setting it to zero or less disables the check.

Default

10485760

hbase.client.scanner.timeout.period
Description

Client scanner lease period in milliseconds.

Default

60000

hbase.client.localityCheck.threadPoolSize
Default

2

hbase.bulkload.retries.number
Description

Maximum retries. This is maximum number of iterations to atomic bulk loads are attempted in the face of splitting operations 0 means never give up.

Default

10

hbase.balancer.period
Description

Period at which the region balancer runs in the Master.

Default

300000

hbase.regions.slop
Description

Rebalance if any regionserver has average + (average * slop) regions.

Default

0.2

hbase.server.thread.wakefrequency
Description

Time to sleep in between searches for work (in milliseconds). Used as sleep interval by service threads such as log roller.

Default

10000

hbase.server.versionfile.writeattempts
Description

How many time to retry attempting to write a version file before just aborting. Each attempt is seperated by the hbase.server.thread.wakefrequency milliseconds.

Default

3

hbase.hregion.memstore.flush.size
Description

Memstore will be flushed to disk if size of the memstore exceeds this number of bytes. Value is checked by a thread that runs every hbase.server.thread.wakefrequency.

Default

134217728

hbase.hregion.percolumnfamilyflush.size.lower.bound
Description

If FlushLargeStoresPolicy is used, then every time that we hit the total memstore limit, we find out all the column families whose memstores exceed this value, and only flush them, while retaining the others whose memstores are lower than this limit. If none of the families have their memstore size more than this, all the memstores will be flushed (just as usual). This value should be less than half of the total memstore threshold (hbase.hregion.memstore.flush.size).

Default

16777216

hbase.hregion.preclose.flush.size
Description

If the memstores in a region are this size or larger when we go to close, run a "pre-flush" to clear out memstores before we put up the region closed flag and take the region offline. On close, a flush is run under the close flag to empty memory. During this time the region is offline and we are not taking on any writes. If the memstore content is large, this flush could take a long time to complete. The preflush is meant to clean out the bulk of the memstore before putting up the close flag and taking the region offline so the flush that runs under the close flag has little to do.

Default

5242880

hbase.hregion.memstore.block.multiplier
Description

Block updates if memstore has hbase.hregion.memstore.block.multiplier times hbase.hregion.memstore.flush.size bytes. Useful preventing runaway memstore during spikes in update traffic. Without an upper-bound, memstore fills such that when it flushes the resultant flush files take a long time to compact or split, or worse, we OOME.

Default

4

hbase.hregion.memstore.mslab.enabled
Description

Enables the MemStore-Local Allocation Buffer, a feature which works to prevent heap fragmentation under heavy write loads. This can reduce the frequency of stop-the-world GC pauses on large heaps.

Default

true

hbase.hregion.max.filesize
Description

Maximum HStoreFile size. If any one of a column families' HStoreFiles has grown to exceed this value, the hosting HRegion is split in two.

Default

10737418240

hbase.hregion.majorcompaction
Description

The time (in miliseconds) between 'major' compactions of all HStoreFiles in a region. Default: Set to 7 days. Major compactions tend to happen exactly when you need them least so enable them such that they run at off-peak for your deploy; or, since this setting is on a periodicity that is unlikely to match your loading, run the compactions via an external invocation out of a cron job or some such.

Default

604800000

hbase.hregion.majorcompaction.jitter
Description

Jitter outer bound for major compactions. On each regionserver, we multiply the hbase.region.majorcompaction interval by some random fraction that is inside the bounds of this maximum. We then add this + or - product to when the next major compaction is to run. The idea is that major compaction does happen on every regionserver at exactly the same time. The smaller this number, the closer the compactions come together.

Default

0.50

hbase.hstore.compactionThreshold
Description

If more than this number of HStoreFiles in any one HStore (one HStoreFile is written per flush of memstore) then a compaction is run to rewrite all HStoreFiles files as one. Larger numbers put off compaction but when it runs, it takes longer to complete.

Default

3

hbase.hstore.flusher.count
Description

The number of flush threads. With less threads, the memstore flushes will be queued. With more threads, the flush will be executed in parallel, increasing the hdfs load. This can lead as well to more compactions.

Default

2

hbase.hstore.blockingStoreFiles
Description

If more than this number of StoreFiles in any one Store (one StoreFile is written per flush of MemStore) then updates are blocked for this HRegion until a compaction is completed, or until hbase.hstore.blockingWaitTime has been exceeded.

Default

10

hbase.hstore.blockingWaitTime
Description

The time an HRegion will block updates for after hitting the StoreFile limit defined by hbase.hstore.blockingStoreFiles. After this time has elapsed, the HRegion will stop blocking updates even if a compaction has not been completed.

Default

90000

hbase.hstore.compaction.max
Description

Max number of HStoreFiles to compact per 'minor' compaction.

Default

10

hbase.hstore.compaction.kv.max
Description

How many KeyValues to read and then write in a batch when flushing or compacting. Do less if big KeyValues and problems with OOME. Do more if wide, small rows.

Default

10

hbase.hstore.time.to.purge.deletes
Description

The amount of time to delay purging of delete markers with future timestamps. If unset, or set to 0, all delete markers, including those with future timestamps, are purged during the next major compaction. Otherwise, a delete marker is kept until the major compaction which occurs after the marker’s timestamp plus the value of this setting, in milliseconds.

Default

0

hbase.storescanner.parallel.seek.enable
Description

Enables StoreFileScanner parallel-seeking in StoreScanner, a feature which can reduce response latency under special conditions.

Default

false

hbase.storescanner.parallel.seek.threads
Description

The default thread pool size if parallel-seeking feature enabled.

Default

10

hfile.block.cache.size
Description

Percentage of maximum heap (-Xmx setting) to allocate to block cache used by HFile/StoreFile. Default of 0.4 means allocate 40%. Set to 0 to disable but it’s not recommended; you need at least enough cache to hold the storefile indices.

Default

0.4

hfile.block.index.cacheonwrite
Description

This allows to put non-root multi-level index blocks into the block cache at the time the index is being written.

Default

false

hfile.index.block.max.size
Description

When the size of a leaf-level, intermediate-level, or root-level index block in a multi-level block index grows to this size, the block is written out and a new block is started.

Default

131072

hbase.bucketcache.ioengine
Description

Where to store the contents of the bucketcache. One of: heap, offheap, or file. If a file, set it to file:PATH_TO_FILE. See http://hbase.apache.org/book.html#offheap.blockcache for more information.

Default

none

hbase.bucketcache.combinedcache.enabled
Description

Whether or not the bucketcache is used in league with the LRU on-heap block cache. In this mode, indices and blooms are kept in the LRU blockcache and the data blocks are kept in the bucketcache.

Default

true

hbase.bucketcache.size
Description

A float that EITHER represents a percentage of total heap memory size to give to the cache (if < 1.0) OR, it is the total capacity in megabytes of BucketCache. Default: 0.0

Default

none

hbase.bucketcache.sizes
Description

A comma-separated list of sizes for buckets for the bucketcache. Can be multiple sizes. List block sizes in order from smallest to largest. The sizes you use will depend on your data access patterns. Must be a multiple of 1024 else you will run into 'java.io.IOException: Invalid HFile block magic' when you go to read from cache. If you specify no values here, then you pick up the default bucketsizes set in code (See BucketAllocator#DEFAULT_BUCKET_SIZES).

Default

none

hfile.format.version
Description

The HFile format version to use for new files. Version 3 adds support for tags in hfiles (See http://hbase.apache.org/book.html#hbase.tags). Distributed Log Replay requires that tags are enabled. Also see the configuration 'hbase.replication.rpc.codec'.

Default

3

hfile.block.bloom.cacheonwrite
Description

Enables cache-on-write for inline blocks of a compound Bloom filter.

Default

false

io.storefile.bloom.block.size
Description

The size in bytes of a single block ("chunk") of a compound Bloom filter. This size is approximate, because Bloom blocks can only be inserted at data block boundaries, and the number of keys per data block varies.

Default

131072

hbase.rs.cacheblocksonwrite
Description

Whether an HFile block should be added to the block cache when the block is finished.

Default

false

hbase.rpc.timeout
Description

This is for the RPC layer to define how long (millisecond) HBase client applications take for a remote call to time out. It uses pings to check connections but will eventually throw a TimeoutException.

Default

60000

hbase.client.operation.timeout
Description

Operation timeout is a top-level restriction (millisecond) that makes sure a blocking operation in Table will not be blocked more than this. In each operation, if rpc request fails because of timeout or other reason, it will retry until success or throw RetriesExhaustedException. But if the total time being blocking reach the operation timeout before retries exhausted, it will break early and throw SocketTimeoutException.

Default

1200000

hbase.cells.scanned.per.heartbeat.check
Description

The number of cells scanned in between heartbeat checks. Heartbeat checks occur during the processing of scans to determine whether or not the server should stop scanning in order to send back a heartbeat message to the client. Heartbeat messages are used to keep the client-server connection alive during long running scans. Small values mean that the heartbeat checks will occur more often and thus will provide a tighter bound on the execution time of the scan. Larger values mean that the heartbeat checks occur less frequently

Default

10000

hbase.rpc.shortoperation.timeout
Description

This is another version of "hbase.rpc.timeout". For those RPC operation within cluster, we rely on this configuration to set a short timeout limitation for short operation. For example, short rpc timeout for region server’s trying to report to active master can benefit quicker master failover process.

Default

10000

hbase.ipc.client.tcpnodelay
Description

Set no delay on rpc socket connections. See http://docs.oracle.com/javase/1.5.0/docs/api/java/net/Socket.html#getTcpNoDelay()

Default

true

hbase.regionserver.hostname
Description

This config is for experts: don’t set its value unless you really know what you are doing. When set to a non-empty value, this represents the (external facing) hostname for the underlying server. See https://issues.apache.org/jira/browse/HBASE-12954 for details.

Default

none

hbase.master.keytab.file
Description

Full path to the kerberos keytab file to use for logging in the configured HMaster server principal.

Default

none

hbase.master.kerberos.principal
Description

Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name that should be used to run the HMaster process. The principal name should be in the form: user/hostname@DOMAIN. If "_HOST" is used as the hostname portion, it will be replaced with the actual hostname of the running instance.

Default

none

hbase.regionserver.keytab.file
Description

Full path to the kerberos keytab file to use for logging in the configured HRegionServer server principal.

Default

none

hbase.regionserver.kerberos.principal
Description

Ex. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name that should be used to run the HRegionServer process. The principal name should be in the form: user/hostname@DOMAIN. If "_HOST" is used as the hostname portion, it will be replaced with the actual hostname of the running instance. An entry for this principal must exist in the file specified in hbase.regionserver.keytab.file

Default

none

hadoop.policy.file
Description

The policy configuration file used by RPC servers to make authorization decisions on client requests. Only used when HBase security is enabled.

Default

hbase-policy.xml

hbase.superuser
Description

List of users or groups (comma-separated), who are allowed full privileges, regardless of stored ACLs, across the cluster. Only used when HBase security is enabled.

Default

none

hbase.auth.key.update.interval
Description

The update interval for master key for authentication tokens in servers in milliseconds. Only used when HBase security is enabled.

Default

86400000

hbase.auth.token.max.lifetime
Description

The maximum lifetime in milliseconds after which an authentication token expires. Only used when HBase security is enabled.

Default

604800000

hbase.ipc.client.fallback-to-simple-auth-allowed
Description

When a client is configured to attempt a secure connection, but attempts to connect to an insecure server, that server may instruct the client to switch to SASL SIMPLE (unsecure) authentication. This setting controls whether or not the client will accept this instruction from the server. When false (the default), the client will not allow the fallback to SIMPLE authentication, and will abort the connection.

Default

false

hbase.coprocessor.enabled
Description

Enables or disables coprocessor loading. If 'false' (disabled), any other coprocessor related configuration will be ignored.

Default

true

hbase.coprocessor.user.enabled
Description

Enables or disables user (aka. table) coprocessor loading. If 'false' (disabled), any table coprocessor attributes in table descriptors will be ignored. If "hbase.coprocessor.enabled" is 'false' this setting has no effect.

Default

true

hbase.coprocessor.region.classes
Description

A comma-separated list of Coprocessors that are loaded by default on all tables. For any override coprocessor method, these classes will be called in order. After implementing your own Coprocessor, just put it in HBase’s classpath and add the fully qualified class name here. A coprocessor can also be loaded on demand by setting HTableDescriptor.

Default

none

hbase.rest.port
Description

The port for the HBase REST server.

Default

8080

hbase.rest.readonly
Description

Defines the mode the REST server will be started in. Possible values are: false: All HTTP methods are permitted - GET/PUT/POST/DELETE. true: Only the GET method is permitted.

Default

false

hbase.rest.threads.max
Description

The maximum number of threads of the REST server thread pool. Threads in the pool are reused to process REST requests. This controls the maximum number of requests processed concurrently. It may help to control the memory used by the REST server to avoid OOM issues. If the thread pool is full, incoming requests will be queued up and wait for some free threads.

Default

100

hbase.rest.threads.min
Description

The minimum number of threads of the REST server thread pool. The thread pool always has at least these number of threads so the REST server is ready to serve incoming requests.

Default

2

hbase.rest.support.proxyuser
Description

Enables running the REST server to support proxy-user mode.

Default

false

hbase.defaults.for.version.skip
Description

Set to true to skip the 'hbase.defaults.for.version' check. Setting this to true can be useful in contexts other than the other side of a maven generation; i.e. running in an ide. You’ll want to set this boolean to true to avoid seeing the RuntimException complaint: "hbase-default.xml file seems to be for and old version of HBase (\${hbase.version}), this version is X.X.X-SNAPSHOT"

Default

false

hbase.coprocessor.master.classes
Description

A comma-separated list of org.apache.hadoop.hbase.coprocessor.MasterObserver coprocessors that are loaded by default on the active HMaster process. For any implemented coprocessor methods, the listed classes will be called in order. After implementing your own MasterObserver, just put it in HBase’s classpath and add the fully qualified class name here.

Default

none

hbase.coprocessor.abortonerror
Description

Set to true to cause the hosting server (master or regionserver) to abort if a coprocessor fails to load, fails to initialize, or throws an unexpected Throwable object. Setting this to false will allow the server to continue execution but the system wide state of the coprocessor in question will become inconsistent as it will be properly executing in only a subset of servers, so this is most useful for debugging only.

Default

true

hbase.online.schema.update.enable
Description

Set true to enable online schema changes.

Default

true

hbase.table.lock.enable
Description

Set to true to enable locking the table in zookeeper for schema change operations. Table locking from master prevents concurrent schema modifications to corrupt table state.

Default

true

hbase.table.max.rowsize
Description

Maximum size of single row in bytes (default is 1 Gb) for Get’ting or Scan’ning without in-row scan flag set. If row size exceeds this limit RowTooBigException is thrown to client.

Default

1073741824

hbase.thrift.minWorkerThreads
Description

The "core size" of the thread pool. New threads are created on every connection until this many threads are created.

Default

16

hbase.thrift.maxWorkerThreads
Description

The maximum size of the thread pool. When the pending request queue overflows, new threads are created until their number reaches this number. After that, the server starts dropping connections.

Default

1000

hbase.thrift.maxQueuedRequests
Description

The maximum number of pending Thrift connections waiting in the queue. If there are no idle threads in the pool, the server queues requests. Only when the queue overflows, new threads are added, up to hbase.thrift.maxQueuedRequests threads.

Default

1000

hbase.thrift.htablepool.size.max
Description

The upper bound for the table pool used in the Thrift gateways server. Since this is per table name, we assume a single table and so with 1000 default worker threads max this is set to a matching number. For other workloads this number can be adjusted as needed.

Default

1000

hbase.regionserver.thrift.framed
Description

Use Thrift TFramedTransport on the server side. This is the recommended transport for thrift servers and requires a similar setting on the client side. Changing this to false will select the default transport, vulnerable to DoS when malformed requests are issued due to THRIFT-601.

Default

false

hbase.regionserver.thrift.framed.max_frame_size_in_mb
Description

Default frame size when using framed transport

Default

2

hbase.regionserver.thrift.compact
Description

Use Thrift TCompactProtocol binary serialization protocol.

Default

false

hbase.rootdir.perms
Description

FS Permissions for the root directory in a secure(kerberos) setup. When master starts, it creates the rootdir with this permissions or sets the permissions if it does not match.

Default

700

hbase.data.umask.enable
Description

Enable, if true, that file permissions should be assigned to the files written by the regionserver

Default

false

hbase.data.umask
Description

File permissions that should be used to write data files when hbase.data.umask.enable is true

Default

000

hbase.metrics.showTableName
Description

Whether to include the prefix "tbl.tablename" in per-column family metrics. If true, for each metric M, per-cf metrics will be reported for tbl.T.cf.CF.M, if false, per-cf metrics will be aggregated by column-family across tables, and reported for cf.CF.M. In both cases, the aggregated metric M across tables and cfs will be reported.

Default

true

hbase.metrics.exposeOperationTimes
Description

Whether to report metrics about time taken performing an operation on the region server. Get, Put, Delete, Increment, and Append can all have their times exposed through Hadoop metrics per CF and per region.

Default

true

hbase.snapshot.enabled
Description

Set to true to allow snapshots to be taken / restored / cloned.

Default

true

hbase.snapshot.restore.take.failsafe.snapshot
Description

Set to true to take a snapshot before the restore operation. The snapshot taken will be used in case of failure, to restore the previous state. At the end of the restore operation this snapshot will be deleted

Default

true

hbase.snapshot.restore.failsafe.name
Description

Name of the failsafe snapshot taken by the restore operation. You can use the {snapshot.name}, {table.name} and {restore.timestamp} variables to create a name based on what you are restoring.

Default

hbase-failsafe-{snapshot.name}-{restore.timestamp}

hbase.server.compactchecker.interval.multiplier
Description

The number that determines how often we scan to see if compaction is necessary. Normally, compactions are done after some events (such as memstore flush), but if region didn’t receive a lot of writes for some time, or due to different compaction policies, it may be necessary to check it periodically. The interval between checks is hbase.server.compactchecker.interval.multiplier multiplied by hbase.server.thread.wakefrequency.

Default

1000

hbase.lease.recovery.timeout
Description

How long we wait on dfs lease recovery in total before giving up.

Default

900000

hbase.lease.recovery.dfs.timeout
Description

How long between dfs recover lease invocations. Should be larger than the sum of the time it takes for the namenode to issue a block recovery command as part of datanode; dfs.heartbeat.interval and the time it takes for the primary datanode, performing block recovery to timeout on a dead datanode; usually dfs.client.socket-timeout. See the end of HBASE-8389 for more.

Default

64000

hbase.column.max.version
Description

New column family descriptors will use this value as the default number of versions to keep.

Default

1

hbase.dfs.client.read.shortcircuit.buffer.size
Description

If the DFSClient configuration dfs.client.read.shortcircuit.buffer.size is unset, we will use what is configured here as the short circuit read default direct byte buffer size. DFSClient native default is 1MB; HBase keeps its HDFS files open so number of file blocks * 1MB soon starts to add up and threaten OOME because of a shortage of direct memory. So, we set it down from the default. Make it > the default hbase block size set in the HColumnDescriptor which is usually 64k.

Default

131072

hbase.regionserver.checksum.verify
Description

If set to true (the default), HBase verifies the checksums for hfile blocks. HBase writes checksums inline with the data when it writes out hfiles. HDFS (as of this writing) writes checksums to a separate file than the data file necessitating extra seeks. Setting this flag saves some on i/o. Checksum verification by HDFS will be internally disabled on hfile streams when this flag is set. If the hbase-checksum verification fails, we will switch back to using HDFS checksums (so do not disable HDFS checksums! And besides this feature applies to hfiles only, not to WALs). If this parameter is set to false, then hbase will not verify any checksums, instead it will depend on checksum verification being done in the HDFS client.

Default

true

hbase.hstore.bytes.per.checksum
Description

Number of bytes in a newly created checksum chunk for HBase-level checksums in hfile blocks.

Default

16384

hbase.hstore.checksum.algorithm
Description

Name of an algorithm that is used to compute checksums. Possible values are NULL, CRC32, CRC32C.

Default

CRC32

hbase.client.scanner.max.result.size
Description

Maximum number of bytes returned when calling a scanner’s next method. Note that when a single row is larger than this limit the row is still returned completely. The default value is 2MB, which is good for 1ge networks. With faster and/or high latency networks this value should be increased.

Default

2097152

hbase.server.scanner.max.result.size
Description

Maximum number of bytes returned when calling a scanner’s next method. Note that when a single row is larger than this limit the row is still returned completely. The default value is 100MB. This is a safety setting to protect the server from OOM situations.

Default

104857600

hbase.status.published
Description

This setting activates the publication by the master of the status of the region server. When a region server dies and its recovery starts, the master will push this information to the client application, to let them cut the connection immediately instead of waiting for a timeout.

Default

false

hbase.status.publisher.class
Description

Implementation of the status publication with a multicast message.

Default

org.apache.hadoop.hbase.master.ClusterStatusPublisher$MulticastPublisher

hbase.status.listener.class
Description

Implementation of the status listener with a multicast message.

Default

org.apache.hadoop.hbase.client.ClusterStatusListener$MulticastListener

hbase.status.multicast.address.ip
Description

Multicast address to use for the status publication by multicast.

Default

226.1.1.3

hbase.status.multicast.address.port
Description

Multicast port to use for the status publication by multicast.

Default

16100

hbase.dynamic.jars.dir
Description

The directory from which the custom filter/co-processor jars can be loaded dynamically by the region server without the need to restart. However, an already loaded filter/co-processor class would not be un-loaded. See HBASE-1936 for more details.

Default

${hbase.rootdir}/lib

hbase.security.authentication
Description

Controls whether or not secure authentication is enabled for HBase. Possible values are 'simple' (no authentication), and 'kerberos'.

Default

simple

hbase.rest.filter.classes
Description

Servlet filters for REST service.

Default

org.apache.hadoop.hbase.rest.filter.GzipFilter

hbase.master.loadbalancer.class
Description

Class used to execute the regions balancing when the period occurs. See the class comment for more on how it works http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/balancer/StochasticLoadBalancer.html It replaces the DefaultLoadBalancer as the default (since renamed as the SimpleLoadBalancer).

Default

org.apache.hadoop.hbase.master.balancer.StochasticLoadBalancer

hbase.security.exec.permission.checks
Description

If this setting is enabled and ACL based access control is active (the AccessController coprocessor is installed either as a system coprocessor or on a table as a table coprocessor) then you must grant all relevant users EXEC privilege if they require the ability to execute coprocessor endpoint calls. EXEC privilege, like any other permission, can be granted globally to a user, or to a user on a per table or per namespace basis. For more information on coprocessor endpoints, see the coprocessor section of the HBase online manual. For more information on granting or revoking permissions using the AccessController, see the security section of the HBase online manual.

Default

false

hbase.procedure.regionserver.classes
Description

A comma-separated list of org.apache.hadoop.hbase.procedure.RegionServerProcedureManager procedure managers that are loaded by default on the active HRegionServer process. The lifecycle methods (init/start/stop) will be called by the active HRegionServer process to perform the specific globally barriered procedure. After implementing your own RegionServerProcedureManager, just put it in HBase’s classpath and add the fully qualified class name here.

Default

none

hbase.procedure.master.classes
Description

A comma-separated list of org.apache.hadoop.hbase.procedure.MasterProcedureManager procedure managers that are loaded by default on the active HMaster process. A procedure is identified by its signature and users can use the signature and an instant name to trigger an execution of a globally barriered procedure. After implementing your own MasterProcedureManager, just put it in HBase’s classpath and add the fully qualified class name here.

Default

none

hbase.coordinated.state.manager.class
Description

Fully qualified name of class implementing coordinated state manager.

Default

org.apache.hadoop.hbase.coordination.ZkCoordinatedStateManager

hbase.regionserver.storefile.refresh.period
Description

The period (in milliseconds) for refreshing the store files for the secondary regions. 0 means this feature is disabled. Secondary regions sees new files (from flushes and compactions) from primary once the secondary region refreshes the list of files in the region (there is no notification mechanism). But too frequent refreshes might cause extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger value is also recommended with this setting.

Default

0

hbase.region.replica.replication.enabled
Description

Whether asynchronous WAL replication to the secondary region replicas is enabled or not. If this is enabled, a replication peer named "region_replica_replication" will be created which will tail the logs and replicate the mutatations to region replicas for tables that have region replication > 1. If this is enabled once, disabling this replication also requires disabling the replication peer using shell or ReplicationAdmin java class. Replication to secondary region replicas works over standard inter-cluster replication. So replication, if disabled explicitly, also has to be enabled by setting "hbase.replication" to true for this feature to work.

Default

false

hbase.http.filter.initializers
Description

A comma separated list of class names. Each class in the list must extend org.apache.hadoop.hbase.http.FilterInitializer. The corresponding Filter will be initialized. Then, the Filter will be applied to all user facing jsp and servlet web pages. The ordering of the list defines the ordering of the filters. The default StaticUserWebFilter add a user principal as defined by the hbase.http.staticuser.user property.

Default

org.apache.hadoop.hbase.http.lib.StaticUserWebFilter

hbase.security.visibility.mutations.checkauths
Description

This property if enabled, will check whether the labels in the visibility expression are associated with the user issuing the mutation

Default

false

hbase.http.max.threads
Description

The maximum number of threads that the HTTP Server will create in its ThreadPool.

Default

10

hbase.replication.rpc.codec
Description

The codec that is to be used when replication is enabled so that the tags are also replicated. This is used along with HFileV3 which supports tags in them. If tags are not used or if the hfile version used is HFileV2 then KeyValueCodec can be used as the replication codec. Note that using KeyValueCodecWithTags for replication when there are no tags causes no harm.

Default

org.apache.hadoop.hbase.codec.KeyValueCodecWithTags

hbase.http.staticuser.user
Description

The user name to filter as, on static web filters while rendering content. An example use is the HDFS web UI (user to be used for browsing files).

Default

dr.stack

hbase.regionserver.handler.abort.on.error.percent
Description

The percent of region server RPC threads failed to abort RS. -1 Disable aborting; 0 Abort if even a single handler has died; 0.x Abort only when this percent of handlers have died; 1 Abort only all of the handers have died.

Default

0.5

hbase.snapshot.master.timeout.millis
Description

Timeout for master for the snapshot procedure execution

Default

300000

hbase.snapshot.region.timeout
Description

Timeout for regionservers to keep threads in snapshot request pool waiting

Default

300000

7.3. hbase-env.sh

Set HBase environment variables in this file. Examples include options to pass the JVM on start of an HBase daemon such as heap size and garbage collector configs. You can also set configurations for HBase configuration, log directories, niceness, ssh options, where to locate process pid files, etc. Open the file at conf/hbase-env.sh and peruse its content. Each option is fairly well documented. Add your own environment variables here if you want them read by HBase daemons on startup.

Changes here will require a cluster restart for HBase to notice the change.

7.4. log4j.properties

Edit this file to change rate at which HBase files are rolled and to change the level at which HBase logs messages.

Changes here will require a cluster restart for HBase to notice the change though log levels can be changed for particular daemons via the HBase UI.

7.5. Client configuration and dependencies connecting to an HBase cluster

If you are running HBase in standalone mode, you don’t need to configure anything for your client to work provided that they are all on the same machine.

Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for current critical locations. ZooKeeper is where all these values are kept. Thus clients require the location of the ZooKeeper ensemble before they can do anything else. Usually this the ensemble location is kept out in the hbase-site.xml and is picked up by the client from the CLASSPATH.

If you are configuring an IDE to run an HBase client, you should include the conf/ directory on your classpath so hbase-site.xml settings can be found (or add src/test/resources to pick up the hbase-site.xml used by tests).

Minimally, a client of HBase needs several libraries in its CLASSPATH when connecting to a cluster, including:

commons-configuration (commons-configuration-1.6.jar)
commons-lang (commons-lang-2.5.jar)
commons-logging (commons-logging-1.1.1.jar)
hadoop-core (hadoop-core-1.0.0.jar)
hbase (hbase-0.92.0.jar)
log4j (log4j-1.2.16.jar)
slf4j-api (slf4j-api-1.5.8.jar)
slf4j-log4j (slf4j-log4j12-1.5.8.jar)
zookeeper (zookeeper-3.4.2.jar)

An example basic hbase-site.xml for client only might look as follows:

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>example1,example2,example3</value>
    <description>The directory shared by region servers.
    </description>
  </property>
</configuration>

7.5.1. Java client configuration

The configuration used by a Java client is kept in an HBaseConfiguration instance.

The factory method on HBaseConfiguration, HBaseConfiguration.create();, on invocation, will read in the content of the first hbase-site.xml found on the client’s CLASSPATH, if one is present (Invocation will also factor in any hbase-default.xml found; an hbase-default.xml ships inside the hbase.X.X.X.jar). It is also possible to specify configuration directly without having to read from a hbase-site.xml. For example, to set the ZooKeeper ensemble for the cluster programmatically do as follows:

Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost");  // Here we are running zookeeper locally

If multiple ZooKeeper instances make up your ZooKeeper ensemble, they may be specified in a comma-separated list (just as in the hbase-site.xml file). This populated Configuration instance can then be passed to an Table, and so on.

8. Example Configurations

8.1. Basic Distributed HBase Install

Here is an example basic configuration for a distributed ten node cluster: * The nodes are named example0, example1, etc., through node example9 in this example. * The HBase Master and the HDFS NameNode are running on the node example0. * RegionServers run on nodes example1-example9. * A 3-node ZooKeeper ensemble runs on example1, example2, and example3 on the default ports. * ZooKeeper data is persisted to the directory /export/zookeeper.

Below we show what the main configuration files — hbase-site.xml, regionservers, and hbase-env.sh — found in the HBase conf directory might look like.

8.1.1. hbase-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>example1,example2,example3</value>
    <description>The directory shared by RegionServers.
    </description>
  </property>
  <property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/export/zookeeper</value>
    <description>Property from ZooKeeper config zoo.cfg.
    The directory where the snapshot is stored.
    </description>
  </property>
  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://example0:8020/hbase</value>
    <description>The directory shared by RegionServers.
    </description>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
    <description>The mode the cluster will be in. Possible values are
      false: standalone and pseudo-distributed setups with managed ZooKeeper
      true: fully-distributed with unmanaged ZooKeeper Quorum (see hbase-env.sh)
    </description>
  </property>
</configuration>

8.1.2. regionservers

In this file you list the nodes that will run RegionServers. In our case, these nodes are example1-example9.

example1
example2
example3
example4
example5
example6
example7
example8
example9

8.1.3. hbase-env.sh

The following lines in the hbase-env.sh file show how to set the JAVA_HOME environment variable (required for HBase 0.98.5 and newer) and set the heap to 4 GB (rather than the default value of 1 GB). If you copy and paste this example, be sure to adjust the JAVA_HOME to suit your environment.

# The java implementation to use.
export JAVA_HOME=/usr/java/jdk1.7.0/

# The maximum amount of heap to use. Default is left to JVM default.
export HBASE_HEAPSIZE=4G

Use rsync to copy the content of the conf directory to all nodes of the cluster.

9. The Important Configurations

Below we list some important configurations. We’ve divided this section into required configuration and worth-a-look recommended configs.

9.1. Required Configurations

Review the os and hadoop sections.

9.1.1. Big Cluster Configurations

If you have a cluster with a lot of regions, it is possible that a Regionserver checks in briefly after the Master starts while all the remaining RegionServers lag behind. This first server to check in will be assigned all regions which is not optimal. To prevent the above scenario from happening, up the hbase.master.wait.on.regionservers.mintostart property from its default value of 1. See HBASE-6389 Modify the conditions to ensure that Master waits for sufficient number of Region Servers before starting region assignments for more detail.

zookeeper.session.timeout

The default timeout is three minutes (specified in milliseconds). This means that if a server crashes, it will be three minutes before the Master notices the crash and starts recovery. You might like to tune the timeout down to a minute or even less so the Master notices failures the sooner. Before changing this value, be sure you have your JVM garbage collection configuration under control otherwise, a long garbage collection that lasts beyond the ZooKeeper session timeout will take out your RegionServer (You might be fine with this — you probably want recovery to start on the server if a RegionServer has been in GC for a long period of time).

To change this configuration, edit hbase-site.xml, copy the changed file around the cluster and restart.

We set this value high to save our having to field questions up on the mailing lists asking why a RegionServer went down during a massive import. The usual cause is that their JVM is untuned and they are running into long GC pauses. Our thinking is that while users are getting familiar with HBase, we’d save them having to know all of its intricacies. Later when they’ve built some confidence, then they can play with configuration such as this.

Number of ZooKeeper Instances

See zookeeper.

9.2.2. HDFS Configurations

dfs.datanode.failed.volumes.tolerated

This is the "…​number of volumes that are allowed to fail before a DataNode stops offering service. By default any volume failure will cause a datanode to shutdown" from the hdfs-default.xml description. You might want to set this to about half the amount of your available disks.

9.2.3. hbase.regionserver.handler.count

This setting defines the number of threads that are kept open to answer incoming requests to user tables. The rule of thumb is to keep this number low when the payload per request approaches the MB (big puts, scans using a large cache) and high when the payload is small (gets, small puts, ICVs, deletes). The total size of the queries in progress is limited by the setting hbase.ipc.server.max.callqueue.size.

It is safe to set that number to the maximum number of incoming clients if their payload is small, the typical example being a cluster that serves a website since puts aren’t typically buffered and most of the operations are gets.

The reason why it is dangerous to keep this setting high is that the aggregate size of all the puts that are currently happening in a region server may impose too much pressure on its memory, or even trigger an OutOfMemoryError. A RegionServer running on low memory will trigger its JVM’s garbage collector to run more frequently up to a point where GC pauses become noticeable (the reason being that all the memory used to keep all the requests' payloads cannot be trashed, no matter how hard the garbage collector tries). After some time, the overall cluster throughput is affected since every request that hits that RegionServer will take longer, which exacerbates the problem even more.

You can get a sense of whether you have too little or too many handlers by rpc.logging on an individual RegionServer then tailing its logs (Queued requests consume memory).

9.2.4. Configuration for large memory machines

HBase ships with a reasonable, conservative configuration that will work on nearly all machine types that people might want to test with. If you have larger machines — HBase has 8G and larger heap — you might the following configuration options helpful. TODO.

9.2.5. Compression

You should consider enabling ColumnFamily compression. There are several options that are near-frictionless and in most all cases boost performance by reducing the size of StoreFiles and thus reducing I/O.

See compression for more information.

9.2.6. Configuring the size and number of WAL files

HBase uses wal to recover the memstore data that has not been flushed to disk in case of an RS failure. These WAL files should be configured to be slightly smaller than HDFS block (by default a HDFS block is 64Mb and a WAL file is ~60Mb).

HBase also has a limit on the number of WAL files, designed to ensure there’s never too much data that needs to be replayed during recovery. This limit needs to be set according to memstore configuration, so that all the necessary data would fit. It is recommended to allocate enough WAL files to store at least that much data (when all memstores are close to full). For example, with 16Gb RS heap, default memstore settings (0.4), and default WAL file size (~60Mb), 16Gb*0.4/60, the starting point for WAL file count is ~109. However, as all memstores are not expected to be full all the time, less WAL files can be allocated.

9.2.7. Managed Splitting

HBase generally handles splitting your regions, based upon the settings in your hbase-default.xml and hbase-site.xml configuration files. Important settings include hbase.regionserver.region.split.policy, hbase.hregion.max.filesize, hbase.regionserver.regionSplitLimit. A simplistic view of splitting is that when a region grows to hbase.hregion.max.filesize, it is split. For most use patterns, most of the time, you should use automatic splitting. See manual region splitting decisions for more information about manual region splitting.

Instead of allowing HBase to split your regions automatically, you can choose to manage the splitting yourself. This feature was added in HBase 0.90.0. Manually managing splits works if you know your keyspace well, otherwise let HBase figure where to split for you. Manual splitting can mitigate region creation and movement under load. It also makes it so region boundaries are known and invariant (if you disable region splitting). If you use manual splits, it is easier doing staggered, time-based major compactions to spread out your network IO load.

Disable Automatic Splitting

To disable automatic splitting, set hbase.hregion.max.filesize to a very large value, such as 100 GB It is not recommended to set it to its absolute maximum value of Long.MAX_VALUE.

Automatic Splitting Is Recommended

If you disable automatic splits to diagnose a problem or during a period of fast data growth, it is recommended to re-enable them when your situation becomes more stable. The potential benefits of managing region splits yourself are not undisputed.
Determine the Optimal Number of Pre-Split Regions

The optimal number of pre-split regions depends on your application and environment. A good rule of thumb is to start with 10 pre-split regions per server and watch as data grows over time. It is better to err on the side of too few regions and perform rolling splits later. The optimal number of regions depends upon the largest StoreFile in your region. The size of the largest StoreFile will increase with time if the amount of data grows. The goal is for the largest region to be just large enough that the compaction selection algorithm only compacts it during a timed major compaction. Otherwise, the cluster can be prone to compaction storms where a large number of regions under compaction at the same time. It is important to understand that the data growth causes compaction storms, and not the manual split decision.

If the regions are split into too many large regions, you can increase the major compaction interval by configuring HConstants.MAJOR_COMPACTION_PERIOD. HBase 0.90 introduced org.apache.hadoop.hbase.util.RegionSplitter, which provides a network-IO-safe rolling split of all regions.

9.2.8. Managed Compactions

By default, major compactions are scheduled to run once in a 7-day period. Prior to HBase 0.96.x, major compactions were scheduled to happen once per day by default.

If you need to control exactly when and how often major compaction runs, you can disable managed major compactions. See the entry for hbase.hregion.majorcompaction in the compaction.parameters table for details.

Do Not Disable Major Compactions

Major compactions are absolutely necessary for StoreFile clean-up. Do not disable them altogether. You can run major compactions manually via the HBase shell or via the Admin API.

For more information about compactions and the compaction file selection process, see compaction

9.2.9. Speculative Execution

Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it is generally advised to turn off Speculative Execution at a system-level unless you need it for a specific case, where it can be configured per-job. Set the properties mapreduce.map.speculative and mapreduce.reduce.speculative to false.

9.3. Other Configurations

9.3.1. Balancer

The balancer is a periodic operation which is run on the master to redistribute regions on the cluster. It is configured via hbase.balancer.period and defaults to 300000 (5 minutes).

See master.processes.loadbalancer for more information on the LoadBalancer.

9.3.2. Disabling Blockcache

Do not turn off block cache (You’d do it by setting hbase.block.cache.size to zero). Currently we do not do well if you do this because the RegionServer will spend all its time loading HFile indices over and over again. If your working set is such that block cache does you no good, at least size the block cache such that HFile indices will stay up in the cache (you can get a rough idea on the size you need by surveying RegionServer UIs; you’ll see index block size accounted near the top of the webpage).

9.3.3. Nagle’s or the small package problem

If a big 40ms or so occasional delay is seen in operations against HBase, try the Nagles' setting. For example, see the user mailing list thread, Inconsistent scan performance with caching set to 1 and the issue cited therein where setting notcpdelay improved scan speeds. You might also see the graphs on the tail of HBASE-7008 Set scanner caching to a better default where our Lars Hofhansl tries various data sizes w/ Nagle’s on and off measuring the effect.

9.3.4. Better Mean Time to Recover (MTTR)

This section is about configurations that will make servers come back faster after a fail. See the Deveraj Das and Nicolas Liochon blog post Introduction to HBase Mean Time to Recover (MTTR) for a brief introduction.

The issue HBASE-8354 forces Namenode into loop with lease recovery requests is messy but has a bunch of good discussion toward the end on low timeouts and how to effect faster recovery including citation of fixes added to HDFS. Read the Varun Sharma comments. The below suggested configurations are Varun’s suggestions distilled and tested. Make sure you are running on a late-version HDFS so you have the fixes he refers too and himself adds to HDFS that help HBase MTTR (e.g. HDFS-3703, HDFS-3712, and HDFS-4791 — Hadoop 2 for sure has them and late Hadoop 1 has some). Set the following in the RegionServer.

<property>
  <name>hbase.lease.recovery.dfs.timeout</name>
  <value>23000</value>
  <description>How much time we allow elapse between calls to recover lease.
  Should be larger than the dfs timeout.</description>
</property>
<property>
  <name>dfs.client.socket-timeout</name>
  <value>10000</value>
  <description>Down the DFS timeout from 60 to 10 seconds.</description>
</property>

And on the NameNode/DataNode side, set the following to enable 'staleness' introduced in HDFS-3703, HDFS-3912.

<property>
  <name>dfs.client.socket-timeout</name>
  <value>10000</value>
  <description>Down the DFS timeout from 60 to 10 seconds.</description>
</property>
<property>
  <name>dfs.datanode.socket.write.timeout</name>
  <value>10000</value>
  <description>Down the DFS timeout from 8 * 60 to 10 seconds.</description>
</property>
<property>
  <name>ipc.client.connect.timeout</name>
  <value>3000</value>
  <description>Down from 60 seconds to 3.</description>
</property>
<property>
  <name>ipc.client.connect.max.retries.on.timeouts</name>
  <value>2</value>
  <description>Down from 45 seconds to 3 (2 == 3 retries).</description>
</property>
<property>
  <name>dfs.namenode.avoid.read.stale.datanode</name>
  <value>true</value>
  <description>Enable stale state in hdfs</description>
</property>
<property>
  <name>dfs.namenode.stale.datanode.interval</name>
  <value>20000</value>
  <description>Down from default 30 seconds</description>
</property>
<property>
  <name>dfs.namenode.avoid.write.stale.datanode</name>
  <value>true</value>
  <description>Enable stale state in hdfs</description>
</property>

9.3.5. JMX

JMX (Java Management Extensions) provides built-in instrumentation that enables you to monitor and manage the Java VM. To enable monitoring and management from remote systems, you need to set system property com.sun.management.jmxremote.port (the port number through which you want to enable JMX RMI connections) when you start the Java VM. See the official documentation for more information. Historically, besides above port mentioned, JMX opens two additional random TCP listening ports, which could lead to port conflict problem. (See HBASE-10289 for details)

As an alternative, You can use the coprocessor-based JMX implementation provided by HBase. To enable it in 0.99 or above, add below property in hbase-site.xml:

<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop.hbase.JMXListener</value>
</property>
DO NOT set com.sun.management.jmxremote.port for Java VM at the same time.

Currently it supports Master and RegionServer Java VM. By default, the JMX listens on TCP port 10102, you can further configure the port using below properties:

<property>
  <name>regionserver.rmi.registry.port</name>
  <value>61130</value>
</property>
<property>
  <name>regionserver.rmi.connector.port</name>
  <value>61140</value>
</property>

The registry port can be shared with connector port in most cases, so you only need to configure regionserver.rmi.registry.port. However if you want to use SSL communication, the 2 ports must be configured to different values.

By default the password authentication and SSL communication is disabled. To enable password authentication, you need to update hbase-env.sh like below:

export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.authenticate=true                  \
                       -Dcom.sun.management.jmxremote.password.file=your_password_file   \
                       -Dcom.sun.management.jmxremote.access.file=your_access_file"

export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "

See example password/access file under $JRE_HOME/lib/management.

To enable SSL communication with password authentication, follow below steps:

#1. generate a key pair, stored in myKeyStore
keytool -genkey -alias jconsole -keystore myKeyStore

#2. export it to file jconsole.cert
keytool -export -alias jconsole -keystore myKeyStore -file jconsole.cert

#3. copy jconsole.cert to jconsole client machine, import it to jconsoleKeyStore
keytool -import -alias jconsole -keystore jconsoleKeyStore -file jconsole.cert

And then update hbase-env.sh like below:

export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=true                         \
                       -Djavax.net.ssl.keyStore=/home/tianq/myKeyStore                 \
                       -Djavax.net.ssl.keyStorePassword=your_password_in_step_1       \
                       -Dcom.sun.management.jmxremote.authenticate=true                \
                       -Dcom.sun.management.jmxremote.password.file=your_password file \
                       -Dcom.sun.management.jmxremote.access.file=your_access_file"

export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE "
export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE "

Finally start jconsole on the client using the key store:

jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore
To enable the HBase JMX implementation on Master, you also need to add below property in hbase-site.xml: 
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.JMXListener</value>
</property>

The corresponding properties for port configuration are master.rmi.registry.port (by default 10101) and master.rmi.connector.port (by default the same as registry.port)

10. Dynamic Configuration

Since HBase 1.0.0, it is possible to change a subset of the configuration without requiring a server restart. In the HBase shell, there are new operators, update_config and update_all_config that will prompt a server or all servers to reload configuration.

Only a subset of all configurations can currently be changed in the running server. Here is an incomplete list: hbase.regionserver.thread.compaction.large, hbase.regionserver.thread.compaction.small, hbase.regionserver.thread.split, hbase.regionserver.thread.merge, as well as compaction policy and configurations and adjustment to offpeak hours. For the full list consult the patch attached to HBASE-12147 Porting Online Config Change from 89-fb.

Upgrading

You cannot skip major versions when upgrading. If you are upgrading from version 0.90.x to 0.94.x, you must first go from 0.90.x to 0.92.x and then go from 0.92.x to 0.94.x.

It may be possible to skip across versions — for example go from 0.92.2 straight to 0.98.0 just following the 0.96.x upgrade instructions — but these scenarios are untested.

Review Apache HBase Configuration, in particular Hadoop. Familiarize yourself with Support and Testing Expectations.

11. HBase version number and compatibility

HBase has two versioning schemes, pre-1.0 and post-1.0. Both are detailed below.

11.1. Post 1.0 versions

Starting with the 1.0.0 release, HBase is working towards Semantic Versioning for its release versioning. In summary:

Given a version number MAJOR.MINOR.PATCH, increment the:

  • MAJOR version when you make incompatible API changes,

  • MINOR version when you add functionality in a backwards-compatible manner, and

  • PATCH version when you make backwards-compatible bug fixes.

  • Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.

Compatibility Dimensions

In addition to the usual API versioning considerations HBase has other compatibility dimensions that we need to consider.

Client-Server wire protocol compatibility

  • Allows updating client and server out of sync.

  • We could only allow upgrading the server first. I.e. the server would be backward compatible to an old client, that way new APIs are OK.

  • Example: A user should be able to use an old client to connect to an upgraded cluster.

Server-Server protocol compatibility

  • Servers of different versions can co-exist in the same cluster.

  • The wire protocol between servers is compatible.

  • Workers for distributed tasks, such as replication and log splitting, can co-exist in the same cluster.

  • Dependent protocols (such as using ZK for coordination) will also not be changed.

  • Example: A user can perform a rolling upgrade.

File format compatibility

  • Support file formats backward and forward compatible

  • Example: File, ZK encoding, directory layout is upgraded automatically as part of an HBase upgrade. User can rollback to the older version and everything will continue to work.

Client API compatibility

  • Allow changing or removing existing client APIs.

  • An API needs to be deprecated for a major version before we will change/remove it.

  • APIs available in a patch version will be available in all later patch versions. However, new APIs may be added which will not be available in earlier patch versions.

  • New APIs introduced in a patch version will only be added in a source compatible way [1]: i.e. code that implements public APIs will continue to compile.

  • Example: A user using a newly deprecated API does not need to modify application code with HBase API calls until the next major version.

Client Binary compatibility

  • Client code written to APIs available in a given patch release can run unchanged (no recompilation needed) against the new jars of later patch versions.

  • Client code written to APIs available in a given patch release might not run against the old jars from an earlier patch version.

  • Example: Old compiled client code will work unchanged with the new jars.

Server-Side Limited API compatibility (taken from Hadoop)

  • Internal APIs are marked as Stable, Evolving, or Unstable

  • This implies binary compatibility for coprocessors and plugins (pluggable classes, including replication) as long as these are only using marked interfaces/classes.

  • Example: Old compiled Coprocessor, Filter, or Plugin code will work unchanged with the new jars.

Dependency Compatibility

  • An upgrade of HBase will not require an incompatible upgrade of a dependent project, including the Java runtime.

  • Example: An upgrade of Hadoop will not invalidate any of the compatibilities guarantees we made.

Operational Compatibility

  • Metric changes

  • Behavioral changes of services

  • JMX APIs exposed via the /jmx/ endpoint

Summary

  • A patch upgrade is a drop-in replacement. Any change that is not Java binary and source compatible would not be allowed.[2] Downgrading versions within patch releases may not be compatible.

  • A minor upgrade requires no application/client code modification. Ideally it would be a drop-in replacement but client code, coprocessors, filters, etc might have to be recompiled if new jars are used.

  • A major upgrade allows the HBase community to make breaking changes.

Table 3. Compatibility Matrix [3]

Major

Minor

Patch

Client-Server wire Compatibility

N

Y

Y

Server-Server Compatibility

N

Y

Y

File Format Compatibility

N [4]

Y

Y

Client API Compatibility

N

Y

Y

Client Binary Compatibility

N

N

Y

Server-Side Limited API Compatibility

Stable

N

Y

Y

Evolving

N

N

Y

Unstable

N

N

N

Dependency Compatibility

N

Y

Y

Operational Compatibility

N

N

Y

11.1.1. HBase API Surface

HBase has a lot of API points, but for the compatibility matrix above, we differentiate between Client API, Limited Private API, and Private API. HBase uses a version of Hadoop’s Interface classification. HBase’s Interface classification classes can be found here.

  • InterfaceAudience: captures the intended audience, possible values are Public (for end users and external projects), LimitedPrivate (for other Projects, Coprocessors or other plugin points), and Private (for internal use).

  • InterfaceStability: describes what types of interface changes are permitted. Possible values are Stable, Evolving, Unstable, and Deprecated.

HBase Client API

HBase Client API consists of all the classes or methods that are marked with InterfaceAudience.Public interface. All main classes in hbase-client and dependent modules have either InterfaceAudience.Public, InterfaceAudience.LimitedPrivate, or InterfaceAudience.Private marker. Not all classes in other modules (hbase-server, etc) have the marker. If a class is not annotated with one of these, it is assumed to be a InterfaceAudience.Private class.

HBase LimitedPrivate API

LimitedPrivate annotation comes with a set of target consumers for the interfaces. Those consumers are coprocessors, phoenix, replication endpoint implementations or similar. At this point, HBase only guarantees source and binary compatibility for these interfaces between patch versions.

HBase Private API

All classes annotated with InterfaceAudience.Private or all classes that do not have the annotation are for HBase internal use only. The interfaces and method signatures can change at any point in time. If you are relying on a particular interface that is marked Private, you should open a jira to propose changing the interface to be Public or LimitedPrivate, or an interface exposed for this purpose.

11.2. Pre 1.0 versions

Before the semantic versioning scheme pre-1.0, HBase tracked either Hadoop’s versions (0.2x) or 0.9x versions. If you are into the arcane, checkout our old wiki page on HBase Versioning which tries to connect the HBase version dots. Below sections cover ONLY the releases before 1.0.

Odd/Even Versioning or "Development" Series Releases

Ahead of big releases, we have been putting up preview versions to start the feedback cycle turning-over earlier. These "Development" Series releases, always odd-numbered, come with no guarantees, not even regards being able to upgrade between two sequential releases (we reserve the right to break compatibility across "Development" Series releases). Needless to say, these releases are not for production deploys. They are a preview of what is coming in the hope that interested parties will take the release for a test drive and flag us early if we there are issues we’ve missed ahead of our rolling a production-worthy release.

Our first "Development" Series was the 0.89 set that came out ahead of HBase 0.90.0. HBase 0.95 is another "Development" Series that portends HBase 0.96.0. 0.99.x is the last series in "developer preview" mode before 1.0. Afterwards, we will be using semantic versioning naming scheme (see above).

Binary Compatibility

When we say two HBase versions are compatible, we mean that the versions are wire and binary compatible. Compatible HBase versions means that clients can talk to compatible but differently versioned servers. It means too that you can just swap out the jars of one version and replace them with the jars of another, compatible version and all will just work. Unless otherwise specified, HBase point versions are (mostly) binary compatible. You can safely do rolling upgrades between binary compatible versions; i.e. across point versions: e.g. from 0.94.5 to 0.94.6. See link:[Does compatibility between versions also mean binary compatibility?] discussion on the HBase dev mailing list.

11.3. Rolling Upgrades

A rolling upgrade is the process by which you update the servers in your cluster a server at a time. You can rolling upgrade across HBase versions if they are binary or wire compatible. See Rolling Upgrade Between Versions that are Binary/Wire Compatible for more on what this means. Coarsely, a rolling upgrade is a graceful stop each server, update the software, and then restart. You do this for each server in the cluster. Usually you upgrade the Master first and then the RegionServers. See Rolling Restart for tools that can help use the rolling upgrade process.

For example, in the below, HBase was symlinked to the actual HBase install. On upgrade, before running a rolling restart over the cluster, we changed the symlink to point at the new HBase software version and then ran

$ HADOOP_HOME=~/hadoop-2.6.0-CRC-SNAPSHOT ~/hbase/bin/rolling-restart.sh --config ~/conf_hbase

The rolling-restart script will first gracefully stop and restart the master, and then each of the RegionServers in turn. Because the symlink was changed, on restart the server will come up using the new HBase version. Check logs for errors as the rolling upgrade proceeds.

Rolling Upgrade Between Versions that are Binary/Wire Compatible

Unless otherwise specified, HBase point versions are binary compatible. You can do a Rolling Upgrades between HBase point versions. For example, you can go to 0.94.6 from 0.94.5 by doing a rolling upgrade across the cluster replacing the 0.94.5 binary with a 0.94.6 binary.

In the minor version-particular sections below, we call out where the versions are wire/protocol compatible and in this case, it is also possible to do a Rolling Upgrades. For example, in Rolling upgrade from 0.98.x to HBase 1.0.0, we state that it is possible to do a rolling upgrade between hbase-0.98.x and hbase-1.0.0.

12. Upgrade Paths

12.1. Upgrading from 0.98.x to 1.0.x

In this section we first note the significant changes that come in with 1.0.0 HBase and then we go over the upgrade process. Be sure to read the significant changes section with care so you avoid surprises.

12.1.1. Changes of Note!

In here we list important changes that are in 1.0.0 since 0.98.x., changes you should be aware that will go into effect once you upgrade.

ZooKeeper 3.4 is required in HBase 1.0.0

See ZooKeeper Requirements.

HBase Default Ports Changed

The ports used by HBase changed. They used to be in the 600XX range. In HBase 1.0.0 they have been moved up out of the ephemeral port range and are 160XX instead (Master web UI was 60010 and is now 16010; the RegionServer web UI was 60030 and is now 16030, etc.). If you want to keep the old port locations, copy the port setting configs from hbase-default.xml into hbase-site.xml, change them back to the old values from the HBase 0.98.x era, and ensure you’ve distributed your configurations before you restart.

HBase Master Port Binding Change

In HBase 1.0.x, the HBase Master binds the RegionServer ports as well as the Master ports. This behavior is changed from HBase versions prior to 1.0. In HBase 1.1 and 2.0 branches, this behavior is reverted to the pre-1.0 behavior of the HBase master not binding the RegionServer ports.

hbase.bucketcache.percentage.in.combinedcache configuration has been REMOVED

You may have made use of this configuration if you are using BucketCache. If NOT using BucketCache, this change does not affect you. Its removal means that your L1 LruBlockCache is now sized using hfile.block.cache.size — i.e. the way you would size the on-heap L1 LruBlockCache if you were NOT doing BucketCache — and the BucketCache size is not whatever the setting for hbase.bucketcache.size is. You may need to adjust configs to get the LruBlockCache and BucketCache sizes set to what they were in 0.98.x and previous. If you did not set this config., its default value was 0.9. If you do nothing, your BucketCache will increase in size by 10%. Your L1 LruBlockCache will become hfile.block.cache.size times your java heap size (hfile.block.cache.size is a float between 0.0 and 1.0). To read more, see HBASE-11520 Simplify offheap cache config by removing the confusing "hbase.bucketcache.percentage.in.combinedcache".

If you have your own customer filters.

See the release notes on the issue HBASE-12068 [Branch-1] Avoid need to always do KeyValueUtil#ensureKeyValue for Filter transformCell; be sure to follow the recommendations therein.

Distributed Log Replay

Distributed Log Replay is off by default in HBase 1.0.0. Enabling it can make a big difference improving HBase MTTR. Enable this feature if you are doing a clean stop/start when you are upgrading. You cannot rolling upgrade to this feature (caveat if you are running on a version of HBase in excess of HBase 0.98.4 — see HBASE-12577 Disable distributed log replay by default for more).

Mismatch Of hbase.client.scanner.max.result.size Between Client and Server

If either the client or server version is lower than 0.98.11/1.0.0 and the server has a smaller value for hbase.client.scanner.max.result.size than the client, scan requests that reach the server’s hbase.client.scanner.max.result.size are likely to miss data. In particular, 0.98.11 defaults hbase.client.scanner.max.result.size to 2 MB but other versions default to larger values. For this reason, be very careful using 0.98.11 servers with any other client version.

12.1.2. Rolling upgrade from 0.98.x to HBase 1.0.0

From 0.96.x to 1.0.0
You cannot do a rolling upgrade from 0.96.x to 1.0.0 without first doing a rolling upgrade to 0.98.x. See comment in HBASE-11164 Document and test rolling updates from 0.98 → 1.0 for the why. Also because HBase 1.0.0 enables HFile v3 by default, HBASE-9801 Change the default HFile version to V3, and support for HFile v3 only arrives in 0.98, this is another reason you cannot rolling upgrade from HBase 0.96.x; if the rolling upgrade stalls, the 0.96.x servers cannot open files written by the servers running the newer HBase 1.0.0 with HFile’s of version 3.

There are no known issues running a rolling upgrade from HBase 0.98.x to HBase 1.0.0.

12.1.3. Upgrading to 1.0 from 0.94

You cannot rolling upgrade from 0.94.x to 1.x.x. You must stop your cluster, install the 1.x.x software, run the migration described at Executing the 0.96 Upgrade (substituting 1.x.x. wherever we make mention of 0.96.x in the section below), and then restart. Be sure to upgrade your ZooKeeper if it is a version less than the required 3.4.x.

12.2. Upgrading from 0.96.x to 0.98.x

A rolling upgrade from 0.96.x to 0.98.x works. The two versions are not binary compatible.

Additional steps are required to take advantage of some of the new features of 0.98.x, including cell visibility labels, cell ACLs, and transparent server side encryption. See Securing Apache HBase for more information. Significant performance improvements include a change to the write ahead log threading model that provides higher transaction throughput under high load, reverse scanners, MapReduce over snapshot files, and striped compaction.

Clients and servers can run with 0.98.x and 0.96.x versions. However, applications may need to be recompiled due to changes in the Java API.

12.3. Upgrading from 0.94.x to 0.98.x

A rolling upgrade from 0.94.x directly to 0.98.x does not work. The upgrade path follows the same procedures as Upgrading from 0.94.x to 0.96.x. Additional steps are required to use some of the new features of 0.98.x. See Upgrading from 0.96.x to 0.98.x for an abbreviated list of these features.

12.4. Upgrading from 0.94.x to 0.96.x

12.4.1. The "Singularity"

HBase 0.96.x was EOL’d, September 1st, 2014
Do not deploy 0.96.x Deploy at least 0.98.x. See EOL 0.96.

You will have to stop your old 0.94.x cluster completely to upgrade. If you are replicating between clusters, both clusters will have to go down to upgrade. Make sure it is a clean shutdown. The less WAL files around, the faster the upgrade will run (the upgrade will split any log files it finds in the filesystem as part of the upgrade process). All clients must be upgraded to 0.96 too.

The API has changed. You will need to recompile your code against 0.96 and you may need to adjust applications to go against new APIs (TODO: List of changes).

12.4.2. Executing the 0.96 Upgrade

HDFS and ZooKeeper must be up!
HDFS and ZooKeeper should be up and running during the upgrade process.

HBase 0.96.0 comes with an upgrade script. Run

$ bin/hbase upgrade

to see its usage. The script has two main modes: -check, and -execute.

check

The check step is run against a running 0.94 cluster. Run it from a downloaded 0.96.x binary. The check step is looking for the presence of HFile v1 files. These are unsupported in HBase 0.96.0. To have them rewritten as HFile v2 you must run a compaction.

The check step prints stats at the end of its run (grep for “Result:” in the log) printing absolute path of the tables it scanned, any HFile v1 files found, the regions containing said files (these regions will need a major compaction), and any corrupted files if found. A corrupt file is unreadable, and so is undefined (neither HFile v1 nor HFile v2).

To run the check step, run

$ bin/hbase upgrade -check

Here is sample output:

Tables Processed:
hdfs://localhost:41020/myHBase/.META.
hdfs://localhost:41020/myHBase/usertable
hdfs://localhost:41020/myHBase/TestTable
hdfs://localhost:41020/myHBase/t

Count of HFileV1: 2
HFileV1:
hdfs://localhost:41020/myHBase/usertable    /fa02dac1f38d03577bd0f7e666f12812/family/249450144068442524
hdfs://localhost:41020/myHBase/usertable    /ecdd3eaee2d2fcf8184ac025555bb2af/family/249450144068442512

Count of corrupted files: 1
Corrupted Files:
hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812/family/1
Count of Regions with HFileV1: 2
Regions to Major Compact:
hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812
hdfs://localhost:41020/myHBase/usertable/ecdd3eaee2d2fcf8184ac025555bb2af

There are some HFileV1, or corrupt files (files with incorrect major version)

In the above sample output, there are two HFile v1 files in two regions, and one corrupt file. Corrupt files should probably be removed. The regions that have HFile v1s need to be major compacted. To major compact, start up the hbase shell and review how to compact an individual region. After the major compaction is done, rerun the check step and the HFile v1 files should be gone, replaced by HFile v2 instances.

By default, the check step scans the HBase root directory (defined as hbase.rootdir in the configuration). To scan a specific directory only, pass the -dir option.

$ bin/hbase upgrade -check -dir /myHBase/testTable

The above command would detect HFile v1 files in the /myHBase/testTable directory.

Once the check step reports all the HFile v1 files have been rewritten, it is safe to proceed with the upgrade.

execute

After the check step shows the cluster is free of HFile v1, it is safe to proceed with the upgrade. Next is the execute step. You must SHUTDOWN YOUR 0.94.x CLUSTER before you can run the execute step. The execute step will not run if it detects running HBase masters or RegionServers.

HDFS and ZooKeeper should be up and running during the upgrade process. If zookeeper is managed by HBase, then you can start zookeeper so it is available to the upgrade by running

$ ./hbase/bin/hbase-daemon.sh start zookeeper

The execute upgrade step is made of three substeps.

  • Namespaces: HBase 0.96.0 has support for namespaces. The upgrade needs to reorder directories in the filesystem for namespaces to work.

  • ZNodes: All znodes are purged so that new ones can be written in their place using a new protobuf’ed format and a few are migrated in place: e.g. replication and table state znodes

  • WAL Log Splitting: If the 0.94.x cluster shutdown was not clean, we’ll split WAL logs as part of migration before we startup on 0.96.0. This WAL splitting runs slower than the native distributed WAL splitting because it is all inside the single upgrade process (so try and get a clean shutdown of the 0.94.0 cluster if you can).

To run the execute step, make sure that first you have copied HBase 0.96.0 binaries everywhere under servers and under clients. Make sure the 0.94.0 cluster is down. Then do as follows:

$ bin/hbase upgrade -execute

Here is some sample output.

Starting Namespace upgrade
Created version file at hdfs://localhost:41020/myHBase with version=7
Migrating table testTable to hdfs://localhost:41020/myHBase/.data/default/testTable
.....
Created version file at hdfs://localhost:41020/myHBase with version=8
Successfully completed NameSpace upgrade.
Starting Znode upgrade
.....
Successfully completed Znode upgrade

Starting Log splitting
...
Successfully completed Log splitting

If the output from the execute step looks good, stop the zookeeper instance you started to do the upgrade:

$ ./hbase/bin/hbase-daemon.sh stop zookeeper

Now start up hbase-0.96.0.

12.5. Troubleshooting

Old Client connecting to 0.96 cluster

It will fail with an exception like the below. Upgrade.

17:22:15  Exception in thread "main" java.lang.IllegalArgumentException: Not a host:port pair: PBUF
17:22:15  *
17:22:15   api-compat-8.ent.cloudera.com ��  ���(
17:22:15    at org.apache.hadoop.hbase.util.Addressing.parseHostname(Addressing.java:60)
17:22:15    at org.apache.hadoop.hbase.ServerName.&init>(ServerName.java:101)
17:22:15    at org.apache.hadoop.hbase.ServerName.parseVersionedServerName(ServerName.java:283)
17:22:15    at org.apache.hadoop.hbase.MasterAddressTracker.bytesToServerName(MasterAddressTracker.java:77)
17:22:15    at org.apache.hadoop.hbase.MasterAddressTracker.getMasterAddress(MasterAddressTracker.java:61)
17:22:15    at org.apache.hadoop.hbase.client.HConnectionManager$HConnectionImplementation.getMaster(HConnectionManager.java:703)
17:22:15    at org.apache.hadoop.hbase.client.HBaseAdmin.&init>(HBaseAdmin.java:126)
17:22:15    at Client_4_3_0.setup(Client_4_3_0.java:716)
17:22:15    at Client_4_3_0.main(Client_4_3_0.java:63)

12.5.1. Upgrading META to use Protocol Buffers (Protobuf)

When you upgrade from versions prior to 0.96, META needs to be converted to use protocol buffers. This is controlled by the configuration option hbase.MetaMigrationConvertingToPB, which is set to true by default. Therefore, by default, no action is required on your part.

The migration is a one-time event. However, every time your cluster starts, META is scanned to ensure that it does not need to be converted. If you have a very large number of regions, this scan can take a long time. Starting in 0.98.5, you can set hbase.MetaMigrationConvertingToPB to false in hbase-site.xml, to disable this start-up scan. This should be considered an expert-level setting.

12.6. Upgrading from 0.92.x to 0.94.x

We used to think that 0.92 and 0.94 were interface compatible and that you can do a rolling upgrade between these versions but then we figured that HBASE-5357 Use builder pattern in HColumnDescriptor changed method signatures so rather than return void they instead return HColumnDescriptor. This will throw java.lang.NoSuchMethodError: org.apache.hadoop.hbase.HColumnDescriptor.setMaxVersions(I)V so 0.92 and 0.94 are NOT compatible. You cannot do a rolling upgrade between them.

12.7. Upgrading from 0.90.x to 0.92.x

12.7.1. Upgrade Guide

You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few things to watch out for upgrading from 0.90.x to 0.92.0.

tl:dr

These are the important things to know before upgrading. . Once you upgrade, you can’t go back.

  1. MSLAB is on by default. Watch that heap usage if you have a lot of regions.

  2. Distributed Log Splitting is on by default. It should make RegionServer failover faster.

  3. There’s a separate tarball for security.

  4. If -XX:MaxDirectMemorySize is set in your hbase-env.sh, it’s going to enable the experimental off-heap cache (You may not want this).
You can’t go back!

To move to 0.92.0, all you need to do is shutdown your cluster, replace your HBase 0.90.x with HBase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x — you must restart). On startup, the .META. table content is rewritten removing the table schema from the info:regioninfo column. Also, any flushes done post first startup will write out data in the new 0.92.0 file format, HBase file format with inline blocks (version 2). This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.

MSLAB is ON by default

In 0.92.0, the hbase.hregion.memstore.mslab.enabled flag is set to true (See Long GC pauses). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per RegionServer in a 0.90.x cluster (and MSLAB was off), you may find yourself OOME’ing on upgrade because the thousands of regions * number of column families * 2MB MSLAB (at a minimum) puts your heap over the top. Set hbase.hregion.memstore.mslab.enabled to false or set the MSLAB size down from 2MB by setting hbase.hregion.memstore.mslab.chunksize to something less.

Distributed Log Splitting is on by default

Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log splitting is done by the cluster (See HBASE-1364 [performance] Distributed splitting of regionserver commit logs or see the blog post Apache HBase Log Splitting). This should cut down significantly on the amount of time it takes splitting logs and getting regions back online again.

Memory accounting is different now

In 0.92.0, HBase file format with inline blocks (version 2) indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem. In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you have less space for block caching. Adjust your block cache accordingly. See the Block Cache for more detail. The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.

On the Hadoop version to use

Run 0.92.0 on Hadoop 1.0.x (or CDH3u3). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. See Hadoop.

If running on Hadoop 1.0.x (or CDH3u3), enable local read. See Practical Caching presentation for ruminations on the performance benefits ‘going local’ (and for how to enable local reads).

HBase 0.92.0 ships with ZooKeeper 3.4.2

If you can, upgrade your ZooKeeper. If you can’t, 3.4.2 clients should work against 3.3.X ensembles (HBase makes use of 3.4.2 API).

Online alter is off by default

In 0.92.0, we’ve added an experimental online schema alter facility (See hbase.online.schema.update.enable). It’s off by default. Enable it at your own risk. Online alter and splitting tables do not play well together so be sure your cluster quiescent using this feature (for now).

WebUI

The web UI has had a few additions made in 0.92.0. It now shows a list of the regions currently transitioning, recent compactions/flushes, and a process list of running processes (usually empty if all is well and requests are being handled promptly). Other additions including requests by region, a debugging servlet dump, etc.

Security tarball

We now ship with two tarballs; secure and insecure HBase. Documentation on how to setup a secure HBase is on the way.

Changes in HBase replication

0.92.0 adds two new features: multi-slave and multi-master replication. The way to enable this is the same as adding a new peer, so in order to have multi-master you would just run add_peer for each cluster that acts as a master to the other slave clusters. Collisions are handled at the timestamp level which may or may not be what you want, this needs to be evaluated on a per use case basis. Replication is still experimental in 0.92 and is disabled by default, run it at your own risk.

RegionServer now aborts if OOME

If an OOME, we now have the JVM kill -9 the RegionServer process so it goes down fast. Previous, a RegionServer might stick around after incurring an OOME limping along in some wounded state. To disable this facility, and recommend you leave it in place, you’d need to edit the bin/hbase file. Look for the addition of the -XX:OnOutOfMemoryError="kill -9 %p" arguments (See HBASE-4769 - ‘Abort RegionServer Immediately on OOME’).

HFile v2 and the “Bigger, Fewer” Tendency

0.92.0 stores data in a new format, HBase file format with inline blocks (version 2). As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run. HFile v2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions. If you have lots of regions now — more than 100s per host — you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is now 1G, up from 256M), and then running online merge tool (See HBASE-1621 merge tool should work on online cluster, but disabled table).

12.8. Upgrading to HBase 0.90.x from 0.20.x or 0.89.x

This version of 0.90.x HBase can be started on data written by HBase 0.20.x or HBase 0.89.x. There is no need of a migration step. HBase 0.89.x and 0.90.x does write out the name of region directories differently — it names them with a md5 hash of the region name rather than a jenkins hash — so this means that once started, there is no going back to HBase 0.20.x.

Be sure to remove the hbase-default.xml from your conf directory on upgrade. A 0.20.x version of this file will have sub-optimal configurations for 0.90.x HBase. The hbase-default.xml file is now bundled into the HBase jar and read from there. If you would like to review the content of this file, see it in the src tree at src/main/resources/hbase-default.xml or see HBase Default Configuration.

Finally, if upgrading from 0.20.x, check your .META. schema in the shell. In the past we would recommend that users run with a 16kb MEMSTORE_FLUSHSIZE. Run

hbase> scan '-ROOT-'

in the shell. This will output the current .META. schema. Check MEMSTORE_FLUSHSIZE size. Is it 16kb (16384)? If so, you will need to change this (The 'normal'/default value is 64MB (67108864)). Run the script bin/set_meta_memstore_size.rb. This will make the necessary edit to your .META. schema. Failure to run this change will make for a slow cluster. See HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE.

The Apache HBase Shell

The Apache HBase Shell is (J)Ruby's IRB with some HBase particular commands added. Anything you can do in IRB, you should be able to do in the HBase Shell.

To run the HBase shell, do as follows:

$ ./bin/hbase shell

Type help and then <RETURN> to see a listing of shell commands and options. Browse at least the paragraphs at the end of the help output for the gist of how variables and command arguments are entered into the HBase shell; in particular note how table names, rows, and columns, etc., must be quoted.

See shell exercises for example basic shell operation.

Here is a nicely formatted listing of all shell commands by Rajeshbabu Chintaguntla.

13. Scripting with Ruby

For examples scripting Apache HBase, look in the HBase bin directory. Look at the files that end in *.rb. To run one of these files, do as follows:

$ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT

14. Running the Shell in Non-Interactive Mode

A new non-interactive mode has been added to the HBase Shell (HBASE-11658). Non-interactive mode captures the exit status (success or failure) of HBase Shell commands and passes that status back to the command interpreter. If you use the normal interactive mode, the HBase Shell will only ever return its own exit status, which will nearly always be 0 for success.

To invoke non-interactive mode, pass the -n or --non-interactive option to HBase Shell.

15. HBase Shell in OS Scripts

You can use the HBase shell from within operating system script interpreters like the Bash shell which is the default command interpreter for most Linux and UNIX distributions. The following guidelines use Bash syntax, but could be adjusted to work with C-style shells such as csh or tcsh, and could probably be modified to work with the Microsoft Windows script interpreter as well. Submissions are welcome.

Spawning HBase Shell commands in this way is slow, so keep that in mind when you are deciding when combining HBase operations with the operating system command line is appropriate.
Example 8. Passing Commands to the HBase Shell

You can pass commands to the HBase Shell in non-interactive mode (see hbase.shell.noninteractive) using the echo command and the | (pipe) operator. Be sure to escape characters in the HBase commands which would otherwise be interpreted by the shell. Some debug-level output has been truncated from the example below.

$ echo "describe 'test1'" | ./hbase shell -n

Version 0.98.3-hadoop2, rd5e65a9144e315bb0a964e7730871af32f5018d5, Sat May 31 19:56:09 PDT 2014

describe 'test1'

DESCRIPTION                                          ENABLED
 'test1', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NON true
 E', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
  VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIO
 NS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =>
 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false'
 , BLOCKCACHE => 'true'}
1 row(s) in 3.2410 seconds

To suppress all output, echo it to /dev/null:

$ echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
Example 9. Checking the Result of a Scripted Command

Since scripts are not designed to be run interactively, you need a way to check whether your command failed or succeeded. The HBase shell uses the standard convention of returning a value of 0 for successful commands, and some non-zero value for failed commands. Bash stores a command’s return value in a special environment variable called $?. Because that variable is overwritten each time the shell runs any command, you should store the result in a different, script-defined variable.

This is a naive script that shows one way to store the return value and make a decision based upon it.

#!/bin/bash

echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1
status=$?
echo "The status was " $status
if ($status == 0); then
    echo "The command succeeded"
else
    echo "The command may have failed."
fi
return $status

15.1. Checking for Success or Failure In Scripts

Getting an exit code of 0 means that the command you scripted definitely succeeded. However, getting a non-zero exit code does not necessarily mean the command failed. The command could have succeeded, but the client lost connectivity, or some other event obscured its success. This is because RPC commands are stateless. The only way to be sure of the status of an operation is to check. For instance, if your script creates a table, but returns a non-zero exit value, you should check whether the table was actually created before trying again to create it.

16. Read HBase Shell Commands from a Command File

You can enter HBase Shell commands into a text file, one command per line, and pass that file to the HBase Shell.

Example 10. Example Command File
create 'test', 'cf'
list 'test'
put 'test', 'row1', 'cf:a', 'value1'
put 'test', 'row2', 'cf:b', 'value2'
put 'test', 'row3', 'cf:c', 'value3'
put 'test', 'row4', 'cf:d', 'value4'
scan 'test'
get 'test', 'row1'
disable 'test'
enable 'test'
Example 11. Directing HBase Shell to Execute the Commands

Pass the path to the command file as the only argument to the hbase shell command. Each command is executed and its output is shown. If you do not include the exit command in your script, you are returned to the HBase shell prompt. There is no way to programmatically check each individual command for success or failure. Also, though you see the output for each command, the commands themselves are not echoed to the screen so it can be difficult to line up the command with its output.

$ ./hbase shell ./sample_commands.txt
0 row(s) in 3.4170 seconds

TABLE
test
1 row(s) in 0.0590 seconds

0 row(s) in 0.1540 seconds

0 row(s) in 0.0080 seconds

0 row(s) in 0.0060 seconds

0 row(s) in 0.0060 seconds

ROW                   COLUMN+CELL
 row1                 column=cf:a, timestamp=1407130286968, value=value1
 row2                 column=cf:b, timestamp=1407130286997, value=value2
 row3                 column=cf:c, timestamp=1407130287007, value=value3
 row4                 column=cf:d, timestamp=1407130287015, value=value4
4 row(s) in 0.0420 seconds

COLUMN                CELL
 cf:a                 timestamp=1407130286968, value=value1
1 row(s) in 0.0110 seconds

0 row(s) in 1.5630 seconds

0 row(s) in 0.4360 seconds

17. Passing VM Options to the Shell

You can pass VM options to the HBase Shell using the HBASE_SHELL_OPTS environment variable. You can set this in your environment, for instance by editing ~/.bashrc, or set it as part of the command to launch HBase Shell. The following example sets several garbage-collection-related variables, just for the lifetime of the VM running the HBase Shell. The command should be run all on a single line, but is broken by the \ character, for readability.

$ HBASE_SHELL_OPTS="-verbose:gc -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCDateStamps \
  -XX:+PrintGCDetails -Xloggc:$HBASE_HOME/logs/gc-hbase.log" ./bin/hbase shell

18. Shell Tricks

18.1. Table variables

HBase 0.95 adds shell commands that provides jruby-style object-oriented references for tables. Previously all of the shell commands that act upon a table have a procedural style that always took the name of the table as an argument. HBase 0.95 introduces the ability to assign a table to a jruby variable. The table reference can be used to perform data read write operations such as puts, scans, and gets well as admin functionality such as disabling, dropping, describing tables.

For example, previously you would always specify a table name:

hbase(main):000:0> create ‘t’, ‘f’
0 row(s) in 1.0970 seconds
hbase(main):001:0> put 't', 'rold', 'f', 'v'
0 row(s) in 0.0080 seconds

hbase(main):002:0> scan 't'
ROW                                COLUMN+CELL
 rold                              column=f:, timestamp=1378473207660, value=v
1 row(s) in 0.0130 seconds

hbase(main):003:0> describe 't'
DESCRIPTION                                                                           ENABLED
 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true
 SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
 ', BLOCKCACHE => 'true'}
1 row(s) in 1.4430 seconds

hbase(main):004:0> disable 't'
0 row(s) in 14.8700 seconds

hbase(main):005:0> drop 't'
0 row(s) in 23.1670 seconds

hbase(main):006:0>

Now you can assign the table to a variable and use the results in jruby shell code.

hbase(main):007 > t = create 't', 'f'
0 row(s) in 1.0970 seconds

=> Hbase::Table - t
hbase(main):008 > t.put 'r', 'f', 'v'
0 row(s) in 0.0640 seconds
hbase(main):009 > t.scan
ROW                           COLUMN+CELL
 r                            column=f:, timestamp=1331865816290, value=v
1 row(s) in 0.0110 seconds
hbase(main):010:0> t.describe
DESCRIPTION                                                                           ENABLED
 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true
 SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2
 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false
 ', BLOCKCACHE => 'true'}
1 row(s) in 0.0210 seconds
hbase(main):038:0> t.disable
0 row(s) in 6.2350 seconds
hbase(main):039:0> t.drop
0 row(s) in 0.2340 seconds

If the table has already been created, you can assign a Table to a variable by using the get_table method:

hbase(main):011 > create 't','f'
0 row(s) in 1.2500 seconds

=> Hbase::Table - t
hbase(main):012:0> tab = get_table 't'
0 row(s) in 0.0010 seconds

=> Hbase::Table - t
hbase(main):013:0> tab.put ‘r1’ ,’f’, ‘v’
0 row(s) in 0.0100 seconds
hbase(main):014:0> tab.scan
ROW                                COLUMN+CELL
 r1                                column=f:, timestamp=1378473876949, value=v
1 row(s) in 0.0240 seconds
hbase(main):015:0>

The list functionality has also been extended so that it returns a list of table names as strings. You can then use jruby to script table operations based on these names. The list_snapshots command also acts similarly.

hbase(main):016 > tables = list(‘t.*’)
TABLE
t
1 row(s) in 0.1040 seconds

=> #<#<Class:0x7677ce29>:0x21d377a4>
hbase(main):017:0> tables.map { |t| disable t ; drop  t}
0 row(s) in 2.2510 seconds

=> [nil]
hbase(main):018:0>

18.2. irbrc

Create an .irbrc file for yourself in your home directory. Add customizations. A useful one is command history so commands are save across Shell invocations:

$ more .irbrc
require 'irb/ext/save-history'
IRB.conf[:SAVE_HISTORY] = 100
IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history"

See the ruby documentation of .irbrc to learn about other possible configurations.

18.3. LOG data to timestamp

To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, do:

hbase(main):021:0> import java.text.SimpleDateFormat
hbase(main):022:0> import java.text.ParsePosition
hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000

To go the other direction:

hbase(main):021:0> import java.util.Date
hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008"

To output in a format that is exactly like that of the HBase log format will take a little messing with SimpleDateFormat.

18.4. Pre-splitting tables with the HBase Shell

You can use a variety of options to pre-split tables when creating them via the HBase Shell create command.

The simplest approach is to specify an array of split points when creating the table. Note that when specifying string literals as split points, these will create split points based on the underlying byte representation of the string. So when specifying a split point of '10', we are actually specifying the byte split point '\x31\30'.

The split points will define n+1 regions where n is the number of split points. The lowest region will contain all keys from the lowest possible key up to but not including the first split point key. The next region will contain keys from the first split point up to, but not including the next split point key. This will continue for all split points up to the last. The last region will be defined from the last split point up to the maximum possible key.

hbase>create 't1','f',SPLITS => ['10','20',30']

In the above example, the table 't1' will be created with column family 'f', pre-split to four regions. Note the first region will contain all keys from '\x00' up to '\x30' (as '\x31' is the ASCII code for '1').

You can pass the split points in a file using following variation. In this example, the splits are read from a file corresponding to the local path on the local filesystem. Each line in the file specifies a split point key.

hbase>create 't14','f',SPLITS_FILE=>'splits.txt'

The other options are to automatically compute splits based on a desired number of regions and a splitting algorithm. HBase supplies algorithms for splitting the key range based on uniform splits or based on hexadecimal keys, but you can provide your own splitting algorithm to subdivide the key range.

# create table with four regions based on random bytes keys
hbase>create 't2','f1', { NUMREGIONS => 4 , SPLITALGO => 'UniformSplit' }

# create table with five regions based on hex keys
hbase>create 't3','f1', { NUMREGIONS => 5, SPLITALGO => 'HexStringSplit' }

As the HBase Shell is effectively a Ruby environment, you can use simple Ruby scripts to compute splits algorithmically.

# generate splits for long (Ruby fixnum) key range from start to end key
hbase(main):070:0> def gen_splits(start_key,end_key,num_regions)
hbase(main):071:1>   results=[]
hbase(main):072:1>   range=end_key-start_key
hbase(main):073:1>   incr=(range/num_regions).floor
hbase(main):074:1>   for i in 1 .. num_regions-1
hbase(main):075:2>     results.push([i*incr+start_key].pack("N"))
hbase(main):076:2>   end
hbase(main):077:1>   return results
hbase(main):078:1> end
hbase(main):079:0>
hbase(main):080:0> splits=gen_splits(1,2000000,10)
=> ["\000\003\r@", "\000\006\032\177", "\000\t'\276", "\000\f4\375", "\000\017B<", "\000\022O{", "\000\025\\\272", "\000\030i\371", "\000\ew8"]
hbase(main):081:0> create 'test_splits','f',SPLITS=>splits
0 row(s) in 0.2670 seconds

=> Hbase::Table - test_splits

Note that the HBase Shell command truncate effectively drops and recreates the table with default options which will discard any pre-splitting. If you need to truncate a pre-split table, you must drop and recreate the table explicitly to re-specify custom split options.

18.5. Debug

18.5.1. Shell debug switch

You can set a debug switch in the shell to see more output — e.g. more of the stack trace on exception — when you run a command:

hbase> debug <RETURN>

18.5.2. DEBUG log level

To enable DEBUG level logging in the shell, launch it with the -d option.

$ ./bin/hbase shell -d

18.6. Commands

18.6.1. count

Count command returns the number of rows in a table. It’s quite fast when configured with the right CACHE

hbase> count '<tablename>', CACHE => 1000

The above count fetches 1000 rows at a time. Set CACHE lower if your rows are big. Default is to fetch one row at a time.

Data Model

In HBase, data is stored in tables, which have rows and columns. This is a terminology overlap with relational databases (RDBMSs), but this is not a helpful analogy. Instead, it can be helpful to think of an HBase table as a multi-dimensional map.

HBase Data Model Terminology
Table

An HBase table consists of multiple rows.

Row

A row in HBase consists of a row key and one or more columns with values associated with them. Rows are sorted alphabetically by the row key as they are stored. For this reason, the design of the row key is very important. The goal is to store data in such a way that related rows are near each other. A common row key pattern is a website domain. If your row keys are domains, you should probably store them in reverse (org.apache.www, org.apache.mail, org.apache.jira). This way, all of the Apache domains are near each other in the table, rather than being spread out based on the first letter of the subdomain.

Column

A column in HBase consists of a column family and a column qualifier, which are delimited by a : (colon) character.

Column Family

Column families physically colocate a set of columns and their values, often for performance reasons. Each column family has a set of storage properties, such as whether its values should be cached in memory, how its data is compressed or its row keys are encoded, and others. Each row in a table has the same column families, though a given row might not store anything in a given column family.

Column Qualifier

A column qualifier is added to a column family to provide the index for a given piece of data. Given a column family content, a column qualifier might be content:html, and another might be content:pdf. Though column families are fixed at table creation, column qualifiers are mutable and may differ greatly between rows.

Cell

A cell is a combination of row, column family, and column qualifier, and contains a value and a timestamp, which represents the value’s version.

Timestamp

A timestamp is written alongside each value, and is the identifier for a given version of a value. By default, the timestamp represents the time on the RegionServer when the data was written, but you can specify a different timestamp value when you put data into the cell.

19. Conceptual View

You can read a very understandable explanation of the HBase data model in the blog post Understanding HBase and BigTable by Jim R. Wilson. Another good explanation is available in the PDF Introduction to Basic Schema Design by Amandeep Khurana.

It may help to read different perspectives to get a solid understanding of HBase schema design. The linked articles cover the same ground as the information in this section.

The following example is a slightly modified form of the one on page 2 of the BigTable paper. There is a table called webtable that contains two rows (com.cnn.www and com.example.www) and three column families named contents, anchor, and people. In this example, for the first row (com.cnn.www), anchor contains two columns (anchor:cssnsi.com, anchor:my.look.ca) and contents contains one column (contents:html). This example contains 5 versions of the row with the row key com.cnn.www, and one version of the row with the row key com.example.www. The contents:html column qualifier contains the entire HTML of a given website. Qualifiers of the anchor column family each contain the external site which links to the site represented by the row, along with the text it used in the anchor of its link. The people column family represents people associated with the site.

Column Names

By convention, a column name is made of its column family prefix and a qualifier. For example, the column contents:html is made up of the column family contents and the html qualifier. The colon character (:) delimits the column family from the column family qualifier.
Table 4. Table webtable
Row Key Time Stamp ColumnFamily contents ColumnFamily anchor ColumnFamily people

"com.cnn.www"

t9

anchor:cnnsi.com = "CNN"

"com.cnn.www"

t8

anchor:my.look.ca = "CNN.com"

"com.cnn.www"

t6

contents:html = "<html>…​"

"com.cnn.www"

t5

contents:html = "<html>…​"

"com.cnn.www"

t3

contents:html = "<html>…​"

Cells in this table that appear to be empty do not take space, or in fact exist, in HBase. This is what makes HBase "sparse." A tabular view is not the only possible way to look at data in HBase, or even the most accurate. The following represents the same information as a multi-dimensional map. This is only a mock-up for illustrative purposes and may not be strictly accurate.

{
  "com.cnn.www": {
    contents: {
      t6: contents:html: "<html>..."
      t5: contents:html: "<html>..."
      t3: contents:html: "<html>..."
    }
    anchor: {
      t9: anchor:cnnsi.com = "CNN"
      t8: anchor:my.look.ca = "CNN.com"
    }
    people: {}
  }
  "com.example.www": {
    contents: {
      t5: contents:html: "<html>..."
    }
    anchor: {}
    people: {
      t5: people:author: "John Doe"
    }
  }
}

20. Physical View

Although at a conceptual level tables may be viewed as a sparse set of rows, they are physically stored by column family. A new column qualifier (column_family:column_qualifier) can be added to an existing column family at any time.

Table 5. ColumnFamily anchor
Row Key Time Stamp Column Family anchor

"com.cnn.www"

t9

anchor:cnnsi.com = "CNN"

"com.cnn.www"

t8

anchor:my.look.ca = "CNN.com"
Table 6. ColumnFamily contents
Row Key Time Stamp ColumnFamily contents:

"com.cnn.www"

t6

contents:html = "<html>…​"

"com.cnn.www"

t5

contents:html = "<html>…​"

"com.cnn.www"

t3

contents:html = "<html>…​"

The empty cells shown in the conceptual view are not stored at all. Thus a request for the value of the contents:html column at time stamp t8 would return no value. Similarly, a request for an anchor:my.look.ca value at time stamp t9 would return no value. However, if no timestamp is supplied, the most recent value for a particular column would be returned. Given multiple versions, the most recent is also the first one found, since timestamps are stored in descending order. Thus a request for the values of all columns in the row com.cnn.www if no timestamp is specified would be: the value of contents:html from timestamp t6, the value of anchor:cnnsi.com from timestamp t9, the value of anchor:my.look.ca from timestamp t8.

For more information about the internals of how Apache HBase stores data, see regions.arch.

21. Namespace

A namespace is a logical grouping of tables analogous to a database in relation database systems. This abstraction lays the groundwork for upcoming multi-tenancy related features:

  • Quota Management (HBASE-8410) - Restrict the amount of resources (i.e. regions, tables) a namespace can consume.

  • Namespace Security Administration (HBASE-9206) - Provide another level of security administration for tenants.

  • Region server groups (HBASE-6721) - A namespace/table can be pinned onto a subset of RegionServers thus guaranteeing a course level of isolation.

21.1. Namespace management

A namespace can be created, removed or altered. Namespace membership is determined during table creation by specifying a fully-qualified table name of the form:

<table namespace>:<table qualifier>
Example 12. Examples
#Create a namespace
create_namespace 'my_ns'
#create my_table in my_ns namespace
create 'my_ns:my_table', 'fam'
#drop namespace
drop_namespace 'my_ns'
#alter namespace
alter_namespace 'my_ns', {METHOD => 'set', 'PROPERTY_NAME' => 'PROPERTY_VALUE'}

21.2. Predefined namespaces

There are two predefined special namespaces:

  • hbase - system namespace, used to contain HBase internal tables

  • default - tables with no explicit specified namespace will automatically fall into this namespace

Example 13. Examples
#namespace=foo and table qualifier=bar
create 'foo:bar', 'fam'

#namespace=default and table qualifier=bar
create 'bar', 'fam'

22. Table

Tables are declared up front at schema definition time.

23. Row

Row keys are uninterpreted bytes. Rows are lexicographically sorted with the lowest order appearing first in a table. The empty byte array is used to denote both the start and end of a tables' namespace.

24. Column Family

Columns in Apache HBase are grouped into column families. All column members of a column family have the same prefix. For example, the columns courses:history and courses:math are both members of the courses column family. The colon character (:) delimits the column family from the column family qualifier. The column family prefix must be composed of printable characters. The qualifying tail, the column family qualifier, can be made of any arbitrary bytes. Column families must be declared up front at schema definition time whereas columns do not need to be defined at schema time but can be conjured on the fly while the table is up and running.

Physically, all column family members are stored together on the filesystem. Because tunings and storage specifications are done at the column family level, it is advised that all column family members have the same general access pattern and size characteristics.

25. Cells

A {row, column, version} tuple exactly specifies a cell in HBase. Cell content is uninterpreted bytes

26. Data Model Operations

The four primary data model operations are Get, Put, Scan, and Delete. Operations are applied via Table instances.

26.1. Get

Get returns attributes for a specified row. Gets are executed via Table.get.

26.2. Put

Put either adds new rows to a table (if the key is new) or can update existing rows (if the key already exists). Puts are executed via Table.put (writeBuffer) or Table.batch (non-writeBuffer).

26.3. Scans

Scan allow iteration over multiple rows for specified attributes.

The following is an example of a Scan on a Table instance. Assume that a table is populated with rows with keys "row1", "row2", "row3", and then another set of rows with the keys "abc1", "abc2", and "abc3". The following example shows how to set a Scan instance to return the rows beginning with "row".

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...

Table table = ...      // instantiate a Table instance

Scan scan = new Scan();
scan.addColumn(CF, ATTR);
scan.setRowPrefixFilter(Bytes.toBytes("row"));
ResultScanner rs = table.getScanner(scan);
try {
  for (Result r = rs.next(); r != null; r = rs.next()) {
    // process result...
  }
} finally {
  rs.close();  // always close the ResultScanner!
}

Note that generally the easiest way to specify a specific stop point for a scan is by using the InclusiveStopFilter class.

26.4. Delete

Delete removes a row from a table. Deletes are executed via Table.delete.

HBase does not modify data in place, and so deletes are handled by creating new markers called tombstones. These tombstones, along with the dead values, are cleaned up on major compactions.

See version.delete for more information on deleting versions of columns, and see compaction for more information on compactions.

27. Versions

A {row, column, version} tuple exactly specifies a cell in HBase. It’s possible to have an unbounded number of cells where the row and column are the same but the cell address differs only in its version dimension.

While rows and column keys are expressed as bytes, the version is specified using a long integer. Typically this long contains time instances such as those returned by java.util.Date.getTime() or System.currentTimeMillis(), that is: the difference, measured in milliseconds, between the current time and midnight, January 1, 1970 UTC.

The HBase version dimension is stored in decreasing order, so that when reading from a store file, the most recent values are found first.

There is a lot of confusion over the semantics of cell versions, in HBase. In particular:

  • If multiple writes to a cell have the same version, only the last written is fetchable.

  • It is OK to write cells in a non-increasing version order.

Below we describe how the version dimension in HBase currently works. See HBASE-2406 for discussion of HBase versions. Bending time in HBase makes for a good read on the version, or time, dimension in HBase. It has more detail on versioning than is provided here. As of this writing, the limitation Overwriting values at existing timestamps mentioned in the article no longer holds in HBase. This section is basically a synopsis of this article by Bruno Dumon.

27.1. Specifying the Number of Versions to Store

The maximum number of versions to store for a given column is part of the column schema and is specified at table creation, or via an alter command, via HColumnDescriptor.DEFAULT_VERSIONS. Prior to HBase 0.96, the default number of versions kept was 3, but in 0.96 and newer has been changed to 1.

Example 14. Modify the Maximum Number of Versions for a Column Family

This example uses HBase Shell to keep a maximum of 5 versions of all columns in column family f1. You could also use HColumnDescriptor.

hbase> alter ‘t1′, NAME => ‘f1′, VERSIONS => 5
Example 15. Modify the Minimum Number of Versions for a Column Family

You can also specify the minimum number of versions to store per column family. By default, this is set to 0, which means the feature is disabled. The following example sets the minimum number of versions on all columns in column family f1 to 2, via HBase Shell. You could also use HColumnDescriptor.

hbase> alter ‘t1′, NAME => ‘f1′, MIN_VERSIONS => 2

Starting with HBase 0.98.2, you can specify a global default for the maximum number of versions kept for all newly-created columns, by setting hbase.column.max.version in hbase-site.xml. See hbase.column.max.version.

27.2. Versions and HBase Operations

In this section we look at the behavior of the version dimension for each of the core HBase operations.

27.2.1. Get/Scan

Gets are implemented on top of Scans. The below discussion of Get applies equally to Scans.

By default, i.e. if you specify no explicit version, when doing a get, the cell whose version has the largest value is returned (which may or may not be the latest one written, see later). The default behavior can be modified in the following ways:

  • to return more than one version, see Get.setMaxVersions()

  • to return versions other than the latest, see Get.setTimeRange()

    To retrieve the latest version that is less than or equal to a given value, thus giving the 'latest' state of the record at a certain point in time, just use a range from 0 to the desired version and set the max versions to 1.

27.2.2. Default Get Example

The following Get will only retrieve the current version of the row

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(Bytes.toBytes("row1"));
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR);  // returns current version of value

27.2.3. Versioned Get Example

The following Get will return the last 3 versions of the row.

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(Bytes.toBytes("row1"));
get.setMaxVersions(3);  // will return last 3 versions of row
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR);  // returns current version of value
List<KeyValue> kv = r.getColumn(CF, ATTR);  // returns all versions of this column

27.2.4. Put

Doing a put always creates a new version of a cell, at a certain timestamp. By default the system uses the server’s currentTimeMillis, but you can specify the version (= the long integer) yourself, on a per-column level. This means you could assign a time in the past or the future, or use the long value for non-time purposes.

To overwrite an existing value, do a put at exactly the same row, column, and version as that of the cell you want to overwrite.

Implicit Version Example

The following Put will be implicitly versioned by HBase with the current time.

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Put put = new Put(Bytes.toBytes(row));
put.add(CF, ATTR, Bytes.toBytes( data));
table.put(put);
Explicit Version Example

The following Put has the version timestamp explicitly set.

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Put put = new Put( Bytes.toBytes(row));
long explicitTimeInMs = 555;  // just an example
put.add(CF, ATTR, explicitTimeInMs, Bytes.toBytes(data));
table.put(put);

Caution: the version timestamp is used internally by HBase for things like time-to-live calculations. It’s usually best to avoid setting this timestamp yourself. Prefer using a separate timestamp attribute of the row, or have the timestamp as a part of the row key, or both.

27.2.5. Delete

There are three different types of internal delete markers. See Lars Hofhansl’s blog for discussion of his attempt adding another, Scanning in HBase: Prefix Delete Marker.

  • Delete: for a specific version of a column.

  • Delete column: for all versions of a column.

  • Delete family: for all columns of a particular ColumnFamily

When deleting an entire row, HBase will internally create a tombstone for each ColumnFamily (i.e., not each individual column).

Deletes work by creating tombstone markers. For example, let’s suppose we want to delete a row. For this you can specify a version, or else by default the currentTimeMillis is used. What this means is delete all cells where the version is less than or equal to this version. HBase never modifies data in place, so for example a delete will not immediately delete (or mark as deleted) the entries in the storage file that correspond to the delete condition. Rather, a so-called tombstone is written, which will mask the deleted values. When HBase does a major compaction, the tombstones are processed to actually remove the dead values, together with the tombstones themselves. If the version you specified when deleting a row is larger than the version of any value in the row, then you can consider the complete row to be deleted.

For an informative discussion on how deletes and versioning interact, see the thread Put w/timestamp → Deleteall → Put w/ timestamp fails up on the user mailing list.

Also see keyvalue for more information on the internal KeyValue format.

Delete markers are purged during the next major compaction of the store, unless the KEEP_DELETED_CELLS option is set in the column family (See Keeping Deleted Cells). To keep the deletes for a configurable amount of time, you can set the delete TTL via the hbase.hstore.time.to.purge.deletes property in hbase-site.xml. If hbase.hstore.time.to.purge.deletes is not set, or set to 0, all delete markers, including those with timestamps in the future, are purged during the next major compaction. Otherwise, a delete marker with a timestamp in the future is kept until the major compaction which occurs after the time represented by the marker’s timestamp plus the value of hbase.hstore.time.to.purge.deletes, in milliseconds.

This behavior represents a fix for an unexpected change that was introduced in HBase 0.94, and was fixed in HBASE-10118. The change has been backported to HBase 0.94 and newer branches.

27.3. Current Limitations

27.3.1. Deletes mask Puts

Deletes mask puts, even puts that happened after the delete was entered. See HBASE-2256. Remember that a delete writes a tombstone, which only disappears after then next major compaction has run. Suppose you do a delete of everything ⇐ T. After this you do a new put with a timestamp ⇐ T. This put, even if it happened after the delete, will be masked by the delete tombstone. Performing the put will not fail, but when you do a get you will notice the put did have no effect. It will start working again after the major compaction has run. These issues should not be a problem if you use always-increasing versions for new puts to a row. But they can occur even if you do not care about time: just do delete and put immediately after each other, and there is some chance they happen within the same millisecond.

27.3.2. Major compactions change query results

…​create three cell versions at t1, t2 and t3, with a maximum-versions setting of 2. So when getting all versions, only the values at t2 and t3 will be returned. But if you delete the version at t2 or t3, the one at t1 will appear again. Obviously, once a major compaction has run, such behavior will not be the case anymore…​ (See Garbage Collection in Bending time in HBase.)

28. Sort Order

All data model operations HBase return data in sorted order. First by row, then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted in reverse, so newest records are returned first).

29. Column Metadata

There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily. Thus, while HBase can support not only a wide number of columns per row, but a heterogeneous set of columns between rows as well, it is your responsibility to keep track of the column names.

The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows. For more information about how HBase stores data internally, see keyvalue.

30. Joins

Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn’t, at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated in this chapter, the read data model operations in HBase are Get and Scan.

However, that doesn’t mean that equivalent join functionality can’t be supported in your application, but you have to do it yourself. The two primary strategies are either denormalizing the data upon writing to HBase, or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS' demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs. hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn’t a single answer that works for every use case.

31. ACID

See ACID Semantics. Lars Hofhansl has also written a note on ACID in HBase.

HBase and Schema Design

A good introduction on the strength and weaknesses modelling on the various non-rdbms datastores is to be found in Ian Varley’s Master thesis, No Relation: The Mixed Blessings of Non-Relational Databases. It is a little dated now but a good background read if you have a moment on how HBase schema modeling differs from how it is done in an RDBMS. Also, read keyvalue for how HBase stores data internally, and the section on schema.casestudies.

The documentation on the Cloud Bigtable website, Designing Your Schema, is pertinent and nicely done and lessons learned there equally apply here in HBase land; just divide any quoted values by ~10 to get what works for HBase: e.g. where it says individual values can be ~10MBs in size, HBase can do similar — perhaps best to go smaller if you can — and where it says a maximum of 100 column families in Cloud Bigtable, think ~10 when modeling on HBase.

32. Schema Creation

HBase schemas can be created or updated using the The Apache HBase Shell or by using Admin in the Java API.

Tables must be disabled when making ColumnFamily modifications, for example:

Configuration config = HBaseConfiguration.create();
Admin admin = new Admin(conf);
TableName table = TableName.valueOf("myTable");

admin.disableTable(table);

HColumnDescriptor cf1 = ...;
admin.addColumn(table, cf1);      // adding new ColumnFamily
HColumnDescriptor cf2 = ...;
admin.modifyColumn(table, cf2);    // modifying existing ColumnFamily

admin.enableTable(table);

See client dependencies for more information about configuring client connections.

online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase requires the table to be disabled.

32.1. Schema Updates

When changes are made to either Tables or ColumnFamilies (e.g. region size, block size), these changes take effect the next time there is a major compaction and the StoreFiles get re-written.

See store for more information on StoreFiles.

33. Table Schema Rules Of Thumb

There are many different data sets, with different access patterns and service-level expectations. Therefore, these rules of thumb are only an overview. Read the rest of this chapter to get more details after you have gone through this list.

  • Aim to have regions sized between 10 and 50 GB.

  • Aim to have cells no larger than 10 MB, or 50 MB if you use mob. Otherwise, consider storing your cell data in HDFS and store a pointer to the data in HBase.

  • A typical schema has between 1 and 3 column families per table. HBase tables should not be designed to mimic RDBMS tables.

  • Around 50-100 regions is a good number for a table with 1 or 2 column families. Remember that a region is a contiguous segment of a column family.

  • Keep your column family names as short as possible. The column family names are stored for every value (ignoring prefix encoding). They should not be self-documenting and descriptive like in a typical RDBMS.

  • If you are storing time-based machine data or logging information, and the row key is based on device ID or service ID plus time, you can end up with a pattern where older data regions never have additional writes beyond a certain age. In this type of situation, you end up with a small number of active regions and a large number of older regions which have no new writes. For these situations, you can tolerate a larger number of regions because your resource consumption is driven by the active regions only.

  • If only one column family is busy with writes, only that column family accomulates memory. Be aware of write patterns when allocating resources.

RegionServer Sizing Rules of Thumb

Lars Hofhansl wrote a great blog post about RegionServer memory sizing. The upshot is that you probably need more memory than you think you need. He goes into the impact of region size, memstore size, HDFS replication factor, and other things to check.

Personally I would place the maximum disk space per machine that can be served exclusively with HBase around 6T, unless you have a very read-heavy workload. In that case the Java heap should be 32GB (20G regions, 128M memstores, the rest defaults).

— Lars Hofhansl
http://hadoop-hbase.blogspot.com/2013/01/hbase-region-server-memory-sizing.html

34. On the number of column families

HBase currently does not do well with anything above two or three column families so keep the number of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so if one column family is carrying the bulk of the data bringing on flushes, the adjacent families will also be flushed even though the amount of data they carry is small. When many column families exist the flushing and compaction interaction can make for a bunch of needless i/o (To be addressed by changing flushing and compaction to work on a per column family basis). For more information on compactions, see Compaction.

Try to make do with one column family if you can in your schemas. Only introduce a second and third column family in the case where data access is usually column scoped; i.e. you query one column family or the other but usually not both at the one time.

34.1. Cardinality of ColumnFamilies

Where multiple ColumnFamilies exist in a single table, be aware of the cardinality (i.e., number of rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion rows, ColumnFamilyA’s data will likely be spread across many, many regions (and RegionServers). This makes mass scans for ColumnFamilyA less efficient.

35. Rowkey Design

35.1. Hotspotting

Rows in HBase are sorted lexicographically by row key. This design optimizes for scans, allowing you to store related rows, or rows that will be read together, near each other. However, poorly designed row keys are a common source of hotspotting. Hotspotting occurs when a large amount of client traffic is directed at one node, or only a few nodes, of a cluster. This traffic may represent reads, writes, or other operations. The traffic overwhelms the single machine responsible for hosting that region, causing performance degradation and potentially leading to region unavailability. This can also have adverse effects on other regions hosted by the same region server as that host is unable to service the requested load. It is important to design data access patterns such that the cluster is fully and evenly utilized.

To prevent hotspotting on writes, design your row keys such that rows that truly do need to be in the same region are, but in the bigger picture, data is being written to multiple regions across the cluster, rather than one at a time. Some common techniques for avoiding hotspotting are described below, along with some of their advantages and drawbacks.

Salting

Salting in this sense has nothing to do with cryptography, but refers to adding random data to the start of a row key. In this case, salting refers to adding a randomly-assigned prefix to the row key to cause it to sort differently than it otherwise would. The number of possible prefixes correspond to the number of regions you want to spread the data across. Salting can be helpful if you have a few "hot" row key patterns which come up over and over amongst other more evenly-distributed rows. Consider the following example, which shows that salting can spread write load across multiple RegionServers, and illustrates some of the negative implications for reads.

Example 16. Salting Example

Suppose you have the following list of row keys, and your table is split such that there is one region for each letter of the alphabet. Prefix 'a' is one region, prefix 'b' is another. In this table, all rows starting with 'f' are in the same region. This example focuses on rows with keys like the following:

foo0001
foo0002
foo0003
foo0004

Now, imagine that you would like to spread these across four different regions. You decide to use four different salts: a, b, c, and d. In this scenario, each of these letter prefixes will be on a different region. After applying the salts, you have the following rowkeys instead. Since you can now write to four separate regions, you theoretically have four times the throughput when writing that you would have if all the writes were going to the same region.

a-foo0003
b-foo0001
c-foo0004
d-foo0002

Then, if you add another row, it will randomly be assigned one of the four possible salt values and end up near one of the existing rows.

a-foo0003
b-foo0001
c-foo0003
c-foo0004
d-foo0002

Since this assignment will be random, you will need to do more work if you want to retrieve the rows in lexicographic order. In this way, salting attempts to increase throughput on writes, but has a cost during reads.

Hashing

Instead of a random assignment, you could use a one-way hash that would cause a given row to always be "salted" with the same prefix, in a way that would spread the load across the RegionServers, but allow for predictability during reads. Using a deterministic hash allows the client to reconstruct the complete rowkey and use a Get operation to retrieve that row as normal.

Example 17. Hashing Example
Given the same situation in the salting example above, you could instead apply a one-way hash that would cause the row with key foo0003 to always, and predictably, receive the a prefix. Then, to retrieve that row, you would already know the key. You could also optimize things so that certain pairs of keys were always in the same region, for instance.
Reversing the Key

A third common trick for preventing hotspotting is to reverse a fixed-width or numeric row key so that the part that changes the most often (the least significant digit) is first. This effectively randomizes row keys, but sacrifices row ordering properties.

See https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-on-designing-hbase-tables, and article on Salted Tables from the Phoenix project, and the discussion in the comments of HBASE-11682 for more information about avoiding hotspotting.

35.2. Monotonically Increasing Row Keys/Timeseries Data

In the HBase chapter of Tom White’s book Hadoop: The Definitive Guide (O’Reilly) there is a an optimization note on watching out for a phenomenon where an import process walks in lock-step with all clients in concert pounding one of the table’s regions (and thus, a single node), then moving onto the next region, etc. With monotonically increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan on why monotonically increasing row keys are problematic in BigTable-like datastores: monotonically increasing values are bad. The pile-up on a single region brought on by monotonically increasing keys can be mitigated by randomizing the input records to not be in sorted order, but in general it’s best to avoid using a timestamp or a sequence (e.g. 1, 2, 3) as the row-key.

If you do need to upload time series data into HBase, you should study OpenTSDB as a successful example. It has a page describing the schema it uses in HBase. The key format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at first glance to contradict the previous advice about not using a timestamp as the key. However, the difference is that the timestamp is not in the lead position of the key, and the design assumption is that there are dozens or hundreds (or more) of different metric types. Thus, even with a continual stream of input data with a mix of metric types, the Puts are distributed across various points of regions in the table.

See schema.casestudies for some rowkey design examples.

35.3. Try to minimize row and column sizes

In HBase, values are always freighted with their coordinates; as a cell value passes through the system, it’ll be accompanied by its row, column name, and timestamp - always. If your rows and column names are large, especially compared to the size of the cell value, then you may run up against some interesting scenarios. One such is the case described by Marc Limotte at the tail of HBASE-3551 (recommended!). Therein, the indices that are kept on HBase storefiles (StoreFile (HFile)) to facilitate random access may end up occupying large chunks of the HBase allotted RAM because the cell value coordinates are large. Mark in the above cited comment suggests upping the block size so entries in the store file index happen at a larger interval or modify the table schema so it makes for smaller rows and column names. Compression will also make for larger indices. See the thread a question storefileIndexSize up on the user mailing list.

Most of the time small inefficiencies don’t matter all that much. Unfortunately, this is a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and rowkeys they could be repeated several billion times in your data.

See keyvalue for more information on HBase stores data internally to see why this is important.

35.3.1. Column Families

Try to keep the ColumnFamily names as small as possible, preferably one character (e.g. "d" for data/default).

See KeyValue for more information on HBase stores data internally to see why this is important.

35.3.2. Attributes

Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to read, prefer shorter attribute names (e.g., "via") to store in HBase.

See keyvalue for more information on HBase stores data internally to see why this is important.

35.3.3. Rowkey Length

Keep them as short as is reasonable such that they can still be useful for required data access (e.g. Get vs. Scan). A short key that is useless for data access is not better than a longer key with better get/scan properties. Expect tradeoffs when designing rowkeys.

35.3.4. Byte Patterns

A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 in those eight bytes. If you stored this number as a String — presuming a byte per character — you need nearly 3x the bytes.

Not convinced? Below is some sample code that you can run on your own.

// long
//
long l = 1234567890L;
byte[] lb = Bytes.toBytes(l);
System.out.println("long bytes length: " + lb.length);   // returns 8

String s = String.valueOf(l);
byte[] sb = Bytes.toBytes(s);
System.out.println("long as string length: " + sb.length);    // returns 10

// hash
//
MessageDigest md = MessageDigest.getInstance("MD5");
byte[] digest = md.digest(Bytes.toBytes(s));
System.out.println("md5 digest bytes length: " + digest.length);    // returns 16

String sDigest = new String(digest);
byte[] sbDigest = Bytes.toBytes(sDigest);
System.out.println("md5 digest as string length: " + sbDigest.length);    // returns 26

Unfortunately, using a binary representation of a type will make your data harder to read outside of your code. For example, this is what you will see in the shell when you increment a value:

hbase(main):001:0> incr 't', 'r', 'f:q', 1
COUNTER VALUE = 1

hbase(main):002:0> get 't', 'r'
COLUMN                                        CELL
 f:q                                          timestamp=1369163040570, value=\x00\x00\x00\x00\x00\x00\x00\x01
1 row(s) in 0.0310 seconds

The shell makes a best effort to print a string, and it this case it decided to just print the hex. The same will happen to your row keys inside the region names. It can be okay if you know what’s being stored, but it might also be unreadable if arbitrary data can be put in the same cells. This is the main trade-off.

35.4. Reverse Timestamps

Reverse Scan API

HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setReversed%28boolean for more information.

A common problem in database processing is quickly finding the most recent version of a value. A technique using reverse timestamps as a part of the key can help greatly with a special case of this problem. Also found in the HBase chapter of Tom White’s book Hadoop: The Definitive Guide (O’Reilly), the technique involves appending (Long.MAX_VALUE - timestamp) to the end of any key, e.g. [key][reverse_timestamp].

The most recent value for [key] in a table can be found by performing a Scan for [key] and obtaining the first record. Since HBase keys are in sorted order, this key sorts before any older row-keys for [key] and thus is first.

This technique would be used instead of using Number of Versions where the intent is to hold onto all versions "forever" (or a very long time) and at the same time quickly obtain access to any other version by using the same Scan technique.

35.5. Rowkeys and ColumnFamilies

Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each ColumnFamily that exists in a table without collision.

35.6. Immutability of Rowkeys

Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row is deleted and then re-inserted. This is a fairly common question on the HBase dist-list so it pays to get the rowkeys right the first time (and/or before you’ve inserted a lot of data).

35.7. Relationship Between RowKeys and Region Splits

If you pre-split your table, it is critical to understand how your rowkey will be distributed across the region boundaries. As an example of why this is important, consider the example of using displayable hex characters as the lead position of the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those key ranges through Bytes.split (which is the split strategy used when creating regions in Admin.createTable(byte[] startKey, byte[] endKey, numRegions) for 10 regions will generate the following splits…​

48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48                                // 0
54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10                 // 6
61 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -68                 // =
68 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -126  // D
75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 72                                // K
82 18 18 18 18 18 18 18 18 18 18 18 18 18 18 14                                // R
88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44                 // X
95 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -102                // _
102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102                // f

(note: the lead byte is listed to the right as a comment.) Given that the first split is a '0' and the last split is an 'f', everything is great, right? Not so fast.

The problem is that all the data is going to pile up in the first 2 regions and the last region thus creating a "lumpy" (and possibly "hot") region problem. To understand why, refer to an ASCII Table. '0' is byte 48, and 'f' is byte 102, but there is a huge gap in byte values (bytes 58 to 96) that will never appear in this keyspace because the only values are [0-9] and [a-f]. Thus, the middle regions will never be used. To make pre-splitting work with this example keyspace, a custom definition of splits (i.e., and not relying on the built-in split method) is required.

Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split them in such a way that all the regions are accessible in the keyspace. While this example demonstrated the problem with a hex-key keyspace, the same problem can happen with any keyspace. Know your data.

Lesson #2: While generally not advisable, using hex-keys (and more generally, displayable data) can still work with pre-split tables as long as all the created regions are accessible in the keyspace.

To conclude this example, the following is an example of how appropriate splits can be pre-created for hex-keys:.

public static boolean createTable(Admin admin, HTableDescriptor table, byte[][] splits)
throws IOException {
  try {
    admin.createTable( table, splits );
    return true;
  } catch (TableExistsException e) {
    logger.info("table " + table.getNameAsString() + " already exists");
    // the table already exists...
    return false;
  }
}

public static byte[][] getHexSplits(String startKey, String endKey, int numRegions) {
  byte[][] splits = new byte[numRegions-1][];
  BigInteger lowestKey = new BigInteger(startKey, 16);
  BigInteger highestKey = new BigInteger(endKey, 16);
  BigInteger range = highestKey.subtract(lowestKey);
  BigInteger regionIncrement = range.divide(BigInteger.valueOf(numRegions));
  lowestKey = lowestKey.add(regionIncrement);
  for(int i=0; i < numRegions-1;i++) {
    BigInteger key = lowestKey.add(regionIncrement.multiply(BigInteger.valueOf(i)));
    byte[] b = String.format("%016x", key).getBytes();
    splits[i] = b;
  }
  return splits;
}

36. Number of Versions

36.1. Maximum Number of Versions

The maximum number of row versions to store is configured per column family via HColumnDescriptor. The default for max versions is 1. This is an important parameter because as described in Data Model section HBase does not overwrite row values, but rather stores different values per row by time (and qualifier). Excess versions are removed during major compactions. The number of max versions may need to be increased or decreased depending on application needs.

It is not recommended setting the number of max versions to an exceedingly high level (e.g., hundreds or more) unless those old values are very dear to you because this will greatly increase StoreFile size.

36.2. Minimum Number of Versions

Like maximum number of row versions, the minimum number of row versions to keep is configured per column family via HColumnDescriptor. The default for min versions is 0, which means the feature is disabled. The minimum number of row versions parameter is used together with the time-to-live parameter and can be combined with the number of row versions parameter to allow configurations such as "keep the last T minutes worth of data, at most N versions, but keep at least M versions around" (where M is the value for minimum number of row versions, M<N). This parameter should only be set when time-to-live is enabled for a column family and must be less than the number of row versions.

37. Supported Datatypes

HBase supports a "bytes-in/bytes-out" interface via Put and Result, so anything that can be converted to an array of bytes can be stored as a value. Input could be strings, numbers, complex objects, or even images as long as they can rendered as bytes.

There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase would probably be too much to ask); search the mailing list for conversations on this topic. All rows in HBase conform to the Data Model, and that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily.

37.1. Counters

One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See Increment in Table.

Synchronization on counters are done on the RegionServer, not in the client.

38. Joins

If you have multiple tables, don’t forget to factor in the potential for Joins into the schema design.

39. Time To Live (TTL)

ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows once the expiration time is reached. This applies to all versions of a row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC.

Store files which contains only expired rows are deleted on minor compaction. Setting hbase.store.delete.expired.storefile to false disables this feature. Setting minimum number of versions to other than 0 also disables this.

See HColumnDescriptor for more information.

Recent versions of HBase also support setting time to live on a per cell basis. See HBASE-10560 for more information. Cell TTLs are submitted as an attribute on mutation requests (Appends, Increments, Puts, etc.) using Mutation#setTTL. If the TTL attribute is set, it will be applied to all cells updated on the server by the operation. There are two notable differences between cell TTL handling and ColumnFamily TTLs:

  • Cell TTLs are expressed in units of milliseconds instead of seconds.

  • A cell TTLs cannot extend the effective lifetime of a cell beyond a ColumnFamily level TTL setting.

40. Keeping Deleted Cells

By default, delete markers extend back to the beginning of time. Therefore, Get or Scan operations will not see a deleted cell (row or column), even when the Get or Scan operation indicates a time range before the delete marker was placed.

ColumnFamilies can optionally keep deleted cells. In this case, deleted cells can still be retrieved, as long as these operations specify a time range that ends before the timestamp of any delete that would affect the cells. This allows for point-in-time queries even in the presence of deletes.

Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete markers.

Example 18. Change the Value of KEEP_DELETED_CELLS Using HBase Shell
hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true
Example 19. Change the Value of KEEP_DELETED_CELLS Using the API
...
HColumnDescriptor.setKeepDeletedCells(true);
...

Let us illustrate the basic effect of setting the KEEP_DELETED_CELLS attribute on a table.

First, without:

create 'test', {NAME=>'e', VERSIONS=>2147483647}
put 'test', 'r1', 'e:c1', 'value', 10
put 'test', 'r1', 'e:c1', 'value', 12
put 'test', 'r1', 'e:c1', 'value', 14
delete 'test', 'r1', 'e:c1',  11

hbase(main):017:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
 r1                                              column=e:c1, timestamp=11, type=DeleteColumn
 r1                                              column=e:c1, timestamp=10, value=value
1 row(s) in 0.0120 seconds

hbase(main):018:0> flush 'test'
0 row(s) in 0.0350 seconds

hbase(main):019:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
 r1                                              column=e:c1, timestamp=11, type=DeleteColumn
1 row(s) in 0.0120 seconds

hbase(main):020:0> major_compact 'test'
0 row(s) in 0.0260 seconds

hbase(main):021:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                              COLUMN+CELL
 r1                                              column=e:c1, timestamp=14, value=value
 r1                                              column=e:c1, timestamp=12, value=value
1 row(s) in 0.0120 seconds

Notice how delete cells are let go.

Now let’s run the same test only with KEEP_DELETED_CELLS set on the table (you can do table or per-column-family):

hbase(main):005:0> create 'test', {NAME=>'e', VERSIONS=>2147483647, KEEP_DELETED_CELLS => true}
0 row(s) in 0.2160 seconds

=> Hbase::Table - test
hbase(main):006:0> put 'test', 'r1', 'e:c1', 'value', 10
0 row(s) in 0.1070 seconds

hbase(main):007:0> put 'test', 'r1', 'e:c1', 'value', 12
0 row(s) in 0.0140 seconds

hbase(main):008:0> put 'test', 'r1', 'e:c1', 'value', 14
0 row(s) in 0.0160 seconds

hbase(main):009:0> delete 'test', 'r1', 'e:c1',  11
0 row(s) in 0.0290 seconds

hbase(main):010:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0550 seconds

hbase(main):011:0> flush 'test'
0 row(s) in 0.2780 seconds

hbase(main):012:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0620 seconds

hbase(main):013:0> major_compact 'test'
0 row(s) in 0.0530 seconds

hbase(main):014:0> scan 'test', {RAW=>true, VERSIONS=>1000}
ROW                                                                                          COLUMN+CELL
 r1                                                                                          column=e:c1, timestamp=14, value=value
 r1                                                                                          column=e:c1, timestamp=12, value=value
 r1                                                                                          column=e:c1, timestamp=11, type=DeleteColumn
 r1                                                                                          column=e:c1, timestamp=10, value=value
1 row(s) in 0.0650 seconds

KEEP_DELETED_CELLS is to avoid removing Cells from HBase when the only reason to remove them is the delete marker. So with KEEP_DELETED_CELLS enabled deleted cells would get removed if either you write more versions than the configured max, or you have a TTL and Cells are in excess of the configured timeout, etc.

41. Secondary Indexes and Alternate Query Paths

This section could also be titled "what if my table rowkey looks like this but I also want to query my table like that." A common example on the dist-list is where a row-key is of the format "user-timestamp" but there are reporting requirements on activity across users for certain time ranges. Thus, selecting by user is easy because it is in the lead position of the key, but time is not.

There is no single answer on the best way to handle this because it depends on…​

  • Number of users

  • Data size and data arrival rate

  • Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. pre-configured ranges)

  • Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an ad-hoc report, whereas it may be too long for others)

and solutions are also influenced by the size of the cluster and how much processing power you have to throw at the solution. Common techniques are in sub-sections below. This is a comprehensive, but not exhaustive, list of approaches.

It should not be a surprise that secondary indexes require additional cluster space and processing. This is precisely what happens in an RDBMS because the act of creating an alternate index requires both space and processing cycles to update. RDBMS products are more advanced in this regard to handle alternative index management out of the box. However, HBase scales better at larger data volumes, so this is a feature trade-off.

Pay attention to Apache HBase Performance Tuning when implementing any of these approaches.

Additionally, see the David Butler response in this dist-list thread HBase, mail # user - Stargate+hbase

41.1. Filter Query

Depending on the case, it may be appropriate to use Client Request Filters. In this case, no secondary index is created. However, don’t try a full-scan on a large table like this from an application (i.e., single-threaded client).

41.2. Periodic-Update Secondary Index

A secondary index could be created in another table which is periodically updated via a MapReduce job. The job could be executed intra-day, but depending on load-strategy it could still potentially be out of sync with the main data table.

See mapreduce.example.readwrite for more information.

41.3. Dual-Write Secondary Index

Another strategy is to build the secondary index while publishing data to the cluster (e.g., write to data table, write to index table). If this is approach is taken after a data table already exists, then bootstrapping will be needed for the secondary index with a MapReduce job (see secondary.indexes.periodic).

41.4. Summary Tables

Where time-ranges are very wide (e.g., year-long report) and where the data is voluminous, summary tables are a common approach. These would be generated with MapReduce jobs into another table.

See mapreduce.example.summary for more information.

41.5. Coprocessor Secondary Index

Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see coprocessors

42. Constraints

HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised usage for Constraints is in enforcing business rules for attributes in the table (e.g. make sure values are in the range 1-10). Constraints could also be used to enforce referential integrity, but this is strongly discouraged as it will dramatically decrease the write throughput of the tables where integrity checking is enabled. Extensive documentation on using Constraints can be found at Constraint since version 0.94.

43. Schema Design Case Studies

The following will describe some typical data ingestion use-cases with HBase, and how the rowkey design and construction can be approached. Note: this is just an illustration of potential approaches, not an exhaustive list. Know your data, and know your processing requirements.

It is highly recommended that you read the rest of the HBase and Schema Design first, before reading these case studies.

The following case studies are described:

  • Log Data / Timeseries Data

  • Log Data / Timeseries on Steroids

  • Customer/Order

  • Tall/Wide/Middle Schema Design

  • List Data

43.1. Case Study - Log Data and Timeseries Data

Assume that the following data elements are being collected.

  • Hostname

  • Timestamp

  • Log event

  • Value/message

We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From these attributes the rowkey will be some combination of hostname, timestamp, and log-event - but what specifically?

43.1.1. Timestamp In The Rowkey Lead Position

The rowkey [timestamp][hostname][log-event] suffers from the monotonically increasing rowkey problem described in Monotonically Increasing Row Keys/Timeseries Data.

There is another pattern frequently mentioned in the dist-lists about "bucketing" timestamps, by performing a mod operation on the timestamp. If time-oriented scans are important, this could be a useful approach. Attention must be paid to the number of buckets, because this will require the same number of scans to return results.

long bucket = timestamp % numBuckets;

to construct:

[bucket][timestamp][hostname][log-event]

As stated above, to select data for a particular timerange, a Scan will need to be performed for each bucket. 100 buckets, for example, will provide a wide distribution in the keyspace but it will require 100 Scans to obtain data for a single timestamp, so there are trade-offs.

43.1.2. Host In The Rowkey Lead Position

The rowkey [hostname][log-event][timestamp] is a candidate if there is a large-ish number of hosts to spread the writes and reads across the keyspace. This approach would be useful if scanning by hostname was a priority.

43.1.3. Timestamp, or Reverse Timestamp?

If the most important access path is to pull most recent events, then storing the timestamps as reverse-timestamps (e.g., timestamp = Long.MAX_VALUE – timestamp) will create the property of being able to do a Scan on [hostname][log-event] to obtain the quickly obtain the most recently captured events.

Neither approach is wrong, it just depends on what is most appropriate for the situation.

Reverse Scan API

HBASE-4811 implements an API to scan a table or a range within a table in reverse, reducing the need to optimize your schema for forward or reverse scanning. This feature is available in HBase 0.98 and later. See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Scan.html#setReversed%28boolean for more information.

43.1.4. Variable Length or Fixed Length Rowkeys?

It is critical to remember that rowkeys are stamped on every column in HBase. If the hostname is a and the event type is e1 then the resulting rowkey would be quite small. However, what if the ingested hostname is myserver1.mycompany.com and the event type is com.package1.subpackage2.subsubpackage3.ImportantService?

It might make sense to use some substitution in the rowkey. There are at least two approaches: hashed and numeric. In the Hostname In The Rowkey Lead Position example, it might look like this:

Composite Rowkey With Hashes:

  • [MD5 hash of hostname] = 16 bytes

  • [MD5 hash of event-type] = 16 bytes

  • [timestamp] = 8 bytes

Composite Rowkey With Numeric Substitution:

For this approach another lookup table would be needed in addition to LOG_DATA, called LOG_TYPES. The rowkey of LOG_TYPES would be:

  • [type] (e.g., byte indicating hostname vs. event-type)

  • [bytes] variable length bytes for raw hostname or event-type.

A column for this rowkey could be a long with an assigned number, which could be obtained by using an HBase counter.

So the resulting composite rowkey would be:

  • [substituted long for hostname] = 8 bytes

  • [substituted long for event type] = 8 bytes

  • [timestamp] = 8 bytes

In either the Hash or Numeric substitution approach, the raw values for hostname and event-type can be stored as columns.

43.2. Case Study - Log Data and Timeseries Data on Steroids

This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows into columns for certain time-periods. For a detailed explanation, see: http://opentsdb.net/schema.html, and Lessons Learned from OpenTSDB from HBaseCon2012.

But this is how the general concept works: data is ingested, for example, in this manner…​

[hostname][log-event][timestamp1]
[hostname][log-event][timestamp2]
[hostname][log-event][timestamp3]

with separate rowkeys for each detailed event, but is re-written like this…​

[hostname][log-event][timerange]

and each of the above events are converted into columns stored with a time-offset relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very advanced processing technique, but HBase makes this possible.

43.3. Case Study - Customer/Order

Assume that HBase is used to store customer and order information. There are two core record-types being ingested: a Customer record type, and Order record type.

The Customer record type would include all the things that you’d typically expect:

  • Customer number

  • Customer name

  • Address (e.g., city, state, zip)

  • Phone numbers, etc.

The Order record type would include things like:

  • Customer number

  • Order number

  • Sales date

  • A series of nested objects for shipping locations and line-items (see Order Object Design for details)

Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose the rowkey, and specifically a composite key such as:

[customer number][order number]

for an ORDER table. However, there are more design decisions to make: are the raw values the best choices for rowkeys?

The same design questions in the Log Data use-case confront us here. What is the keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a reasonable spread in the keyspace, similar options appear:

Composite Rowkey With Hashes:

  • [MD5 of customer number] = 16 bytes

  • [MD5 of order number] = 16 bytes

Composite Numeric/Hash Combo Rowkey:

  • [substituted long for customer number] = 8 bytes

  • [MD5 of order number] = 16 bytes

43.3.1. Single Table? Multiple Tables?

A traditional design approach would have separate tables for CUSTOMER and SALES. Another option is to pack multiple record types into a single table (e.g., CUSTOMER++).

Customer Record Type Rowkey:

  • [customer-id]

  • [type] = type indicating `1' for customer record type

Order Record Type Rowkey:

  • [customer-id]

  • [type] = type indicating `2' for order record type

  • [order]

The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id (e.g., a single scan could get you everything about that customer). The disadvantage is that it’s not as easy to scan for a particular record-type.

43.3.2. Order Object Design

Now we need to address how to model the Order object. Assume that the class structure is as follows:

Order

(an Order can have multiple ShippingLocations

LineItem

(a ShippingLocation can have multiple LineItems

there are multiple options on storing this data.

Completely Normalized

With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM.

The ORDER table’s rowkey was described above: schema.casestudies.custorder

The SHIPPING_LOCATION’s composite rowkey would be something like this:

  • [order-rowkey]

  • [shipping location number] (e.g., 1st location, 2nd, etc.)

The LINE_ITEM table’s composite rowkey would be something like this:

  • [order-rowkey]

  • [shipping location number] (e.g., 1st location, 2nd, etc.)

  • [line item number] (e.g., 1st lineitem, 2nd, etc.)

Such a normalized model is likely to be the approach with an RDBMS, but that’s not your only option with HBase. The cons of such an approach is that to retrieve information about any Order, you will need:

  • Get on the ORDER table for the Order

  • Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances

  • Scan on the LINE_ITEM for each ShippingLocation

granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase you’re just more aware of this fact.

Single Table With Record Types

With this approach, there would exist a single table ORDER that would contain

The Order rowkey was described above: schema.casestudies.custorder

  • [order-rowkey]

  • [ORDER record type]

The ShippingLocation composite rowkey would be something like this:

  • [order-rowkey]

  • [SHIPPING record type]

  • [shipping location number] (e.g., 1st location, 2nd, etc.)

The LineItem composite rowkey would be something like this:

  • [order-rowkey]

  • [LINE record type]

  • [shipping location number] (e.g., 1st location, 2nd, etc.)

  • [line item number] (e.g., 1st lineitem, 2nd, etc.)

Denormalized

A variant of the Single Table With Record Types approach is to denormalize and flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.

The LineItem composite rowkey would be something like this:

  • [order-rowkey]

  • [LINE record type]

  • [line item number] (e.g., 1st lineitem, 2nd, etc., care must be taken that there are unique across the entire order)

and the LineItem columns would be something like this:

  • itemNumber

  • quantity

  • price

  • shipToLine1 (denormalized from ShippingLocation)

  • shipToLine2 (denormalized from ShippingLocation)

  • shipToCity (denormalized from ShippingLocation)

  • shipToState (denormalized from ShippingLocation)

  • shipToZip (denormalized from ShippingLocation)

The pros of this approach include a less complex object hierarchy, but one of the cons is that updating gets more complicated in case any of this information changes.

Object BLOB

With this approach, the entire Order object graph is treated, in one way or another, as a BLOB. For example, the ORDER table’s rowkey was described above: schema.casestudies.custorder, and a single column called "order" would contain an object that could be deserialized that contained a container Order, ShippingLocations, and LineItems.

There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc. All of them are variants of the same approach: encode the object graph to a byte-array. Care should be taken with this approach to ensure backward compatibility in case the object model changes such that older persisted structures can still be read back out of HBase.

Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per Order in this example), but the cons include the aforementioned warning about backward compatibility of serialization, language dependencies of serialization (e.g., Java Serialization only works with Java clients), the fact that you have to deserialize the entire object to get any piece of information inside the BLOB, and the difficulty in getting frameworks like Hive to work with custom objects like this.

43.4. Case Study - "Tall/Wide/Middle" Schema Design Smackdown

This section will describe additional schema design questions that appear on the dist-list, specifically about tall and wide tables. These are general guidelines and not laws - each application must consider its own needs.

43.4.1. Rows vs. Versions

A common question is whether one should prefer rows or HBase’s built-in-versioning. The context is typically where there are "a lot" of versions of a row to be retained (e.g., where it is significantly above the HBase default of 1 max versions). The rows-approach would require storing a timestamp in some portion of the rowkey so that they would not overwrite with each successive update.

Preference: Rows (generally speaking).

43.4.2. Rows vs. Columns

Another common question is whether one should prefer rows or columns. The context is typically in extreme cases of wide tables, such as having 1 row with 1 million attributes, or 1 million rows with 1 columns apiece.

Preference: Rows (generally speaking). To be clear, this guideline is in the context is in extremely wide cases, not in the standard use-case where one needs to store a few dozen or hundred columns. But there is also a middle path between these two options, and that is "Rows as Columns."

43.4.3. Rows as Columns

The middle path between Rows vs. Columns is packing data that would be a separate row into columns, for certain rows. OpenTSDB is the best example of this case where a single row represents a defined time-range, and then discrete events are treated as columns. This approach is often more complex, and may require the additional complexity of re-writing your data, but has the advantage of being I/O efficient. For an overview of this approach, see schema.casestudies.log-steroids.

43.5. Case Study - List Data

The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in Apache HBase.

  • QUESTION *

We’re looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense. One option is store the majority of the data in a key, so we could have something like:

<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
<FixedWidthUserName><FixedWidthValueId3>:"" (no value)

The other option we had was to do this entirely using:

<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...

where each row would contain multiple values. So in one case reading the first thirty values would be:

scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}

And in the second case it would be

get 'FixedWidthUserName\x00\x00\x00\x00'

The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists. Some users would have ⇐ 30 total values in these lists, and some users would have millions (i.e. power-law distribution)

The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility. Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?

My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we’ll always need the same page size. I’ve ended up hearing different people tell me opposite things about performance. I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case. I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we’d need to update all subsequent rows).

Thanks for help / suggestions / follow-up questions.

  • ANSWER *

If I understand you correctly, you’re ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:

"user123, firstname, Paul",
"user234, lastname, Smith"

(But the usernames are fixed width, and the valueids are fixed width).

And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?

The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you’re really sure it is needed.

Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you’re giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn’t sound like you need that. Doing it this way is generally recommended (see here http://hbase.apache.org/book.html#schema.smackdown).

Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row. I’m guessing you jumped to the "paginated" version because you’re assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you’re not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn’t be fundamentally worse. The client has methods that allow you to get specific slices of columns.

Note that neither case fundamentally uses more disk space than the other; you’re just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars George’s excellent video about understanding HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk).

A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc. That seems significantly more complex. It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out. If you don’t have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! :)

44. Operational and Performance Configuration Options

See the Performance section perf.schema for more information operational and performance schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.

HBase and MapReduce

Apache MapReduce is a software framework used to analyze large amounts of data, and is the framework used most often with Apache Hadoop. MapReduce itself is out of the scope of this document. A good place to get started with MapReduce is http://hadoop.apache.org/docs/r2.6.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html. MapReduce version 2 (MR2)is now part of YARN.

This chapter discusses specific configuration steps you need to take to use MapReduce on data within HBase. In addition, it discusses other interactions and issues between HBase and MapReduce jobs. Finally, it discusses Cascading, an alternative API for MapReduce.

mapred and mapreduce

There are two mapreduce packages in HBase as in MapReduce itself: org.apache.hadoop.hbase.mapred and org.apache.hadoop.hbase.mapreduce. The former does old-style API and the latter the new style. The latter has more facility though you can usually find an equivalent in the older package. Pick the package that goes with your MapReduce deploy. When in doubt or starting over, pick the org.apache.hadoop.hbase.mapreduce. In the notes below, we refer to o.a.h.h.mapreduce but replace with the o.a.h.h.mapred if that is what you are using.

45. HBase, MapReduce, and the CLASSPATH

By default, MapReduce jobs deployed to a MapReduce cluster do not have access to either the HBase configuration under $HBASE_CONF_DIR or the HBase classes.

To give the MapReduce jobs the access they need, you could add hbase-site.xml to $HADOOP_HOME/conf and add HBase jars to the $HADOOP_HOME/lib directory. You would then need to copy these changes across your cluster. Or you can edit $HADOOP_HOME/conf/hadoop-env.sh and add them to the HADOOP_CLASSPATH variable. However, this approach is not recommended because it will pollute your Hadoop install with HBase references. It also requires you to restart the Hadoop cluster before Hadoop can use the HBase data.

The recommended approach is to let HBase add its dependency jars itself and use HADOOP_CLASSPATH or -libjars.

Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself. The dependencies only need to be available on the local CLASSPATH. The following example runs the bundled HBase RowCounter MapReduce job against a table named usertable. If you have not set the environment variables expected in the command (the parts prefixed by a $ sign and surrounded by curly braces), you can use the actual system paths instead. Be sure to use the correct version of the HBase JAR for your system. The backticks (` symbols) cause the shell to execute the sub-commands, setting the output of hbase classpath (the command to dump HBase CLASSPATH) to HADOOP_CLASSPATH. This example assumes you use a BASH-compatible shell.

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-server-VERSION.jar rowcounter usertable

When the command runs, internally, the HBase JAR finds the dependencies it needs and adds them to the MapReduce job configuration. See the source at TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done.

The command hbase mapredcp can also help you dump the CLASSPATH entries required by MapReduce, which are the same jars TableMapReduceUtil#addDependencyJars would add. You can add them together with HBase conf directory to HADOOP_CLASSPATH. For jobs that do not package their dependencies or call TableMapReduceUtil#addDependencyJars, the following command structure is necessary:

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase mapredcp`:${HBASE_HOME}/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(${HBASE_HOME}/bin/hbase mapredcp | tr ':' ',') ...

The example may not work if you are running HBase from its build directory rather than an installed location. You may see an error like the following:

java.lang.RuntimeException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper

If this occurs, try modifying the command as follows, so that it uses the HBase JARs from the target/ directory within the build environment.

$ HADOOP_CLASSPATH=${HBASE_BUILD_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar:`${HBASE_BUILD_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_BUILD_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar rowcounter usertable
Notice to MapReduce users of HBase between 0.96.1 and 0.98.4

Some MapReduce jobs that use HBase fail to launch. The symptom is an exception similar to the following:

Exception in thread "main" java.lang.IllegalAccessError: class
    com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass
    com.google.protobuf.LiteralByteString
    at java.lang.ClassLoader.defineClass1(Native Method)
    at java.lang.ClassLoader.defineClass(ClassLoader.java:792)
    at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
    at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
    at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    at
    org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:818)
    at
    org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToString(TableMapReduceUtil.java:433)
    at
    org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:186)
    at
    org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:147)
    at
    org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:270)
    at
    org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:100)
...

This is caused by an optimization introduced in HBASE-9867 that inadvertently introduced a classloader dependency.

This affects both jobs using the -libjars option and "fat jar," those which package their runtime dependencies in a nested lib folder.

In order to satisfy the new classloader requirements, hbase-protocol.jar must be included in Hadoop’s classpath. See HBase, MapReduce, and the CLASSPATH for current recommendations for resolving classpath errors. The following is included for historical purposes.

This can be resolved system-wide by including a reference to the hbase-protocol.jar in Hadoop’s lib directory, via a symlink or by copying the jar into the new location.

This can also be achieved on a per-job launch basis by including it in the HADOOP_CLASSPATH environment variable at job submission time. When launching jobs that package their dependencies, all three of the following job launching commands satisfy this requirement:

$ HADOOP_CLASSPATH=/path/to/hbase-protocol.jar:/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass
$ HADOOP_CLASSPATH=$(hbase mapredcp):/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass
$ HADOOP_CLASSPATH=$(hbase classpath) hadoop jar MyJob.jar MyJobMainClass

For jars that do not package their dependencies, the following command structure is necessary:

$ HADOOP_CLASSPATH=$(hbase mapredcp):/etc/hbase/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(hbase mapredcp | tr ':' ',') ...

See also HBASE-10304 for further discussion of this issue.

46. MapReduce Scan Caching

TableMapReduceUtil now restores the option to set scanner caching (the number of rows which are cached before returning the result to the client) on the Scan object that is passed in. This functionality was lost due to a bug in HBase 0.95 (HBASE-11558), which is fixed for HBase 0.98.5 and 0.96.3. The priority order for choosing the scanner caching is as follows:

  1. Caching settings which are set on the scan object.

  2. Caching settings which are specified via the configuration option hbase.client.scanner.caching, which can either be set manually in hbase-site.xml or via the helper method TableMapReduceUtil.setScannerCaching().

  3. The default value HConstants.DEFAULT_HBASE_CLIENT_SCANNER_CACHING, which is set to 100.

Optimizing the caching settings is a balance between the time the client waits for a result and the number of sets of results the client needs to receive. If the caching setting is too large, the client could end up waiting for a long time or the request could even time out. If the setting is too small, the scan needs to return results in several pieces. If you think of the scan as a shovel, a bigger cache setting is analogous to a bigger shovel, and a smaller cache setting is equivalent to more shoveling in order to fill the bucket.

The list of priorities mentioned above allows you to set a reasonable default, and override it for specific operations.

See the API documentation for Scan for more details.

47. Bundled HBase MapReduce Jobs

The HBase JAR also serves as a Driver for some bundled MapReduce jobs. To learn about the bundled MapReduce jobs, run the following command.

$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar
An example program must be given as the first argument.
Valid program names are:
  copytable: Export a table from local cluster to peer cluster
  completebulkload: Complete a bulk data load.
  export: Write table data to HDFS.
  import: Import data written by Export.
  importtsv: Import data in TSV format.
  rowcounter: Count rows in HBase table

Each of the valid program names are bundled MapReduce jobs. To run one of the jobs, model your command after the following example.

$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter myTable

48. HBase as a MapReduce Job Data Source and Data Sink

HBase can be used as a data source, TableInputFormat, and data sink, TableOutputFormat or MultiTableOutputFormat, for MapReduce jobs. Writing MapReduce jobs that read or write HBase, it is advisable to subclass TableMapper and/or TableReducer. See the do-nothing pass-through classes IdentityTableMapper and IdentityTableReducer for basic usage. For a more involved example, see RowCounter or review the org.apache.hadoop.hbase.mapreduce.TestTableMapReduce unit test.

If you run MapReduce jobs that use HBase as source or sink, need to specify source and sink table and column names in your configuration.

When you read from HBase, the TableInputFormat requests the list of regions from HBase and makes a map, which is either a map-per-region or mapreduce.job.maps map, whichever is smaller. If your job only has two maps, raise mapreduce.job.maps to a number greater than the number of regions. Maps will run on the adjacent TaskTracker/NodeManager if you are running a TaskTracer/NodeManager and RegionServer per node. When writing to HBase, it may make sense to avoid the Reduce step and write back into HBase from within your map. This approach works when your job does not need the sort and collation that MapReduce does on the map-emitted data. On insert, HBase 'sorts' so there is no point double-sorting (and shuffling data around your MapReduce cluster) unless you need to. If you do not need the Reduce, your map might emit counts of records processed for reporting at the end of the job, or set the number of Reduces to zero and use TableOutputFormat. If running the Reduce step makes sense in your case, you should typically use multiple reducers so that load is spread across the HBase cluster.

A new HBase partitioner, the HRegionPartitioner, can run as many reducers the number of existing regions. The HRegionPartitioner is suitable when your table is large and your upload will not greatly alter the number of existing regions upon completion. Otherwise use the default partitioner.

49. Writing HFiles Directly During Bulk Import

If you are importing into a new table, you can bypass the HBase API and write your content directly to the filesystem, formatted into HBase data files (HFiles). Your import will run faster, perhaps an order of magnitude faster. For more on how this mechanism works, see Bulk Loading.

50. RowCounter Example

The included RowCounter MapReduce job uses TableInputFormat and does a count of all rows in the specified table. To run it, use the following command:

$ ./bin/hadoop jar hbase-X.X.X.jar

This will invoke the HBase MapReduce Driver class. Select rowcounter from the choice of jobs offered. This will print rowcounter usage advice to standard output. Specify the tablename, column to count, and output directory. If you have classpath errors, see HBase, MapReduce, and the CLASSPATH.

51. Map-Task Splitting

51.1. The Default HBase MapReduce Splitter

When TableInputFormat is used to source an HBase table in a MapReduce job, its splitter will make a map task for each region of the table. Thus, if there are 100 regions in the table, there will be 100 map-tasks for the job - regardless of how many column families are selected in the Scan.

51.2. Custom Splitters

For those interested in implementing custom splitters, see the method getSplits in TableInputFormatBase. That is where the logic for map-task assignment resides.

52. HBase MapReduce Examples

52.1. HBase MapReduce Read Example

The following is an example of using HBase as a MapReduce source in read-only manner. Specifically, there is a Mapper instance but no Reducer, and nothing is being emitted from the Mapper. The job would be defined as follows…​

Configuration config = HBaseConfiguration.create();
Job job = new Job(config, "ExampleRead");
job.setJarByClass(MyReadJob.class);     // class that contains mapper

Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs
...

TableMapReduceUtil.initTableMapperJob(
  tableName,        // input HBase table name
  scan,             // Scan instance to control CF and attribute selection
  MyMapper.class,   // mapper
  null,             // mapper output key
  null,             // mapper output value
  job);
job.setOutputFormatClass(NullOutputFormat.class);   // because we aren't emitting anything from mapper

boolean b = job.waitForCompletion(true);
if (!b) {
  throw new IOException("error with job!");
}

…​and the mapper instance would extend TableMapper…​

public static class MyMapper extends TableMapper<Text, Text> {

  public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException {
    // process data for the row from the Result instance.
   }
}

52.2. HBase MapReduce Read/Write Example

The following is an example of using HBase both as a source and as a sink with MapReduce. This example will simply copy data from one table to another.

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleReadWrite");
job.setJarByClass(MyReadWriteJob.class);    // class that contains mapper

Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs

TableMapReduceUtil.initTableMapperJob(
  sourceTable,      // input table
  scan,             // Scan instance to control CF and attribute selection
  MyMapper.class,   // mapper class
  null,             // mapper output key
  null,             // mapper output value
  job);
TableMapReduceUtil.initTableReducerJob(
  targetTable,      // output table
  null,             // reducer class
  job);
job.setNumReduceTasks(0);

boolean b = job.waitForCompletion(true);
if (!b) {
    throw new IOException("error with job!");
}

An explanation is required of what TableMapReduceUtil is doing, especially with the reducer. TableOutputFormat is being used as the outputFormat class, and several parameters are being set on the config (e.g., TableOutputFormat.OUTPUT_TABLE), as well as setting the reducer output key to ImmutableBytesWritable and reducer value to Writable. These could be set by the programmer on the job and conf, but TableMapReduceUtil tries to make things easier.

The following is the example mapper, which will create a Put and matching the input Result and emit it. Note: this is what the CopyTable utility does.

public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put>  {

  public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
    // this example is just copying the data from the source table...
      context.write(row, resultToPut(row,value));
    }

    private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
      Put put = new Put(key.get());
      for (KeyValue kv : result.raw()) {
        put.add(kv);
      }
      return put;
    }
}

There isn’t actually a reducer step, so TableOutputFormat takes care of sending the Put to the target table.

This is just an example, developers could choose not to use TableOutputFormat and connect to the target table themselves.

52.3. HBase MapReduce Read/Write Example With Multi-Table Output

TODO: example for MultiTableOutputFormat.

52.4. HBase MapReduce Summary to HBase Example

The following example uses HBase as a MapReduce source and sink with a summarization step. This example will count the number of distinct instances of a value in a table and write those summarized counts in another table.

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummary");
job.setJarByClass(MySummaryJob.class);     // class that contains mapper and reducer

Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs

TableMapReduceUtil.initTableMapperJob(
  sourceTable,        // input table
  scan,               // Scan instance to control CF and attribute selection
  MyMapper.class,     // mapper class
  Text.class,         // mapper output key
  IntWritable.class,  // mapper output value
  job);
TableMapReduceUtil.initTableReducerJob(
  targetTable,        // output table
  MyTableReducer.class,    // reducer class
  job);
job.setNumReduceTasks(1);   // at least one, adjust as required

boolean b = job.waitForCompletion(true);
if (!b) {
  throw new IOException("error with job!");
}

In this example mapper a column with a String-value is chosen as the value to summarize upon. This value is used as the key to emit from the mapper, and an IntWritable represents an instance counter.

public static class MyMapper extends TableMapper<Text, IntWritable>  {
  public static final byte[] CF = "cf".getBytes();
  public static final byte[] ATTR1 = "attr1".getBytes();

  private final IntWritable ONE = new IntWritable(1);
  private Text text = new Text();

  public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
    String val = new String(value.getValue(CF, ATTR1));
    text.set(val);     // we can only emit Writables...
    context.write(text, ONE);
  }
}

In the reducer, the "ones" are counted (just like any other MR example that does this), and then emits a Put.

public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable>  {
  public static final byte[] CF = "cf".getBytes();
  public static final byte[] COUNT = "count".getBytes();

  public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
    int i = 0;
    for (IntWritable val : values) {
      i += val.get();
    }
    Put put = new Put(Bytes.toBytes(key.toString()));
    put.add(CF, COUNT, Bytes.toBytes(i));

    context.write(null, put);
  }
}

52.5. HBase MapReduce Summary to File Example

This very similar to the summary example above, with exception that this is using HBase as a MapReduce source but HDFS as the sink. The differences are in the job setup and in the reducer. The mapper remains the same.

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleSummaryToFile");
job.setJarByClass(MySummaryFileJob.class);     // class that contains mapper and reducer

Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs

TableMapReduceUtil.initTableMapperJob(
  sourceTable,        // input table
  scan,               // Scan instance to control CF and attribute selection
  MyMapper.class,     // mapper class
  Text.class,         // mapper output key
  IntWritable.class,  // mapper output value
  job);
job.setReducerClass(MyReducer.class);    // reducer class
job.setNumReduceTasks(1);    // at least one, adjust as required
FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile"));  // adjust directories as required

boolean b = job.waitForCompletion(true);
if (!b) {
  throw new IOException("error with job!");
}

As stated above, the previous Mapper can run unchanged with this example. As for the Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting Puts.

public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable>  {

  public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
    int i = 0;
    for (IntWritable val : values) {
      i += val.get();
    }
    context.write(key, new IntWritable(i));
  }
}

52.6. HBase MapReduce Summary to HBase Without Reducer

It is also possible to perform summaries without a reducer - if you use HBase as the reducer.

An HBase target table would need to exist for the job summary. The Table method incrementColumnValue would be used to atomically increment values. From a performance perspective, it might make sense to keep a Map of values with their values to be incremented for each map-task, and make one update per key at during the cleanup method of the mapper. However, your mileage may vary depending on the number of rows to be processed and unique keys.

In the end, the summary results are in HBase.

52.7. HBase MapReduce Summary to RDBMS

Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, it is possible to generate summaries directly to an RDBMS via a custom reducer. The setup method can connect to an RDBMS (the connection information can be passed via custom parameters in the context) and the cleanup method can close the connection.

It is critical to understand that number of reducers for the job affects the summarization implementation, and you’ll have to design this into your reducer. Specifically, whether it is designed to run as a singleton (one reducer) or multiple reducers. Neither is right or wrong, it depends on your use-case. Recognize that the more reducers that are assigned to the job, the more simultaneous connections to the RDBMS will be created - this will scale, but only to a point.

public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable>  {

  private Connection c = null;

  public void setup(Context context) {
    // create DB connection...
  }

  public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
    // do summarization
    // in this example the keys are Text, but this is just an example
  }

  public void cleanup(Context context) {
    // close db connection
  }

}

In the end, the summary results are written to your RDBMS table/s.

53. Accessing Other HBase Tables in a MapReduce Job

Although the framework currently allows one HBase table as input to a MapReduce job, other HBase tables can be accessed as lookup tables, etc., in a MapReduce job via creating an Table instance in the setup method of the Mapper.

public class MyMapper extends TableMapper<Text, LongWritable> {
  private Table myOtherTable;

  public void setup(Context context) {
    // In here create a Connection to the cluster and save it or use the Connection
    // from the existing table
    myOtherTable = connection.getTable("myOtherTable");
  }

  public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
    // process Result...
    // use 'myOtherTable' for lookups
  }

54. Speculative Execution

It is generally advisable to turn off speculative execution for MapReduce jobs that use HBase as a source. This can either be done on a per-Job basis through properties, or on the entire cluster. Especially for longer running jobs, speculative execution will create duplicate map-tasks which will double-write your data to HBase; this is probably not what you want.

See spec.ex for more information.

55. Cascading

Cascading is an alternative API for MapReduce, which actually uses MapReduce, but allows you to write your MapReduce code in a simplified way.

The following example shows a Cascading Flow which "sinks" data into an HBase cluster. The same hBaseTap API could be used to "source" data as well.

// read data from the default filesystem
// emits two fields: "offset" and "line"
Tap source = new Hfs( new TextLine(), inputFileLhs );

// store data in an HBase cluster
// accepts fields "num", "lower", and "upper"
// will automatically scope incoming fields to their proper familyname, "left" or "right"
Fields keyFields = new Fields( "num" );
String[] familyNames = {"left", "right"};
Fields[] valueFields = new Fields[] {new Fields( "lower" ), new Fields( "upper" ) };
Tap hBaseTap = new HBaseTap( "multitable", new HBaseScheme( keyFields, familyNames, valueFields ), SinkMode.REPLACE );

// a simple pipe assembly to parse the input into fields
// a real app would likely chain multiple Pipes together for more complex processing
Pipe parsePipe = new Each( "insert", new Fields( "line" ), new RegexSplitter( new Fields( "num", "lower", "upper" ), " " ) );

// "plan" a cluster executable Flow
// this connects the source Tap and hBaseTap (the sink Tap) to the parsePipe
Flow parseFlow = new FlowConnector( properties ).connect( source, hBaseTap, parsePipe );

// start the flow, and block until complete
parseFlow.complete();

// open an iterator on the HBase table we stuffed data into
TupleEntryIterator iterator = parseFlow.openSink();

while(iterator.hasNext())
  {
  // print out each tuple from HBase
  System.out.println( "iterator.next() = " + iterator.next() );
  }

iterator.close();

Securing Apache HBase

Reporting Security Bugs
To protect existing HBase installations from exploitation, please do not use JIRA to report security-related bugs. Instead, send your report to the mailing list private@apache.org, which allows anyone to send messages, but restricts who can read them. Someone on that list will contact you to follow up on your report.

HBase adheres to the Apache Software Foundation’s policy on reported vulnerabilities, available at http://apache.org/security/.

If you wish to send an encrypted report, you can use the GPG details provided for the general ASF security list. This will likely increase the response time to your report.

HBase provides mechanisms to secure various components and aspects of HBase and how it relates to the rest of the Hadoop infrastructure, as well as clients and resources outside Hadoop.

56. Using Secure HTTP (HTTPS) for the Web UI

A default HBase install uses insecure HTTP connections for Web UIs for the master and region servers. To enable secure HTTP (HTTPS) connections instead, set hbase.ssl.enabled to true in hbase-site.xml. This does not change the port used by the Web UI. To change the port for the web UI for a given HBase component, configure that port’s setting in hbase-site.xml. These settings are:

  • hbase.master.info.port

  • hbase.regionserver.info.port

If you enable HTTPS, clients should avoid using the non-secure HTTP connection.

If you enable secure HTTP, clients should connect to HBase using the https:// URL. Clients using the http:// URL will receive an HTTP response of 200, but will not receive any data. The following exception is logged:

javax.net.ssl.SSLException: Unrecognized SSL message, plaintext connection?

This is because the same port is used for HTTP and HTTPS.

HBase uses Jetty for the Web UI. Without modifying Jetty itself, it does not seem possible to configure Jetty to redirect one port to another on the same host. See Nick Dimiduk’s contribution on this Stack Overflow thread for more information. If you know how to fix this without opening a second port for HTTPS, patches are appreciated.

57. Using SPNEGO for Kerberos authentication with Web UIs

Kerberos-authentication to HBase Web UIs can be enabled via configuring SPNEGO with the hbase.security.authentication.ui property in hbase-site.xml. Enabling this authentication requires that HBase is also configured to use Kerberos authentication for RPCs (e.g hbase.security.authentication = kerberos).

<property>
  <name>hbase.security.authentication.ui</name>
  <value>kerberos</value>
  <description>Controls what kind of authentication should be used for the HBase web UIs.</description>
</property>
<property>
  <name>hbase.security.authentication</name>
  <value>kerberos</value>
  <description>The Kerberos keytab file to use for SPNEGO authentication by the web server.</description>
</property>

A number of properties exist to configure SPNEGO authentication for the web server:

<property>
  <name>hbase.security.authentication.spnego.kerberos.principal</name>
  <value>HTTP/_HOST@EXAMPLE.COM</value>
  <description>Required for SPNEGO, the Kerberos principal to use for SPNEGO authentication by the
  web server. The _HOST keyword will be automatically substituted with the node's
  hostname.</description>
</property>
<property>
  <name>hbase.security.authentication.spnego.kerberos.keytab</name>
  <value>/etc/security/keytabs/spnego.service.keytab</value>
  <description>Required for SPNEGO, the Kerberos keytab file to use for SPNEGO authentication by the
  web server.</description>
</property>
<property>
  <name>hbase.security.authentication.spnego.kerberos.name.rules</name>
  <value></value>
  <description>Optional, Hadoop-style `auth_to_local` rules which will be parsed and used in the
  handling of Kerberos principals</description>
</property>
<property>
  <name>hbase.security.authentication.signature.secret.file</name>
  <value></value>
  <description>Optional, a file whose contents will be used as a secret to sign the HTTP cookies
  as a part of the SPNEGO authentication handshake. If this is not provided, Java's `Random` library
  will be used for the secret.</description>
</property>

58. Secure Client Access to Apache HBase

Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also Matteo Bertozzi’s article on Understanding User Authentication and Authorization in Apache HBase.

This describes how to set up Apache HBase and clients for connection to secure HBase resources.

58.1. Prerequisites

Hadoop Authentication Configuration

To run HBase RPC with strong authentication, you must set hbase.security.authentication to kerberos. In this case, you must also set hadoop.security.authentication to kerberos in core-site.xml. Otherwise, you would be using strong authentication for HBase but not for the underlying HDFS, which would cancel out any benefit.

Kerberos KDC

You need to have a working Kerberos KDC.

58.2. Server-side Configuration for Secure Operation

First, refer to security.prerequisites and ensure that your underlying HDFS configuration is secure.

Add the following to the hbase-site.xml file on every server machine in the cluster:

<property>
  <name>hbase.security.authentication</name>
  <value>kerberos</value>
</property>
<property>
  <name>hbase.security.authorization</name>
  <value>true</value>
</property>
<property>
<name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>

A full shutdown and restart of HBase service is required when deploying these configuration changes.

58.3. Client-side Configuration for Secure Operation

First, refer to Prerequisites and ensure that your underlying HDFS configuration is secure.

Add the following to the hbase-site.xml file on every client:

<property>
  <name>hbase.security.authentication</name>
  <value>kerberos</value>
</property>

The client environment must be logged in to Kerberos from KDC or keytab via the kinit command before communication with the HBase cluster will be possible.

Be advised that if the hbase.security.authentication in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.

Once HBase is configured for secure RPC it is possible to optionally configure encrypted communication. To do so, add the following to the hbase-site.xml file on every client:

<property>
  <name>hbase.rpc.protection</name>
  <value>privacy</value>
</property>

This configuration property can also be set on a per-connection basis. Set it in the Configuration supplied to Table:

Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
conf.set("hbase.rpc.protection", "privacy");
try (Connection connection = ConnectionFactory.createConnection(conf)) {
  try (Table table = connection.getTable(TableName.valueOf(tablename)) {
  .... do your stuff
  }
}

Expect a ~10% performance penalty for encrypted communication.

58.4. Client-side Configuration for Secure Operation - Thrift Gateway

Add the following to the hbase-site.xml file for every Thrift gateway:

<property>
  <name>hbase.thrift.keytab.file</name>
  <value>/etc/hbase/conf/hbase.keytab</value>
</property>
<property>
  <name>hbase.thrift.kerberos.principal</name>
  <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
  <!-- TODO: This may need to be  HTTP/_HOST@<REALM> and _HOST may not work.
   You may have  to put the concrete full hostname.
   -->
</property>
<!-- Add these if you need to configure a different DNS interface from the default -->
<property>
  <name>hbase.thrift.dns.interface</name>
  <value>default</value>
</property>
<property>
  <name>hbase.thrift.dns.nameserver</name>
  <value>default</value>
</property>

Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.

In order to use the Thrift API principal to interact with HBase, it is also necessary to add the hbase.thrift.kerberos.principal to the acl table. For example, to give the Thrift API principal, thrift_server, administrative access, a command such as this one will suffice:

grant 'thrift_server', 'RWCA'

For more information about ACLs, please see the Access Control Labels (ACLs) section

The Thrift gateway will authenticate with HBase using the supplied credential. No authentication will be performed by the Thrift gateway itself. All client access via the Thrift gateway will use the Thrift gateway’s credential and have its privilege.

58.5. Configure the Thrift Gateway to Authenticate on Behalf of the Client

Client-side Configuration for Secure Operation - Thrift Gateway describes how to authenticate a Thrift client to HBase using a fixed user. As an alternative, you can configure the Thrift gateway to authenticate to HBase on the client’s behalf, and to access HBase using a proxy user. This was implemented in HBASE-11349 for Thrift 1, and HBASE-11474 for Thrift 2.

Limitations with Thrift Framed Transport

If you use framed transport, you cannot yet take advantage of this feature, because SASL does not work with Thrift framed transport at this time.

To enable it, do the following.

  1. Be sure Thrift is running in secure mode, by following the procedure described in Client-side Configuration for Secure Operation - Thrift Gateway.

  2. Be sure that HBase is configured to allow proxy users, as described in REST Gateway Impersonation Configuration.

  3. In hbase-site.xml for each cluster node running a Thrift gateway, set the property hbase.thrift.security.qop to one of the following three values:

    • privacy - authentication, integrity, and confidentiality checking.

    • integrity - authentication and integrity checking

    • authentication - authentication checking only

  4. Restart the Thrift gateway processes for the changes to take effect. If a node is running Thrift, the output of the jps command will list a ThriftServer process. To stop Thrift on a node, run the command bin/hbase-daemon.sh stop thrift. To start Thrift on a node, run the command bin/hbase-daemon.sh start thrift.

58.6. Configure the Thrift Gateway to Use the doAs Feature

Configure the Thrift Gateway to Authenticate on Behalf of the Client describes how to configure the Thrift gateway to authenticate to HBase on the client’s behalf, and to access HBase using a proxy user. The limitation of this approach is that after the client is initialized with a particular set of credentials, it cannot change these credentials during the session. The doAs feature provides a flexible way to impersonate multiple principals using the same client. This feature was implemented in HBASE-12640 for Thrift 1, but is currently not available for Thrift 2.

To enable the doAs feature, add the following to the hbase-site.xml file for every Thrift gateway:

<property>
  <name>hbase.regionserver.thrift.http</name>
  <value>true</value>
</property>
<property>
  <name>hbase.thrift.support.proxyuser</name>
  <value>true/value>
</property>

To allow proxy users when using doAs impersonation, add the following to the hbase-site.xml file for every HBase node:

<property>
  <name>hadoop.security.authorization</name>
  <value>true</value>
</property>
<property>
  <name>hadoop.proxyuser.$USER.groups</name>
  <value>$GROUPS</value>
</property>
<property>
  <name>hadoop.proxyuser.$USER.hosts</name>
  <value>$GROUPS</value>
</property>

Take a look at the demo client to get an overall idea of how to use this feature in your client.

58.7. Client-side Configuration for Secure Operation - REST Gateway

Add the following to the hbase-site.xml file for every REST gateway:

<property>
  <name>hbase.rest.keytab.file</name>
  <value>$KEYTAB</value>
</property>
<property>
  <name>hbase.rest.kerberos.principal</name>
  <value>$USER/_HOST@HADOOP.LOCALDOMAIN</value>
</property>

Substitute the appropriate credential and keytab for $USER and $KEYTAB respectively.

The REST gateway will authenticate with HBase using the supplied credential.

In order to use the REST API principal to interact with HBase, it is also necessary to add the hbase.rest.kerberos.principal to the acl table. For example, to give the REST API principal, rest_server, administrative access, a command such as this one will suffice:

grant 'rest_server', 'RWCA'

For more information about ACLs, please see the Access Control Labels (ACLs) section

HBase REST gateway supports SPNEGO HTTP authentication for client access to the gateway. To enable REST gateway Kerberos authentication for client access, add the following to the hbase-site.xml file for every REST gateway.

<property>
  <name>hbase.rest.support.proxyuser</name>
  <value>true</value>
</property>
<property>
  <name>hbase.rest.authentication.type</name>
  <value>kerberos</value>
</property>
<property>
  <name>hbase.rest.authentication.kerberos.principal</name>
  <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
</property>
<property>
  <name>hbase.rest.authentication.kerberos.keytab</name>
  <value>$KEYTAB</value>
</property>
<!-- Add these if you need to configure a different DNS interface from the default -->
<property>
  <name>hbase.rest.dns.interface</name>
  <value>default</value>
</property>
<property>
  <name>hbase.rest.dns.nameserver</name>
  <value>default</value>
</property>

Substitute the keytab for HTTP for $KEYTAB.

HBase REST gateway supports different 'hbase.rest.authentication.type': simple, kerberos. You can also implement a custom authentication by implementing Hadoop AuthenticationHandler, then specify the full class name as 'hbase.rest.authentication.type' value. For more information, refer to SPNEGO HTTP authentication.

58.8. REST Gateway Impersonation Configuration

By default, the REST gateway doesn’t support impersonation. It accesses the HBase on behalf of clients as the user configured as in the previous section. To the HBase server, all requests are from the REST gateway user. The actual users are unknown. You can turn on the impersonation support. With impersonation, the REST gateway user is a proxy user. The HBase server knows the actual/real user of each request. So it can apply proper authorizations.

To turn on REST gateway impersonation, we need to configure HBase servers (masters and region servers) to allow proxy users; configure REST gateway to enable impersonation.

To allow proxy users, add the following to the hbase-site.xml file for every HBase server:

<property>
  <name>hadoop.security.authorization</name>
  <value>true</value>
</property>
<property>
  <name>hadoop.proxyuser.$USER.groups</name>
  <value>$GROUPS</value>
</property>
<property>
  <name>hadoop.proxyuser.$USER.hosts</name>
  <value>$GROUPS</value>
</property>

Substitute the REST gateway proxy user for $USER, and the allowed group list for $GROUPS.

To enable REST gateway impersonation, add the following to the hbase-site.xml file for every REST gateway.

<property>
  <name>hbase.rest.authentication.type</name>
  <value>kerberos</value>
</property>
<property>
  <name>hbase.rest.authentication.kerberos.principal</name>
  <value>HTTP/_HOST@HADOOP.LOCALDOMAIN</value>
</property>
<property>
  <name>hbase.rest.authentication.kerberos.keytab</name>
  <value>$KEYTAB</value>
</property>

Substitute the keytab for HTTP for $KEYTAB.

59. Simple User Access to Apache HBase

Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also Matteo Bertozzi’s article on Understanding User Authentication and Authorization in Apache HBase.

This describes how to set up Apache HBase and clients for simple user access to HBase resources.

59.1. Simple versus Secure Access

The following section shows how to set up simple user access. Simple user access is not a secure method of operating HBase. This method is used to prevent users from making mistakes. It can be used to mimic the Access Control using on a development system without having to set up Kerberos.

This method is not used to prevent malicious or hacking attempts. To make HBase secure against these types of attacks, you must configure HBase for secure operation. Refer to the section Secure Client Access to Apache HBase and complete all of the steps described there.

59.2. Prerequisites

None

59.3. Server-side Configuration for Simple User Access Operation

Add the following to the hbase-site.xml file on every server machine in the cluster:

<property>
  <name>hbase.security.authentication</name>
  <value>simple</value>
</property>
<property>
  <name>hbase.security.authorization</name>
  <value>true</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>

For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster:

<property>
  <name>hbase.rpc.engine</name>
  <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>

A full shutdown and restart of HBase service is required when deploying these configuration changes.

59.4. Client-side Configuration for Simple User Access Operation

Add the following to the hbase-site.xml file on every client:

<property>
  <name>hbase.security.authentication</name>
  <value>simple</value>
</property>

For 0.94, add the following to the hbase-site.xml file on every server machine in the cluster:

<property>
  <name>hbase.rpc.engine</name>
  <value>org.apache.hadoop.hbase.ipc.SecureRpcEngine</value>
</property>

Be advised that if the hbase.security.authentication in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.

59.4.1. Client-side Configuration for Simple User Access Operation - Thrift Gateway

The Thrift gateway user will need access. For example, to give the Thrift API user, thrift_server, administrative access, a command such as this one will suffice:

grant 'thrift_server', 'RWCA'

For more information about ACLs, please see the Access Control Labels (ACLs) section

The Thrift gateway will authenticate with HBase using the supplied credential. No authentication will be performed by the Thrift gateway itself. All client access via the Thrift gateway will use the Thrift gateway’s credential and have its privilege.

59.4.2. Client-side Configuration for Simple User Access Operation - REST Gateway

The REST gateway will authenticate with HBase using the supplied credential. No authentication will be performed by the REST gateway itself. All client access via the REST gateway will use the REST gateway’s credential and have its privilege.

The REST gateway user will need access. For example, to give the REST API user, rest_server, administrative access, a command such as this one will suffice:

grant 'rest_server', 'RWCA'

For more information about ACLs, please see the Access Control Labels (ACLs) section

It should be possible for clients to authenticate with the HBase cluster through the REST gateway in a pass-through manner via SPNEGO HTTP authentication. This is future work.

60. Securing Access to HDFS and ZooKeeper

Secure HBase requires secure ZooKeeper and HDFS so that users cannot access and/or modify the metadata and data from under HBase. HBase uses HDFS (or configured file system) to keep its data files as well as write ahead logs (WALs) and other data. HBase uses ZooKeeper to store some metadata for operations (master address, table locks, recovery state, etc).

60.1. Securing ZooKeeper Data

ZooKeeper has a pluggable authentication mechanism to enable access from clients using different methods. ZooKeeper even allows authenticated and un-authenticated clients at the same time. The access to znodes can be restricted by providing Access Control Lists (ACLs) per znode. An ACL contains two components, the authentication method and the principal. ACLs are NOT enforced hierarchically. See ZooKeeper Programmers Guide for details.

HBase daemons authenticate to ZooKeeper via SASL and kerberos (See SASL Authentication with ZooKeeper). HBase sets up the znode ACLs so that only the HBase user and the configured hbase superuser (hbase.superuser) can access and modify the data. In cases where ZooKeeper is used for service discovery or sharing state with the client, the znodes created by HBase will also allow anyone (regardless of authentication) to read these znodes (clusterId, master address, meta location, etc), but only the HBase user can modify them.

60.2. Securing File System (HDFS) Data

All of the data under management is kept under the root directory in the file system (hbase.rootdir). Access to the data and WAL files in the filesystem should be restricted so that users cannot bypass the HBase layer, and peek at the underlying data files from the file system. HBase assumes the filesystem used (HDFS or other) enforces permissions hierarchically. If sufficient protection from the file system (both authorization and authentication) is not provided, HBase level authorization control (ACLs, visibility labels, etc) is meaningless since the user can always access the data from the file system.

HBase enforces the posix-like permissions 700 (rwx------) to its root directory. It means that only the HBase user can read or write the files in FS. The default setting can be changed by configuring hbase.rootdir.perms in hbase-site.xml. A restart of the active master is needed so that it changes the used permissions. For versions before 1.2.0, you can check whether HBASE-13780 is committed, and if not, you can manually set the permissions for the root directory if needed. Using HDFS, the command would be:

sudo -u hdfs hadoop fs -chmod 700 /hbase

You should change /hbase if you are using a different hbase.rootdir.

In secure mode, SecureBulkLoadEndpoint should be configured and used for properly handing of users files created from MR jobs to the HBase daemons and HBase user. The staging directory in the distributed file system used for bulk load (hbase.bulkload.staging.dir, defaults to /tmp/hbase-staging) should have (mode 711, or rwx—​x—​x) so that users can access the staging directory created under that parent directory, but cannot do any other operation. See Secure Bulk Load for how to configure SecureBulkLoadEndPoint.

61. Securing Access To Your Data

After you have configured secure authentication between HBase client and server processes and gateways, you need to consider the security of your data itself. HBase provides several strategies for securing your data:

  • Role-based Access Control (RBAC) controls which users or groups can read and write to a given HBase resource or execute a coprocessor endpoint, using the familiar paradigm of roles.

  • Visibility Labels which allow you to label cells and control access to labelled cells, to further restrict who can read or write to certain subsets of your data. Visibility labels are stored as tags. See hbase.tags for more information.

  • Transparent encryption of data at rest on the underlying filesystem, both in HFiles and in the WAL. This protects your data at rest from an attacker who has access to the underlying filesystem, without the need to change the implementation of the client. It can also protect against data leakage from improperly disposed disks, which can be important for legal and regulatory compliance.

Server-side configuration, administration, and implementation details of each of these features are discussed below, along with any performance trade-offs. An example security configuration is given at the end, to show these features all used together, as they might be in a real-world scenario.

All aspects of security in HBase are in active development and evolving rapidly. Any strategy you employ for security of your data should be thoroughly tested. In addition, some of these features are still in the experimental stage of development. To take advantage of many of these features, you must be running HBase 0.98+ and using the HFile v3 file format.
Protecting Sensitive Files

Several procedures in this section require you to copy files between cluster nodes. When copying keys, configuration files, or other files containing sensitive strings, use a secure method, such as ssh, to avoid leaking sensitive data.
Procedure: Basic Server-Side Configuration

  1. Enable HFile v3, by setting hfile.format.version to 3 in hbase-site.xml. This is the default for HBase 1.0 and newer.

    <property>
  <name>hfile.format.version</name>
  <value>3</value>
</property>

  2. Enable SASL and Kerberos authentication for RPC and ZooKeeper, as described in security.prerequisites and SASL Authentication with ZooKeeper.

61.1. Tags

Tags are a feature of HFile v3. A tag is a piece of metadata which is part of a cell, separate from the key, value, and version. Tags are an implementation detail which provides a foundation for other security-related features such as cell-level ACLs and visibility labels. Tags are stored in the HFiles themselves. It is possible that in the future, tags will be used to implement other HBase features. You don’t need to know a lot about tags in order to use the security features they enable.

61.1.1. Implementation Details

Every cell can have zero or more tags. Every tag has a type and the actual tag byte array.

Just as row keys, column families, qualifiers and values can be encoded (see data.block.encoding.types), tags can also be encoded as well. You can enable or disable tag encoding at the level of the column family, and it is enabled by default. Use the HColumnDescriptor#setCompressionTags(boolean compressTags) method to manage encoding settings on a column family. You also need to enable the DataBlockEncoder for the column family, for encoding of tags to take effect.

You can enable compression of each tag in the WAL, if WAL compression is also enabled, by setting the value of hbase.regionserver.wal.tags.enablecompression to true in hbase-site.xml. Tag compression uses dictionary encoding.

Tag compression is not supported when using WAL encryption.

61.2. Access Control Labels (ACLs)

61.2.1. How It Works

ACLs in HBase are based upon a user’s membership in or exclusion from groups, and a given group’s permissions to access a given resource. ACLs are implemented as a coprocessor called AccessController.

HBase does not maintain a private group mapping, but relies on a Hadoop group mapper, which maps between entities in a directory such as LDAP or Active Directory, and HBase users. Any supported Hadoop group mapper will work. Users are then granted specific permissions (Read, Write, Execute, Create, Admin) against resources (global, namespaces, tables, cells, or endpoints).

With Kerberos and Access Control enabled, client access to HBase is authenticated and user data is private unless access has been explicitly granted.

HBase has a simpler security model than relational databases, especially in terms of client operations. No distinction is made between an insert (new record) and update (of existing record), for example, as both collapse down into a Put.

Understanding Access Levels

HBase access levels are granted independently of each other and allow for different types of operations at a given scope.

  • Read (R) - can read data at the given scope

  • Write (W) - can write data at the given scope

  • Execute (X) - can execute coprocessor endpoints at the given scope

  • Create (C) - can create tables or drop tables (even those they did not create) at the given scope

  • Admin (A) - can perform cluster operations such as balancing the cluster or assigning regions at the given scope

The possible scopes are:

  • Superuser - superusers can perform any operation available in HBase, to any resource. The user who runs HBase on your cluster is a superuser, as are any principals assigned to the configuration property hbase.superuser in hbase-site.xml on the HMaster.

  • Global - permissions granted at global scope allow the admin to operate on all tables of the cluster.

  • Namespace - permissions granted at namespace scope apply to all tables within a given namespace.

  • Table - permissions granted at table scope apply to data or metadata within a given table.

  • ColumnFamily - permissions granted at ColumnFamily scope apply to cells within that ColumnFamily.

  • Cell - permissions granted at cell scope apply to that exact cell coordinate (key, value, timestamp). This allows for policy evolution along with data.

    To change an ACL on a specific cell, write an updated cell with new ACL to the precise coordinates of the original.

    If you have a multi-versioned schema and want to update ACLs on all visible versions, you need to write new cells for all visible versions. The application has complete control over policy evolution.

    The exception to the above rule is append and increment processing. Appends and increments can carry an ACL in the operation. If one is included in the operation, then it will be applied to the result of the append or increment. Otherwise, the ACL of the existing cell you are appending to or incrementing is preserved.

The combination of access levels and scopes creates a matrix of possible access levels that can be granted to a user. In a production environment, it is useful to think of access levels in terms of what is needed to do a specific job. The following list describes appropriate access levels for some common types of HBase users. It is important not to grant more access than is required for a given user to perform their required tasks.

  • Superusers - In a production system, only the HBase user should have superuser access. In a development environment, an administrator may need superuser access in order to quickly control and manage the cluster. However, this type of administrator should usually be a Global Admin rather than a superuser.

  • Global Admins - A global admin can perform tasks and access every table in HBase. In a typical production environment, an admin should not have Read or Write permissions to data within tables.

  • A global admin with Admin permissions can perform cluster-wide operations on the cluster, such as balancing, assigning or unassigning regions, or calling an explicit major compaction. This is an operations role.

  • A global admin with Create permissions can create or drop any table within HBase. This is more of a DBA-type role.

    In a production environment, it is likely that different users will have only one of Admin and Create permissions.

    In the current implementation, a Global Admin with Admin permission can grant himself Read and Write permissions on a table and gain access to that table’s data. For this reason, only grant Global Admin permissions to trusted user who actually need them.

    Also be aware that a Global Admin with Create permission can perform a Put operation on the ACL table, simulating a grant or revoke and circumventing the authorization check for Global Admin permissions.

    Due to these issues, be cautious with granting Global Admin privileges.

  • Namespace Admins - a namespace admin with Create permissions can create or drop tables within that namespace, and take and restore snapshots. A namespace admin with Admin permissions can perform operations such as splits or major compactions on tables within that namespace.

  • Table Admins - A table admin can perform administrative operations only on that table. A table admin with Create permissions can create snapshots from that table or restore that table from a snapshot. A table admin with Admin permissions can perform operations such as splits or major compactions on that table.

  • Users - Users can read or write data, or both. Users can also execute coprocessor endpoints, if given Executable permissions.

Table 7. Real-World Example of Access Levels
Job Title Scope Permissions Description

Senior Administrator

Global

Access, Create

Manages the cluster and gives access to Junior Administrators.

Junior Administrator

Global

Create

Creates tables and gives access to Table Administrators.

Table Administrator

Table

Access

Maintains a table from an operations point of view.

Data Analyst

Table

Read

Creates reports from HBase data.

Web Application

Table

Read, Write

Puts data into HBase and uses HBase data to perform operations.
ACL Matrix

For more details on how ACLs map to specific HBase operations and tasks, see appendix acl matrix.

Implementation Details

Cell-level ACLs are implemented using tags (see Tags). In order to use cell-level ACLs, you must be using HFile v3 and HBase 0.98 or newer.

  1. Files created by HBase are owned by the operating system user running the HBase process. To interact with HBase files, you should use the API or bulk load facility.

  2. HBase does not model "roles" internally in HBase. Instead, group names can be granted permissions. This allows external modeling of roles via group membership. Groups are created and manipulated externally to HBase, via the Hadoop group mapping service.

Server-Side Configuration

  1. As a prerequisite, perform the steps in Procedure: Basic Server-Side Configuration.

  2. Install and configure the AccessController coprocessor, by setting the following properties in hbase-site.xml. These properties take a list of classes.

    If you use the AccessController along with the VisibilityController, the AccessController must come first in the list, because with both components active, the VisibilityController will delegate access control on its system tables to the AccessController. For an example of using both together, see Security Configuration Example. 
    <property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.security.exec.permission.checks</name>
  <value>true</value>
</property>

    Optionally, you can enable transport security, by setting hbase.rpc.protection to privacy. This requires HBase 0.98.4 or newer.

  3. Set up the Hadoop group mapper in the Hadoop namenode’s core-site.xml. This is a Hadoop file, not an HBase file. Customize it to your site’s needs. Following is an example.

    <property>
  <name>hadoop.security.group.mapping</name>
  <value>org.apache.hadoop.security.LdapGroupsMapping</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.url</name>
  <value>ldap://server</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.bind.user</name>
  <value>Administrator@example-ad.local</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.bind.password</name>
  <value>****</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.base</name>
  <value>dc=example-ad,dc=local</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
  <value>(&amp;(objectClass=user)(sAMAccountName={0}))</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
  <value>(objectClass=group)</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
  <value>member</value>
</property>

<property>
  <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
  <value>cn</value>
</property>

  4. Optionally, enable the early-out evaluation strategy. Prior to HBase 0.98.0, if a user was not granted access to a column family, or at least a column qualifier, an AccessDeniedException would be thrown. HBase 0.98.0 removed this exception in order to allow cell-level exceptional grants. To restore the old behavior in HBase 0.98.0-0.98.6, set hbase.security.access.early_out to true in hbase-site.xml. In HBase 0.98.6, the default has been returned to true.

  5. Distribute your configuration and restart your cluster for changes to take effect.

  6. To test your configuration, log into HBase Shell as a given user and use the whoami command to report the groups your user is part of. In this example, the user is reported as being a member of the services group.

    hbase> whoami
service (auth:KERBEROS)
    groups: services
Administration

Administration tasks can be performed from HBase Shell or via an API.

API Examples

Many of the API examples below are taken from source files hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java and hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/SecureTestUtil.java.

Neither the examples, nor the source files they are taken from, are part of the public HBase API, and are provided for illustration only. Refer to the official API for usage instructions.

  1. User and Group Administration

    Users and groups are maintained external to HBase, in your directory.

  2. Granting Access To A Namespace, Table, Column Family, or Cell

    There are a few different types of syntax for grant statements. The first, and most familiar, is as follows, with the table and column family being optional:

    grant 'user', 'RWXCA', 'TABLE', 'CF', 'CQ'

    Groups and users are granted access in the same way, but groups are prefixed with an @ symbol. In the same way, tables and namespaces are specified in the same way, but namespaces are prefixed with an @ symbol.

    It is also possible to grant multiple permissions against the same resource in a single statement, as in this example. The first sub-clause maps users to ACLs and the second sub-clause specifies the resource.

    HBase Shell support for granting and revoking access at the cell level is for testing and verification support, and should not be employed for production use because it won’t apply the permissions to cells that don’t exist yet. The correct way to apply cell level permissions is to do so in the application code when storing the values.
    ACL Granularity and Evaluation Order

    ACLs are evaluated from least granular to most granular, and when an ACL is reached that grants permission, evaluation stops. This means that cell ACLs do not override ACLs at less granularity.

    Example 20. HBase Shell

    • Global:

      hbase> grant '@admins', 'RWXCA'

    • Namespace:

      hbase> grant 'service', 'RWXCA', '@test-NS'

    • Table:

      hbase> grant 'service', 'RWXCA', 'user'

    • Column Family:

      hbase> grant '@developers', 'RW', 'user', 'i'

    • Column Qualifier:

      hbase> grant 'service, 'RW', 'user', 'i', 'foo'

    • Cell:

      The syntax for granting cell ACLs uses the following syntax:

      grant <table>, \
  { '<user-or-group>' => \
    '<permissions>', ... }, \
  { <scanner-specification> }

    • <user-or-group> is the user or group name, prefixed with @ in the case of a group.

    • <permissions> is a string containing any or all of "RWXCA", though only R and W are meaningful at cell scope.

    • <scanner-specification> is the scanner specification syntax and conventions used by the 'scan' shell command. For some examples of scanner specifications, issue the following HBase Shell command.

      hbase> help "scan"

      This example grants read access to the 'testuser' user and read/write access to the 'developers' group, on cells in the 'pii' column which match the filter.

      hbase> grant 'user', \
  { '@developers' => 'RW', 'testuser' => 'R' }, \
  { COLUMNS => 'pii', FILTER => "(PrefixFilter ('test'))" }

      The shell will run a scanner with the given criteria, rewrite the found cells with new ACLs, and store them back to their exact coordinates.

    Example 21. API

    The following example shows how to grant access at the table level.

    public static void grantOnTable(final HBaseTestingUtility util, final String user,
    final TableName table, final byte[] family, final byte[] qualifier,
    final Permission.Action... actions) throws Exception {
  SecureTestUtil.updateACLs(util, new Callable<Void>() {
    @Override
    public Void call() throws Exception {
      Configuration conf = HBaseConfiguration.create();
      Connection connection = ConnectionFactory.createConnection(conf);
      try (Connection connection = ConnectionFactory.createConnection(conf)) {
        try (Table table = connection.getTable(TableName.valueOf(tablename)) {
          AccessControlLists.ACL_TABLE_NAME);
          try {
            BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
            AccessControlService.BlockingInterface protocol =
                AccessControlService.newBlockingStub(service);
            ProtobufUtil.grant(protocol, user, table, family, qualifier, actions);
          } finally {
            acl.close();
          }
          return null;
        }
      }
    }
  }
}

    To grant permissions at the cell level, you can use the Mutation.setACL method:

    Mutation.setACL(String user, Permission perms)
Mutation.setACL(Map<String, Permission> perms)

    Specifically, this example provides read permission to a user called user1 on any cells contained in a particular Put operation:

    put.setACL(“user1”, new Permission(Permission.Action.READ))

  3. Revoking Access Control From a Namespace, Table, Column Family, or Cell

    The revoke command and API are twins of the grant command and API, and the syntax is exactly the same. The only exception is that you cannot revoke permissions at the cell level. You can only revoke access that has previously been granted, and a revoke statement is not the same thing as explicit denial to a resource.

    HBase Shell support for granting and revoking access is for testing and verification support, and should not be employed for production use because it won’t apply the permissions to cells that don’t exist yet. The correct way to apply cell-level permissions is to do so in the application code when storing the values.
    Example 22. Revoking Access To a Table
    public static void revokeFromTable(final HBaseTestingUtility util, final String user,
    final TableName table, final byte[] family, final byte[] qualifier,
    final Permission.Action... actions) throws Exception {
  SecureTestUtil.updateACLs(util, new Callable<Void>() {
    @Override
    public Void call() throws Exception {
      Configuration conf = HBaseConfiguration.create();
      Connection connection = ConnectionFactory.createConnection(conf);
      Table acl = connection.getTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME);
      try {
        BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
        AccessControlService.BlockingInterface protocol =
            AccessControlService.newBlockingStub(service);
        ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions);
      } finally {
        acl.close();
      }
      return null;
    }
  });
}

  4. Showing a User’s Effective Permissions

    Example 23. HBase Shell
    hbase> user_permission 'user'

hbase> user_permission '.*'

hbase> user_permission JAVA_REGEX
Example 24. API
public static void verifyAllowed(User user, AccessTestAction action, int count) throws Exception {
  try {
    Object obj = user.runAs(action);
    if (obj != null && obj instanceof List&lt;?&gt;) {
      List&lt;?&gt; results = (List&lt;?&gt;) obj;
      if (results != null && results.isEmpty()) {
        fail("Empty non null results from action for user '" ` user.getShortName() ` "'");
      }
      assertEquals(count, results.size());
    }
  } catch (AccessDeniedException ade) {
    fail("Expected action to pass for user '" ` user.getShortName() ` "' but was denied");
  }
}

61.3. Visibility Labels

Visibility labels control can be used to only permit users or principals associated with a given label to read or access cells with that label. For instance, you might label a cell top-secret, and only grant access to that label to the managers group. Visibility labels are implemented using Tags, which are a feature of HFile v3, and allow you to store metadata on a per-cell basis. A label is a string, and labels can be combined into expressions by using logical operators (&, |, or !), and using parentheses for grouping. HBase does not do any kind of validation of expressions beyond basic well-formedness. Visibility labels have no meaning on their own, and may be used to denote sensitivity level, privilege level, or any other arbitrary semantic meaning.

If a user’s labels do not match a cell’s label or expression, the user is denied access to the cell.

In HBase 0.98.6 and newer, UTF-8 encoding is supported for visibility labels and expressions. When creating labels using the addLabels(conf, labels) method provided by the org.apache.hadoop.hbase.security.visibility.VisibilityClient class and passing labels in Authorizations via Scan or Get, labels can contain UTF-8 characters, as well as the logical operators normally used in visibility labels, with normal Java notations, without needing any escaping method. However, when you pass a CellVisibility expression via a Mutation, you must enclose the expression with the CellVisibility.quote() method if you use UTF-8 characters or logical operators. See TestExpressionParser and the source file hbase-client/src/test/java/org/apache/hadoop/hbase/client/TestScan.java.

A user adds visibility expressions to a cell during a Put operation. In the default configuration, the user does not need to have access to a label in order to label cells with it. This behavior is controlled by the configuration option hbase.security.visibility.mutations.checkauths. If you set this option to true, the labels the user is modifying as part of the mutation must be associated with the user, or the mutation will fail. Whether a user is authorized to read a labelled cell is determined during a Get or Scan, and results which the user is not allowed to read are filtered out. This incurs the same I/O penalty as if the results were returned, but reduces load on the network.

Visibility labels can also be specified during Delete operations. For details about visibility labels and Deletes, see HBASE-10885.

The user’s effective label set is built in the RPC context when a request is first received by the RegionServer. The way that users are associated with labels is pluggable. The default plugin passes through labels specified in Authorizations added to the Get or Scan and checks those against the calling user’s authenticated labels list. When the client passes labels for which the user is not authenticated, the default plugin drops them. You can pass a subset of user authenticated labels via the Get#setAuthorizations(Authorizations(String,…​)) and Scan#setAuthorizations(Authorizations(String,…​)); methods.

Groups can be granted visibility labels the same way as users. Groups are prefixed with an @ symbol. When checking visibility labels of a user, the server will include the visibility labels of the groups of which the user is a member, together with the user’s own labels. When the visibility labels are retrieved using API VisibilityClient#getAuths or Shell command get_auths for a user, we will return labels added specifically for that user alone, not the group level labels.

Visibility label access checking is performed by the VisibilityController coprocessor. You can use interface VisibilityLabelService to provide a custom implementation and/or control the way that visibility labels are stored with cells. See the source file hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabelsWithCustomVisLabService.java for one example.

Visibility labels can be used in conjunction with ACLs.

The labels have to be explicitly defined before they can be used in visibility labels. See below for an example of how this can be done.
There is currently no way to determine which labels have been applied to a cell. See HBASE-12470 for details.
Visibility labels are not currently applied for superusers.
Table 8. Examples of Visibility Expressions
Expression Interpretation 
fulltime

Allow access to users associated with the fulltime label.

 
!public

Allow access to users not associated with the public label.

 
( secret | topsecret ) & !probationary

Allow access to users associated with either the secret or topsecret label and not associated with the probationary label.

61.3.1. Server-Side Configuration

  1. As a prerequisite, perform the steps in Procedure: Basic Server-Side Configuration.

  2. Install and configure the VisibilityController coprocessor by setting the following properties in hbase-site.xml. These properties take a list of class names.

    <property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
    If you use the AccessController and VisibilityController coprocessors together, the AccessController must come first in the list, because with both components active, the VisibilityController will delegate access control on its system tables to the AccessController.

  3. Adjust Configuration

    By default, users can label cells with any label, including labels they are not associated with, which means that a user can Put data that he cannot read. For example, a user could label a cell with the (hypothetical) 'topsecret' label even if the user is not associated with that label. If you only want users to be able to label cells with labels they are associated with, set hbase.security.visibility.mutations.checkauths to true. In that case, the mutation will fail if it makes use of labels the user is not associated with.

  4. Distribute your configuration and restart your cluster for changes to take effect.

61.3.2. Administration

Administration tasks can be performed using the HBase Shell or the Java API. For defining the list of visibility labels and associating labels with users, the HBase Shell is probably simpler.

API Examples

Many of the Java API examples in this section are taken from the source file hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabels.java. Refer to that file or the API documentation for more context.

Neither these examples, nor the source file they were taken from, are part of the public HBase API, and are provided for illustration only. Refer to the official API for usage instructions.

  1. Define the List of Visibility Labels

    Example 25. HBase Shell
    hbase> add_labels [ 'admin', 'service', 'developer', 'test' ]
    Example 26. Java API
    public static void addLabels() throws Exception {
  PrivilegedExceptionAction<VisibilityLabelsResponse> action = new PrivilegedExceptionAction<VisibilityLabelsResponse>() {
    public VisibilityLabelsResponse run() throws Exception {
      String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE, COPYRIGHT, ACCENT,
          UNICODE_VIS_TAG, UC1, UC2 };
      try {
        VisibilityClient.addLabels(conf, labels);
      } catch (Throwable t) {
        throw new IOException(t);
      }
      return null;
    }
  };
  SUPERUSER.runAs(action);
}

  2. Associate Labels with Users

    Example 27. HBase Shell
    hbase> set_auths 'service', [ 'service' ]
    hbase> set_auths 'testuser', [ 'test' ]
    hbase> set_auths 'qa', [ 'test', 'developer' ]
    hbase> set_auths '@qagroup', [ 'test' ]
    Example 28. Java API
    public void testSetAndGetUserAuths() throws Throwable {
  final String user = "user1";
  PrivilegedExceptionAction<Void> action = new PrivilegedExceptionAction<Void>() {
    public Void run() throws Exception {
      String[] auths = { SECRET, CONFIDENTIAL };
      try {
        VisibilityClient.setAuths(conf, auths, user);
      } catch (Throwable e) {
      }
      return null;
    }
    ...

  3. Clear Labels From Users

    Example 29. HBase Shell
    hbase> clear_auths 'service', [ 'service' ]
    hbase> clear_auths 'testuser', [ 'test' ]
    hbase> clear_auths 'qa', [ 'test', 'developer' ]
    hbase> clear_auths '@qagroup', [ 'test', 'developer' ]
    Example 30. Java API
    ...
auths = new String[] { SECRET, PUBLIC, CONFIDENTIAL };
VisibilityLabelsResponse response = null;
try {
  response = VisibilityClient.clearAuths(conf, auths, user);
} catch (Throwable e) {
  fail("Should not have failed");
  ...
}

  4. Apply a Label or Expression to a Cell

    The label is only applied when data is written. The label is associated with a given version of the cell.

    Example 31. HBase Shell
    hbase> set_visibility 'user', 'admin|service|developer', { COLUMNS => 'i' }
    hbase> set_visibility 'user', 'admin|service', { COLUMNS => 'pii' }
    hbase> set_visibility 'user', 'test', { COLUMNS => [ 'i', 'pii' ], FILTER => "(PrefixFilter ('test'))" }
    HBase Shell support for applying labels or permissions to cells is for testing and verification support, and should not be employed for production use because it won’t apply the labels to cells that don’t exist yet. The correct way to apply cell level labels is to do so in the application code when storing the values.
    Example 32. Java API
    static Table createTableAndWriteDataWithLabels(TableName tableName, String... labelExps)
    throws Exception {
  Configuration conf = HBaseConfiguration.create();
  Connection connection = ConnectionFactory.createConnection(conf);
  Table table = NULL;
  try {
    table = TEST_UTIL.createTable(tableName, fam);
    int i = 1;
    List<Put> puts = new ArrayList<Put>();
    for (String labelExp : labelExps) {
      Put put = new Put(Bytes.toBytes("row" + i));
      put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value);
      put.setCellVisibility(new CellVisibility(labelExp));
      puts.add(put);
      i++;
    }
    table.put(puts);
  } finally {
    if (table != null) {
      table.flushCommits();
    }
  }

61.3.3. Reading Cells with Labels

When you issue a Scan or Get, HBase uses your default set of authorizations to filter out cells that you do not have access to. A superuser can set the default set of authorizations for a given user by using the set_auths HBase Shell command or the ],%20java.lang.String)[VisibilityClient.setAuths() method.

You can specify a different authorization during the Scan or Get, by passing the AUTHORIZATIONS option in HBase Shell, or the setAuthorizations() method if you use the API. This authorization will be combined with your default set as an additional filter. It will further filter your results, rather than giving you additional authorization.

Example 33. HBase Shell
hbase> get_auths 'myUser'
hbase> scan 'table1', AUTHORIZATIONS => ['private']
Example 34. Java API
...
public Void run() throws Exception {
  String[] auths1 = { SECRET, CONFIDENTIAL };
  GetAuthsResponse authsResponse = null;
  try {
    VisibilityClient.setAuths(conf, auths1, user);
    try {
      authsResponse = VisibilityClient.getAuths(conf, user);
    } catch (Throwable e) {
      fail("Should not have failed");
    }
  } catch (Throwable e) {
  }
  List<String> authsList = new ArrayList<String>();
  for (ByteString authBS : authsResponse.getAuthList()) {
    authsList.add(Bytes.toString(authBS.toByteArray()));
  }
  assertEquals(2, authsList.size());
  assertTrue(authsList.contains(SECRET));
  assertTrue(authsList.contains(CONFIDENTIAL));
  return null;
}
...

61.3.4. Implementing Your Own Visibility Label Algorithm

Interpreting the labels authenticated for a given get/scan request is a pluggable algorithm.

You can specify a custom plugin or plugins by using the property hbase.regionserver.scan.visibility.label.generator.class. The output for the first ScanLabelGenerator will be the input for the next one, until the end of the list.

The default implementation, which was implemented in HBASE-12466, loads two plugins, FeedUserAuthScanLabelGenerator and DefinedSetFilterScanLabelGenerator. See Reading Cells with Labels.

61.3.5. Replicating Visibility Tags as Strings

As mentioned in the above sections, the interface VisibilityLabelService could be used to implement a different way of storing the visibility expressions in the cells. Clusters with replication enabled also must replicate the visibility expressions to the peer cluster. If DefaultVisibilityLabelServiceImpl is used as the implementation for VisibilityLabelService, all the visibility expression are converted to the corresponding expression based on the ordinals for each visibility label stored in the labels table. During replication, visible cells are also replicated with the ordinal-based expression intact. The peer cluster may not have the same labels table with the same ordinal mapping for the visibility labels. In that case, replicating the ordinals makes no sense. It would be better if the replication occurred with the visibility expressions transmitted as strings. To replicate the visibility expression as strings to the peer cluster, create a RegionServerObserver configuration which works based on the implementation of the VisibilityLabelService interface. The configuration below enables replication of visibility expressions to peer clusters as strings. See HBASE-11639 for more details.

<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop.hbase.security.visibility.VisibilityController$VisibilityReplication</value>
</property>

61.4. Transparent Encryption of Data At Rest

HBase provides a mechanism for protecting your data at rest, in HFiles and the WAL, which reside within HDFS or another distributed filesystem. A two-tier architecture is used for flexible and non-intrusive key rotation. "Transparent" means that no implementation changes are needed on the client side. When data is written, it is encrypted. When it is read, it is decrypted on demand.

61.4.1. How It Works

The administrator provisions a master key for the cluster, which is stored in a key provider accessible to every trusted HBase process, including the HMaster, RegionServers, and clients (such as HBase Shell) on administrative workstations. The default key provider is integrated with the Java KeyStore API and any key management systems with support for it. Other custom key provider implementations are possible. The key retrieval mechanism is configured in the hbase-site.xml configuration file. The master key may be stored on the cluster servers, protected by a secure KeyStore file, or on an external keyserver, or in a hardware security module. This master key is resolved as needed by HBase processes through the configured key provider.

Next, encryption use can be specified in the schema, per column family, by creating or modifying a column descriptor to include two additional attributes: the name of the encryption algorithm to use (currently only "AES" is supported), and optionally, a data key wrapped (encrypted) with the cluster master key. If a data key is not explicitly configured for a ColumnFamily, HBase will create a random data key per HFile. This provides an incremental improvement in security over the alternative. Unless you need to supply an explicit data key, such as in a case where you are generating encrypted HFiles for bulk import with a given data key, only specify the encryption algorithm in the ColumnFamily schema metadata and let HBase create data keys on demand. Per Column Family keys facilitate low impact incremental key rotation and reduce the scope of any external leak of key material. The wrapped data key is stored in the ColumnFamily schema metadata, and in each HFile for the Column Family, encrypted with the cluster master key. After the Column Family is configured for encryption, any new HFiles will be written encrypted. To ensure encryption of all HFiles, trigger a major compaction after enabling this feature.

When the HFile is opened, the data key is extracted from the HFile, decrypted with the cluster master key, and used for decryption of the remainder of the HFile. The HFile will be unreadable if the master key is not available. If a remote user somehow acquires access to the HFile data because of some lapse in HDFS permissions, or from inappropriately discarded media, it will not be possible to decrypt either the data key or the file data.

It is also possible to encrypt the WAL. Even though WALs are transient, it is necessary to encrypt the WALEdits to avoid circumventing HFile protections for encrypted column families, in the event that the underlying filesystem is compromised. When WAL encryption is enabled, all WALs are encrypted, regardless of whether the relevant HFiles are encrypted.

61.4.2. Server-Side Configuration

This procedure assumes you are using the default Java keystore implementation. If you are using a custom implementation, check its documentation and adjust accordingly.

  1. Create a secret key of appropriate length for AES encryption, using the keytool utility.

    $ keytool -keystore /path/to/hbase/conf/hbase.jks \
  -storetype jceks -storepass **** \
  -genseckey -keyalg AES -keysize 128 \
  -alias <alias>

    Replace **** with the password for the keystore file and <alias> with the username of the HBase service account, or an arbitrary string. If you use an arbitrary string, you will need to configure HBase to use it, and that is covered below. Specify a keysize that is appropriate. Do not specify a separate password for the key, but press Return when prompted.

  2. Set appropriate permissions on the keyfile and distribute it to all the HBase servers.

    The previous command created a file called hbase.jks in the HBase conf/ directory. Set the permissions and ownership on this file such that only the HBase service account user can read the file, and securely distribute the key to all HBase servers.

  3. Configure the HBase daemons.

    Set the following properties in hbase-site.xml on the region servers, to configure HBase daemons to use a key provider backed by the KeyStore file or retrieving the cluster master key. In the example below, replace **** with the password.

    <property>
  <name>hbase.crypto.keyprovider</name>
  <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
</property>
<property>
  <name>hbase.crypto.keyprovider.parameters</name>
  <value>jceks:///path/to/hbase/conf/hbase.jks?password=****</value>
</property>

    By default, the HBase service account name will be used to resolve the cluster master key. However, you can store it with an arbitrary alias (in the keytool command). In that case, set the following property to the alias you used.

    <property>
  <name>hbase.crypto.master.key.name</name>
  <value>my-alias</value>
</property>

    You also need to be sure your HFiles use HFile v3, in order to use transparent encryption. This is the default configuration for HBase 1.0 onward. For previous versions, set the following property in your hbase-site.xml file.

    <property>
  <name>hfile.format.version</name>
  <value>3</value>
</property>

    Optionally, you can use a different cipher provider, either a Java Cryptography Encryption (JCE) algorithm provider or a custom HBase cipher implementation.

    • JCE:

      • Install a signed JCE provider (supporting AES/CTR/NoPadding mode with 128 bit keys)

      • Add it with highest preference to the JCE site configuration file $JAVA_HOME/lib/security/java.security.

      • Update hbase.crypto.algorithm.aes.provider and hbase.crypto.algorithm.rng.provider options in hbase-site.xml.

    • Custom HBase Cipher:

      • Implement org.apache.hadoop.hbase.io.crypto.CipherProvider.

      • Add the implementation to the server classpath.

      • Update hbase.crypto.cipherprovider in hbase-site.xml.

  4. Configure WAL encryption.

    Configure WAL encryption in every RegionServer’s hbase-site.xml, by setting the following properties. You can include these in the HMaster’s hbase-site.xml as well, but the HMaster does not have a WAL and will not use them.

    <property>
  <name>hbase.regionserver.hlog.reader.impl</name>
  <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
</property>
<property>
  <name>hbase.regionserver.hlog.writer.impl</name>
  <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
</property>
<property>
  <name>hbase.regionserver.wal.encryption</name>
  <value>true</value>
</property>

  5. Configure permissions on the hbase-site.xml file.

    Because the keystore password is stored in the hbase-site.xml, you need to ensure that only the HBase user can read the hbase-site.xml file, using file ownership and permissions.

  6. Restart your cluster.

    Distribute the new configuration file to all nodes and restart your cluster.

61.4.3. Administration

Administrative tasks can be performed in HBase Shell or the Java API.

Java API

Java API examples in this section are taken from the source file hbase-server/src/test/java/org/apache/hadoop/hbase/util/TestHBaseFsckEncryption.java. .

Neither these examples, nor the source files they are taken from, are part of the public HBase API, and are provided for illustration only. Refer to the official API for usage instructions.
Enable Encryption on a Column Family

To enable encryption on a column family, you can either use HBase Shell or the Java API. After enabling encryption, trigger a major compaction. When the major compaction completes, the HFiles will be encrypted.

Rotate the Data Key

To rotate the data key, first change the ColumnFamily key in the column descriptor, then trigger a major compaction. When compaction is complete, all HFiles will be re-encrypted using the new data key. Until the compaction completes, the old HFiles will still be readable using the old key.

Switching Between Using a Random Data Key and Specifying A Key

If you configured a column family to use a specific key and you want to return to the default behavior of using a randomly-generated key for that column family, use the Java API to alter the HColumnDescriptor so that no value is sent with the key ENCRYPTION_KEY.

Rotate the Master Key

To rotate the master key, first generate and distribute the new key. Then update the KeyStore to contain a new master key, and keep the old master key in the KeyStore using a different alias. Next, configure fallback to the old master key in the hbase-site.xml file.

61.5. Secure Bulk Load

Bulk loading in secure mode is a bit more involved than normal setup, since the client has to transfer the ownership of the files generated from the MapReduce job to HBase. Secure bulk loading is implemented by a coprocessor, named SecureBulkLoadEndpoint, which uses a staging directory configured by the configuration property hbase.bulkload.staging.dir, which defaults to /tmp/hbase-staging/.

Secure Bulk Load Algorithm

  • One time only, create a staging directory which is world-traversable and owned by the user which runs HBase (mode 711, or rwx—​x—​x). A listing of this directory will look similar to the following:

    $ ls -ld /tmp/hbase-staging
drwx--x--x  2 hbase  hbase  68  3 Sep 14:54 /tmp/hbase-staging

  • A user writes out data to a secure output directory owned by that user. For example, /user/foo/data.

  • Internally, HBase creates a secret staging directory which is globally readable/writable (-rwxrwxrwx, 777). For example, /tmp/hbase-staging/averylongandrandomdirectoryname. The name and location of this directory is not exposed to the user. HBase manages creation and deletion of this directory.

  • The user makes the data world-readable and world-writable, moves it into the random staging directory, then calls the SecureBulkLoadClient#bulkLoadHFiles method.

The strength of the security lies in the length and randomness of the secret directory.

To enable secure bulk load, add the following properties to hbase-site.xml.

<property>
  <name>hbase.bulkload.staging.dir</name>
  <value>/tmp/hbase-staging</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.token.TokenProvider,
  org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
</property>

62. Security Configuration Example

This configuration example includes support for HFile v3, ACLs, Visibility Labels, and transparent encryption of data at rest and the WAL. All options have been discussed separately in the sections above.

Example 35. Example Security Settings in hbase-site.xml
<!-- HFile v3 Support -->
<property>
  <name>hfile.format.version</name>
  <value>3</value>
</property>
<!-- HBase Superuser -->
<property>
  <name>hbase.superuser</name>
  <value>hbase, admin</value>
</property>
<!-- Coprocessors for ACLs and Visibility Tags -->
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.visibility.VisibilityController,
  org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop/hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.access.VisibilityController</value>
</property>
<!-- Executable ACL for Coprocessor Endpoints -->
<property>
  <name>hbase.security.exec.permission.checks</name>
  <value>true</value>
</property>
<!-- Whether a user needs authorization for a visibility tag to set it on a cell -->
<property>
  <name>hbase.security.visibility.mutations.checkauth</name>
  <value>false</value>
</property>
<!-- Secure RPC Transport -->
<property>
  <name>hbase.rpc.protection</name>
  <value>privacy</value>
 </property>
 <!-- Transparent Encryption -->
<property>
  <name>hbase.crypto.keyprovider</name>
  <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
</property>
<property>
  <name>hbase.crypto.keyprovider.parameters</name>
  <value>jceks:///path/to/hbase/conf/hbase.jks?password=***</value>
</property>
<property>
  <name>hbase.crypto.master.key.name</name>
  <value>hbase</value>
</property>
<!-- WAL Encryption -->
<property>
  <name>hbase.regionserver.hlog.reader.impl</name>
  <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
</property>
<property>
  <name>hbase.regionserver.hlog.writer.impl</name>
  <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
</property>
<property>
  <name>hbase.regionserver.wal.encryption</name>
  <value>true</value>
</property>
<!-- For key rotation -->
<property>
  <name>hbase.crypto.master.alternate.key.name</name>
  <value>hbase.old</value>
</property>
<!-- Secure Bulk Load -->
<property>
  <name>hbase.bulkload.staging.dir</name>
  <value>/tmp/hbase-staging</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.token.TokenProvider,
  org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
</property>
Example 36. Example Group Mapper in Hadoop core-site.xml

Adjust these settings to suit your environment.

<property>
  <name>hadoop.security.group.mapping</name>
  <value>org.apache.hadoop.security.LdapGroupsMapping</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.url</name>
  <value>ldap://server</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.bind.user</name>
  <value>Administrator@example-ad.local</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.bind.password</name>
  <value>****</value> <!-- Replace with the actual password -->
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.base</name>
  <value>dc=example-ad,dc=local</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.search.filter.user</name>
  <value>(&amp;(objectClass=user)(sAMAccountName={0}))</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.search.filter.group</name>
  <value>(objectClass=group)</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.search.attr.member</name>
  <value>member</value>
</property>
<property>
  <name>hadoop.security.group.mapping.ldap.search.attr.group.name</name>
  <value>cn</value>
</property>

Architecture

63. Overview

63.1. NoSQL?

HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn’t an RDBMS which supports SQL as its primary access language, but there are many types of NoSQL databases: BerkeleyDB is an example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking, HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS, such as typed columns, secondary indexes, triggers, and advanced query languages, etc.

However, HBase has many features which supports both linear and modular scaling. HBase clusters expand by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20 RegionServers, for example, it doubles both in terms of storage and as well as processing capacity. An RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best performance requires specialized hardware and storage devices. HBase features of note are:

  • Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This makes it very suitable for tasks such as high-speed counter aggregation.

  • Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are automatically split and re-distributed as your data grows.

  • Automatic RegionServer failover

  • Hadoop/HDFS Integration: HBase supports HDFS out of the box as its distributed file system.

  • MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both source and sink.

  • Java Client API: HBase supports an easy to use Java API for programmatic access.

  • Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.

  • Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.

  • Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.

63.2. When Should I Use HBase?

HBase isn’t suitable for every problem.

First, make sure you have enough data. If you have hundreds of millions or billions of rows, then HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS might be a better choice due to the fact that all of your data might wind up on a single node (or two) and the rest of the cluster may be sitting idle.

Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns, secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a complete redesign as opposed to a port.

Third, make sure you have enough hardware. Even HDFS doesn’t do well with anything less than 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.

HBase can run quite well stand-alone on a laptop - but this should be considered a development configuration only.

63.3. What Is The Difference Between HBase and Hadoop/HDFS?

HDFS is a distributed file system that is well suited for the storage of large files. Its documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files. HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables. This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist on HDFS for high-speed lookups. See the Data Model and the rest of this chapter for more information on how HBase achieves its goals.

64. Catalog Tables

The catalog table hbase:meta exists as an HBase table and is filtered out of the HBase shell’s list command, but is in fact a table just like any other.

64.1. -ROOT-

The -ROOT- table was removed in HBase 0.96.0. Information here should be considered historical.

The -ROOT- table kept track of the location of the .META table (the previous name for the table now called hbase:meta) prior to HBase 0.96. The -ROOT- table structure was as follows:

Key

  • .META. region key (.META.,,1)

Values

  • info:regioninfo (serialized HRegionInfo instance of hbase:meta)

  • info:server (server:port of the RegionServer holding hbase:meta)

  • info:serverstartcode (start-time of the RegionServer process holding hbase:meta)

64.2. hbase:meta

The hbase:meta table (previously called .META.) keeps a list of all regions in the system. The location of hbase:meta was previously tracked within the -ROOT- table, but is now stored in ZooKeeper.

The hbase:meta table structure is as follows:

Key

  • Region key of the format ([table],[region start key],[region id])

Values

  • info:regioninfo (serialized HRegionInfo instance for this region)

  • info:server (server:port of the RegionServer containing this region)

  • info:serverstartcode (start-time of the RegionServer process containing this region)

When a table is in the process of splitting, two other columns will be created, called info:splitA and info:splitB. These columns represent the two daughter regions. The values for these columns are also serialized HRegionInfo instances. After the region has been split, eventually this row will be deleted.

Note on HRegionInfo

The empty key is used to denote table start and table end. A region with an empty start key is the first region in a table. If a region has both an empty start and an empty end key, it is the only region in the table

In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see the Writables utility.

64.3. Startup Sequencing

First, the location of hbase:meta is looked up in ZooKeeper. Next, hbase:meta is updated with server and startcode values.

For information on region-RegionServer assignment, see Region-RegionServer Assignment.

65. Client

The HBase client finds the RegionServers that are serving the particular row range of interest. It does this by querying the hbase:meta table. See hbase:meta for details. After locating the required region(s), the client contacts the RegionServer serving that region, rather than going through the master, and issues the read or write request. This information is cached in the client so that subsequent requests need not go through the lookup process. Should a region be reassigned either by the master load balancer or because a RegionServer has died, the client will requery the catalog tables to determine the new location of the user region.

See Runtime Impact for more information about the impact of the Master on HBase Client communication.

Administrative functions are done via an instance of Admin

65.1. Cluster Connections

The API changed in HBase 1.0. For connection configuration information, see Client configuration and dependencies connecting to an HBase cluster.

65.1.1. API as of HBase 1.0.0

It’s been cleaned up and users are returned Interfaces to work against rather than particular types. In HBase 1.0, obtain a Connection object from ConnectionFactory and thereafter, get from it instances of Table, Admin, and RegionLocator on an as-need basis. When done, close the obtained instances. Finally, be sure to cleanup your Connection instance before exiting. Connections are heavyweight objects but thread-safe so you can create one for your application and keep the instance around. Table, Admin and RegionLocator instances are lightweight. Create as you go and then let go as soon as you are done by closing them. See the Client Package Javadoc Description for example usage of the new HBase 1.0 API.

65.1.2. API before HBase 1.0.0

Instances of HTable are the way to interact with an HBase cluster earlier than 1.0.0. Table instances are not thread-safe. Only one thread can use an instance of Table at any given time. When creating Table instances, it is advisable to use the same HBaseConfiguration instance. This will ensure sharing of ZooKeeper and socket instances to the RegionServers which is usually what you want. For example, this is preferred:

HBaseConfiguration conf = HBaseConfiguration.create();
HTable table1 = new HTable(conf, "myTable");
HTable table2 = new HTable(conf, "myTable");

as opposed to this:

HBaseConfiguration conf1 = HBaseConfiguration.create();
HTable table1 = new HTable(conf1, "myTable");
HBaseConfiguration conf2 = HBaseConfiguration.create();
HTable table2 = new HTable(conf2, "myTable");

For more information about how connections are handled in the HBase client, see ConnectionFactory.

Connection Pooling

For applications which require high-end multithreaded access (e.g., web-servers or application servers that may serve many application threads in a single JVM), you can pre-create a Connection, as shown in the following example:

Example 37. Pre-Creating a Connection
// Create a connection to the cluster.
Configuration conf = HBaseConfiguration.create();
try (Connection connection = ConnectionFactory.createConnection(conf)) {
  try (Table table = connection.getTable(TableName.valueOf(tablename)) {
    // use table as needed, the table returned is lightweight
  }
}

Constructing HTableInterface implementation is very lightweight and resources are controlled.

HTablePool is Deprecated

Previous versions of this guide discussed HTablePool, which was deprecated in HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by HBASE-6500, or HConnection, which is deprecated in HBase 1.0 by Connection. Please use Connection instead.

65.2. WriteBuffer and Batch Methods

In HBase 1.0 and later, HTable is deprecated in favor of Table. Table does not use autoflush. To do buffered writes, use the BufferedMutator class.

Before a Table or HTable instance is discarded, invoke either close() or flushCommits(), so `Put`s will not be lost.

For additional information on write durability, review the ACID semantics page.

For fine-grained control of batching of Puts or Deletes, see the batch methods on Table.

65.3. External Clients

Information on non-Java clients and custom protocols is covered in Apache HBase External APIs

66. Client Request Filters

Get and Scan instances can be optionally configured with filters which are applied on the RegionServer.

Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups of Filter functionality.

66.1. Structural

Structural Filters contain other Filters.

66.1.1. FilterList

FilterList represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or' between two Filters (checking for either 'my value' or 'my other value' on the same attribute).

FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
  cf,
  column,
  CompareOp.EQUAL,
  Bytes.toBytes("my value")
  );
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
  cf,
  column,
  CompareOp.EQUAL,
  Bytes.toBytes("my other value")
  );
list.add(filter2);
scan.setFilter(list);

66.2. Column Value

66.2.1. SingleColumnValueFilter

A SingleColumnValueFilter (see: http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SingleColumnValueFilter.html) can be used to test column values for equivalence (CompareOp.EQUAL), inequality (CompareOp.NOT_EQUAL), or ranges (e.g., CompareOp.GREATER). The following is an example of testing equivalence of a column to a String value "my value"…​

SingleColumnValueFilter filter = new SingleColumnValueFilter(
  cf,
  column,
  CompareOp.EQUAL,
  Bytes.toBytes("my value")
  );
scan.setFilter(filter);

66.3. Column Value Comparators

There are several Comparator classes in the Filter package that deserve special mention. These Comparators are used in concert with other Filters, such as SingleColumnValueFilter.

66.3.1. RegexStringComparator

RegexStringComparator supports regular expressions for value comparisons.

RegexStringComparator comp = new RegexStringComparator("my.");   // any value that starts with 'my'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
  cf,
  column,
  CompareOp.EQUAL,
  comp
  );
scan.setFilter(filter);

See the Oracle JavaDoc for supported RegEx patterns in Java.

66.3.2. SubstringComparator

SubstringComparator can be used to determine if a given substring exists in a value. The comparison is case-insensitive.

SubstringComparator comp = new SubstringComparator("y val");   // looking for 'my value'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
  cf,
  column,
  CompareOp.EQUAL,
  comp
  );
scan.setFilter(filter);

66.3.3. BinaryPrefixComparator

See BinaryPrefixComparator.

66.3.4. BinaryComparator

See BinaryComparator.

66.4. KeyValue Metadata

As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate the existence of keys (i.e., ColumnFamily:Column qualifiers) for a row, as opposed to values the previous section.

66.4.1. FamilyFilter

FamilyFilter can be used to filter on the ColumnFamily. It is generally a better idea to select ColumnFamilies in the Scan than to do it with a Filter.

66.4.2. QualifierFilter

QualifierFilter can be used to filter based on Column (aka Qualifier) name.

66.4.3. ColumnPrefixFilter

ColumnPrefixFilter can be used to filter based on the lead portion of Column (aka Qualifier) names.

A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently get a subset of the columns in very wide rows.

Note: The same column qualifier can be used in different column families. This filter returns all matching columns.

Example: Find all columns in a row and family that start with "abc"

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] prefix = Bytes.toBytes("abc");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnPrefixFilter(prefix);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

66.4.4. MultipleColumnPrefixFilter

MultipleColumnPrefixFilter behaves like ColumnPrefixFilter but allows specifying multiple prefixes.

Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes. It can be used to efficiently get discontinuous sets of columns from very wide rows.

Example: Find all columns in a row and family that start with "abc" or "xyz"

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new MultipleColumnPrefixFilter(prefixes);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

66.4.5. ColumnRangeFilter

A ColumnRangeFilter allows efficient intra row scanning.

A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently get a 'slice' of the columns of a very wide row. i.e. you have a million columns in a row but you only want to look at columns bbbb-bbdd.

Note: The same column qualifier can be used in different column families. This filter returns all matching columns.

Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] startColumn = Bytes.toBytes("bbbb");
byte[] endColumn = Bytes.toBytes("bbdd");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

Note: Introduced in HBase 0.92

66.5. RowKey

66.5.1. RowFilter

It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however RowFilter can also be used.

66.6. Utility

66.6.1. FirstKeyOnlyFilter

This is primarily used for rowcount jobs. See FirstKeyOnlyFilter.

67. Master

HMaster is the implementation of the Master Server. The Master server is responsible for monitoring all RegionServer instances in the cluster, and is the interface for all metadata changes. In a distributed cluster, the Master typically runs on the NameNode. J Mohamed Zahoor goes into some more detail on the Master Architecture in this blog posting, HBase HMaster Architecture .

67.1. Startup Behavior

If run in a multi-Master environment, all Masters compete to run the cluster. If the active Master loses its lease in ZooKeeper (or the Master shuts down), then the remaining Masters jostle to take over the Master role.

67.2. Runtime Impact

A common dist-list question involves what happens to an HBase cluster when the Master goes down. Because the HBase client talks directly to the RegionServers, the cluster can still function in a "steady state". Additionally, per Catalog Tables, hbase:meta exists as an HBase table and is not resident in the Master. However, the Master controls critical functions such as RegionServer failover and completing region splits. So while the cluster can still run for a short time without the Master, the Master should be restarted as soon as possible.

67.3. Interface

The methods exposed by HMasterInterface are primarily metadata-oriented methods:

  • Table (createTable, modifyTable, removeTable, enable, disable)

  • ColumnFamily (addColumn, modifyColumn, removeColumn)

  • Region (move, assign, unassign) For example, when the Admin method disableTable is invoked, it is serviced by the Master server.

67.4. Processes

The Master runs several background threads:

67.4.1. LoadBalancer

Periodically, and when there are no regions in transition, a load balancer will run and move regions around to balance the cluster’s load. See Balancer for configuring this property.

See Region-RegionServer Assignment for more information on region assignment.

67.4.2. CatalogJanitor

Periodically checks and cleans up the hbase:meta table. See <arch.catalog.meta>> for more information on the meta table.

68. RegionServer

HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a DataNode.

68.1. Interface

The methods exposed by HRegionRegionInterface contain both data-oriented and region-maintenance methods:

  • Data (get, put, delete, next, etc.)

  • Region (splitRegion, compactRegion, etc.) For example, when the Admin method majorCompact is invoked on a table, the client is actually iterating through all regions for the specified table and requesting a major compaction directly to each region.

68.2. Processes

The RegionServer runs a variety of background threads:

68.2.1. CompactSplitThread

Checks for splits and handle minor compactions.

68.2.2. MajorCompactionChecker

Checks for major compactions.

68.2.3. MemStoreFlusher

Periodically flushes in-memory writes in the MemStore to StoreFiles.

68.2.4. LogRoller

Periodically checks the RegionServer’s WAL.

68.3. Coprocessors

Coprocessors were added in 0.92. There is a thorough Blog Overview of CoProcessors posted. Documentation will eventually move to this reference guide, but the blog is the most current information available at this time.

68.4. Block Cache

HBase provides two different BlockCache implementations: the default on-heap LruBlockCache and the BucketCache, which is (usually) off-heap. This section discusses benefits and drawbacks of each implementation, how to choose the appropriate option, and configuration options for each.

Block Cache Reporting: UI

See the RegionServer UI for detail on caching deploy. Since HBase 0.98.4, the Block Cache detail has been significantly extended showing configurations, sizings, current usage, time-in-the-cache, and even detail on block counts and types.

68.4.1. Cache Choices

LruBlockCache is the original implementation, and is entirely within the Java heap. BucketCache is mainly intended for keeping block cache data off-heap, although BucketCache can also keep data on-heap and serve from a file-backed cache.

BucketCache is production ready as of HBase 0.98.6

To run with BucketCache, you need HBASE-11678. This was included in 0.98.6.

Fetching will always be slower when fetching from BucketCache, as compared to the native on-heap LruBlockCache. However, latencies tend to be less erratic across time, because there is less garbage collection when you use BucketCache since it is managing BlockCache allocations, not the GC. If the BucketCache is deployed in off-heap mode, this memory is not managed by the GC at all. This is why you’d use BucketCache, so your latencies are less erratic and to mitigate GCs and heap fragmentation. See Nick Dimiduk’s BlockCache 101 for comparisons running on-heap vs off-heap tests. Also see Comparing BlockCache Deploys which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC), use BucketCache.

When you enable BucketCache, you are enabling a two tier caching system, an L1 cache which is implemented by an instance of LruBlockCache and an off-heap L2 cache which is implemented by BucketCache. Management of these two tiers and the policy that dictates how blocks move between them is done by CombinedBlockCache. It keeps all DATA blocks in the L2 BucketCache and meta blocks — INDEX and BLOOM blocks — on-heap in the L1 LruBlockCache. See Off-heap Block Cache for more detail on going off-heap.

68.4.2. General Cache Configurations

Apart from the cache implementation itself, you can set some general configuration options to control how the cache performs. See http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html. After setting any of these options, restart or rolling restart your cluster for the configuration to take effect. Check logs for errors or unexpected behavior.

See also Prefetch Option for Blockcache, which discusses a new option introduced in HBASE-9857.

68.4.3. LruBlockCache Design

The LruBlockCache is an LRU cache that contains three levels of block priority to allow for scan-resistance and in-memory ColumnFamilies:

  • Single access priority: The first time a block is loaded from HDFS it normally has this priority and it will be part of the first group to be considered during evictions. The advantage is that scanned blocks are more likely to get evicted than blocks that are getting more usage.

  • Multi access priority: If a block in the previous priority group is accessed again, it upgrades to this priority. It is thus part of the second group considered during evictions.

  • In-memory access priority: If the block’s family was configured to be "in-memory", it will be part of this priority disregarding the number of times it was accessed. Catalog tables are configured like this. This group is the last one considered during evictions.

    To mark a column family as in-memory, call

HColumnDescriptor.setInMemory(true);

if creating a table from java, or set IN_MEMORY ⇒ true when creating or altering a table in the shell: e.g.

hbase(main):003:0> create  't', {NAME => 'f', IN_MEMORY => 'true'}

For more information, see the LruBlockCache source

68.4.4. LruBlockCache Usage

Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache. This might be good for a large number of use cases, but further tunings are usually required in order to achieve better performance. An important concept is the working set size, or WSS, which is: "the amount of memory needed to compute the answer to a problem". For a website, this would be the data that’s needed to answer the queries over a short amount of time.

The way to calculate how much memory is available in HBase for caching is:

number of region servers * heap size * hfile.block.cache.size * 0.99

The default value for the block cache is 0.25 which represents 25% of the available heap. The last value (99%) is the default acceptable loading factor in the LRU cache after which eviction is started. The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would make the process blocking from the point where it loads new blocks. Here are some examples:

  • One region server with the heap size set to 1 GB and the default block cache size will have 253 MB of block cache available.

  • 20 region servers with the heap size set to 8 GB and a default block cache size will have 39.6 of block cache.

  • 100 region servers with the heap size set to 24 GB and a block cache size of 0.5 will have about 1.16 TB of block cache.

Your data is not the only resident of the block cache. Here are others that you may have to take into account:

Catalog Tables

The -ROOT- (prior to HBase 0.96, see arch.catalog.root) and hbase:meta tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. The former never uses more than a few hundred bytes while the latter can occupy a few MBs (depending on the number of regions).

HFiles Indexes

An HFile is the file format that HBase uses to store data in HDFS. It contains a multi-layered index which allows HBase to seek to the data without having to read the whole file. The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing. For big data sets it’s not unusual to see numbers around 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren’t used.

Keys

The values that are stored are only half the picture, since each value is stored along with its keys (row key, family qualifier, and timestamp). See Try to minimize row and column sizes.

Bloom Filters

Just like the HFile indexes, those data structures (when enabled) are stored in the LRU.

Currently the recommended way to measure HFile indexes and bloom filters sizes is to look at the region server web UI and checkout the relevant metrics. For keys, sampling can be done by using the HFile command line tool and look for the average key size metric. Since HBase 0.98.3, you can view details on BlockCache stats and metrics in a special Block Cache section in the UI.

It’s generally bad to use block caching when the WSS doesn’t fit in memory. This is the case when you have for example 40GB available across all your region servers' block caches but you need to process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily. Here are two use cases:

  • Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close to 0. Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM. For more information on monitoring GC, see JVM Garbage Collection Logs.

  • Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there’s no need to put them into the block cache. The Scan object has the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access. An example would be counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that’s currently in use.

Caching META blocks only (DATA blocks in fscache)

An interesting setup is one where we cache META blocks only and we read DATA blocks in on each access. If the DATA blocks fit inside fscache, this alternative may make sense when access is completely random across a very large dataset. To enable this setup, alter your table and for each column family set BLOCKCACHE ⇒ 'false'. You are 'disabling' the BlockCache for this column family only. You can never disable the caching of META blocks. Since HBASE-4683 Always cache index and bloom blocks, we will cache META blocks even if the BlockCache is disabled.

68.4.5. Off-heap Block Cache

How to Enable BucketCache

The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an L1 on-heap cache implemented by LruBlockCache and a second L2 cache implemented with BucketCache. The managing class is CombinedBlockCache by default. The previous link describes the caching 'policy' implemented by CombinedBlockCache. In short, it works by keeping meta blocks — INDEX and BLOOM in the L1, on-heap LruBlockCache tier — and DATA blocks are kept in the L2, BucketCache tier. It is possible to amend this behavior in HBase since version 1.0 and ask that a column family have both its meta and DATA blocks hosted on-heap in the L1 tier by setting cacheDataInL1 via (HColumnDescriptor.setCacheDataInL1(true) or in the shell, creating or amending column families setting CACHE_DATA_IN_L1 to true: e.g.

hbase(main):003:0> create 't', {NAME => 't', CONFIGURATION => {CACHE_DATA_IN_L1 => 'true'}}

The BucketCache Block Cache can be deployed on-heap, off-heap, or file based. You set which via the hbase.bucketcache.ioengine setting. Setting it to heap will have BucketCache deployed inside the allocated Java heap. Setting it to offheap will have BucketCache make its allocations off-heap, and an ioengine setting of file:PATH_TO_FILE will direct BucketCache to use a file caching (Useful in particular if you have some fast I/O attached to the box such as SSDs).

It is possible to deploy an L1+L2 setup where we bypass the CombinedBlockCache policy and have BucketCache working as a strict L2 cache to the L1 LruBlockCache. For such a setup, set CacheConfig.BUCKET_CACHE_COMBINED_KEY to false. In this mode, on eviction from L1, blocks go to L2. When a block is cached, it is cached first in L1. When we go to look for a cached block, we look first in L1 and if none found, then search L2. Let us call this deploy format, Raw L1+L2.

Other BucketCache configs include: specifying a location to persist cache to across restarts, how many threads to use writing the cache, etc. See the CacheConfig.html class for configuration options and descriptions.

BucketCache Example Configuration

This sample provides a configuration for a 4 GB off-heap BucketCache with a 1 GB on-heap cache.

Configuration is performed on the RegionServer.

Setting hbase.bucketcache.ioengine and hbase.bucketcache.size > 0 enables CombinedBlockCache. Let us presume that the RegionServer has been set to run with a 5G heap: i.e. HBASE_HEAPSIZE=5g.

  1. First, edit the RegionServer’s hbase-env.sh and set HBASE_OFFHEAPSIZE to a value greater than the off-heap size wanted, in this case, 4 GB (expressed as 4G). Let’s set it to 5G. That’ll be 4G for our off-heap cache and 1G for any other uses of off-heap memory (there are other users of off-heap memory other than BlockCache; e.g. DFSClient in RegionServer can make use of off-heap memory). See Direct Memory Usage In HBase.

    HBASE_OFFHEAPSIZE=5G

  2. Next, add the following configuration to the RegionServer’s hbase-site.xml.

    <property>
  <name>hbase.bucketcache.ioengine</name>
  <value>offheap</value>
</property>
<property>
  <name>hfile.block.cache.size</name>
  <value>0.2</value>
</property>
<property>
  <name>hbase.bucketcache.size</name>
  <value>4196</value>
</property>

  3. Restart or rolling restart your cluster, and check the logs for any issues.

In the above, we set the BucketCache to be 4G. We configured the on-heap LruBlockCache have 20% (0.2) of the RegionServer’s heap size (0.2 * 5G = 1G). In other words, you configure the L1 LruBlockCache as you would normally (as if there were no L2 cache present).

HBASE-10641 introduced the ability to configure multiple sizes for the buckets of the BucketCache, in HBase 0.98 and newer. To configurable multiple bucket sizes, configure the new property hfile.block.cache.sizes (instead of hfile.block.cache.size) to a comma-separated list of block sizes, ordered from smallest to largest, with no spaces. The goal is to optimize the bucket sizes based on your data access patterns. The following example configures buckets of size 4096 and 8192.

<property>
  <name>hfile.block.cache.sizes</name>
  <value>4096,8192</value>
</property>
Direct Memory Usage In HBase

The default maximum direct memory varies by JVM. Traditionally it is 64M or some relation to allocated heap size (-Xmx) or no limit at all (JDK7 apparently). HBase servers use direct memory, in particular short-circuit reading, the hosted DFSClient will allocate direct memory buffers. If you do off-heap block caching, you’ll be making use of direct memory. Starting your JVM, make sure the -XX:MaxDirectMemorySize setting in conf/hbase-env.sh is set to some value that is higher than what you have allocated to your off-heap BlockCache (hbase.bucketcache.size). It should be larger than your off-heap block cache and then some for DFSClient usage (How much the DFSClient uses is not easy to quantify; it is the number of open HFiles * hbase.dfs.client.read.shortcircuit.buffer.size where hbase.dfs.client.read.shortcircuit.buffer.size is set to 128k in HBase — see hbase-default.xml default configurations). Direct memory, which is part of the Java process heap, is separate from the object heap allocated by -Xmx. The value allocated by MaxDirectMemorySize must not exceed physical RAM, and is likely to be less than the total available RAM due to other memory requirements and system constraints.

You can see how much memory — on-heap and off-heap/direct — a RegionServer is configured to use and how much it is using at any one time by looking at the Server Metrics: Memory tab in the UI. It can also be gotten via JMX. In particular the direct memory currently used by the server can be found on the java.nio.type=BufferPool,name=direct bean. Terracotta has a good write up on using off-heap memory in Java. It is for their product BigMemory but a lot of the issues noted apply in general to any attempt at going off-heap. Check it out.
hbase.bucketcache.percentage.in.combinedcache

This is a pre-HBase 1.0 configuration removed because it was confusing. It was a float that you would set to some value between 0.0 and 1.0. Its default was 0.9. If the deploy was using CombinedBlockCache, then the LruBlockCache L1 size was calculated to be (1 - hbase.bucketcache.percentage.in.combinedcache) * size-of-bucketcache and the BucketCache size was hbase.bucketcache.percentage.in.combinedcache * size-of-bucket-cache. where size-of-bucket-cache itself is EITHER the value of the configuration hbase.bucketcache.size IF it was specified as Megabytes OR hbase.bucketcache.size * -XX:MaxDirectMemorySize if hbase.bucketcache.size is between 0 and 1.0.

In 1.0, it should be more straight-forward. L1 LruBlockCache size is set as a fraction of java heap using hfile.block.cache.size setting (not the best name) and L2 is set as above either in absolute Megabytes or as a fraction of allocated maximum direct memory.

68.4.6. Compressed BlockCache

HBASE-11331 introduced lazy BlockCache decompression, more simply referred to as compressed BlockCache. When compressed BlockCache is enabled data and encoded data blocks are cached in the BlockCache in their on-disk format, rather than being decompressed and decrypted before caching.

For a RegionServer hosting more data than can fit into cache, enabling this feature with SNAPPY compression has been shown to result in 50% increase in throughput and 30% improvement in mean latency while, increasing garbage collection by 80% and increasing overall CPU load by 2%. See HBASE-11331 for more details about how performance was measured and achieved. For a RegionServer hosting data that can comfortably fit into cache, or if your workload is sensitive to extra CPU or garbage-collection load, you may receive less benefit.

The compressed BlockCache is disabled by default. To enable it, set hbase.block.data.cachecompressed to true in hbase-site.xml on all RegionServers.

68.5. RegionServer Splitting Implementation

As write requests are handled by the region server, they accumulate in an in-memory storage system called the memstore. Once the memstore fills, its content are written to disk as additional store files. This event is called a memstore flush. As store files accumulate, the RegionServer will compact them into fewer, larger files. After each flush or compaction finishes, the amount of data stored in the region has changed. The RegionServer consults the region split policy to determine if the region has grown too large or should be split for another policy-specific reason. A region split request is enqueued if the policy recommends it.

Logically, the process of splitting a region is simple. We find a suitable point in the keyspace of the region where we should divide the region in half, then split the region’s data into two new regions at that point. The details of the process however are not simple. When a split happens, the newly created daughter regions do not rewrite all the data into new files immediately. Instead, they create small files similar to symbolic link files, named Reference files, which point to either the top or bottom part of the parent store file according to the split point. The reference file is used just like a regular data file, but only half of the records are considered. The region can only be split if there are no more references to the immutable data files of the parent region. Those reference files are cleaned gradually by compactions, so that the region will stop referring to its parents files, and can be split further.

Although splitting the region is a local decision made by the RegionServer, the split process itself must coordinate with many actors. The RegionServer notifies the Master before and after the split, updates the .META. table so that clients can discover the new daughter regions, and rearranges the directory structure and data files in HDFS. Splitting is a multi-task process. To enable rollback in case of an error, the RegionServer keeps an in-memory journal about the execution state. The steps taken by the RegionServer to execute the split are illustrated in RegionServer Split Process. Each step is labeled with its step number. Actions from RegionServers or Master are shown in red, while actions from the clients are show in green.

Region Split Process
Figure 1. RegionServer Split Process

  1. The RegionServer decides locally to split the region, and prepares the split. THE SPLIT TRANSACTION IS STARTED. As a first step, the RegionServer acquires a shared read lock on the table to prevent schema modifications during the splitting process. Then it creates a znode in zookeeper under /hbase/region-in-transition/region-name, and sets the znode’s state to SPLITTING.

  2. The Master learns about this znode, since it has a watcher for the parent region-in-transition znode.

  3. The RegionServer creates a sub-directory named .splits under the parent’s region directory in HDFS.

  4. The RegionServer closes the parent region and marks the region as offline in its local data structures. THE SPLITTING REGION IS NOW OFFLINE. At this point, client requests coming to the parent region will throw NotServingRegionException. The client will retry with some backoff. The closing region is flushed.

  5. The RegionServer creates region directories under the .splits directory, for daughter regions A and B, and creates necessary data structures. Then it splits the store files, in the sense that it creates two Reference files per store file in the parent region. Those reference files will point to the parent region’s files.

  6. The RegionServer creates the actual region directory in HDFS, and moves the reference files for each daughter.

  7. The RegionServer sends a Put request to the .META. table, to set the parent as offline in the .META. table and add information about daughter regions. At this point, there won’t be individual entries in .META. for the daughters. Clients will see that the parent region is split if they scan .META., but won’t know about the daughters until they appear in .META.. Also, if this Put to .META. succeeds, the parent will be effectively split. If the RegionServer fails before this RPC succeeds, Master and the next Region Server opening the region will clean dirty state about the region split. After the .META. update, though, the region split will be rolled-forward by Master.

  8. The RegionServer opens daughters A and B in parallel.

  9. The RegionServer adds the daughters A and B to .META., together with information that it hosts the regions. THE SPLIT REGIONS (DAUGHTERS WITH REFERENCES TO PARENT) ARE NOW ONLINE. After this point, clients can discover the new regions and issue requests to them. Clients cache the .META. entries locally, but when they make requests to the RegionServer or .META., their caches will be invalidated, and they will learn about the new regions from .META..

  10. The RegionServer updates znode /hbase/region-in-transition/region-name in ZooKeeper to state SPLIT, so that the master can learn about it. The balancer can freely re-assign the daughter regions to other region servers if necessary. THE SPLIT TRANSACTION IS NOW FINISHED.

  11. After the split, .META. and HDFS will still contain references to the parent region. Those references will be removed when compactions in daughter regions rewrite the data files. Garbage collection tasks in the master periodically check whether the daughter regions still refer to the parent region’s files. If not, the parent region will be removed.

68.6. Write Ahead Log (WAL)

68.6.1. Purpose

The Write Ahead Log (WAL) records all changes to data in HBase, to file-based storage. Under normal operations, the WAL is not needed because data changes move from the MemStore to StoreFiles. However, if a RegionServer crashes or becomes unavailable before the MemStore is flushed, the WAL ensures that the changes to the data can be replayed. If writing to the WAL fails, the entire operation to modify the data fails.

HBase uses an implementation of the WAL interface. Usually, there is only one instance of a WAL per RegionServer. The RegionServer records Puts and Deletes to it, before recording them to the MemStore for the affected Store.

The HLog

Prior to 2.0, the interface for WALs in HBase was named HLog. In 0.94, HLog was the name of the implementation of the WAL. You will likely find references to the HLog in documentation tailored to these older versions.

The WAL resides in HDFS in the /hbase/WALs/ directory (prior to HBase 0.94, they were stored in /hbase/.logs/), with subdirectories per region.

For more general information about the concept of write ahead logs, see the Wikipedia Write-Ahead Log article.

68.6.2. MultiWAL

With a single WAL per RegionServer, the RegionServer must write to the WAL serially, because HDFS files must be sequential. This causes the WAL to be a performance bottleneck.

HBase 1.0 introduces support MultiWal in HBASE-5699. MultiWAL allows a RegionServer to write multiple WAL streams in parallel, by using multiple pipelines in the underlying HDFS instance, which increases total throughput during writes. This parallelization is done by partitioning incoming edits by their Region. Thus, the current implementation will not help with increasing the throughput to a single Region.

RegionServers using the original WAL implementation and those using the MultiWAL implementation can each handle recovery of either set of WALs, so a zero-downtime configuration update is possible through a rolling restart.

Configure MultiWAL

To configure MultiWAL for a RegionServer, set the value of the property hbase.wal.provider to multiwal by pasting in the following XML:

<property>
  <name>hbase.wal.provider</name>
  <value>multiwal</value>
</property>

Restart the RegionServer for the changes to take effect.

To disable MultiWAL for a RegionServer, unset the property and restart the RegionServer.

68.6.3. WAL Flushing

TODO (describe).

68.6.4. WAL Splitting

A RegionServer serves many regions. All of the regions in a region server share the same active WAL file. Each edit in the WAL file includes information about which region it belongs to. When a region is opened, the edits in the WAL file which belong to that region need to be replayed. Therefore, edits in the WAL file must be grouped by region so that particular sets can be replayed to regenerate the data in a particular region. The process of grouping the WAL edits by region is called log splitting. It is a critical process for recovering data if a region server fails.

Log splitting is done by the HMaster during cluster start-up or by the ServerShutdownHandler as a region server shuts down. So that consistency is guaranteed, affected regions are unavailable until data is restored. All WAL edits need to be recovered and replayed before a given region can become available again. As a result, regions affected by log splitting are unavailable until the process completes.

Procedure: Log Splitting, Step by Step

  1. The /hbase/WALs/<host>,<port>,<startcode> directory is renamed.

    Renaming the directory is important because a RegionServer may still be up and accepting requests even if the HMaster thinks it is down. If the RegionServer does not respond immediately and does not heartbeat its ZooKeeper session, the HMaster may interpret this as a RegionServer failure. Renaming the logs directory ensures that existing, valid WAL files which are still in use by an active but busy RegionServer are not written to by accident.

    The new directory is named according to the following pattern:

    /hbase/WALs/<host>,<port>,<startcode>-splitting

    An example of such a renamed directory might look like the following:

    /hbase/WALs/srv.example.com,60020,1254173957298-splitting

  2. Each log file is split, one at a time.

    The log splitter reads the log file one edit entry at a time and puts each edit entry into the buffer corresponding to the edit’s region. At the same time, the splitter starts several writer threads. Writer threads pick up a corresponding buffer and write the edit entries in the buffer to a temporary recovered edit file. The temporary edit file is stored to disk with the following naming pattern:

    /hbase/<table_name>/<region_id>/recovered.edits/.temp

    This file is used to store all the edits in the WAL log for this region. After log splitting completes, the .temp file is renamed to the sequence ID of the first log written to the file.

    To determine whether all edits have been written, the sequence ID is compared to the sequence of the last edit that was written to the HFile. If the sequence of the last edit is greater than or equal to the sequence ID included in the file name, it is clear that all writes from the edit file have been completed.

  3. After log splitting is complete, each affected region is assigned to a RegionServer.

    When the region is opened, the recovered.edits folder is checked for recovered edits files. If any such files are present, they are replayed by reading the edits and saving them to the MemStore. After all edit files are replayed, the contents of the MemStore are written to disk (HFile) and the edit files are deleted.

Handling of Errors During Log Splitting

If you set the hbase.hlog.split.skip.errors option to true, errors are treated as follows:

  • Any error encountered during splitting will be logged.

  • The problematic WAL log will be moved into the .corrupt directory under the hbase rootdir,

  • Processing of the WAL will continue

If the hbase.hlog.split.skip.errors option is set to false, the default, the exception will be propagated and the split will be logged as failed. See HBASE-2958 When hbase.hlog.split.skip.errors is set to false, we fail the split but that’s it. We need to do more than just fail split if this flag is set.

How EOFExceptions are treated when splitting a crashed RegionServer’s WALs

If an EOFException occurs while splitting logs, the split proceeds even when hbase.hlog.split.skip.errors is set to false. An EOFException while reading the last log in the set of files to split is likely, because the RegionServer was likely in the process of writing a record at the time of a crash. For background, see HBASE-2643 Figure how to deal with eof splitting logs

Performance Improvements during Log Splitting

WAL log splitting and recovery can be resource intensive and take a long time, depending on the number of RegionServers involved in the crash and the size of the regions. Enabling or Disabling Distributed Log Splitting was developed to improve performance during log splitting.

Enabling or Disabling Distributed Log Splitting

Distributed log processing is enabled by default since HBase 0.92. The setting is controlled by the hbase.master.distributed.log.splitting property, which can be set to true or false, but defaults to true.

Distributed Log Splitting, Step by Step

After configuring distributed log splitting, the HMaster controls the process. The HMaster enrolls each RegionServer in the log splitting process, and the actual work of splitting the logs is done by the RegionServers. The general process for log splitting, as described in Distributed Log Splitting, Step by Step still applies here.

  1. If distributed log processing is enabled, the HMaster creates a split log manager instance when the cluster is started.

    1. The split log manager manages all log files which need to be scanned and split.

    2. The split log manager places all the logs into the ZooKeeper splitlog node (/hbase/splitlog) as tasks.

    3. You can view the contents of the splitlog by issuing the following zkCli command. Example output is shown.

      ls /hbase/splitlog
[hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost8.sample.com%2C57020%2C1340474893275-splitting%2Fhost8.sample.com%253A57020.1340474893900,
hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost3.sample.com%2C57020%2C1340474893299-splitting%2Fhost3.sample.com%253A57020.1340474893931,
hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost4.sample.com%2C57020%2C1340474893287-splitting%2Fhost4.sample.com%253A57020.1340474893946]

      The output contains some non-ASCII characters. When decoded, it looks much more simple:

      [hdfs://host2.sample.com:56020/hbase/.logs
/host8.sample.com,57020,1340474893275-splitting
/host8.sample.com%3A57020.1340474893900,
hdfs://host2.sample.com:56020/hbase/.logs
/host3.sample.com,57020,1340474893299-splitting
/host3.sample.com%3A57020.1340474893931,
hdfs://host2.sample.com:56020/hbase/.logs
/host4.sample.com,57020,1340474893287-splitting
/host4.sample.com%3A57020.1340474893946]

      The listing represents WAL file names to be scanned and split, which is a list of log splitting tasks.

  2. The split log manager monitors the log-splitting tasks and workers.

    The split log manager is responsible for the following ongoing tasks:

    • Once the split log manager publishes all the tasks to the splitlog znode, it monitors these task nodes and waits for them to be processed.

    • Checks to see if there are any dead split log workers queued up. If it finds tasks claimed by unresponsive workers, it will resubmit those tasks. If the resubmit fails due to some ZooKeeper exception, the dead worker is queued up again for retry.

    • Checks to see if there are any unassigned tasks. If it finds any, it create an ephemeral rescan node so that each split log worker is notified to re-scan unassigned tasks via the nodeChildrenChanged ZooKeeper event.

    • Checks for tasks which are assigned but expired. If any are found, they are moved back to TASK_UNASSIGNED state again so that they can be retried. It is possible that these tasks are assigned to slow workers, or they may already be finished. This is not a problem, because log splitting tasks have the property of idempotence. In other words, the same log splitting task can be processed many times without causing any problem.

    • The split log manager watches the HBase split log znodes constantly. If any split log task node data is changed, the split log manager retrieves the node data. The node data contains the current state of the task. You can use the zkCli get command to retrieve the current state of a task. In the example output below, the first line of the output shows that the task is currently unassigned.

      get /hbase/splitlog/hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost6.sample.com%2C57020%2C1340474893287-splitting%2Fhost6.sample.com%253A57020.1340474893945

unassigned host2.sample.com:57000
cZxid = 0×7115
ctime = Sat Jun 23 11:13:40 PDT 2012
...

      Based on the state of the task whose data is changed, the split log manager does one of the following:

    • Resubmit the task if it is unassigned

    • Heartbeat the task if it is assigned

    • Resubmit or fail the task if it is resigned (see Reasons a Task Will Fail)

    • Resubmit or fail the task if it is completed with errors (see Reasons a Task Will Fail)

    • Resubmit or fail the task if it could not complete due to errors (see Reasons a Task Will Fail)

    • Delete the task if it is successfully completed or failed

      Reasons a Task Will Fail

      • The task has been deleted.

      • The node no longer exists.

      • The log status manager failed to move the state of the task to TASK_UNASSIGNED.

      • The number of resubmits is over the resubmit threshold.

  3. Each RegionServer’s split log worker performs the log-splitting tasks.

    Each RegionServer runs a daemon thread called the split log worker, which does the work to split the logs. The daemon thread starts when the RegionServer starts, and registers itself to watch HBase znodes. If any splitlog znode children change, it notifies a sleeping worker thread to wake up and grab more tasks. If a worker’s current task’s node data is changed, the worker checks to see if the task has been taken by another worker. If so, the worker thread stops work on the current task.

    The worker monitors the splitlog znode constantly. When a new task appears, the split log worker retrieves the task paths and checks each one until it finds an unclaimed task, which it attempts to claim. If the claim was successful, it attempts to perform the task and updates the task’s state property based on the splitting outcome. At this point, the split log worker scans for another unclaimed task.

    How the Split Log Worker Approaches a Task

    • It queries the task state and only takes action if the task is in `TASK_UNASSIGNED `state.

    • If the task is in TASK_UNASSIGNED state, the worker attempts to set the state to TASK_OWNED by itself. If it fails to set the state, another worker will try to grab it. The split log manager will also ask all workers to rescan later if the task remains unassigned.

    • If the worker succeeds in taking ownership of the task, it tries to get the task state again to make sure it really gets it asynchronously. In the meantime, it starts a split task executor to do the actual work:

      • Get the HBase root folder, create a temp folder under the root, and split the log file to the temp folder.

      • If the split was successful, the task executor sets the task to state TASK_DONE.

      • If the worker catches an unexpected IOException, the task is set to state TASK_ERR.

      • If the worker is shutting down, set the task to state TASK_RESIGNED.

      • If the task is taken by another worker, just log it.

  4. The split log manager monitors for uncompleted tasks.

    The split log manager returns when all tasks are completed successfully. If all tasks are completed with some failures, the split log manager throws an exception so that the log splitting can be retried. Due to an asynchronous implementation, in very rare cases, the split log manager loses track of some completed tasks. For that reason, it periodically checks for remaining uncompleted task in its task map or ZooKeeper. If none are found, it throws an exception so that the log splitting can be retried right away instead of hanging there waiting for something that won’t happen.

Distributed Log Replay

After a RegionServer fails, its failed regions are assigned to another RegionServer, which are marked as "recovering" in ZooKeeper. A split log worker directly replays edits from the WAL of the failed RegionServer to the regions at its new location. When a region is in "recovering" state, it can accept writes but no reads (including Append and Increment), region splits or merges.

Distributed Log Replay extends the Enabling or Disabling Distributed Log Splitting framework. It works by directly replaying WAL edits to another RegionServer instead of creating recovered.edits files. It provides the following advantages over distributed log splitting alone:

  • It eliminates the overhead of writing and reading a large number of recovered.edits files. It is not unusual for thousands of recovered.edits files to be created and written concurrently during a RegionServer recovery. Many small random writes can degrade overall system performance.

  • It allows writes even when a region is in recovering state. It only takes seconds for a recovering region to accept writes again.

Enabling Distributed Log Replay

To enable distributed log replay, set hbase.master.distributed.log.replay to true. This will be the default for HBase 0.99 (HBASE-10888).

You must also enable HFile version 3 (which is the default HFile format starting in HBase 0.99. See HBASE-10855). Distributed log replay is unsafe for rolling upgrades.

68.6.5. Disabling the WAL

It is possible to disable the WAL, to improve performance in certain specific situations. However, disabling the WAL puts your data at risk. The only situation where this is recommended is during a bulk load. This is because, in the event of a problem, the bulk load can be re-run with no risk of data loss.

The WAL is disabled by calling the HBase client field Mutation.writeToWAL(false). Use the Mutation.setDurability(Durability.SKIP_WAL) and Mutation.getDurability() methods to set and get the field’s value. There is no way to disable the WAL for only a specific table.

If you disable the WAL for anything other than bulk loads, your data is at risk.

69. Regions

Regions are the basic element of availability and distribution for tables, and are comprised of a Store per Column Family. The hierarchy of objects is as follows:

Table                    (HBase table)
    Region               (Regions for the table)
        Store            (Store per ColumnFamily for each Region for the table)
            MemStore     (MemStore for each Store for each Region for the table)
            StoreFile    (StoreFiles for each Store for each Region for the table)
                Block    (Blocks within a StoreFile within a Store for each Region for the table)

For a description of what HBase files look like when written to HDFS, see Browsing HDFS for HBase Objects.

69.1. Considerations for Number of Regions

In general, HBase is designed to run with a small (20-200) number of relatively large (5-20Gb) regions per server. The considerations for this are as follows:

69.1.1. Why should I keep my Region count low?

Typically you want to keep your region count low on HBase for numerous reasons. Usually right around 100 regions per RegionServer has yielded the best results. Here are some of the reasons below for keeping region count low:

  1. MSLAB (MemStore-local allocation buffer) requires 2MB per MemStore (that’s 2MB per family per region). 1000 regions that have 2 families each is 3.9GB of heap used, and it’s not even storing data yet. NB: the 2MB value is configurable.

  2. If you fill all the regions at somewhat the same rate, the global memory usage makes it that it forces tiny flushes when you have too many regions which in turn generates compactions. Rewriting the same data tens of times is the last thing you want. An example is filling 1000 regions (with one family) equally and let’s consider a lower bound for global MemStore usage of 5GB (the region server would have a big heap). Once it reaches 5GB it will force flush the biggest region, at that point they should almost all have about 5MB of data so it would flush that amount. 5MB inserted later, it would flush another region that will now have a bit over 5MB of data, and so on. This is currently the main limiting factor for the number of regions; see Number of regions per RS - upper bound for detailed formula.

  3. The master as is is allergic to tons of regions, and will take a lot of time assigning them and moving them around in batches. The reason is that it’s heavy on ZK usage, and it’s not very async at the moment (could really be improved — and has been improved a bunch in 0.96 HBase).

  4. In older versions of HBase (pre-HFile v2, 0.90 and previous), tons of regions on a few RS can cause the store file index to rise, increasing heap usage and potentially creating memory pressure or OOME on the RSs

Another issue is the effect of the number of regions on MapReduce jobs; it is typical to have one mapper per HBase region. Thus, hosting only 5 regions per RS may not be enough to get sufficient number of tasks for a MapReduce job, while 1000 regions will generate far too many tasks.

See Determining region count and size for configuration guidelines.

69.2. Region-RegionServer Assignment

This section describes how Regions are assigned to RegionServers.

69.2.1. Startup

When HBase starts regions are assigned as follows (short version):

  1. The Master invokes the AssignmentManager upon startup.

  2. The AssignmentManager looks at the existing region assignments in hbase:meta.

  3. If the region assignment is still valid (i.e., if the RegionServer is still online) then the assignment is kept.

  4. If the assignment is invalid, then the LoadBalancerFactory is invoked to assign the region. The load balancer (StochasticLoadBalancer by default in HBase 1.0) assign the region to a RegionServer.

  5. hbase:meta is updated with the RegionServer assignment (if needed) and the RegionServer start codes (start time of the RegionServer process) upon region opening by the RegionServer.

69.2.2. Failover

When a RegionServer fails:

  1. The regions immediately become unavailable because the RegionServer is down.

  2. The Master will detect that the RegionServer has failed.

  3. The region assignments will be considered invalid and will be re-assigned just like the startup sequence.

  4. In-flight queries are re-tried, and not lost.

  5. Operations are switched to a new RegionServer within the following amount of time:

    ZooKeeper session timeout + split time + assignment/replay time

69.2.3. Region Load Balancing

Regions can be periodically moved by the LoadBalancer.

69.2.4. Region State Transition

HBase maintains a state for each region and persists the state in hbase:meta. The state of the hbase:meta region itself is persisted in ZooKeeper. You can see the states of regions in transition in the Master web UI. Following is the list of possible region states.

Possible Region States

  • OFFLINE: the region is offline and not opening

  • OPENING: the region is in the process of being opened

  • OPEN: the region is open and the RegionServer has notified the master

  • FAILED_OPEN: the RegionServer failed to open the region

  • CLOSING: the region is in the process of being closed

  • CLOSED: the RegionServer has closed the region and notified the master

  • FAILED_CLOSE: the RegionServer failed to close the region

  • SPLITTING: the RegionServer notified the master that the region is splitting

  • SPLIT: the RegionServer notified the master that the region has finished splitting

  • SPLITTING_NEW: this region is being created by a split which is in progress

  • MERGING: the RegionServer notified the master that this region is being merged with another region

  • MERGED: the RegionServer notified the master that this region has been merged

  • MERGING_NEW: this region is being created by a merge of two regions

region states
Figure 2. Region State Transitions
Graph Legend

  • Brown: Offline state, a special state that can be transient (after closed before opening), terminal (regions of disabled tables), or initial (regions of newly created tables)

  • Palegreen: Online state that regions can serve requests

  • Lightblue: Transient states

  • Red: Failure states that need OPS attention

  • Gold: Terminal states of regions split/merged

  • Grey: Initial states of regions created through split/merge

Transition State Descriptions

  1. The master moves a region from OFFLINE to OPENING state and tries to assign the region to a RegionServer. The RegionServer may or may not have received the open region request. The master retries sending the open region request to the RegionServer until the RPC goes through or the master runs out of retries. After the RegionServer receives the open region request, the RegionServer begins opening the region.

  2. If the master is running out of retries, the master prevents the RegionServer from opening the region by moving the region to CLOSING state and trying to close it, even if the RegionServer is starting to open the region.

  3. After the RegionServer opens the region, it continues to try to notify the master until the master moves the region to OPEN state and notifies the RegionServer. The region is now open.

  4. If the RegionServer cannot open the region, it notifies the master. The master moves the region to CLOSED state and tries to open the region on a different RegionServer.

  5. If the master cannot open the region on any of a certain number of regions, it moves the region to FAILED_OPEN state, and takes no further action until an operator intervenes from the HBase shell, or the server is dead.

  6. The master moves a region from OPEN to CLOSING state. The RegionServer holding the region may or may not have received the close region request. The master retries sending the close request to the server until the RPC goes through or the master runs out of retries.

  7. If the RegionServer is not online, or throws NotServingRegionException, the master moves the region to OFFLINE state and re-assigns it to a different RegionServer.

  8. If the RegionServer is online, but not reachable after the master runs out of retries, the master moves the region to FAILED_CLOSE state and takes no further action until an operator intervenes from the HBase shell, or the server is dead.

  9. If the RegionServer gets the close region request, it closes the region and notifies the master. The master moves the region to CLOSED state and re-assigns it to a different RegionServer.

  10. Before assigning a region, the master moves the region to OFFLINE state automatically if it is in CLOSED state.

  11. When a RegionServer is about to split a region, it notifies the master. The master moves the region to be split from OPEN to SPLITTING state and add the two new regions to be created to the RegionServer. These two regions are in SPLITTING_NEW state initially.

  12. After notifying the master, the RegionServer starts to split the region. Once past the point of no return, the RegionServer notifies the master again so the master can update the hbase:meta table. However, the master does not update the region states until it is notified by the server that the split is done. If the split is successful, the splitting region is moved from SPLITTING to SPLIT state and the two new regions are moved from SPLITTING_NEW to OPEN state.

  13. If the split fails, the splitting region is moved from SPLITTING back to OPEN state, and the two new regions which were created are moved from SPLITTING_NEW to OFFLINE state.

  14. When a RegionServer is about to merge two regions, it notifies the master first. The master moves the two regions to be merged from OPEN to MERGING state, and adds the new region which will hold the contents of the merged regions region to the RegionServer. The new region is in MERGING_NEW state initially.

  15. After notifying the master, the RegionServer starts to merge the two regions. Once past the point of no return, the RegionServer notifies the master again so the master can update the META. However, the master does not update the region states until it is notified by the RegionServer that the merge has completed. If the merge is successful, the two merging regions are moved from MERGING to MERGED state and the new region is moved from MERGING_NEW to OPEN state.

  16. If the merge fails, the two merging regions are moved from MERGING back to OPEN state, and the new region which was created to hold the contents of the merged regions is moved from MERGING_NEW to OFFLINE state.

  17. For regions in FAILED_OPEN or FAILED_CLOSE states, the master tries to close them again when they are reassigned by an operator via HBase Shell.

69.3. Region-RegionServer Locality

Over time, Region-RegionServer locality is achieved via HDFS block replication. The HDFS client does the following by default when choosing locations to write replicas:

  1. First replica is written to local node

  2. Second replica is written to a random node on another rack

  3. Third replica is written on the same rack as the second, but on a different node chosen randomly

  4. Subsequent replicas are written on random nodes on the cluster. See Replica Placement: The First Baby Steps on this page: HDFS Architecture

Thus, HBase eventually achieves locality for a region after a flush or a compaction. In a RegionServer failover situation a RegionServer may be assigned regions with non-local StoreFiles (because none of the replicas are local), however as new data is written in the region, or the table is compacted and StoreFiles are re-written, they will become "local" to the RegionServer.

For more information, see Replica Placement: The First Baby Steps on this page: HDFS Architecture and also Lars George’s blog on HBase and HDFS locality.

69.4. Region Splits

Regions split when they reach a configured threshold. Below we treat the topic in short. For a longer exposition, see Apache HBase Region Splitting and Merging by our Enis Soztutar.

Splits run unaided on the RegionServer; i.e. the Master does not participate. The RegionServer splits a region, offlines the split region and then adds the daughter regions to hbase:meta, opens daughters on the parent’s hosting RegionServer and then reports the split to the Master. See Managed Splitting for how to manually manage splits (and for why you might do this).

69.4.1. Custom Split Policies

You can override the default split policy using a custom RegionSplitPolicy(HBase 0.94+). Typically a custom split policy should extend HBase’s default split policy: IncreasingToUpperBoundRegionSplitPolicy.

The policy can set globally through the HBase configuration or on a per-table basis.

Configuring the Split Policy Globally in hbase-site.xml
<property>
  <name>hbase.regionserver.region.split.policy</name>
  <value>org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy</value>
</property>
Configuring a Split Policy On a Table Using the Java API
HTableDescriptor tableDesc = new HTableDescriptor("test");
tableDesc.setValue(HTableDescriptor.SPLIT_POLICY, ConstantSizeRegionSplitPolicy.class.getName());
tableDesc.addFamily(new HColumnDescriptor(Bytes.toBytes("cf1")));
admin.createTable(tableDesc);
----
Configuring the Split Policy On a Table Using HBase Shell
hbase> create 'test', {METADATA => {'SPLIT_POLICY' => 'org.apache.hadoop.hbase.regionserver.ConstantSizeRegionSplitPolicy'}},{NAME => 'cf1'}

The default split policy can be overwritten using a custom RegionSplitPolicy(HBase 0.94+). Typically a custom split policy should extend HBase’s default split policy: ConstantSizeRegionSplitPolicy.

The policy can be set globally through the HBaseConfiguration used or on a per table basis:

HTableDescriptor myHtd = ...;
myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());
The DisabledRegionSplitPolicy policy blocks manual region splitting.

69.5. Manual Region Splitting

It is possible to manually split your table, either at table creation (pre-splitting), or at a later time as an administrative action. You might choose to split your region for one or more of the following reasons. There may be other valid reasons, but the need to manually split your table might also point to problems with your schema design.

Reasons to Manually Split Your Table

  • Your data is sorted by timeseries or another similar algorithm that sorts new data at the end of the table. This means that the Region Server holding the last region is always under load, and the other Region Servers are idle, or mostly idle. See also Monotonically Increasing Row Keys/Timeseries Data.

  • You have developed an unexpected hotspot in one region of your table. For instance, an application which tracks web searches might be inundated by a lot of searches for a celebrity in the event of news about that celebrity. See perf.one.region for more discussion about this particular scenario.

  • After a big increase in the number of RegionServers in your cluster, to get the load spread out quickly.

  • Before a bulk-load which is likely to cause unusual and uneven load across regions.

See Managed Splitting for a discussion about the dangers and possible benefits of managing splitting completely manually.

The DisabledRegionSplitPolicy policy blocks manual region splitting.

69.5.1. Determining Split Points

The goal of splitting your table manually is to improve the chances of balancing the load across the cluster in situations where good rowkey design alone won’t get you there. Keeping that in mind, the way you split your regions is very dependent upon the characteristics of your data. It may be that you already know the best way to split your table. If not, the way you split your table depends on what your keys are like.

Alphanumeric Rowkeys

If your rowkeys start with a letter or number, you can split your table at letter or number boundaries. For instance, the following command creates a table with regions that split at each vowel, so the first region has A-D, the second region has E-H, the third region has I-N, the fourth region has O-V, and the fifth region has U-Z.

Using a Custom Algorithm

The RegionSplitter tool is provided with HBase, and uses a SplitAlgorithm to determine split points for you. As parameters, you give it the algorithm, desired number of regions, and column families. It includes two split algorithms. The first is the HexStringSplit algorithm, which assumes the row keys are hexadecimal strings. The second, UniformSplit, assumes the row keys are random byte arrays. You will probably need to develop your own SplitAlgorithm, using the provided ones as models.

69.6. Online Region Merges

Both Master and RegionServer participate in the event of online region merges. Client sends merge RPC to the master, then the master moves the regions together to the RegionServer where the more heavily loaded region resided. Finally the master sends the merge request to this RegionServer which then runs the merge. Similar to process of region splitting, region merges run as a local transaction on the RegionServer. It offlines the regions and then merges two regions on the file system, atomically delete merging regions from hbase:meta and adds the merged region to hbase:meta, opens the merged region on the RegionServer and reports the merge to the Master.

An example of region merges in the HBase shell

$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME'
$ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME', true

It’s an asynchronous operation and call returns immediately without waiting merge completed. Passing true as the optional third parameter will force a merge. Normally only adjacent regions can be merged. The force parameter overrides this behaviour and is for expert use only.

69.7. Store

A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region.

69.7.1. MemStore

The MemStore holds in-memory modifications to the Store. Modifications are Cells/KeyValues. When a flush is requested, the current MemStore is moved to a snapshot and is cleared. HBase continues to serve edits from the new MemStore and backing snapshot until the flusher reports that the flush succeeded. At this point, the snapshot is discarded. Note that when the flush happens, MemStores that belong to the same region will all be flushed.

69.7.2. MemStore Flush

A MemStore flush can be triggered under any of the conditions listed below. The minimum flush unit is per region, not at individual MemStore level.

  1. When a MemStore reaches the size specified by hbase.hregion.memstore.flush.size, all MemStores that belong to its region will be flushed out to disk.

  2. When the overall MemStore usage reaches the value specified by hbase.regionserver.global.memstore.upperLimit, MemStores from various regions will be flushed out to disk to reduce overall MemStore usage in a RegionServer.

    The flush order is based on the descending order of a region’s MemStore usage.

    Regions will have their MemStores flushed until the overall MemStore usage drops to or slightly below hbase.regionserver.global.memstore.lowerLimit.

  3. When the number of WAL log entries in a given region server’s WAL reaches the value specified in hbase.regionserver.max.logs, MemStores from various regions will be flushed out to disk to reduce the number of logs in the WAL.

    The flush order is based on time.

    Regions with the oldest MemStores are flushed first until WAL count drops below hbase.regionserver.max.logs.

69.7.3. Scans

  • When a client issues a scan against a table, HBase generates RegionScanner objects, one per region, to serve the scan request.

  • The RegionScanner object contains a list of StoreScanner objects, one per column family.

  • Each StoreScanner object further contains a list of StoreFileScanner objects, corresponding to each StoreFile and HFile of the corresponding column family, and a list of KeyValueScanner objects for the MemStore.

  • The two lists are merged into one, which is sorted in ascending order with the scan object for the MemStore at the end of the list.

  • When a StoreFileScanner object is constructed, it is associated with a MultiVersionConcurrencyControl read point, which is the current memstoreTS, filtering out any new updates beyond the read point.

69.7.4. StoreFile (HFile)

StoreFiles are where your data lives.

HFile Format

The HFile file format is based on the SSTable file described in the BigTable [2006] paper and on Hadoop’s TFile (The unit test suite and the compression harness were taken directly from TFile). Schubert Zhang’s blog post on HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs makes for a thorough introduction to HBase’s HFile. Matteo Bertozzi has also put up a helpful description, HBase I/O: HFile.

For more information, see the HFile source code. Also see HBase file format with inline blocks (version 2) for information about the HFile v2 format that was included in 0.92.

HFile Tool

To view a textualized version of HFile content, you can use the org.apache.hadoop.hbase.io.hfile.HFile tool. Type the following to see usage:

$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile

For example, to view the content of the file hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475, type the following:

 $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475

If you leave off the option -v to see just a summary on the HFile. See usage for other things to do with the HFile tool.

StoreFile Directory Structure on HDFS

For more information of what StoreFiles look like on HDFS with respect to the directory structure, see Browsing HDFS for HBase Objects.

69.7.5. Blocks

StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis.

Compression happens at the block level within StoreFiles. For more information on compression, see Compression and Data Block Encoding In HBase.

For more information on blocks, see the HFileBlock source code.

69.7.6. KeyValue

The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into the passed array which specify where to start interpreting the content as KeyValue.

The KeyValue format inside a byte array is:

  • keylength

  • valuelength

  • key

  • value

The Key is further decomposed as:

  • rowlength

  • row (i.e., the rowkey)

  • columnfamilylength

  • columnfamily

  • columnqualifier

  • timestamp

  • keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily)

KeyValue instances are not split across blocks. For example, if there is an 8 MB KeyValue, even if the block-size is 64kb this KeyValue will be read in as a coherent block. For more information, see the KeyValue source code.

Example

To emphasize the points above, examine what happens with two Puts for two different columns for the same row:

  • Put #1: rowkey=row1, cf:attr1=value1

  • Put #2: rowkey=row1, cf:attr2=value2

Even though these are for the same row, a KeyValue is created for each column:

Key portion for Put #1:

  • rowlength -----------→ 4

  • row -----------------→ row1

  • columnfamilylength --→ 2

  • columnfamily --------→ cf

  • columnqualifier -----→ attr1

  • timestamp -----------→ server time of Put

  • keytype -------------→ Put

Key portion for Put #2:

  • rowlength -----------→ 4

  • row -----------------→ row1

  • columnfamilylength --→ 2

  • columnfamily --------→ cf

  • columnqualifier -----→ attr2

  • timestamp -----------→ server time of Put

  • keytype -------------→ Put

It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are embedded within the KeyValue instance. The longer these identifiers are, the bigger the KeyValue is.

69.7.7. Compaction

Ambiguous Terminology

  • A StoreFile is a facade of HFile. In terms of compaction, use of StoreFile seems to have prevailed in the past.

  • A Store is the same thing as a ColumnFamily. StoreFiles are related to a Store, or ColumnFamily.

  • If you want to read more about StoreFiles versus HFiles and Stores versus ColumnFamilies, see HBASE-11316.

When the MemStore reaches a given size (hbase.hregion.memstore.flush.size), it flushes its contents to a StoreFile. The number of StoreFiles in a Store increases over time. Compaction is an operation which reduces the number of StoreFiles in a Store, by merging them together, in order to increase performance on read operations. Compactions can be resource-intensive to perform, and can either help or hinder performance depending on many factors.

Compactions fall into two categories: minor and major. Minor and major compactions differ in the following ways.

Minor compactions usually select a small number of small, adjacent StoreFiles and rewrite them as a single StoreFile. Minor compactions do not drop (filter out) deletes or expired versions, because of potential side effects. See Compaction and Deletions and Compaction and Versions for information on how deletes and versions are handled in relation to compactions. The end result of a minor compaction is fewer, larger StoreFiles for a given Store.

The end result of a major compaction is a single StoreFile per Store. Major compactions also process delete markers and max versions. See Compaction and Deletions and Compaction and Versions for information on how deletes and versions are handled in relation to compactions.

Compaction and Deletions

When an explicit deletion occurs in HBase, the data is not actually deleted. Instead, a tombstone marker is written. The tombstone marker prevents the data from being returned with queries. During a major compaction, the data is actually deleted, and the tombstone marker is removed from the StoreFile. If the deletion happens because of an expired TTL, no tombstone is created. Instead, the expired data is filtered out and is not written back to the compacted StoreFile.

Compaction and Versions

When you create a Column Family, you can specify the maximum number of versions to keep, by specifying HColumnDescriptor.setMaxVersions(int versions). The default value is 3. If more versions than the specified maximum exist, the excess versions are filtered out and not written back to the compacted StoreFile.

Major Compactions Can Impact Query Results

In some situations, older versions can be inadvertently resurrected if a newer version is explicitly deleted. See Major compactions change query results for a more in-depth explanation. This situation is only possible before the compaction finishes.

In theory, major compactions improve performance. However, on a highly loaded system, major compactions can require an inappropriate number of resources and adversely affect performance. In a default configuration, major compactions are scheduled automatically to run once in a 7-day period. This is sometimes inappropriate for systems in production. You can manage major compactions manually. See Managed Compactions.

Compactions do not perform region merges. See Merge for more information on region merging.

Compaction Policy - HBase 0.96.x and newer

Compacting large StoreFiles, or too many StoreFiles at once, can cause more IO load than your cluster is able to handle without causing performance problems. The method by which HBase selects which StoreFiles to include in a compaction (and whether the compaction is a minor or major compaction) is called the compaction policy.

Prior to HBase 0.96.x, there was only one compaction policy. That original compaction policy is still available as RatioBasedCompactionPolicy. The new compaction default policy, called ExploringCompactionPolicy, was subsequently backported to HBase 0.94 and HBase 0.95, and is the default in HBase 0.96 and newer. It was implemented in HBASE-7842. In short, ExploringCompactionPolicy attempts to select the best possible set of StoreFiles to compact with the least amount of work, while the RatioBasedCompactionPolicy selects the first set that meets the criteria.

Regardless of the compaction policy used, file selection is controlled by several configurable parameters and happens in a multi-step approach. These parameters will be explained in context, and then will be given in a table which shows their descriptions, defaults, and implications of changing them.

Being Stuck

When the MemStore gets too large, it needs to flush its contents to a StoreFile. However, a Store can only have hbase.hstore.blockingStoreFiles files, so the MemStore needs to wait for the number of StoreFiles to be reduced by one or more compactions. However, if the MemStore grows larger than hbase.hregion.memstore.flush.size, it is not able to flush its contents to a StoreFile. If the MemStore is too large and the number of StoreFiles is also too high, the algorithm is said to be "stuck". The compaction algorithm checks for this "stuck" situation and provides mechanisms to alleviate it.

The ExploringCompactionPolicy Algorithm

The ExploringCompactionPolicy algorithm considers each possible set of adjacent StoreFiles before choosing the set where compaction will have the most benefit.

One situation where the ExploringCompactionPolicy works especially well is when you are bulk-loading data and the bulk loads create larger StoreFiles than the StoreFiles which are holding data older than the bulk-loaded data. This can "trick" HBase into choosing to perform a major compaction each time a compaction is needed, and cause a lot of extra overhead. With the ExploringCompactionPolicy, major compactions happen much less frequently because minor compactions are more efficient.

In general, ExploringCompactionPolicy is the right choice for most situations, and thus is the default compaction policy. You can also use ExploringCompactionPolicy along with Experimental: Stripe Compactions.

The logic of this policy can be examined in hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/compactions/ExploringCompactionPolicy.java. The following is a walk-through of the logic of the ExploringCompactionPolicy.

  1. Make a list of all existing StoreFiles in the Store. The rest of the algorithm filters this list to come up with the subset of HFiles which will be chosen for compaction.

  2. If this was a user-requested compaction, attempt to perform the requested compaction type, regardless of what would normally be chosen. Note that even if the user requests a major compaction, it may not be possible to perform a major compaction. This may be because not all StoreFiles in the Column Family are available to compact or because there are too many Stores in the Column Family.

  3. Some StoreFiles are automatically excluded from consideration. These include:

    • StoreFiles that are larger than hbase.hstore.compaction.max.size

    • StoreFiles that were created by a bulk-load operation which explicitly excluded compaction. You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this, specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the bulk load operation.

  4. Iterate through the list from step 1, and make a list of all potential sets of StoreFiles to compact together. A potential set is a grouping of hbase.hstore.compaction.min contiguous StoreFiles in the list. For each set, perform some sanity-checking and figure out whether this is the best compaction that could be done:

    • If the number of StoreFiles in this set (not the size of the StoreFiles) is fewer than hbase.hstore.compaction.min or more than hbase.hstore.compaction.max, take it out of consideration.

    • Compare the size of this set of StoreFiles with the size of the smallest possible compaction that has been found in the list so far. If the size of this set of StoreFiles represents the smallest compaction that could be done, store it to be used as a fall-back if the algorithm is "stuck" and no StoreFiles would otherwise be chosen. See Being Stuck.

    • Do size-based sanity checks against each StoreFile in this set of StoreFiles.

      • If the size of this StoreFile is larger than hbase.hstore.compaction.max.size, take it out of consideration.

      • If the size is greater than or equal to hbase.hstore.compaction.min.size, sanity-check it against the file-based ratio to see whether it is too large to be considered.

        The sanity-checking is successful if:

      • There is only one StoreFile in this set, or

      • For each StoreFile, its size multiplied by hbase.hstore.compaction.ratio (or hbase.hstore.compaction.ratio.offpeak if off-peak hours are configured and it is during off-peak hours) is less than the sum of the sizes of the other HFiles in the set.

  5. If this set of StoreFiles is still in consideration, compare it to the previously-selected best compaction. If it is better, replace the previously-selected best compaction with this one.

  6. When the entire list of potential compactions has been processed, perform the best compaction that was found. If no StoreFiles were selected for compaction, but there are multiple StoreFiles, assume the algorithm is stuck (see Being Stuck) and if so, perform the smallest compaction that was found in step 3.

RatioBasedCompactionPolicy Algorithm

The RatioBasedCompactionPolicy was the only compaction policy prior to HBase 0.96, though ExploringCompactionPolicy has now been backported to HBase 0.94 and 0.95. To use the RatioBasedCompactionPolicy rather than the ExploringCompactionPolicy, set hbase.hstore.defaultengine.compactionpolicy.class to RatioBasedCompactionPolicy in the hbase-site.xml file. To switch back to the ExploringCompactionPolicy, remove the setting from the hbase-site.xml.

The following section walks you through the algorithm used to select StoreFiles for compaction in the RatioBasedCompactionPolicy.

  1. The first phase is to create a list of all candidates for compaction. A list is created of all StoreFiles not already in the compaction queue, and all StoreFiles newer than the newest file that is currently being compacted. This list of StoreFiles is ordered by the sequence ID. The sequence ID is generated when a Put is appended to the write-ahead log (WAL), and is stored in the metadata of the HFile.

  2. Check to see if the algorithm is stuck (see Being Stuck, and if so, a major compaction is forced. This is a key area where The ExploringCompactionPolicy Algorithm is often a better choice than the RatioBasedCompactionPolicy.

  3. If the compaction was user-requested, try to perform the type of compaction that was requested. Note that a major compaction may not be possible if all HFiles are not available for compaction or if too many StoreFiles exist (more than hbase.hstore.compaction.max).

  4. Some StoreFiles are automatically excluded from consideration. These include:

    • StoreFiles that are larger than hbase.hstore.compaction.max.size

    • StoreFiles that were created by a bulk-load operation which explicitly excluded compaction. You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this, specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the bulk load operation.

  5. The maximum number of StoreFiles allowed in a major compaction is controlled by the hbase.hstore.compaction.max parameter. If the list contains more than this number of StoreFiles, a minor compaction is performed even if a major compaction would otherwise have been done. However, a user-requested major compaction still occurs even if there are more than hbase.hstore.compaction.max StoreFiles to compact.

  6. If the list contains fewer than hbase.hstore.compaction.min StoreFiles to compact, a minor compaction is aborted. Note that a major compaction can be performed on a single HFile. Its function is to remove deletes and expired versions, and reset locality on the StoreFile.

  7. The value of the hbase.hstore.compaction.ratio parameter is multiplied by the sum of StoreFiles smaller than a given file, to determine whether that StoreFile is selected for compaction during a minor compaction. For instance, if hbase.hstore.compaction.ratio is 1.2, FileX is 5MB, FileY is 2MB, and FileZ is 3MB:

    5 <= 1.2 x (2 + 3)            or            5 <= 6

    In this scenario, FileX is eligible for minor compaction. If FileX were 7MB, it would not be eligible for minor compaction. This ratio favors smaller StoreFile. You can configure a different ratio for use in off-peak hours, using the parameter hbase.hstore.compaction.ratio.offpeak, if you also configure hbase.offpeak.start.hour and hbase.offpeak.end.hour.

  8. If the last major compaction was too long ago and there is more than one StoreFile to be compacted, a major compaction is run, even if it would otherwise have been minor. By default, the maximum time between major compactions is 7 days, plus or minus a 4.8 hour period, and determined randomly within those parameters. Prior to HBase 0.96, the major compaction period was 24 hours. See hbase.hregion.majorcompaction in the table below to tune or disable time-based major compactions.

Parameters Used by Compaction Algorithm

This table contains the main configuration parameters for compaction. This list is not exhaustive. To tune these parameters from the defaults, edit the hbase-default.xml file. For a full list of all configuration parameters available, see config.files

hbase.hstore.compaction.min

The minimum number of StoreFiles which must be eligible for compaction before compaction can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with too many tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction each time you have two StoreFiles in a Store, and this is probably not appropriate. If you set this value too high, all the other values will need to be adjusted accordingly. For most cases, the default value is appropriate. In previous versions of HBase, the parameter hbase.hstore.compaction.min was called hbase.hstore.compactionThreshold.

Default: 3

hbase.hstore.compaction.max

The maximum number of StoreFiles which will be selected for a single minor compaction, regardless of the number of eligible StoreFiles. Effectively, the value of hbase.hstore.compaction.max controls the length of time it takes a single compaction to complete. Setting it larger means that more StoreFiles are included in a compaction. For most cases, the default value is appropriate.

Default: 10

hbase.hstore.compaction.min.size

A StoreFile smaller than this size will always be eligible for minor compaction. StoreFiles this size or larger are evaluated by hbase.hstore.compaction.ratio to determine if they are eligible. Because this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to be reduced in write-heavy environments where many files in the 1-2 MB range are being flushed, because every StoreFile will be targeted for compaction and the resulting StoreFiles may still be under the minimum size and require further compaction. If this parameter is lowered, the ratio check is triggered more quickly. This addressed some issues seen in earlier versions of HBase but changing this parameter is no longer necessary in most situations.

Default:128 MB

hbase.hstore.compaction.max.size

A StoreFile larger than this size will be excluded from compaction. The effect of raising hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get compacted often. If you feel that compaction is happening too often without much benefit, you can try raising this value.

Default: Long.MAX_VALUE

hbase.hstore.compaction.ratio

For minor compaction, this ratio is used to determine whether a given StoreFile which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction of large StoreFile. The value of hbase.hstore.compaction.ratio is expressed as a floating-point decimal.

  • A large ratio, such as 10, will produce a single giant StoreFile. Conversely, a value of .25, will produce behavior similar to the BigTable compaction algorithm, producing four StoreFiles.

  • A moderate value of between 1.0 and 1.4 is recommended. When tuning this value, you are balancing write costs with read costs. Raising the value (to something like 1.4) will have more write costs, because you will compact larger StoreFiles. However, during reads, HBase will need to seek through fewer StoreFiles to accomplish the read. Consider this approach if you cannot take advantage of Bloom Filters.

  • Alternatively, you can lower this value to something like 1.0 to reduce the background cost of writes, and use to limit the number of StoreFiles touched during reads. For most cases, the default value is appropriate.

    Default: 1.2F

hbase.hstore.compaction.ratio.offpeak

The compaction ratio used during off-peak compactions, if off-peak hours are also configured (see below). Expressed as a floating-point decimal. This allows for more aggressive (or less aggressive, if you set it lower than hbase.hstore.compaction.ratio) compaction during a set time period. Ignored if off-peak is disabled (default). This works the same as hbase.hstore.compaction.ratio.

Default: 5.0F

hbase.offpeak.start.hour

The start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.

Default: -1 (disabled)

hbase.offpeak.end.hour

The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.

Default: -1 (disabled)

hbase.regionserver.thread.compaction.throttle

There are two different thread pools for compactions, one for large compactions and the other for small compactions. This helps to keep compaction of lean tables (such as hbase:meta) fast. If a compaction is larger than this threshold, it goes into the large compaction pool. In most cases, the default value is appropriate.

Default: 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults to 128)

hbase.hregion.majorcompaction

Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based automatic major compactions. User-requested and size-based major compactions will still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at a somewhat-random time during a given window of time.

Default: 7 days (604800000 milliseconds)

hbase.hregion.majorcompaction.jitter

A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur a given amount of time either side of hbase.hregion.majorcompaction. The smaller the number, the closer the compactions will happen to the hbase.hregion.majorcompaction interval. Expressed as a floating-point decimal.

Default: .50F

Compaction File Selection
Legacy Information

This section has been preserved for historical reasons and refers to the way compaction worked prior to HBase 0.96.x. You can still use this behavior if you enable RatioBasedCompactionPolicy Algorithm. For information on the way that compactions work in HBase 0.96.x and later, see Compaction.

To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source code that will serve as useful reference.

It has been copied below:

/* normal skew:
 *
 *         older ----> newer
 *     _
 *    | |   _
 *    | |  | |   _
 *  --|-|- |-|- |-|---_-------_-------  minCompactSize
 *    | |  | |  | |  | |  _  | |
 *    | |  | |  | |  | | | | | |
 *    | |  | |  | |  | | | | | |
 */
Important knobs:

  • hbase.hstore.compaction.ratio Ratio used in compaction file selection algorithm (default 1.2f).

  • hbase.hstore.compaction.min (in HBase v 0.90 this is called hbase.hstore.compactionThreshold) (files) Minimum number of StoreFiles per Store to be selected for a compaction to occur (default 2).

  • hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor compaction (default 10).

  • hbase.hstore.compaction.min.size (bytes) Any StoreFile smaller than this setting with automatically be a candidate for compaction. Defaults to hbase.hregion.memstore.flush.size (128 mb).

  • hbase.hstore.compaction.max.size (.92) (bytes) Any StoreFile larger than this setting with automatically be excluded from compaction (default Long.MAX_VALUE).

The minor compaction StoreFile selection logic is size based, and selects a file for compaction when the file ⇐ sum(smaller_files) * hbase.hstore.compaction.ratio.

Minor Compaction File Selection - Example #1 (Basic Example)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.

Why?

  • 100 → No, because sum(50, 23, 12, 12) * 1.0 = 97.

  • 50 → No, because sum(23, 12, 12) * 1.0 = 47.

  • 23 → Yes, because sum(12, 12) * 1.0 = 24.

  • 12 → Yes, because the previous file has been included, and because this does not exceed the max-file limit of 5

  • 12 → Yes, because the previous file had been included, and because this does not exceed the max-file limit of 5.

Minor Compaction File Selection - Example #2 (Not Enough Files ToCompact)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest). With the above parameters, no compaction will be started.

Why?

  • 100 → No, because sum(25, 12, 12) * 1.0 = 47

  • 25 → No, because sum(12, 12) * 1.0 = 24

  • 12 → No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less than the threshold of 3

  • 12 → No. Candidate because the previous StoreFile was, but there are not enough files to compact

Minor Compaction File Selection - Example #3 (Limiting Files To Compact)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.

Why?

  • 7 → Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size

  • 6 → Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.

  • 5 → Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.

  • 4 → Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.

  • 3 → Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.

  • 2 → No. Candidate because previous file was selected and 2 is less than the min-size, but the max-number of files to compact has been reached.

  • 1 → No. Candidate because previous file was selected and 1 is less than the min-size, but max-number of files to compact has been reached.

Impact of Key Configuration Options
This information is now included in the configuration parameter table in Parameters Used by Compaction Algorithm.
Date Tiered Compaction

Date tiered compaction is a date-aware store file compaction strategy that is beneficial for time-range scans for time-series data.

When To Use Date Tiered Compactions

Consider using Date Tiered Compaction for reads for limited time ranges, especially scans of recent data

Don’t use it for

  • random gets without a limited time range

  • frequent deletes and updates

  • Frequent out of order data writes creating long tails, especially writes with future timestamps

  • frequent bulk loads with heavily overlapping time ranges

Performance Improvements

Performance testing has shown that the performance of time-range scans improve greatly for limited time ranges, especially scans of recent data.

Enabling Date Tiered Compaction

You can enable Date Tiered compaction for a table or a column family, by setting its hbase.hstore.engine.class to org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine.

You also need to set hbase.hstore.blockingStoreFiles to a high number, such as 60, if using all default settings, rather than the default value of 12). Use 1.5~2 x projected file count if changing the parameters, Projected file count = windows per tier x tier count + incoming window min + files older than max age

You also need to set hbase.hstore.compaction.max to the same value as hbase.hstore.blockingStoreFiles to unblock major compaction.

Procedure: Enable Date Tiered Compaction

  1. Run one of following commands in the HBase shell. Replace the table name orders_table with the name of your table.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'}
alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'}}
create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DateTieredStoreEngine', 'hbase.hstore.blockingStoreFiles' => '60', 'hbase.hstore.compaction.min'=>'2', 'hbase.hstore.compaction.max'=>'60'}

  2. Configure other options if needed. See Configuring Date Tiered Compaction for more information.

Procedure: Disable Date Tiered Compaction

  1. Set the hbase.hstore.engine.class option to either nil or org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. Either option has the same effect. Make sure you set the other options you changed to the original settings too.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.DefaultStoreEngine'， 'hbase.hstore.blockingStoreFiles' => '12', 'hbase.hstore.compaction.min'=>'6', 'hbase.hstore.compaction.max'=>'12'}}

When you change the store engine either way, a major compaction will likely be performed on most regions. This is not necessary on new tables.

Configuring Date Tiered Compaction

Each of the settings for date tiered compaction should be configured at the table or column family level. If you use HBase shell, the general command pattern is as follows:

alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}}
Tier Parameters

You can configure your date tiers by changing the settings for the following parameters:

Table 9. Date Tier Parameters
Setting Notes

hbase.hstore.compaction.date.tiered.max.storefile.age.millis

Files with max-timestamp smaller than this will no longer be compacted.Default at Long.MAX_VALUE.

hbase.hstore.compaction.date.tiered.base.window.millis

Base window size in milliseconds. Default at 6 hours.

hbase.hstore.compaction.date.tiered.windows.per.tier

Number of windows per tier. Default at 4.

hbase.hstore.compaction.date.tiered.incoming.window.min

Minimal number of files to compact in the incoming window. Set it to expected number of files in the window to avoid wasteful compaction. Default at 6.

hbase.hstore.compaction.date.tiered.window.policy.class

The policy to select store files within the same time window. It doesn’t apply to the incoming window. Default at exploring compaction. This is to avoid wasteful compaction.
Compaction Throttler

With tiered compaction all servers in the cluster will promote windows to higher tier at the same time, so using a compaction throttle is recommended: Set hbase.regionserver.throughput.controller to org.apache.hadoop.hbase.regionserver.compactions.PressureAwareCompactionThroughputController.

For more information about date tiered compaction, please refer to the design specification at https://docs.google.com/document/d/1_AmlNb2N8Us1xICsTeGDLKIqL6T-oHoRLZ323MG_uy8
Experimental: Stripe Compactions

Stripe compactions is an experimental feature added in HBase 0.98 which aims to improve compactions for large regions or non-uniformly distributed row keys. In order to achieve smaller and/or more granular compactions, the StoreFiles within a region are maintained separately for several row-key sub-ranges, or "stripes", of the region. The stripes are transparent to the rest of HBase, so other operations on the HFiles or data work without modification.

Stripe compactions change the HFile layout, creating sub-regions within regions. These sub-regions are easier to compact, and should result in fewer major compactions. This approach alleviates some of the challenges of larger regions.

Stripe compaction is fully compatible with Compaction and works in conjunction with either the ExploringCompactionPolicy or RatioBasedCompactionPolicy. It can be enabled for existing tables, and the table will continue to operate normally if it is disabled later.

When To Use Stripe Compactions

Consider using stripe compaction if you have either of the following:

  • Large regions. You can get the positive effects of smaller regions without additional overhead for MemStore and region management overhead.

  • Non-uniform keys, such as time dimension in a key. Only the stripes receiving the new keys will need to compact. Old data will not compact as often, if at all

Performance Improvements

Performance testing has shown that the performance of reads improves somewhat, and variability of performance of reads and writes is greatly reduced. An overall long-term performance improvement is seen on large non-uniform-row key regions, such as a hash-prefixed timestamp key. These performance gains are the most dramatic on a table which is already large. It is possible that the performance improvement might extend to region splits.

Enabling Stripe Compaction

You can enable stripe compaction for a table or a column family, by setting its hbase.hstore.engine.class to org.apache.hadoop.hbase.regionserver.StripeStoreEngine. You also need to set the hbase.hstore.blockingStoreFiles to a high number, such as 100 (rather than the default value of 10).

Procedure: Enable Stripe Compaction

  1. Run one of following commands in the HBase shell. Replace the table name orders_table with the name of your table.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}
alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}}
create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}

  2. Configure other options if needed. See Configuring Stripe Compaction for more information.

  3. Enable the table.

Procedure: Disable Stripe Compaction

  1. Set the hbase.hstore.engine.class option to either nil or org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. Either option has the same effect.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'rg.apache.hadoop.hbase.regionserver.DefaultStoreEngine'}

  2. Enable the table.

When you enable a large table after changing the store engine either way, a major compaction will likely be performed on most regions. This is not necessary on new tables.

Configuring Stripe Compaction

Each of the settings for stripe compaction should be configured at the table or column family level. If you use HBase shell, the general command pattern is as follows:

alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}}
Region and stripe sizing

You can configure your stripe sizing based upon your region sizing. By default, your new regions will start with one stripe. On the next compaction after the stripe has grown too large (16 x MemStore flushes size), it is split into two stripes. Stripe splitting continues as the region grows, until the region is large enough to split.

You can improve this pattern for your own data. A good rule is to aim for a stripe size of at least 1 GB, and about 8-12 stripes for uniform row keys. For example, if your regions are 30 GB, 12 x 2.5 GB stripes might be a good starting point.

Table 10. Stripe Sizing Settings
Setting Notes

hbase.store.stripe.initialStripeCount

The number of stripes to create when stripe compaction is enabled. You can use it as follows:

  • For relatively uniform row keys, if you know the approximate target number of stripes from the above, you can avoid some splitting overhead by starting with several stripes (2, 5, 10…​). If the early data is not representative of overall row key distribution, this will not be as efficient.

  • For existing tables with a large amount of data, this setting will effectively pre-split your stripes.

  • For keys such as hash-prefixed sequential keys, with more than one hash prefix per region, pre-splitting may make sense.

hbase.store.stripe.sizeToSplit

The maximum size a stripe grows before splitting. Use this in conjunction with hbase.store.stripe.splitPartCount to control the target stripe size (sizeToSplit = splitPartsCount * target stripe size), according to the above sizing considerations.

hbase.store.stripe.splitPartCount

The number of new stripes to create when splitting a stripe. The default is 2, which is appropriate for most cases. For non-uniform row keys, you can experiment with increasing the number to 3 or 4, to isolate the arriving updates into narrower slice of the region without additional splits being required.
MemStore Size Settings

By default, the flush creates several files from one MemStore, according to existing stripe boundaries and row keys to flush. This approach minimizes write amplification, but can be undesirable if the MemStore is small and there are many stripes, because the files will be too small.

In this type of situation, you can set hbase.store.stripe.compaction.flushToL0 to true. This will cause a MemStore flush to create a single file instead. When at least hbase.store.stripe.compaction.minFilesL0 such files (by default, 4) accumulate, they will be compacted into striped files.

Normal Compaction Configuration and Stripe Compaction

All the settings that apply to normal compactions (see Parameters Used by Compaction Algorithm) apply to stripe compactions. The exceptions are the minimum and maximum number of files, which are set to higher values by default because the files in stripes are smaller. To control these for stripe compactions, use hbase.store.stripe.compaction.minFiles and hbase.store.stripe.compaction.maxFiles, rather than hbase.hstore.compaction.min and hbase.hstore.compaction.max.

70. Bulk Loading

70.1. Overview

HBase includes several methods of loading data into tables. The most straightforward method is to either use the TableOutputFormat class from a MapReduce job, or use the normal client APIs; however, these are not always the most efficient methods.

The bulk load feature uses a MapReduce job to output table data in HBase’s internal data format, and then directly loads the generated StoreFiles into a running cluster. Using bulk load will use less CPU and network resources than simply using the HBase API.

70.2. Bulk Load Limitations

As bulk loading bypasses the write path, the WAL doesn’t get written to as part of the process. Replication works by reading the WAL files so it won’t see the bulk loaded data – and the same goes for the edits that use Put.setDurability(SKIP_WAL). One way to handle that is to ship the raw files or the HFiles to the other cluster and do the other processing there.

70.3. Bulk Load Architecture

The HBase bulk load process consists of two main steps.

70.3.1. Preparing data via a MapReduce job

The first step of a bulk load is to generate HBase data files (StoreFiles) from a MapReduce job using HFileOutputFormat2. This output format writes out data in HBase’s internal storage format so that they can be later loaded very efficiently into the cluster.

In order to function efficiently, HFileOutputFormat2 must be configured such that each output HFile fits within a single region. In order to do this, jobs whose output will be bulk loaded into HBase use Hadoop’s TotalOrderPartitioner class to partition the map output into disjoint ranges of the key space, corresponding to the key ranges of the regions in the table.

HFileOutputFormat2 includes a convenience function, configureIncrementalLoad(), which automatically sets up a TotalOrderPartitioner based on the current region boundaries of a table.

70.3.2. Completing the data load

After a data import has been prepared, either by using the importtsv tool with the “importtsv.bulk.output” option or by some other MapReduce job using the HFileOutputFormat, the completebulkload tool is used to import the data into the running cluster. This command line tool iterates through the prepared data files, and for each one determines the region the file belongs to. It then contacts the appropriate RegionServer which adopts the HFile, moving it into its storage directory and making the data available to clients.

If the region boundaries have changed during the course of bulk load preparation, or between the preparation and completion steps, the completebulkload utility will automatically split the data files into pieces corresponding to the new boundaries. This process is not optimally efficient, so users should take care to minimize the delay between preparing a bulk load and importing it into the cluster, especially if other clients are simultaneously loading data through other means.

$ hadoop jar hbase-server-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable

The -c config-file option can be used to specify a file containing the appropriate hbase parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is NOT managed by HBase).

If the target table does not already exist in HBase, this tool will create the table automatically.

70.4. See Also

For more information about the referenced utilities, see ImportTsv and CompleteBulkLoad.

See How-to: Use HBase Bulk Loading, and Why for a recent blog on current state of bulk loading.

70.5. Advanced Usage

Although the importtsv tool is useful in many cases, advanced users may want to generate data programmatically, or import data from other formats. To get started doing so, dig into ImportTsv.java and check the JavaDoc for HFileOutputFormat.

The import step of the bulk load can also be done programmatically. See the LoadIncrementalHFiles class for more information.

71. HDFS

As HBase runs on HDFS (and each StoreFile is written as a file on HDFS), it is important to have an understanding of the HDFS Architecture especially in terms of how it stores files, handles failovers, and replicates blocks.

See the Hadoop documentation on HDFS Architecture for more information.

71.1. NameNode

The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link for more information.

71.2. DataNode

The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link for more information.

72. Timeline-consistent High Available Reads

72.1. Introduction

HBase, architecturally, always had the strong consistency guarantee from the start. All reads and writes are routed through a single region server, which guarantees that all writes happen in an order, and all reads are seeing the most recent committed data.

However, because of this single homing of the reads to a single location, if the server becomes unavailable, the regions of the table that were hosted in the region server become unavailable for some time. There are three phases in the region recovery process - detection, assignment, and recovery. Of these, the detection is usually the longest and is presently in the order of 20-30 seconds depending on the ZooKeeper session timeout. During this time and before the recovery is complete, the clients will not be able to read the region data.

However, for some use cases, either the data may be read-only, or doing reads against some stale data is acceptable. With timeline-consistent high available reads, HBase can be used for these kind of latency-sensitive use cases where the application can expect to have a time bound on the read completion.

For achieving high availability for reads, HBase provides a feature called region replication. In this model, for each region of a table, there will be multiple replicas that are opened in different RegionServers. By default, the region replication is set to 1, so only a single region replica is deployed and there will not be any changes from the original model. If region replication is set to 2 or more, then the master will assign replicas of the regions of the table. The Load Balancer ensures that the region replicas are not co-hosted in the same region servers and also in the same rack (if possible).

All of the replicas for a single region will have a unique replica_id, starting from 0. The region replica having replica_id==0 is called the primary region, and the others secondary regions or secondaries. Only the primary can accept writes from the client, and the primary will always contain the latest changes. Since all writes still have to go through the primary region, the writes are not highly-available (meaning they might block for some time if the region becomes unavailable).

72.2. Timeline Consistency

With this feature, HBase introduces a Consistency definition, which can be provided per read operation (get or scan).

public enum Consistency {
    STRONG,
    TIMELINE
}

Consistency.STRONG is the default consistency model provided by HBase. In case the table has region replication = 1, or in a table with region replicas but the reads are done with this consistency, the read is always performed by the primary regions, so that there will not be any change from the previous behaviour, and the client always observes the latest data.

In case a read is performed with Consistency.TIMELINE, then the read RPC will be sent to the primary region server first. After a short interval (hbase.client.primaryCallTimeout.get, 10ms by default), parallel RPC for secondary region replicas will also be sent if the primary does not respond back. After this, the result is returned from whichever RPC is finished first. If the response came back from the primary region replica, we can always know that the data is latest. For this Result.isStale() API has been added to inspect the staleness. If the result is from a secondary region, then Result.isStale() will be set to true. The user can then inspect this field to possibly reason about the data.

In terms of semantics, TIMELINE consistency as implemented by HBase differs from pure eventual consistency in these respects:

  • Single homed and ordered updates: Region replication or not, on the write side, there is still only 1 defined replica (primary) which can accept writes. This replica is responsible for ordering the edits and preventing conflicts. This guarantees that two different writes are not committed at the same time by different replicas and the data diverges. With this, there is no need to do read-repair or last-timestamp-wins kind of conflict resolution.

  • The secondaries also apply the edits in the order that the primary committed them. This way the secondaries will contain a snapshot of the primaries data at any point in time. This is similar to RDBMS replications and even HBase’s own multi-datacenter replication, however in a single cluster.

  • On the read side, the client can detect whether the read is coming from up-to-date data or is stale data. Also, the client can issue reads with different consistency requirements on a per-operation basis to ensure its own semantic guarantees.

  • The client can still observe edits out-of-order, and can go back in time, if it observes reads from one secondary replica first, then another secondary replica. There is no stickiness to region replicas or a transaction-id based guarantee. If required, this can be implemented later though.

Timeline Consistency
Figure 3. Timeline Consistency

To better understand the TIMELINE semantics, let’s look at the above diagram. Let’s say that there are two clients, and the first one writes x=1 at first, then x=2 and x=3 later. As above, all writes are handled by the primary region replica. The writes are saved in the write ahead log (WAL), and replicated to the other replicas asynchronously. In the above diagram, notice that replica_id=1 received 2 updates, and its data shows that x=2, while the replica_id=2 only received a single update, and its data shows that x=1.

If client1 reads with STRONG consistency, it will only talk with the replica_id=0, and thus is guaranteed to observe the latest value of x=3. In case of a client issuing TIMELINE consistency reads, the RPC will go to all replicas (after primary timeout) and the result from the first response will be returned back. Thus the client can see either 1, 2 or 3 as the value of x. Let’s say that the primary region has failed and log replication cannot continue for some time. If the client does multiple reads with TIMELINE consistency, she can observe x=2 first, then x=1, and so on.

72.3. Tradeoffs

Having secondary regions hosted for read availability comes with some tradeoffs which should be carefully evaluated per use case. Following are advantages and disadvantages.

Advantages

  • High availability for read-only tables

  • High availability for stale reads

  • Ability to do very low latency reads with very high percentile (99.9%+) latencies for stale reads

Disadvantages

  • Double / Triple MemStore usage (depending on region replication count) for tables with region replication > 1

  • Increased block cache usage

  • Extra network traffic for log replication

  • Extra backup RPCs for replicas

To serve the region data from multiple replicas, HBase opens the regions in secondary mode in the region servers. The regions opened in secondary mode will share the same data files with the primary region replica, however each secondary region replica will have its own MemStore to keep the unflushed data (only primary region can do flushes). Also to serve reads from secondary regions, the blocks of data files may be also cached in the block caches for the secondary regions.

72.4. Where is the code

This feature is delivered in two phases, Phase 1 and 2. The first phase is done in time for HBase-1.0.0 release. Meaning that using HBase-1.0.x, you can use all the features that are marked for Phase 1. Phase 2 is committed in HBase-1.1.0, meaning all HBase versions after 1.1.0 should contain Phase 2 items.

72.5. Propagating writes to region replicas

As discussed above writes only go to the primary region replica. For propagating the writes from the primary region replica to the secondaries, there are two different mechanisms. For read-only tables, you do not need to use any of the following methods. Disabling and enabling the table should make the data available in all region replicas. For mutable tables, you have to use only one of the following mechanisms: storefile refresher, or async wal replication. The latter is recommended.

72.5.1. StoreFile Refresher

The first mechanism is store file refresher which is introduced in HBase-1.0+. Store file refresher is a thread per region server, which runs periodically, and does a refresh operation for the store files of the primary region for the secondary region replicas. If enabled, the refresher will ensure that the secondary region replicas see the new flushed, compacted or bulk loaded files from the primary region in a timely manner. However, this means that only flushed data can be read back from the secondary region replicas, and after the refresher is run, making the secondaries lag behind the primary for an a longer time.

For turning this feature on, you should configure hbase.regionserver.storefile.refresh.period to a non-zero value. See Configuration section below.

72.5.2. Asnyc WAL replication

The second mechanism for propagation of writes to secondaries is done via “Async WAL Replication” feature and is only available in HBase-1.1+. This works similarly to HBase’s multi-datacenter replication, but instead the data from a region is replicated to the secondary regions. Each secondary replica always receives and observes the writes in the same order that the primary region committed them. In some sense, this design can be thought of as “in-cluster replication”, where instead of replicating to a different datacenter, the data goes to secondary regions to keep secondary region’s in-memory state up to date. The data files are shared between the primary region and the other replicas, so that there is no extra storage overhead. However, the secondary regions will have recent non-flushed data in their memstores, which increases the memory overhead. The primary region writes flush, compaction, and bulk load events to its WAL as well, which are also replicated through wal replication to secondaries. When they observe the flush/compaction or bulk load event, the secondary regions replay the event to pick up the new files and drop the old ones.

Committing writes in the same order as in primary ensures that the secondaries won’t diverge from the primary regions data, but since the log replication is asynchronous, the data might still be stale in secondary regions. Since this feature works as a replication endpoint, the performance and latency characteristics is expected to be similar to inter-cluster replication.

Async WAL Replication is disabled by default. You can enable this feature by setting hbase.region.replica.replication.enabled to true. Asyn WAL Replication feature will add a new replication peer named region_replica_replication as a replication peer when you create a table with region replication > 1 for the first time. Once enabled, if you want to disable this feature, you need to do two actions: * Set configuration property hbase.region.replica.replication.enabled to false in hbase-site.xml (see Configuration section below) * Disable the replication peer named region_replica_replication in the cluster using hbase shell or ReplicationAdmin class:

        hbase> disable_peer 'region_replica_replication'

72.6. Store File TTL

In both of the write propagation approaches mentioned above, store files of the primary will be opened in secondaries independent of the primary region. So for files that the primary compacted away, the secondaries might still be referring to these files for reading. Both features are using HFileLinks to refer to files, but there is no protection (yet) for guaranteeing that the file will not be deleted prematurely. Thus, as a guard, you should set the configuration property hbase.master.hfilecleaner.ttl to a larger value, such as 1 hour to guarantee that you will not receive IOExceptions for requests going to replicas.

72.7. Region replication for META table’s region

Currently, Async WAL Replication is not done for the META table’s WAL. The meta table’s secondary replicas still refreshes themselves from the persistent store files. Hence the hbase.regionserver.meta.storefile.refresh.period needs to be set to a certain non-zero value for refreshing the meta store files. Note that this configuration is configured differently than hbase.regionserver.storefile.refresh.period.

72.8. Memory accounting

The secondary region replicas refer to the data files of the primary region replica, but they have their own memstores (in HBase-1.1+) and uses block cache as well. However, one distinction is that the secondary region replicas cannot flush the data when there is memory pressure for their memstores. They can only free up memstore memory when the primary region does a flush and this flush is replicated to the secondary. Since in a region server hosting primary replicas for some regions and secondaries for some others, the secondaries might cause extra flushes to the primary regions in the same host. In extreme situations, there can be no memory left for adding new writes coming from the primary via wal replication. For unblocking this situation (and since secondary cannot flush by itself), the secondary is allowed to do a “store file refresh” by doing a file system list operation to pick up new files from primary, and possibly dropping its memstore. This refresh will only be performed if the memstore size of the biggest secondary region replica is at least hbase.region.replica.storefile.refresh.memstore.multiplier (default 4) times bigger than the biggest memstore of a primary replica. One caveat is that if this is performed, the secondary can observe partial row updates across column families (since column families are flushed independently). The default should be good to not do this operation frequently. You can set this value to a large number to disable this feature if desired, but be warned that it might cause the replication to block forever.

72.9. Secondary replica failover

When a secondary region replica first comes online, or fails over, it may have served some edits from its memstore. Since the recovery is handled differently for secondary replicas, the secondary has to ensure that it does not go back in time before it starts serving requests after assignment. For doing that, the secondary waits until it observes a full flush cycle (start flush, commit flush) or a “region open event” replicated from the primary. Until this happens, the secondary region replica will reject all read requests by throwing an IOException with message “The region’s reads are disabled”. However, the other replicas will probably still be available to read, thus not causing any impact for the rpc with TIMELINE consistency. To facilitate faster recovery, the secondary region will trigger a flush request from the primary when it is opened. The configuration property hbase.region.replica.wait.for.primary.flush (enabled by default) can be used to disable this feature if needed.

72.10. Configuration properties

To use highly available reads, you should set the following properties in hbase-site.xml file. There is no specific configuration to enable or disable region replicas. Instead you can change the number of region replicas per table to increase or decrease at the table creation or with alter table. The following configuration is for using async wal replication and using meta replicas of 3.

72.10.1. Server side properties

<property>
    <name>hbase.regionserver.storefile.refresh.period</name>
    <value>0</value>
    <description>
      The period (in milliseconds) for refreshing the store files for the secondary regions. 0 means this feature is disabled. Secondary regions sees new files (from flushes and compactions) from primary once the secondary region refreshes the list of files in the region (there is no notification mechanism). But too frequent refreshes might cause extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger value is also recommended with this setting.
    </description>
</property>

<property>
    <name>hbase.regionserver.meta.storefile.refresh.period</name>
    <value>300000</value>
    <description>
      The period (in milliseconds) for refreshing the store files for the hbase:meta tables secondary regions. 0 means this feature is disabled. Secondary regions sees new files (from flushes and compactions) from primary once the secondary region refreshes the list of files in the region (there is no notification mechanism). But too frequent refreshes might cause extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger value is also recommended with this setting. This should be a non-zero number if meta replicas are enabled (via hbase.meta.replica.count set to greater than 1).
    </description>
</property>

<property>
    <name>hbase.region.replica.replication.enabled</name>
    <value>true</value>
    <description>
      Whether asynchronous WAL replication to the secondary region replicas is enabled or not. If this is enabled, a replication peer named "region_replica_replication" will be created which will tail the logs and replicate the mutations to region replicas for tables that have region replication > 1. If this is enabled once, disabling this replication also      requires disabling the replication peer using shell or ReplicationAdmin java class. Replication to secondary region replicas works over standard inter-cluster replication.
    </description>
</property>
<property>
  <name>hbase.region.replica.replication.memstore.enabled</name>
  <value>true</value>
  <description>
    If you set this to `false`, replicas do not receive memstore updates from
    the primary RegionServer. If you set this to `true`, you can still disable
    memstore replication on a per-table basis, by setting the table's
    `REGION_MEMSTORE_REPLICATION` configuration property to `false`. If
    memstore replication is disabled, the secondaries will only receive
    updates for events like flushes and bulkloads, and will not have access to
    data which the primary has not yet flushed. This preserves the guarantee
    of row-level consistency, even when the read requests `Consistency.TIMELINE`.
  </description>
</property>

<property>
    <name>hbase.master.hfilecleaner.ttl</name>
    <value>3600000</value>
    <description>
      The period (in milliseconds) to keep store files in the archive folder before deleting them from the file system.</description>
</property>

<property>
    <name>hbase.meta.replica.count</name>
    <value>3</value>
    <description>
      Region replication count for the meta regions. Defaults to 1.
    </description>
</property>


<property>
    <name>hbase.region.replica.storefile.refresh.memstore.multiplier</name>
    <value>4</value>
    <description>
      The multiplier for a “store file refresh” operation for the secondary region replica. If a region server has memory pressure, the secondary region will refresh it’s store files if the memstore size of the biggest secondary replica is bigger this many times than the memstore size of the biggest primary replica. Set this to a very big value to disable this feature (not recommended).
    </description>
</property>

<property>
 <name>hbase.region.replica.wait.for.primary.flush</name>
    <value>true</value>
    <description>
      Whether to wait for observing a full flush cycle from the primary before start serving data in a secondary. Disabling this might cause the secondary region replicas to go back in time for reads between region movements.
    </description>
</property>

One thing to keep in mind also is that, region replica placement policy is only enforced by the StochasticLoadBalancer which is the default balancer. If you are using a custom load balancer property in hbase-site.xml (hbase.master.loadbalancer.class) replicas of regions might end up being hosted in the same server.

72.10.2. Client side properties

Ensure to set the following for all clients (and servers) that will use region replicas.

<property>
    <name>hbase.ipc.client.specificThreadForWriting</name>
    <value>true</value>
    <description>
      Whether to enable interruption of RPC threads at the client side. This is required for region replicas with fallback RPC’s to secondary regions.
    </description>
</property>
<property>
  <name>hbase.client.primaryCallTimeout.get</name>
  <value>10000</value>
  <description>
    The timeout (in microseconds), before secondary fallback RPC’s are submitted for get requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies.
  </description>
</property>
<property>
  <name>hbase.client.primaryCallTimeout.multiget</name>
  <value>10000</value>
  <description>
      The timeout (in microseconds), before secondary fallback RPC’s are submitted for multi-get requests (Table.get(List<Get>)) with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies.
  </description>
</property>
<property>
  <name>hbase.client.replicaCallTimeout.scan</name>
  <value>1000000</value>
  <description>
    The timeout (in microseconds), before secondary fallback RPC’s are submitted for scan requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 1 sec. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies.
  </description>
</property>
<property>
    <name>hbase.meta.replicas.use</name>
    <value>true</value>
    <description>
      Whether to use meta table replicas or not. Default is false.
    </description>
</property>

Note HBase-1.0.x users should use hbase.ipc.client.allowsInterrupt rather than hbase.ipc.client.specificThreadForWriting.

72.11. User Interface

In the masters user interface, the region replicas of a table are also shown together with the primary regions. You can notice that the replicas of a region will share the same start and end keys and the same region name prefix. The only difference would be the appended replica_id (which is encoded as hex), and the region encoded name will be different. You can also see the replica ids shown explicitly in the UI.

72.12. Creating a table with region replication

Region replication is a per-table property. All tables have REGION_REPLICATION = 1 by default, which means that there is only one replica per region. You can set and change the number of replicas per region of a table by supplying the REGION_REPLICATION property in the table descriptor.

72.12.1. Shell

create 't1', 'f1', {REGION_REPLICATION => 2}

describe 't1'
for i in 1..100
put 't1', "r#{i}", 'f1:c1', i
end
flush 't1'

72.12.2. Java

HTableDescriptor htd = new HTableDescriptor(TableName.valueOf(“test_table”));
htd.setRegionReplication(2);
...
admin.createTable(htd);

You can also use setRegionReplication() and alter table to increase, decrease the region replication for a table.

72.13. Read API and Usage

72.13.1. Shell

You can do reads in shell using a the Consistency.TIMELINE semantics as follows

hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}

You can simulate a region server pausing or becoming unavailable and do a read from the secondary replica:

$ kill -STOP <pid or primary region server>

hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}

Using scans is also similar

hbase> scan 't1', {CONSISTENCY => 'TIMELINE'}

72.13.2. Java

You can set the consistency for Gets and Scans and do requests as follows.

Get get = new Get(row);
get.setConsistency(Consistency.TIMELINE);
...
Result result = table.get(get);

You can also pass multiple gets:

Get get1 = new Get(row);
get1.setConsistency(Consistency.TIMELINE);
...
ArrayList<Get> gets = new ArrayList<Get>();
gets.add(get1);
...
Result[] results = table.get(gets);

And Scans:

Scan scan = new Scan();
scan.setConsistency(Consistency.TIMELINE);
...
ResultScanner scanner = table.getScanner(scan);

You can inspect whether the results are coming from primary region or not by calling the Result.isStale() method:

Result result = table.get(get);
if (result.isStale()) {
  ...
}

72.14. Resources

  1. More information about the design and implementation can be found at the jira issue: HBASE-10070

  2. HBaseCon 2014 talk also contains some details and slides.

73. Storing Medium-sized Objects (MOB)

Data comes in many sizes, and saving all of your data in HBase, including binary data such as images and documents, is ideal. While HBase can technically handle binary objects with cells that are larger than 100 KB in size, HBase’s normal read and write paths are optimized for values smaller than 100KB in size. When HBase deals with large numbers of objects over this threshold, referred to here as medium objects, or MOBs, performance is degraded due to write amplification caused by splits and compactions. When using MOBs, ideally your objects will be between 100KB and 10MB. HBase FIX_VERSION_NUMBER adds support for better managing large numbers of MOBs while maintaining performance, consistency, and low operational overhead. MOB support is provided by the work done in HBASE-11339. To take advantage of MOB, you need to use HFile version 3. Optionally, configure the MOB file reader’s cache settings for each RegionServer (see Configuring the MOB Cache), then configure specific columns to hold MOB data. Client code does not need to change to take advantage of HBase MOB support. The feature is transparent to the client.

73.1. Configuring Columns for MOB

You can configure columns to support MOB during table creation or alteration, either in HBase Shell or via the Java API. The two relevant properties are the boolean IS_MOB and the MOB_THRESHOLD, which is the number of bytes at which an object is considered to be a MOB. Only IS_MOB is required. If you do not specify the MOB_THRESHOLD, the default threshold value of 100 KB is used.

Example 38. Configure a Column for MOB Using HBase Shell
hbase> create 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400}
hbase> alter 't1', {NAME => 'f1', IS_MOB => true, MOB_THRESHOLD => 102400}
Example 39. Configure a Column for MOB Using the Java API
...
HColumnDescriptor hcd = new HColumnDescriptor(“f”);
hcd.setMobEnabled(true);
...
hcd.setMobThreshold(102400L);
...

73.2. Testing MOB

The utility org.apache.hadoop.hbase.IntegrationTestIngestMOB is provided to assist with testing the MOB feature. The utility is run as follows:

$ sudo -u hbase hbase org.apache.hadoop.hbase.IntegrationTestIngestMOB \
            -threshold 102400 \
            -minMobDataSize 512 \
            -maxMobDataSize 5120

  • threshold is the threshold at which cells are considered to be MOBs. The default is 1 kB, expressed in bytes.

  • minMobDataSize is the minimum value for the size of MOB data. The default is 512 B, expressed in bytes.

  • maxMobDataSize is the maximum value for the size of MOB data. The default is 5 kB, expressed in bytes.

73.3. Configuring the MOB Cache

Because there can be a large number of MOB files at any time, as compared to the number of HFiles, MOB files are not always kept open. The MOB file reader cache is a LRU cache which keeps the most recently used MOB files open. To configure the MOB file reader’s cache on each RegionServer, add the following properties to the RegionServer’s hbase-site.xml, customize the configuration to suit your environment, and restart or rolling restart the RegionServer.

Example 40. Example MOB Cache Configuration
<property>
    <name>hbase.mob.file.cache.size</name>
    <value>1000</value>
    <description>
      Number of opened file handlers to cache.
      A larger value will benefit reads by providing more file handlers per mob
      file cache and would reduce frequent file opening and closing.
      However, if this is set too high, this could lead to a "too many opened file handers"
      The default value is 1000.
    </description>
</property>
<property>
    <name>hbase.mob.cache.evict.period</name>
    <value>3600</value>
    <description>
      The amount of time in seconds after which an unused file is evicted from the
      MOB cache. The default value is 3600 seconds.
    </description>
</property>
<property>
    <name>hbase.mob.cache.evict.remain.ratio</name>
    <value>0.5f</value>
    <description>
      A multiplier (between 0.0 and 1.0), which determines how many files remain cached
      after the threshold of files that remains cached after a cache eviction occurs
      which is triggered by reaching the `hbase.mob.file.cache.size` threshold.
      The default value is 0.5f, which means that half the files (the least-recently-used
      ones) are evicted.
    </description>
</property>

73.4. MOB Optimization Tasks

73.4.1. Manually Compacting MOB Files

To manually compact MOB files, rather than waiting for the configuration to trigger compaction, use the compact_mob or major_compact_mob HBase shell commands. These commands require the first argument to be the table name, and take an optional column family as the second argument. If the column family is omitted, all MOB-enabled column families are compacted.

hbase> compact_mob 't1', 'c1'
hbase> compact_mob 't1'
hbase> major_compact_mob 't1', 'c1'
hbase> major_compact_mob 't1'

These commands are also available via Admin.compactMob and Admin.majorCompactMob methods.

73.4.2. MOB Sweeper

HBase MOB a MapReduce job called the Sweeper tool for optimization. The Sweeper tool coalesces small MOB files or MOB files with many deletions or updates. The Sweeper tool is not required if you use native MOB compaction, which does not rely on MapReduce.

To configure the Sweeper tool, set the following options:

<property>
    <name>hbase.mob.sweep.tool.compaction.ratio</name>
    <value>0.5f</value>
    <description>
      If there are too many cells deleted in a mob file, it's regarded
      as an invalid file and needs to be merged.
      If existingCellsSize/mobFileSize is less than ratio, it's regarded
      as an invalid file. The default value is 0.5f.
    </description>
</property>
<property>
    <name>hbase.mob.sweep.tool.compaction.mergeable.size</name>
    <value>134217728</value>
    <description>
      If the size of a mob file is less than this value, it's regarded as a small
      file and needs to be merged. The default value is 128MB.
    </description>
</property>
<property>
    <name>hbase.mob.sweep.tool.compaction.memstore.flush.size</name>
    <value>134217728</value>
    <description>
      The flush size for the memstore used by sweep job. Each sweep reducer owns such a memstore.
      The default value is 128MB.
    </description>
</property>
<property>
    <name>hbase.master.mob.ttl.cleaner.period</name>
    <value>86400</value>
    <description>
      The period that ExpiredMobFileCleanerChore runs. The unit is second.
      The default value is one day.
    </description>
</property>

Next, add the HBase install directory, `$HBASE_HOME`/*, and HBase library directory to yarn-site.xml Adjust this example to suit your environment.

<property>
    <description>Classpath for typical applications.</description>
    <name>yarn.application.classpath</name>
    <value>
        $HADOOP_CONF_DIR,
        $HADOOP_COMMON_HOME/*,$HADOOP_COMMON_HOME/lib/*,
        $HADOOP_HDFS_HOME/*,$HADOOP_HDFS_HOME/lib/*,
        $HADOOP_MAPRED_HOME/*,$HADOOP_MAPRED_HOME/lib/*,
        $HADOOP_YARN_HOME/*,$HADOOP_YARN_HOME/lib/*,
        $HBASE_HOME/*, $HBASE_HOME/lib/*
    </value>
</property>

Finally, run the sweeper tool for each column which is configured for MOB.

$ org.apache.hadoop.hbase.mob.compactions.Sweeper _tableName_ _familyName_

Apache HBase APIs

This chapter provides information about performing operations using HBase native APIs. This information is not exhaustive, and provides a quick reference in addition to the User API Reference. The examples here are not comprehensive or complete, and should be used for purposes of illustration only.

Apache HBase also works with multiple external APIs. See Apache HBase External APIs for more information.

74. Examples

Example 41. Create, modify and delete a Table Using Java
package com.example.hbase.admin;

package util;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Admin;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.io.compress.Compression.Algorithm;

public class Example {

  private static final String TABLE_NAME = "MY_TABLE_NAME_TOO";
  private static final String CF_DEFAULT = "DEFAULT_COLUMN_FAMILY";

  public static void createOrOverwrite(Admin admin, HTableDescriptor table) throws IOException {
    if (admin.tableExists(table.getTableName())) {
      admin.disableTable(table.getTableName());
      admin.deleteTable(table.getTableName());
    }
    admin.createTable(table);
  }

  public static void createSchemaTables(Configuration config) throws IOException {
    try (Connection connection = ConnectionFactory.createConnection(config);
         Admin admin = connection.getAdmin()) {

      HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME));
      table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY));

      System.out.print("Creating table. ");
      createOrOverwrite(admin, table);
      System.out.println(" Done.");
    }
  }

  public static void modifySchema (Configuration config) throws IOException {
    try (Connection connection = ConnectionFactory.createConnection(config);
         Admin admin = connection.getAdmin()) {

      TableName tableName = TableName.valueOf(TABLE_NAME);
      if (admin.tableExists(tableName)) {
        System.out.println("Table does not exist.");
        System.exit(-1);
      }

      HTableDescriptor table = new HTableDescriptor(tableName);

      // Update existing table
      HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF");
      newColumn.setCompactionCompressionType(Algorithm.GZ);
      newColumn.setMaxVersions(HConstants.ALL_VERSIONS);
      admin.addColumn(tableName, newColumn);

      // Update existing column family
      HColumnDescriptor existingColumn = new HColumnDescriptor(CF_DEFAULT);
      existingColumn.setCompactionCompressionType(Algorithm.GZ);
      existingColumn.setMaxVersions(HConstants.ALL_VERSIONS);
      table.modifyFamily(existingColumn);
      admin.modifyTable(tableName, table);

      // Disable an existing table
      admin.disableTable(tableName);

      // Delete an existing column family
      admin.deleteColumn(tableName, CF_DEFAULT.getBytes("UTF-8"));

      // Delete a table (Need to be disabled first)
      admin.deleteTable(tableName);
    }
  }

  public static void main(String... args) throws IOException {
    Configuration config = HBaseConfiguration.create();

    //Add any necessary configuration files (hbase-site.xml, core-site.xml)
    config.addResource(new Path(System.getenv("HBASE_CONF_DIR"), "hbase-site.xml"));
    config.addResource(new Path(System.getenv("HADOOP_CONF_DIR"), "core-site.xml"));
    createSchemaTables(config);
    modifySchema(config);
  }
}

Apache HBase External APIs

This chapter will cover access to Apache HBase either through non-Java languages and through custom protocols. For information on using the native HBase APIs, refer to User API Reference and the HBase APIs chapter.

75. REST

Representational State Transfer (REST) was introduced in 2000 in the doctoral dissertation of Roy Fielding, one of the principal authors of the HTTP specification.

REST itself is out of the scope of this documentation, but in general, REST allows client-server interactions via an API that is tied to the URL itself. This section discusses how to configure and run the REST server included with HBase, which exposes HBase tables, rows, cells, and metadata as URL specified resources. There is also a nice series of blogs on How-to: Use the Apache HBase REST Interface by Jesse Anderson.

75.1. Starting and Stopping the REST Server

The included REST server can run as a daemon which starts an embedded Jetty servlet container and deploys the servlet into it. Use one of the following commands to start the REST server in the foreground or background. The port is optional, and defaults to 8080.

# Foreground
$ bin/hbase rest start -p <port>

# Background, logging to a file in $HBASE_LOGS_DIR
$ bin/hbase-daemon.sh start rest -p <port>

To stop the REST server, use Ctrl-C if you were running it in the foreground, or the following command if you were running it in the background.

$ bin/hbase-daemon.sh stop rest

75.2. Configuring the REST Server and Client

For information about configuring the REST server and client for SSL, as well as doAs impersonation for the REST server, see Configure the Thrift Gateway to Authenticate on Behalf of the Client and other portions of the Securing Apache HBase chapter.

75.3. Using REST Endpoints

The following examples use the placeholder server http://example.com:8000, and the following commands can all be run using curl or wget commands. You can request plain text (the default), XML , or JSON output by adding no header for plain text, or the header "Accept: text/xml" for XML, "Accept: application/json" for JSON, or "Accept: application/x-protobuf" to for protocol buffers.

Unless specified, use GET requests for queries, PUT or POST requests for creation or mutation, and DELETE for deletion.
Table 11. Cluster-Wide Endpoints
Endpoint HTTP Verb Description Example

/version/cluster

GET

Version of HBase running on this cluster

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/version/cluster"

/status/cluster

GET

Cluster status

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/status/cluster"

/

GET

List of all non-system tables

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/"
Table 12. Namespace Endpoints
Endpoint HTTP Verb Description Example

/namespaces

GET

List all namespaces

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/namespaces/"

/namespaces/namespace

GET

Describe a specific namespace

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/namespaces/special_ns"

/namespaces/namespace

POST

Create a new namespace

 
curl -vi -X POST \
  -H "Accept: text/xml" \
  "example.com:8000/namespaces/special_ns"

/namespaces/namespace/tables

GET

List all tables in a specific namespace

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/namespaces/special_ns/tables"

/namespaces/namespace

PUT

Alter an existing namespace. Currently not used.

 
curl -vi -X PUT \
  -H "Accept: text/xml" \
  "http://example.com:8000/namespaces/special_ns

/namespaces/namespace

DELETE

Delete a namespace. The namespace must be empty.

 
curl -vi -X DELETE \
  -H "Accept: text/xml" \
  "example.com:8000/namespaces/special_ns"
Table 13. Table Endpoints
Endpoint HTTP Verb Description Example

/table/schema

GET

Describe the schema of the specified table.

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/schema"

/table/schema

POST

Create a new table, or replace an existing table’s schema

 
curl -vi -X POST \
  -H "Accept: text/xml" \
  -H "Content-Type: text/xml" \
  -d '<?xml version="1.0" encoding="UTF-8"?><TableSchema name="users"><ColumnSchema name="cf" /></TableSchema>' \
  "http://example.com:8000/users/schema"

/table/schema

PUT

Update an existing table with the provided schema fragment

 
curl -vi -X PUT \
  -H "Accept: text/xml" \
  -H "Content-Type: text/xml" \
  -d '<?xml version="1.0" encoding="UTF-8"?><TableSchema name="users"><ColumnSchema name="cf" KEEP_DELETED_CELLS="true" /></TableSchema>' \
  "http://example.com:8000/users/schema"

/table/schema

DELETE

Delete the table. You must use the /table/schema endpoint, not just /table/.

 
curl -vi -X DELETE \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/schema"

/table/regions

GET

List the table regions

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/regions
Table 14. Endpoints for Get Operations
Endpoint HTTP Verb Description Example

/table/row/column:qualifier/timestamp

GET

Get the value of a single row. Values are Base-64 encoded.

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/row1"

curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/row1/cf:a/1458586888395"

/table/row/column:qualifier

GET

Get the value of a single column. Values are Base-64 encoded.

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/row1/cf:a"

curl -vi -X GET \
  -H "Accept: text/xml" \
   "http://example.com:8000/users/row1/cf:a/"

/table/row/column:qualifier/?v=number_of_versions

GET

Multi-Get a specified number of versions of a given cell. Values are Base-64 encoded.

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/row1/cf:a?v=2"
Table 15. Endpoints for Scan Operations
Endpoint HTTP Verb Description Example

/table/scanner/

PUT

Get a Scanner object. Required by all other Scan operations. Adjust the batch parameter to the number of rows the scan should return in a batch. See the next example for adding filters to your scanner. The scanner endpoint URL is returned as the Location in the HTTP response. The other examples in this table assume that the scanner endpoint is http://example.com:8000/users/scanner/145869072824375522207.

 
curl -vi -X PUT \
  -H "Accept: text/xml" \
  -H "Content-Type: text/xml" \
  -d '<Scanner batch="1"/>' \
  "http://example.com:8000/users/scanner/"

/table/scanner/

PUT

To supply filters to the Scanner object or configure the Scanner in any other way, you can create a text file and add your filter to the file. For example, to return only rows for which keys start with <codeph>u123</codeph> and use a batch size of 100, the filter file would look like this: 

<Scanner batch="100">
  <filter>
    {
      "type": "PrefixFilter",
      "value": "u123"
    }
  </filter>
</Scanner>

Pass the file to the -d argument of the curl request.

 
curl -vi -X PUT \
  -H "Accept: text/xml" \
  -H "Content-Type:text/xml" \
  -d @filter.txt \
  "http://example.com:8000/users/scanner/"

/table/scanner/scanner-id

GET

Get the next batch from the scanner. Cell values are byte-encoded. If the scanner has been exhausted, HTTP status 204 is returned.

 
curl -vi -X GET \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/scanner/145869072824375522207"

table/scanner/scanner-id

DELETE

Deletes the scanner and frees the resources it used.

 
curl -vi -X DELETE \
  -H "Accept: text/xml" \
  "http://example.com:8000/users/scanner/145869072824375522207"
Table 16. Endpoints for Put Operations
Endpoint HTTP Verb Description Example

/table/row_key

PUT

Write a row to a table. The row, column qualifier, and value must each be Base-64 encoded. To encode a string, use the base64 command-line utility. To decode the string, use base64 -d. The payload is in the --data argument, and the /users/fakerow value is a placeholder. Insert multiple rows by adding them to the <CellSet> element. You can also save the data to be inserted to a file and pass it to the -d parameter with syntax like -d @filename.txt.

 
curl -vi -X PUT \
  -H "Accept: text/xml" \
  -H "Content-Type: text/xml" \
  -d '<?xml version="1.0" encoding="UTF-8" standalone="yes"?><CellSet><Row key="cm93NQo="><Cell column="Y2Y6ZQo=">dmFsdWU1Cg==</Cell></Row></CellSet>' \
  "http://example.com:8000/users/fakerow"

curl -vi -X PUT \
  -H "Accept: text/json" \
  -H "Content-Type: text/json" \
  -d '{"Row":[{"key":"cm93NQo=", "Cell": [{"column":"Y2Y6ZQo=", "$":"dmFsdWU1Cg=="}]}]}'' \
  "example.com:8000/users/fakerow"

75.4. REST XML Schema

<schema xmlns="http://www.w3.org/2001/XMLSchema" xmlns:tns="RESTSchema">

  <element name="Version" type="tns:Version"></element>

  <complexType name="Version">
    <attribute name="REST" type="string"></attribute>
    <attribute name="JVM" type="string"></attribute>
    <attribute name="OS" type="string"></attribute>
    <attribute name="Server" type="string"></attribute>
    <attribute name="Jersey" type="string"></attribute>
  </complexType>

  <element name="TableList" type="tns:TableList"></element>

  <complexType name="TableList">
    <sequence>
      <element name="table" type="tns:Table" maxOccurs="unbounded" minOccurs="1"></element>
    </sequence>
  </complexType>

  <complexType name="Table">
    <sequence>
      <element name="name" type="string"></element>
    </sequence>
  </complexType>

  <element name="TableInfo" type="tns:TableInfo"></element>

  <complexType name="TableInfo">
    <sequence>
      <element name="region" type="tns:TableRegion" maxOccurs="unbounded" minOccurs="1"></element>
    </sequence>
    <attribute name="name" type="string"></attribute>
  </complexType>

  <complexType name="TableRegion">
    <attribute name="name" type="string"></attribute>
    <attribute name="id" type="int"></attribute>
    <attribute name="startKey" type="base64Binary"></attribute>
    <attribute name="endKey" type="base64Binary"></attribute>
    <attribute name="location" type="string"></attribute>
  </complexType>

  <element name="TableSchema" type="tns:TableSchema"></element>

  <complexType name="TableSchema">
    <sequence>
      <element name="column" type="tns:ColumnSchema" maxOccurs="unbounded" minOccurs="1"></element>
    </sequence>
    <attribute name="name" type="string"></attribute>
    <anyAttribute></anyAttribute>
  </complexType>

  <complexType name="ColumnSchema">
    <attribute name="name" type="string"></attribute>
    <anyAttribute></anyAttribute>
  </complexType>

  <element name="CellSet" type="tns:CellSet"></element>

  <complexType name="CellSet">
    <sequence>
      <element name="row" type="tns:Row" maxOccurs="unbounded" minOccurs="1"></element>
    </sequence>
  </complexType>

  <element name="Row" type="tns:Row"></element>

  <complexType name="Row">
    <sequence>
      <element name="key" type="base64Binary"></element>
      <element name="cell" type="tns:Cell" maxOccurs="unbounded" minOccurs="1"></element>
    </sequence>
  </complexType>

  <element name="Cell" type="tns:Cell"></element>

  <complexType name="Cell">
    <sequence>
      <element name="value" maxOccurs="1" minOccurs="1">
        <simpleType><restriction base="base64Binary">
        </simpleType>
      </element>
    </sequence>
    <attribute name="column" type="base64Binary" />
    <attribute name="timestamp" type="int" />
  </complexType>

  <element name="Scanner" type="tns:Scanner"></element>

  <complexType name="Scanner">
    <sequence>
      <element name="column" type="base64Binary" minOccurs="0" maxOccurs="unbounded"></element>
    </sequence>
    <sequence>
      <element name="filter" type="string" minOccurs="0" maxOccurs="1"></element>
    </sequence>
    <attribute name="startRow" type="base64Binary"></attribute>
    <attribute name="endRow" type="base64Binary"></attribute>
    <attribute name="batch" type="int"></attribute>
    <attribute name="startTime" type="int"></attribute>
    <attribute name="endTime" type="int"></attribute>
  </complexType>

  <element name="StorageClusterVersion" type="tns:StorageClusterVersion" />

  <complexType name="StorageClusterVersion">
    <attribute name="version" type="string"></attribute>
  </complexType>

  <element name="StorageClusterStatus"
    type="tns:StorageClusterStatus">
  </element>

  <complexType name="StorageClusterStatus">
    <sequence>
      <element name="liveNode" type="tns:Node"
        maxOccurs="unbounded" minOccurs="0">
      </element>
      <element name="deadNode" type="string" maxOccurs="unbounded"
        minOccurs="0">
      </element>
    </sequence>
    <attribute name="regions" type="int"></attribute>
    <attribute name="requests" type="int"></attribute>
    <attribute name="averageLoad" type="float"></attribute>
  </complexType>

  <complexType name="Node">
    <sequence>
      <element name="region" type="tns:Region"
   maxOccurs="unbounded" minOccurs="0">
      </element>
    </sequence>
    <attribute name="name" type="string"></attribute>
    <attribute name="startCode" type="int"></attribute>
    <attribute name="requests" type="int"></attribute>
    <attribute name="heapSizeMB" type="int"></attribute>
    <attribute name="maxHeapSizeMB" type="int"></attribute>
  </complexType>

  <complexType name="Region">
    <attribute name="name" type="base64Binary"></attribute>
    <attribute name="stores" type="int"></attribute>
    <attribute name="storefiles" type="int"></attribute>
    <attribute name="storefileSizeMB" type="int"></attribute>
    <attribute name="memstoreSizeMB" type="int"></attribute>
    <attribute name="storefileIndexSizeMB" type="int"></attribute>
  </complexType>

</schema>

75.5. REST Protobufs Schema

message Version {
  optional string restVersion = 1;
  optional string jvmVersion = 2;
  optional string osVersion = 3;
  optional string serverVersion = 4;
  optional string jerseyVersion = 5;
}

message StorageClusterStatus {
  message Region {
    required bytes name = 1;
    optional int32 stores = 2;
    optional int32 storefiles = 3;
    optional int32 storefileSizeMB = 4;
    optional int32 memstoreSizeMB = 5;
    optional int32 storefileIndexSizeMB = 6;
  }
  message Node {
    required string name = 1;    // name:port
    optional int64 startCode = 2;
    optional int32 requests = 3;
    optional int32 heapSizeMB = 4;
    optional int32 maxHeapSizeMB = 5;
    repeated Region regions = 6;
  }
  // node status
  repeated Node liveNodes = 1;
  repeated string deadNodes = 2;
  // summary statistics
  optional int32 regions = 3;
  optional int32 requests = 4;
  optional double averageLoad = 5;
}

message TableList {
  repeated string name = 1;
}

message TableInfo {
  required string name = 1;
  message Region {
    required string name = 1;
    optional bytes startKey = 2;
    optional bytes endKey = 3;
    optional int64 id = 4;
    optional string location = 5;
  }
  repeated Region regions = 2;
}

message TableSchema {
  optional string name = 1;
  message Attribute {
    required string name = 1;
    required string value = 2;
  }
  repeated Attribute attrs = 2;
  repeated ColumnSchema columns = 3;
  // optional helpful encodings of commonly used attributes
  optional bool inMemory = 4;
  optional bool readOnly = 5;
}

message ColumnSchema {
  optional string name = 1;
  message Attribute {
    required string name = 1;
    required string value = 2;
  }
  repeated Attribute attrs = 2;
  // optional helpful encodings of commonly used attributes
  optional int32 ttl = 3;
  optional int32 maxVersions = 4;
  optional string compression = 5;
}

message Cell {
  optional bytes row = 1;       // unused if Cell is in a CellSet
  optional bytes column = 2;
  optional int64 timestamp = 3;
  optional bytes data = 4;
}

message CellSet {
  message Row {
    required bytes key = 1;
    repeated Cell values = 2;
  }
  repeated Row rows = 1;
}

message Scanner {
  optional bytes startRow = 1;
  optional bytes endRow = 2;
  repeated bytes columns = 3;
  optional int32 batch = 4;
  optional int64 startTime = 5;
  optional int64 endTime = 6;
}

76. Thrift

Documentation about Thrift has moved to Thrift API and Filter Language.

77. C/C++ Apache HBase Client

FB’s Chip Turner wrote a pure C/C++ client. Check it out.

78. Using Java Data Objects (JDO) with HBase

Java Data Objects (JDO) is a standard way to access persistent data in databases, using plain old Java objects (POJO) to represent persistent data.

Dependencies

This code example has the following dependencies:

  1. HBase 0.90.x or newer

  2. commons-beanutils.jar (http://commons.apache.org/)

  3. commons-pool-1.5.5.jar (http://commons.apache.org/)

  4. transactional-tableindexed for HBase 0.90 (https://github.com/hbase-trx/hbase-transactional-tableindexed)

Download hbase-jdo

Download the code from http://code.google.com/p/hbase-jdo/.

Example 42. JDO Example

This example uses JDO to create a table and an index, insert a row into a table, get a row, get a column value, perform a query, and do some additional HBase operations.

package com.apache.hadoop.hbase.client.jdo.examples;

import java.io.File;
import java.io.FileInputStream;
import java.io.InputStream;
import java.util.Hashtable;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.tableindexed.IndexedTable;

import com.apache.hadoop.hbase.client.jdo.AbstractHBaseDBO;
import com.apache.hadoop.hbase.client.jdo.HBaseBigFile;
import com.apache.hadoop.hbase.client.jdo.HBaseDBOImpl;
import com.apache.hadoop.hbase.client.jdo.query.DeleteQuery;
import com.apache.hadoop.hbase.client.jdo.query.HBaseOrder;
import com.apache.hadoop.hbase.client.jdo.query.HBaseParam;
import com.apache.hadoop.hbase.client.jdo.query.InsertQuery;
import com.apache.hadoop.hbase.client.jdo.query.QSearch;
import com.apache.hadoop.hbase.client.jdo.query.SelectQuery;
import com.apache.hadoop.hbase.client.jdo.query.UpdateQuery;

/**
 * Hbase JDO Example.
 *
 * dependency library.
 * - commons-beanutils.jar
 * - commons-pool-1.5.5.jar
 * - hbase0.90.0-transactionl.jar
 *
 * you can expand Delete,Select,Update,Insert Query classes.
 *
 */
public class HBaseExample {
  public static void main(String[] args) throws Exception {
    AbstractHBaseDBO dbo = new HBaseDBOImpl();

    //*drop if table is already exist.*
    if(dbo.isTableExist("user")){
     dbo.deleteTable("user");
    }

    //*create table*
    dbo.createTableIfNotExist("user",HBaseOrder.DESC,"account");
    //dbo.createTableIfNotExist("user",HBaseOrder.ASC,"account");

    //create index.
    String[] cols={"id","name"};
    dbo.addIndexExistingTable("user","account",cols);

    //insert
    InsertQuery insert = dbo.createInsertQuery("user");
    UserBean bean = new UserBean();
    bean.setFamily("account");
    bean.setAge(20);
    bean.setEmail("ncanis@gmail.com");
    bean.setId("ncanis");
    bean.setName("ncanis");
    bean.setPassword("1111");
    insert.insert(bean);

    //select 1 row
    SelectQuery select = dbo.createSelectQuery("user");
    UserBean resultBean = (UserBean)select.select(bean.getRow(),UserBean.class);

    // select column value.
    String value = (String)select.selectColumn(bean.getRow(),"account","id",String.class);

    // search with option (QSearch has EQUAL, NOT_EQUAL, LIKE)
    // select id,password,name,email from account where id='ncanis' limit startRow,20
    HBaseParam param = new HBaseParam();
    param.setPage(bean.getRow(),20);
    param.addColumn("id","password","name","email");
    param.addSearchOption("id","ncanis",QSearch.EQUAL);
    select.search("account", param, UserBean.class);

    // search column value is existing.
    boolean isExist = select.existColumnValue("account","id","ncanis".getBytes());

    // update password.
    UpdateQuery update = dbo.createUpdateQuery("user");
    Hashtable<String, byte[]> colsTable = new Hashtable<String, byte[]>();
    colsTable.put("password","2222".getBytes());
    update.update(bean.getRow(),"account",colsTable);

    //delete
    DeleteQuery delete = dbo.createDeleteQuery("user");
    delete.deleteRow(resultBean.getRow());

    ////////////////////////////////////
    // etc

    // HTable pool with apache commons pool
    // borrow and release. HBasePoolManager(maxActive, minIdle etc..)
    IndexedTable table = dbo.getPool().borrow("user");
    dbo.getPool().release(table);

    // upload bigFile by hadoop directly.
    HBaseBigFile bigFile = new HBaseBigFile();
    File file = new File("doc/movie.avi");
    FileInputStream fis = new FileInputStream(file);
    Path rootPath = new Path("/files/");
    String filename = "movie.avi";
    bigFile.uploadFile(rootPath,filename,fis,true);

    // receive file stream from hadoop.
    Path p = new Path(rootPath,filename);
    InputStream is = bigFile.path2Stream(p,4096);

  }
}

79. Scala

79.1. Setting the Classpath

To use Scala with HBase, your CLASSPATH must include HBase’s classpath as well as the Scala JARs required by your code. First, use the following command on a server running the HBase RegionServer process, to get HBase’s classpath.

$ ps aux |grep regionserver| awk -F 'java.library.path=' {'print $2'} | awk {'print $1'}

/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-64

Set the $CLASSPATH environment variable to include the path you found in the previous step, plus the path of scala-library.jar and each additional Scala-related JAR needed for your project.

$ export CLASSPATH=$CLASSPATH:/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-64:/path/to/scala-library.jar

79.2. Scala SBT File

Your build.sbt file needs the following resolvers and libraryDependencies to work with HBase.

resolvers += "Apache HBase" at "https://repository.apache.org/content/repositories/releases"

resolvers += "Thrift" at "http://people.apache.org/~rawson/repo/"

libraryDependencies ++= Seq(
    "org.apache.hadoop" % "hadoop-core" % "0.20.2",
    "org.apache.hbase" % "hbase" % "0.90.4"
)

79.3. Example Scala Code

This example lists HBase tables, creates a new table, and adds a row to it.

import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.{Connection,ConnectionFactory,HBaseAdmin,HTable,Put,Get}
import org.apache.hadoop.hbase.util.Bytes


val conf = new HBaseConfiguration()
val connection = ConnectionFactory.createConnection(conf);
val admin = connection.getAdmin();

// list the tables
val listtables=admin.listTables()
listtables.foreach(println)

// let's insert some data in 'mytable' and get the row

val table = new HTable(conf, "mytable")

val theput= new Put(Bytes.toBytes("rowkey1"))

theput.add(Bytes.toBytes("ids"),Bytes.toBytes("id1"),Bytes.toBytes("one"))
table.put(theput)

val theget= new Get(Bytes.toBytes("rowkey1"))
val result=table.get(theget)
val value=result.value()
println(Bytes.toString(value))

80. Jython

80.1. Setting the Classpath

To use Jython with HBase, your CLASSPATH must include HBase’s classpath as well as the Jython JARs required by your code. First, use the following command on a server running the HBase RegionServer process, to get HBase’s classpath.

$ ps aux |grep regionserver| awk -F 'java.library.path=' {'print $2'} | awk {'print $1'}

/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-64

Set the $CLASSPATH environment variable to include the path you found in the previous step, plus the path to jython.jar and each additional Jython-related JAR needed for your project.

$ export CLASSPATH=$CLASSPATH:/usr/lib/hadoop/lib/native:/usr/lib/hbase/lib/native/Linux-amd64-64:/path/to/jython.jar

Start a Jython shell with HBase and Hadoop JARs in the classpath: $ bin/hbase org.python.util.jython

80.2. Jython Code Examples

Example 43. Table Creation, Population, Get, and Delete with Jython

The following Jython code example creates a table, populates it with data, fetches the data, and deletes the table.

import java.lang
from org.apache.hadoop.hbase import HBaseConfiguration, HTableDescriptor, HColumnDescriptor, HConstants, TableName
from org.apache.hadoop.hbase.client import HBaseAdmin, HTable, Get
from org.apache.hadoop.hbase.io import Cell, RowResult

# First get a conf object.  This will read in the configuration
# that is out in your hbase-*.xml files such as location of the
# hbase master node.
conf = HBaseConfiguration()

# Create a table named 'test' that has two column families,
# one named 'content, and the other 'anchor'.  The colons
# are required for column family names.
tablename = TableName.valueOf("test")

desc = HTableDescriptor(tablename)
desc.addFamily(HColumnDescriptor("content:"))
desc.addFamily(HColumnDescriptor("anchor:"))
admin = HBaseAdmin(conf)

# Drop and recreate if it exists
if admin.tableExists(tablename):
    admin.disableTable(tablename)
    admin.deleteTable(tablename)
admin.createTable(desc)

tables = admin.listTables()
table = HTable(conf, tablename)

# Add content to 'column:' on a row named 'row_x'
row = 'row_x'
update = Get(row)
update.put('content:', 'some content')
table.commit(update)

# Now fetch the content just added, returns a byte[]
data_row = table.get(row, "content:")
data = java.lang.String(data_row.value, "UTF8")

print "The fetched row contains the value '%s'" % data

# Delete the table.
admin.disableTable(desc.getName())
admin.deleteTable(desc.getName())
Example 44. Table Scan Using Jython

This example scans a table and returns the results that match a given family qualifier.

# Print all rows that are members of a particular column family
# by passing a regex for family qualifier

import java.lang

from org.apache.hadoop.hbase import HBaseConfiguration
from org.apache.hadoop.hbase.client import HTable

conf = HBaseConfiguration()

table = HTable(conf, "wiki")
col = "title:.*$"

scanner = table.getScanner([col], "")
while 1:
    result = scanner.next()
    if not result:
        break
    print java.lang.String(result.row), java.lang.String(result.get('title:').value)

Thrift API and Filter Language

Apache Thrift is a cross-platform, cross-language development framework. HBase includes a Thrift API and filter language. The Thrift API relies on client and server processes.

You can configure Thrift for secure authentication at the server and client side, by following the procedures in Client-side Configuration for Secure Operation - Thrift Gateway and Configure the Thrift Gateway to Authenticate on Behalf of the Client.

The rest of this chapter discusses the filter language provided by the Thrift API.

81. Filter Language

Thrift Filter Language was introduced in HBase 0.92. It allows you to perform server-side filtering when accessing HBase over Thrift or in the HBase shell. You can find out more about shell integration by using the scan help command in the shell.

You specify a filter as a string, which is parsed on the server to construct the filter.

81.1. General Filter String Syntax

A simple filter expression is expressed as a string:

“FilterName (argument, argument,... , argument)”

Keep the following syntax guidelines in mind.

  • Specify the name of the filter followed by the comma-separated argument list in parentheses.

  • If the argument represents a string, it should be enclosed in single quotes (').

  • Arguments which represent a boolean, an integer, or a comparison operator (such as <, >, or !=), should not be enclosed in quotes

  • The filter name must be a single word. All ASCII characters are allowed except for whitespace, single quotes and parentheses.

  • The filter’s arguments can contain any ASCII character. If single quotes are present in the argument, they must be escaped by an additional preceding single quote.

81.2. Compound Filters and Operators

Binary Operators
AND

If the AND operator is used, the key-value must satisfy both filters.

OR

If the OR operator is used, the key-value must satisfy at least one of the filters.

Unary Operators
SKIP

For a particular row, if any of the key-values fail the filter condition, the entire row is skipped.

WHILE

For a particular row, key-values will be emitted until a key-value is reached that fails the filter condition.

Example 45. Compound Operators

You can combine multiple operators to create a hierarchy of filters, such as the following example:

(Filter1 AND Filter2) OR (Filter3 AND Filter4)

81.3. Order of Evaluation

  1. Parentheses have the highest precedence.

  2. The unary operators SKIP and WHILE are next, and have the same precedence.

  3. The binary operators follow. AND has highest precedence, followed by OR.

Example 46. Precedence Example
Filter1 AND Filter2 OR Filter
is evaluated as
(Filter1 AND Filter2) OR Filter3
Filter1 AND SKIP Filter2 OR Filter3
is evaluated as
(Filter1 AND (SKIP Filter2)) OR Filter3

You can use parentheses to explicitly control the order of evaluation.

81.4. Compare Operator

The following compare operators are provided:

  1. LESS (<)

  2. LESS_OR_EQUAL (⇐)

  3. EQUAL (=)

  4. NOT_EQUAL (!=)

  5. GREATER_OR_EQUAL (>=)

  6. GREATER (>)

  7. NO_OP (no operation)

The client should use the symbols (<, ⇐, =, !=, >, >=) to express compare operators.

81.5. Comparator

A comparator can be any of the following:

  1. BinaryComparator - This lexicographically compares against the specified byte array using Bytes.compareTo(byte[], byte[])

  2. BinaryPrefixComparator - This lexicographically compares against a specified byte array. It only compares up to the length of this byte array.

  3. RegexStringComparator - This compares against the specified byte array using the given regular expression. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator

  4. SubStringComparator - This tests if the given substring appears in a specified byte array. The comparison is case insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator

The general syntax of a comparator is: ComparatorType:ComparatorValue

The ComparatorType for the various comparators is as follows:

  1. BinaryComparator - binary

  2. BinaryPrefixComparator - binaryprefix

  3. RegexStringComparator - regexstring

  4. SubStringComparator - substring

The ComparatorValue can be any value.

Example ComparatorValues

  1. binary:abc will match everything that is lexicographically greater than "abc"

  2. binaryprefix:abc will match everything whose first 3 characters are lexicographically equal to "abc"

  3. regexstring:ab*yz will match everything that doesn’t begin with "ab" and ends with "yz"

  4. substring:abc123 will match everything that begins with the substring "abc123"

81.6. Example PHP Client Program that uses the Filter Language

<?
  $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
  require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
  flib_init(FLIB_CONTEXT_SCRIPT);
  require_module('storage/hbase');
  $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift server is running>);
  $hbase->open();
  $client = $hbase->getClient();
  $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
  $to_print = $client->scannerGetList($result,1);
  while ($to_print) {
    print_r($to_print);
    $to_print = $client->scannerGetList($result,1);
  }
  $client->scannerClose($result);
?>

81.7. Example Filter Strings

  • "PrefixFilter ('Row') AND PageFilter (1) AND FirstKeyOnlyFilter ()" will return all key-value pairs that match the following conditions:

    1. The row containing the key-value should have prefix Row

    2. The key-value must be located in the first row of the table

    3. The key-value pair must be the first key-value in the row

  • "(RowFilter (=, 'binary:Row 1') AND TimeStampsFilter (74689, 89734)) OR ColumnRangeFilter ('abc', true, 'xyz', false))" will return all key-value pairs that match both the following conditions:

    • The key-value is in a row having row key Row 1

    • The key-value must have a timestamp of either 74689 or 89734.

    • Or it must match the following condition:

      • The key-value pair must be in a column that is lexicographically >= abc and < xyz 

  • "SKIP ValueFilter (0)" will skip the entire row if any of the values in the row is not 0

81.8. Individual Filter Syntax

KeyOnlyFilter

This filter doesn’t take any arguments. It returns only the key component of each key-value.

FirstKeyOnlyFilter

This filter doesn’t take any arguments. It returns only the first key-value from each row.

PrefixFilter

This filter takes one argument – a prefix of a row key. It returns only those key-values present in a row that starts with the specified row prefix

ColumnPrefixFilter

This filter takes one argument – a column prefix. It returns only those key-values present in a column that starts with the specified column prefix. The column prefix must be of the form: “qualifier”.

MultipleColumnPrefixFilter

This filter takes a list of column prefixes. It returns key-values that are present in a column that starts with any of the specified column prefixes. Each of the column prefixes must be of the form: “qualifier”.

ColumnCountGetFilter

This filter takes one argument – a limit. It returns the first limit number of columns in the table.

PageFilter

This filter takes one argument – a page size. It returns page size number of rows from the table.

ColumnPaginationFilter

This filter takes two arguments – a limit and offset. It returns limit number of columns after offset number of columns. It does this for all the rows.

InclusiveStopFilter

This filter takes one argument – a row key on which to stop scanning. It returns all key-values present in rows up to and including the specified row.

TimeStampsFilter

This filter takes a list of timestamps. It returns those key-values whose timestamps matches any of the specified timestamps.

RowFilter

This filter takes a compare operator and a comparator. It compares each row key with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that row.

Family Filter

This filter takes a compare operator and a comparator. It compares each column family name with the comparator using the compare operator and if the comparison returns true, it returns all the Cells in that column family.

QualifierFilter

This filter takes a compare operator and a comparator. It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column.

ValueFilter

This filter takes a compare operator and a comparator. It compares each value with the comparator using the compare operator and if the comparison returns true, it returns that key-value.

DependentColumnFilter

This filter takes two arguments – a family and a qualifier. It tries to locate this column in each row and returns all key-values in that row that have the same timestamp. If the row doesn’t contain the specified column – none of the key-values in that row will be returned.

SingleColumnValueFilter

This filter takes a column family, a qualifier, a compare operator and a comparator. If the specified column is not found – all the columns of that row will be emitted. If the column is found and the comparison with the comparator returns true, all the columns of the row will be emitted. If the condition fails, the row will not be emitted.

SingleColumnValueExcludeFilter

This filter takes the same arguments and behaves same as SingleColumnValueFilter – however, if the column is found and the condition passes, all the columns of the row will be emitted except for the tested column value.

ColumnRangeFilter

This filter is used for selecting only those keys with columns that are between minColumn and maxColumn. It also takes two boolean variables to indicate whether to include the minColumn and maxColumn or not.

HBase and Spark

Apache Spark is a software framework that is used to process data in memory in a distributed manner, and is replacing MapReduce in many use cases.

Spark itself is out of scope of this document, please refer to the Spark site for more information on the Spark project and subprojects. This document will focus on 4 main interaction points between Spark and HBase. Those interaction points are:

Basic Spark

The ability to have an HBase Connection at any point in your Spark DAG.

Spark Streaming

The ability to have an HBase Connection at any point in your Spark Streaming application.

Spark Bulk Load

The ability to write directly to HBase HFiles for bulk insertion into HBase

SparkSQL/DataFrames

The ability to write SparkSQL that draws on tables that are represented in HBase.

The following sections will walk through examples of all these interaction points.

82. Basic Spark

This section discusses Spark HBase integration at the lowest and simplest levels. All the other interaction points are built upon the concepts that will be described here.

At the root of all Spark and HBase integration is the HBaseContext. The HBaseContext takes in HBase configurations and pushes them to the Spark executors. This allows us to have an HBase Connection per Spark Executor in a static location.

For reference, Spark Executors can be on the same nodes as the Region Servers or on different nodes there is no dependence of co-location. Think of every Spark Executor as a multi-threaded client application. This allows any Spark Tasks running on the executors to access the shared Connection object.

Example 47. HBaseContext Usage Example

This example shows how HBaseContext can be used to do a foreachPartition on a RDD in Scala:

val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()

...

val hbaseContext = new HBaseContext(sc, config)

rdd.hbaseForeachPartition(hbaseContext, (it, conn) => {
 val bufferedMutator = conn.getBufferedMutator(TableName.valueOf("t1"))
 it.foreach((putRecord) => {
. val put = new Put(putRecord._1)
. putRecord._2.foreach((putValue) => put.addColumn(putValue._1, putValue._2, putValue._3))
. bufferedMutator.mutate(put)
 })
 bufferedMutator.flush()
 bufferedMutator.close()
})

Here is the same example implemented in Java:

JavaSparkContext jsc = new JavaSparkContext(sparkConf);

try {
  List<byte[]> list = new ArrayList<>();
  list.add(Bytes.toBytes("1"));
  ...
  list.add(Bytes.toBytes("5"));

  JavaRDD<byte[]> rdd = jsc.parallelize(list);
  Configuration conf = HBaseConfiguration.create();

  JavaHBaseContext hbaseContext = new JavaHBaseContext(jsc, conf);

  hbaseContext.foreachPartition(rdd,
      new VoidFunction<Tuple2<Iterator<byte[]>, Connection>>() {
   public void call(Tuple2<Iterator<byte[]>, Connection> t)
        throws Exception {
    Table table = t._2().getTable(TableName.valueOf(tableName));
    BufferedMutator mutator = t._2().getBufferedMutator(TableName.valueOf(tableName));
    while (t._1().hasNext()) {
      byte[] b = t._1().next();
      Result r = table.get(new Get(b));
      if (r.getExists()) {
       mutator.mutate(new Put(b));
      }
    }

    mutator.flush();
    mutator.close();
    table.close();
   }
  });
} finally {
  jsc.stop();
}

All functionality between Spark and HBase will be supported both in Scala and in Java, with the exception of SparkSQL which will support any language that is supported by Spark. For the remaining of this documentation we will focus on Scala examples for now.

The examples above illustrate how to do a foreachPartition with a connection. A number of other Spark base functions are supported out of the box:

bulkPut

For massively parallel sending of puts to HBase

bulkDelete

For massively parallel sending of deletes to HBase

bulkGet

For massively parallel sending of gets to HBase to create a new RDD

mapPartition

To do a Spark Map function with a Connection object to allow full access to HBase

hBaseRDD

To simplify a distributed scan to create a RDD

For examples of all these functionalities, see the HBase-Spark Module.

83. Spark Streaming

Spark Streaming is a micro batching stream processing framework built on top of Spark. HBase and Spark Streaming make great companions in that HBase can help serve the following benefits alongside Spark Streaming.

  • A place to grab reference data or profile data on the fly

  • A place to store counts or aggregates in a way that supports Spark Streaming promise of only once processing.

The HBase-Spark module’s integration points with Spark Streaming are similar to its normal Spark integration points, in that the following commands are possible straight off a Spark Streaming DStream.

bulkPut

For massively parallel sending of puts to HBase

bulkDelete

For massively parallel sending of deletes to HBase

bulkGet

For massively parallel sending of gets to HBase to create a new RDD

mapPartition

To do a Spark Map function with a Connection object to allow full access to HBase

hBaseRDD

To simplify a distributed scan to create a RDD

Example 48. bulkPut Example with DStreams

Below is an example of bulkPut with DStreams. It is very close in feel to the RDD bulk put.

val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()

val hbaseContext = new HBaseContext(sc, config)
val ssc = new StreamingContext(sc, Milliseconds(200))

val rdd1 = ...
val rdd2 = ...

val queue = mutable.Queue[RDD[(Array[Byte], Array[(Array[Byte],
    Array[Byte], Array[Byte])])]]()

queue += rdd1
queue += rdd2

val dStream = ssc.queueStream(queue)

dStream.hbaseBulkPut(
  hbaseContext,
  TableName.valueOf(tableName),
  (putRecord) => {
   val put = new Put(putRecord._1)
   putRecord._2.foreach((putValue) => put.addColumn(putValue._1, putValue._2, putValue._3))
   put
  })

There are three inputs to the hbaseBulkPut function. . The hbaseContext that carries the configuration boardcast information link us to the HBase Connections in the executors . The table name of the table we are putting data into . A function that will convert a record in the DStream into an HBase Put object.

84. Bulk Load

There are two options for bulk loading data into HBase with Spark. There is the basic bulk load functionality that will work for cases where your rows have millions of columns and cases where your columns are not consolidated and partitions before the on the map side of the Spark bulk load process.

There is also a thin record bulk load option with Spark, this second option is designed for tables that have less then 10k columns per row. The advantage of this second option is higher throughput and less over all load on the Spark shuffle operation.

Both implementations work more or less like the MapReduce bulk load process in that a partitioner partitions the rowkeys based on region splits and the row keys are sent to the reducers in order, so that HFiles can be written out directly from the reduce phase.

In Spark terms, the bulk load will be implemented around a the Spark repartitionAndSortWithinPartitions followed by a Spark foreachPartition.

First lets look at an example of using the basic bulk load functionality

Example 49. Bulk Loading Example

The following example shows bulk loading with Spark.

val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()

val hbaseContext = new HBaseContext(sc, config)

val stagingFolder = ...
val rdd = sc.parallelize(Array(
      (Bytes.toBytes("1"),
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"), Bytes.toBytes("foo1"))),
      (Bytes.toBytes("3"),
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"), Bytes.toBytes("foo2.b"))), ...

rdd.hbaseBulkLoad(TableName.valueOf(tableName),
  t => {
   val rowKey = t._1
   val family:Array[Byte] = t._2(0)._1
   val qualifier = t._2(0)._2
   val value = t._2(0)._3

   val keyFamilyQualifier= new KeyFamilyQualifier(rowKey, family, qualifier)

   Seq((keyFamilyQualifier, value)).iterator
  },
  stagingFolder.getPath)

val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
  conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))

The hbaseBulkLoad function takes three required parameters:

  1. The table name of the table we intend to bulk load too

  2. A function that will convert a record in the RDD to a tuple key value par. With the tuple key being a KeyFamilyQualifer object and the value being the cell value. The KeyFamilyQualifer object will hold the RowKey, Column Family, and Column Qualifier. The shuffle will partition on the RowKey but will sort by all three values.

  3. The temporary path for the HFile to be written out too

Following the Spark bulk load command, use the HBase’s LoadIncrementalHFiles object to load the newly created HFiles into HBase.

Additional Parameters for Bulk Loading with Spark

You can set the following attributes with additional parameter options on hbaseBulkLoad.

  • Max file size of the HFiles

  • A flag to exclude HFiles from compactions

  • Column Family settings for compression, bloomType, blockSize, and dataBlockEncoding

Example 50. Using Additional Parameters
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()

val hbaseContext = new HBaseContext(sc, config)

val stagingFolder = ...
val rdd = sc.parallelize(Array(
      (Bytes.toBytes("1"),
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"), Bytes.toBytes("foo1"))),
      (Bytes.toBytes("3"),
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"), Bytes.toBytes("foo2.b"))), ...

val familyHBaseWriterOptions = new java.util.HashMap[Array[Byte], FamilyHFileWriteOptions]
val f1Options = new FamilyHFileWriteOptions("GZ", "ROW", 128, "PREFIX")

familyHBaseWriterOptions.put(Bytes.toBytes("columnFamily1"), f1Options)

rdd.hbaseBulkLoad(TableName.valueOf(tableName),
  t => {
   val rowKey = t._1
   val family:Array[Byte] = t._2(0)._1
   val qualifier = t._2(0)._2
   val value = t._2(0)._3

   val keyFamilyQualifier= new KeyFamilyQualifier(rowKey, family, qualifier)

   Seq((keyFamilyQualifier, value)).iterator
  },
  stagingFolder.getPath,
  familyHBaseWriterOptions,
  compactionExclude = false,
  HConstants.DEFAULT_MAX_FILE_SIZE)

val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
  conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))

Now lets look at how you would call the thin record bulk load implementation

Example 51. Using thin record bulk load
val sc = new SparkContext("local", "test")
val config = new HBaseConfiguration()

val hbaseContext = new HBaseContext(sc, config)

val stagingFolder = ...
val rdd = sc.parallelize(Array(
      ("1",
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("a"), Bytes.toBytes("foo1"))),
      ("3",
        (Bytes.toBytes(columnFamily1), Bytes.toBytes("b"), Bytes.toBytes("foo2.b"))), ...

rdd.hbaseBulkLoadThinRows(hbaseContext,
      TableName.valueOf(tableName),
      t => {
        val rowKey = t._1

        val familyQualifiersValues = new FamiliesQualifiersValues
        t._2.foreach(f => {
          val family:Array[Byte] = f._1
          val qualifier = f._2
          val value:Array[Byte] = f._3

          familyQualifiersValues +=(family, qualifier, value)
        })
        (new ByteArrayWrapper(Bytes.toBytes(rowKey)), familyQualifiersValues)
      },
      stagingFolder.getPath,
      new java.util.HashMap[Array[Byte], FamilyHFileWriteOptions],
      compactionExclude = false,
      20)

val load = new LoadIncrementalHFiles(config)
load.doBulkLoad(new Path(stagingFolder.getPath),
  conn.getAdmin, table, conn.getRegionLocator(TableName.valueOf(tableName)))

Note that the big difference in using bulk load for thin rows is the function returns a tuple with the first value being the row key and the second value being an object of FamiliesQualifiersValues, which will contain all the values for this row for all column families.

85. SparkSQL/DataFrames

HBase-Spark Connector (in HBase-Spark Module) leverages DataSource API (SPARK-3247) introduced in Spark-1.2.0, bridges the gap between simple HBase KV store and complex relational SQL queries and enables users to perform complex data analytical work on top of HBase using Spark. HBase Dataframe is a standard Spark Dataframe, and is able to interact with any other data sources such as Hive, Orc, Parquet, JSON, etc. HBase-Spark Connector applies critical techniques such as partition pruning, column pruning, predicate pushdown and data locality.

To use HBase-Spark connector, users need to define the Catalog for the schema mapping between HBase and Spark tables, prepare the data and populate the HBase table, then load HBase DataFrame. After that, users can do integrated query and access records in HBase table with SQL query. Following illustrates the basic procedure.

85.1. Define catalog

def catalog = s"""{
       |"table":{"namespace":"default", "name":"table1"},
       |"rowkey":"key",
       |"columns":{
         |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
         |"col1":{"cf":"cf1", "col":"col1", "type":"boolean"},
         |"col2":{"cf":"cf2", "col":"col2", "type":"double"},
         |"col3":{"cf":"cf3", "col":"col3", "type":"float"},
         |"col4":{"cf":"cf4", "col":"col4", "type":"int"},
         |"col5":{"cf":"cf5", "col":"col5", "type":"bigint"},
         |"col6":{"cf":"cf6", "col":"col6", "type":"smallint"},
         |"col7":{"cf":"cf7", "col":"col7", "type":"string"},
         |"col8":{"cf":"cf8", "col":"col8", "type":"tinyint"}
       |}
     |}""".stripMargin

Catalog defines a mapping between HBase and Spark tables. There are two critical parts of this catalog. One is the rowkey definition and the other is the mapping between table column in Spark and the column family and column qualifier in HBase. The above defines a schema for a HBase table with name as table1, row key as key and a number of columns (col1 - col8). Note that the rowkey also has to be defined in details as a column (col0), which has a specific cf (rowkey).

85.2. Save the DataFrame

case class HBaseRecord(
   col0: String,
   col1: Boolean,
   col2: Double,
   col3: Float,
   col4: Int,       
   col5: Long,
   col6: Short,
   col7: String,
   col8: Byte)

object HBaseRecord
{                                                                                                             
   def apply(i: Int, t: String): HBaseRecord = {
      val s = s"""row${"%03d".format(i)}"""       
      HBaseRecord(s,
      i % 2 == 0,
      i.toDouble,
      i.toFloat,  
      i,
      i.toLong,
      i.toShort,  
      s"String$i: $t",      
      i.toByte)
  }
}

val data = (0 to 255).map { i =>  HBaseRecord(i, "extra")}

sc.parallelize(data).toDF.write.options(
 Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
 .format("org.apache.hadoop.hbase.spark ")
 .save()

data prepared by the user is a local Scala collection which has 256 HBaseRecord objects. sc.parallelize(data) function distributes data to form an RDD. toDF returns a DataFrame. write function returns a DataFrameWriter used to write the DataFrame to external storage systems (e.g. HBase here). Given a DataFrame with specified schema catalog, save function will create an HBase table with 5 regions and save the DataFrame inside.

85.3. Load the DataFrame

def withCatalog(cat: String): DataFrame = {
  sqlContext
  .read
  .options(Map(HBaseTableCatalog.tableCatalog->cat))
  .format("org.apache.hadoop.hbase.spark")
  .load()
}
val df = withCatalog(catalog)

In ‘withCatalog’ function, sqlContext is a variable of SQLContext, which is the entry point for working with structured data (rows and columns) in Spark. read returns a DataFrameReader that can be used to read data in as a DataFrame. option function adds input options for the underlying data source to the DataFrameReader, and format function specifies the input data source format for the DataFrameReader. The load() function loads input in as a DataFrame. The date frame df returned by withCatalog function could be used to access HBase table, such as 4.4 and 4.5.

85.4. Language Integrated Query

val s = df.filter(($"col0" <= "row050" && $"col0" > "row040") ||
  $"col0" === "row005" ||
  $"col0" <= "row005")
  .select("col0", "col1", "col4")
s.show

DataFrame can do various operations, such as join, sort, select, filter, orderBy and so on. df.filter above filters rows using the given SQL expression. select selects a set of columns: col0, col1 and col4.

85.5. SQL Query

df.registerTempTable("table1")
sqlContext.sql("select count(col1) from table1").show

registerTempTable registers df DataFrame as a temporary table using the table name table1. The lifetime of this temporary table is tied to the SQLContext that was used to create df. sqlContext.sql function allows the user to execute SQL queries.

85.6. Others

Example 52. Query with different timestamps

In HBaseSparkConf, four parameters related to timestamp can be set. They are TIMESTAMP, MIN_TIMESTAMP, MAX_TIMESTAMP and MAX_VERSIONS respectively. Users can query records with different timestamps or time ranges with MIN_TIMESTAMP and MAX_TIMESTAMP. In the meantime, use concrete value instead of tsSpecified and oldMs in the examples below.

The example below shows how to load df DataFrame with different timestamps. tsSpecified is specified by the user. HBaseTableCatalog defines the HBase and Relation relation schema. writeCatalog defines catalog for the schema mapping.

val df = sqlContext.read
      .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.TIMESTAMP -> tsSpecified.toString))
      .format("org.apache.hadoop.hbase.spark")
      .load()

The example below shows how to load df DataFrame with different time ranges. oldMs is specified by the user.

val df = sqlContext.read
      .options(Map(HBaseTableCatalog.tableCatalog -> writeCatalog, HBaseSparkConf.MIN_TIMESTAMP -> "0",
        HBaseSparkConf.MAX_TIMESTAMP -> oldMs.toString))
      .format("org.apache.hadoop.hbase.spark")
      .load()

After loading df DataFrame, users can query data.

df.registerTempTable("table")
sqlContext.sql("select count(col1) from table").show
Example 53. Native Avro support

HBase-Spark Connector support different data formats like Avro, Jason, etc. The use case below shows how spark supports Avro. User can persist the Avro record into HBase directly. Internally, the Avro schema is converted to a native Spark Catalyst data type automatically. Note that both key-value parts in an HBase table can be defined in Avro format.

1) Define catalog for the schema mapping:

def catalog = s"""{
                     |"table":{"namespace":"default", "name":"Avrotable"},
                      |"rowkey":"key",
                      |"columns":{
                      |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
                      |"col1":{"cf":"cf1", "col":"col1", "type":"binary"}
                      |}
                      |}""".stripMargin

catalog is a schema for a HBase table named Avrotable. row key as key and one column col1. The rowkey also has to be defined in details as a column (col0), which has a specific cf (rowkey).

2) Prepare the Data:

 object AvroHBaseRecord {
   val schemaString =
     s"""{"namespace": "example.avro",
         |   "type": "record",      "name": "User",
         |    "fields": [
         |        {"name": "name", "type": "string"},
         |        {"name": "favorite_number",  "type": ["int", "null"]},
         |        {"name": "favorite_color", "type": ["string", "null"]},
         |        {"name": "favorite_array", "type": {"type": "array", "items": "string"}},
         |        {"name": "favorite_map", "type": {"type": "map", "values": "int"}}
         |      ]    }""".stripMargin

   val avroSchema: Schema = {
     val p = new Schema.Parser
     p.parse(schemaString)
   }

   def apply(i: Int): AvroHBaseRecord = {
     val user = new GenericData.Record(avroSchema);
     user.put("name", s"name${"%03d".format(i)}")
     user.put("favorite_number", i)
     user.put("favorite_color", s"color${"%03d".format(i)}")
     val favoriteArray = new GenericData.Array[String](2, avroSchema.getField("favorite_array").schema())
     favoriteArray.add(s"number${i}")
     favoriteArray.add(s"number${i+1}")
     user.put("favorite_array", favoriteArray)
     import collection.JavaConverters._
     val favoriteMap = Map[String, Int](("key1" -> i), ("key2" -> (i+1))).asJava
     user.put("favorite_map", favoriteMap)
     val avroByte = AvroSedes.serialize(user, avroSchema)
     AvroHBaseRecord(s"name${"%03d".format(i)}", avroByte)
   }
 }

 val data = (0 to 255).map { i =>
    AvroHBaseRecord(i)
 }

schemaString is defined first, then it is parsed to get avroSchema. avroSchema is used to generate AvroHBaseRecord. data prepared by users is a local Scala collection which has 256 AvroHBaseRecord objects.

3) Save DataFrame:

 sc.parallelize(data).toDF.write.options(
     Map(HBaseTableCatalog.tableCatalog -> catalog, HBaseTableCatalog.newTable -> "5"))
     .format("org.apache.spark.sql.execution.datasources.hbase")
     .save()

Given a data frame with specified schema catalog, above will create an HBase table with 5 regions and save the data frame inside.

4) Load the DataFrame

def avroCatalog = s"""{
            |"table":{"namespace":"default", "name":"avrotable"},
            |"rowkey":"key",
            |"columns":{
              |"col0":{"cf":"rowkey", "col":"key", "type":"string"},
              |"col1":{"cf":"cf1", "col":"col1", "avro":"avroSchema"}
            |}
          |}""".stripMargin

 def withCatalog(cat: String): DataFrame = {
     sqlContext
         .read
         .options(Map("avroSchema" -> AvroHBaseRecord.schemaString, HBaseTableCatalog.tableCatalog -> avroCatalog))
         .format("org.apache.spark.sql.execution.datasources.hbase")
         .load()
 }
 val df = withCatalog(catalog)

In withCatalog function, read returns a DataFrameReader that can be used to read data in as a DataFrame. The option function adds input options for the underlying data source to the DataFrameReader. There are two options: one is to set avroSchema as AvroHBaseRecord.schemaString, and one is to set HBaseTableCatalog.tableCatalog as avroCatalog. The load() function loads input in as a DataFrame. The date frame df returned by withCatalog function could be used to access the HBase table.

5) SQL Query

 df.registerTempTable("avrotable")
 val c = sqlContext.sql("select count(1) from avrotable").

After loading df DataFrame, users can query data. registerTempTable registers df DataFrame as a temporary table using the table name avrotable. sqlContext.sql function allows the user to execute SQL queries.

Apache HBase Coprocessors

HBase Coprocessors are modeled after Google BigTable’s coprocessor implementation (http://research.google.com/people/jeff/SOCC2010-keynote-slides.pdf pages 41-42.).

The coprocessor framework provides mechanisms for running your custom code directly on the RegionServers managing your data. Efforts are ongoing to bridge gaps between HBase’s implementation and BigTable’s architecture. For more information see HBASE-4047.

The information in this chapter is primarily sourced and heavily reused from the following resources:

  1. Mingjie Lai’s blog post Coprocessor Introduction.

  2. Gaurav Bhardwaj’s blog post The How To Of HBase Coprocessors.

Use Coprocessors At Your Own Risk

Coprocessors are an advanced feature of HBase and are intended to be used by system developers only. Because coprocessor code runs directly on the RegionServer and has direct access to your data, they introduce the risk of data corruption, man-in-the-middle attacks, or other malicious data access. Currently, there is no mechanism to prevent data corruption by coprocessors, though work is underway on HBASE-4047.

+ In addition, there is no resource isolation, so a well-intentioned but misbehaving coprocessor can severely degrade cluster performance and stability.

86. Coprocessor Overview

In HBase, you fetch data using a Get or Scan, whereas in an RDBMS you use a SQL query. In order to fetch only the relevant data, you filter it using a HBase Filter , whereas in an RDBMS you use a WHERE predicate.

After fetching the data, you perform computations on it. This paradigm works well for "small data" with a few thousand rows and several columns. However, when you scale to billions of rows and millions of columns, moving large amounts of data across your network will create bottlenecks at the network layer, and the client needs to be powerful enough and have enough memory to handle the large amounts of data and the computations. In addition, the client code can grow large and complex.

In this scenario, coprocessors might make sense. You can put the business computation code into a coprocessor which runs on the RegionServer, in the same location as the data, and returns the result to the client.

This is only one scenario where using coprocessors can provide benefit. Following are some analogies which may help to explain some of the benefits of coprocessors.

86.1. Coprocessor Analogies

Triggers and Stored Procedure

An Observer coprocessor is similar to a trigger in a RDBMS in that it executes your code either before or after a specific event (such as a Get or Put) occurs. An endpoint coprocessor is similar to a stored procedure in a RDBMS because it allows you to perform custom computations on the data on the RegionServer itself, rather than on the client.

MapReduce

MapReduce operates on the principle of moving the computation to the location of the data. Coprocessors operate on the same principal.

AOP

If you are familiar with Aspect Oriented Programming (AOP), you can think of a coprocessor as applying advice by intercepting a request and then running some custom code, before passing the request on to its final destination (or even changing the destination).

86.2. Coprocessor Implementation Overview

  1. Either your class should extend one of the Coprocessor classes, such as BaseRegionObserver, or it should implement the Coprocessor or CoprocessorService interface.

  2. Load the coprocessor, either statically (from the configuration) or dynamically, using HBase Shell. For more details see Loading Coprocessors.

  3. Call the coprocessor from your client-side code. HBase handles the coprocessor transparently.

The framework API is provided in the coprocessor package.

87. Types of Coprocessors

87.1. Observer Coprocessors

Observer coprocessors are triggered either before or after a specific event occurs. Observers that happen before an event use methods that start with a pre prefix, such as prePut. Observers that happen just after an event override methods that start with a post prefix, such as postPut.

87.1.1. Use Cases for Observer Coprocessors

Security

Before performing a Get or Put operation, you can check for permission using preGet or prePut methods.

Referential Integrity

HBase does not directly support the RDBMS concept of refential integrity, also known as foreign keys. You can use a coprocessor to enforce such integrity. For instance, if you have a business rule that every insert to the users table must be followed by a corresponding entry in the user_daily_attendance table, you could implement a coprocessor to use the prePut method on user to insert a record into user_daily_attendance.

Secondary Indexes

You can use a coprocessor to maintain secondary indexes. For more information, see SecondaryIndexing.

87.1.2. Types of Observer Coprocessor

RegionObserver

A RegionObserver coprocessor allows you to observe events on a region, such as Get and Put operations. See RegionObserver. Consider overriding the convenience class BaseRegionObserver, which implements the RegionObserver interface and will not break if new methods are added.

RegionServerObserver

A RegionServerObserver allows you to observe events related to the RegionServer’s operation, such as starting, stopping, or performing merges, commits, or rollbacks. See RegionServerObserver. Consider overriding the convenience class BaseMasterAndRegionObserver which implements both MasterObserver and RegionServerObserver interfaces and will not break if new methods are added.

MasterOvserver

A MasterObserver allows you to observe events related to the HBase Master, such as table creation, deletion, or schema modification. See MasterObserver. Consider overriding the convenience class BaseMasterAndRegionObserver, which implements both MasterObserver and RegionServerObserver interfaces and will not break if new methods are added.

WalObserver

A WalObserver allows you to observe events related to writes to the Write-Ahead Log (WAL). See WALObserver. Consider overriding the convenience class BaseWALObserver, which implements the WalObserver interface and will not break if new methods are added.

Examples provides working examples of observer coprocessors.

87.2. Endpoint Coprocessor

Endpoint processors allow you to perform computation at the location of the data. See Coprocessor Analogy. An example is the need to calculate a running average or summation for an entire table which spans hundreds of regions.

In contract to observer coprocessors, where your code is run transparently, endpoint coprocessors must be explicitly invoked using the CoprocessorService() method available in Table, HTableInterface, or HTable.

Starting with HBase 0.96, endpoint coprocessors are implemented using Google Protocol Buffers (protobuf). For more details on protobuf, see Google’s Protocol Buffer Guide. Endpoints Coprocessor written in version 0.94 are not compatible with version 0.96 or later. See HBASE-5448). To upgrade your HBase cluster from 0.94 or earlier to 0.96 or later, you need to reimplement your coprocessor.

Examples provides working examples of endpoint coprocessors.

88. Loading Coprocessors

To make your coprocessor available to HBase, it must be loaded, either statically (through the HBase configuration) or dynamically (using HBase Shell or the Java API).

88.1. Static Loading

Follow these steps to statically load your coprocessor. Keep in mind that you must restart HBase to unload a coprocessor that has been loaded statically.

  1. Define the Coprocessor in hbase-site.xml, with a <property> element with a <name> and a <value> sub-element. The <name> should be one of the following:

    • hbase.coprocessor.region.classes for RegionObservers and Endpoints.

    • hbase.coprocessor.wal.classes for WALObservers.

    • hbase.coprocessor.master.classes for MasterObservers.

      <value> must contain the fully-qualified class name of your coprocessor’s implementation class.

      For example to load a Coprocessor (implemented in class SumEndPoint.java) you have to create following entry in RegionServer’s 'hbase-site.xml' file (generally located under 'conf' directory):

      <property>
    <name>hbase.coprocessor.region.classes</name>
    <value>org.myname.hbase.coprocessor.endpoint.SumEndPoint</value>
</property>

      If multiple classes are specified for loading, the class names must be comma-separated. The framework attempts to load all the configured classes using the default class loader. Therefore, the jar file must reside on the server-side HBase classpath.

      Coprocessors which are loaded in this way will be active on all regions of all tables. These are also called system Coprocessor. The first listed Coprocessors will be assigned the priority Coprocessor.Priority.SYSTEM. Each subsequent coprocessor in the list will have its priority value incremented by one (which reduces its priority, because priorities have the natural sort order of Integers).

      When calling out to registered observers, the framework executes their callbacks methods in the sorted order of their priority.
      Ties are broken arbitrarily.

  2. Put your code HBase’s classpath. One easy way to do this is to drop the jar (containing you code and all the dependencies) into the lib/ directory in the HBase installation.

  3. Restart HBase.

88.2. Static Unloading

  1. Delete the coprocessor’s <property> element, including sub-elements, from hbase-site.xml.

  2. Restart HBase.

  3. Optionally, remove the coprocessor’s JAR file from the classpath or HBase’s lib/ directory.

88.3. Dynamic Loading

You can also load a coprocessor dynamically, without restarting HBase. This may seem preferable to static loading, but dynamically loaded coprocessors are loaded on a per-table basis, and are only available to the table for which they were loaded. For this reason, dynamically loaded tables are sometimes called Table Coprocessor.

In addition, dynamically loading a coprocessor acts as a schema change on the table, and the table must be taken offline to load the coprocessor.

There are three ways to dynamically load Coprocessor.

Assumptions

The below mentioned instructions makes the following assumptions:

  • A JAR called coprocessor.jar contains the Coprocessor implementation along with all of its dependencies.

  • The JAR is available in HDFS in some location like hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar.

88.3.1. Using HBase Shell

  1. Disable the table using HBase Shell:

    hbase> disable 'users'

  2. Load the Coprocessor, using a command like the following:

    hbase alter 'users', METHOD => 'table_att', 'Coprocessor'=>'hdfs://<namenode>:<port>/
user/<hadoop-user>/coprocessor.jar| org.myname.hbase.Coprocessor.RegionObserverExample|1073741823|
arg1=1,arg2=2'

    The Coprocessor framework will try to read the class information from the coprocessor table attribute value. The value contains four pieces of information which are separated by the pipe (|) character.

    • File path: The jar file containing the Coprocessor implementation must be in a location where all region servers can read it.
      You could copy the file onto the local disk on each region server, but it is recommended to store it in HDFS.
      HBASE-14548 allows a directory containing the jars or some wildcards to be specified, such as: hdfs://<namenode>:<port>/user/<hadoop-user>/ or hdfs://<namenode>:<port>/user/<hadoop-user>/*.jar. Please note that if a directory is specified, all jar files(.jar) directly in the directory are added, but it does not search files in the subtree rooted in the directory. And do not contain any wildcard if you would like to specify a directory. This enhancement applies to the ways of using the JAVA API as well.

    • Class name: The full class name of the Coprocessor.

    • Priority: An integer. The framework will determine the execution sequence of all configured observers registered at the same hook using priorities. This field can be left blank. In that case the framework will assign a default priority value.

    • Arguments (Optional): This field is passed to the Coprocessor implementation. This is optional.

  3. Enable the table.

    hbase(main):003:0> enable 'users'

  4. Verify that the coprocessor loaded:

    hbase(main):04:0> describe 'users'

    The coprocessor should be listed in the TABLE_ATTRIBUTES.

88.3.2. Using the Java API (all HBase versions)

The following Java code shows how to use the setValue() method of HTableDescriptor to load a coprocessor on the users table.

TableName tableName = TableName.valueOf("users");
String path = "hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar";
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
hTableDescriptor.setValue("COPROCESSOR$1", path + "|"
+ RegionObserverExample.class.getCanonicalName() + "|"
+ Coprocessor.PRIORITY_USER);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);

88.3.3. Using the Java API (HBase 0.96+ only)

In HBase 0.96 and newer, the addCoprocessor() method of HTableDescriptor provides an easier way to load a coprocessor dynamically.

TableName tableName = TableName.valueOf("users");
Path path = new Path("hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar");
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
hTableDescriptor.addCoprocessor(RegionObserverExample.class.getCanonicalName(), path,
Coprocessor.PRIORITY_USER, null);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);
There is no guarantee that the framework will load a given Coprocessor successfully. For example, the shell command neither guarantees a jar file exists at a particular location nor verifies whether the given class is actually contained in the jar file.

88.4. Dynamic Unloading

88.4.1. Using HBase Shell

  1. Disable the table.

    hbase> disable 'users'

  2. Alter the table to remove the coprocessor.

    hbase> alter 'users', METHOD => 'table_att_unset', NAME => 'coprocessor$1'

  3. Enable the table.

    hbase> enable 'users'

88.4.2. Using the Java API

Reload the table definition without setting the value of the coprocessor either by using setValue() or addCoprocessor() methods. This will remove any coprocessor attached to the table.

TableName tableName = TableName.valueOf("users");
String path = "hdfs://<namenode>:<port>/user/<hadoop-user>/coprocessor.jar";
Configuration conf = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(conf);
Admin admin = connection.getAdmin();
admin.disableTable(tableName);
HTableDescriptor hTableDescriptor = new HTableDescriptor(tableName);
HColumnDescriptor columnFamily1 = new HColumnDescriptor("personalDet");
columnFamily1.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily1);
HColumnDescriptor columnFamily2 = new HColumnDescriptor("salaryDet");
columnFamily2.setMaxVersions(3);
hTableDescriptor.addFamily(columnFamily2);
admin.modifyTable(tableName, hTableDescriptor);
admin.enableTable(tableName);

In HBase 0.96 and newer, you can instead use the removeCoprocessor() method of the HTableDescriptor class.

89. Examples

HBase ships examples for Observer Coprocessor in ZooKeeperScanPolicyObserver and for Endpoint Coprocessor in RowCountEndpoint

A more detailed example is given below.

These examples assume a table called users, which has two column families personalDet and salaryDet, containing personal and salary details. Below is the graphical representation of the users table.

Table 17. Users Table
personalDet salaryDet

jverne

Jules

Verne

02/08/1828

12000

9000

3000

rowkey

name

lastname

dob

gross

net

allowances

admin

Admin

Admin

cdickens

Charles

Dickens

02/07/1812

10000

8000

2000

89.1. Observer Example

The following Observer coprocessor prevents the details of the user admin from being returned in a Get or Scan of the users table.

  1. Write a class that extends the BaseRegionObserver class.

  2. Override the preGetOp() method (the preGet() method is deprecated) to check whether the client has queried for the rowkey with value admin. If so, return an empty result. Otherwise, process the request as normal.

  3. Put your code and dependencies in a JAR file.

  4. Place the JAR in HDFS where HBase can locate it.

  5. Load the Coprocessor.

  6. Write a simple program to test it.

Following are the implementation of the above steps:

public class RegionObserverExample extends BaseRegionObserver {

    private static final byte[] ADMIN = Bytes.toBytes("admin");
    private static final byte[] COLUMN_FAMILY = Bytes.toBytes("details");
    private static final byte[] COLUMN = Bytes.toBytes("Admin_det");
    private static final byte[] VALUE = Bytes.toBytes("You can't see Admin details");

    @Override
    public void preGetOp(final ObserverContext<RegionCoprocessorEnvironment> e, final Get get, final List<Cell> results)
    throws IOException {

        if (Bytes.equals(get.getRow(),ADMIN)) {
            Cell c = CellUtil.createCell(get.getRow(),COLUMN_FAMILY, COLUMN,
            System.currentTimeMillis(), (byte)4, VALUE);
            results.add(c);
            e.bypass();
        }
    }
}

Overriding the preGetOp() will only work for Get operations. You also need to override the preScannerOpen() method to filter the admin row from scan results.

@Override
public RegionScanner preScannerOpen(final ObserverContext<RegionCoprocessorEnvironment> e, final Scan scan,
final RegionScanner s) throws IOException {

    Filter filter = new RowFilter(CompareOp.NOT_EQUAL, new BinaryComparator(ADMIN));
    scan.setFilter(filter);
    return s;
}

This method works but there is a side effect. If the client has used a filter in its scan, that filter will be replaced by this filter. Instead, you can explicitly remove any admin results from the scan:

@Override
public boolean postScannerNext(final ObserverContext<RegionCoprocessorEnvironment> e, final InternalScanner s,
final List<Result> results, final int limit, final boolean hasMore) throws IOException {
        Result result = null;
    Iterator<Result> iterator = results.iterator();
    while (iterator.hasNext()) {
    result = iterator.next();
        if (Bytes.equals(result.getRow(), ROWKEY)) {
            iterator.remove();
            break;
        }
    }
    return hasMore;
}

89.2. Endpoint Example

Still using the users table, this example implements a coprocessor to calculate the sum of all employee salaries, using an endpoint coprocessor.

  1. Create a '.proto' file defining your service.

    option java_package = "org.myname.hbase.coprocessor.autogenerated";
option java_outer_classname = "Sum";
option java_generic_services = true;
option java_generate_equals_and_hash = true;
option optimize_for = SPEED;
message SumRequest {
    required string family = 1;
    required string column = 2;
}

message SumResponse {
  required int64 sum = 1 [default = 0];
}

service SumService {
  rpc getSum(SumRequest)
    returns (SumResponse);
}

  2. Execute the protoc command to generate the Java code from the above .proto' file.

    $ mkdir src
$ protoc --java_out=src ./sum.proto

    This will generate a class call Sum.java.

  3. Write a class that extends the generated service class, implement the Coprocessor and CoprocessorService classes, and override the service method.

    If you load a coprocessor from hbase-site.xml and then load the same coprocessor again using HBase Shell, it will be loaded a second time. The same class will exist twice, and the second instance will have a higher ID (and thus a lower priority). The effect is that the duplicate coprocessor is effectively ignored. 
    public class SumEndPoint extends SumService implements Coprocessor, CoprocessorService {

    private RegionCoprocessorEnvironment env;

    @Override
    public Service getService() {
        return this;
    }

    @Override
    public void start(CoprocessorEnvironment env) throws IOException {
        if (env instanceof RegionCoprocessorEnvironment) {
            this.env = (RegionCoprocessorEnvironment)env;
        } else {
            throw new CoprocessorException("Must be loaded on a table region!");
        }
    }

    @Override
    public void stop(CoprocessorEnvironment env) throws IOException {
        // do mothing
    }

    @Override
    public void getSum(RpcController controller, SumRequest request, RpcCallback done) {
        Scan scan = new Scan();
        scan.addFamily(Bytes.toBytes(request.getFamily()));
        scan.addColumn(Bytes.toBytes(request.getFamily()), Bytes.toBytes(request.getColumn()));
        SumResponse response = null;
        InternalScanner scanner = null;
        try {
            scanner = env.getRegion().getScanner(scan);
            List results = new ArrayList();
            boolean hasMore = false;
                        long sum = 0L;
                do {
                        hasMore = scanner.next(results);
                        for (Cell cell : results) {
                            sum = sum + Bytes.toLong(CellUtil.cloneValue(cell));
                     }
                        results.clear();
                } while (hasMore);

                response = SumResponse.newBuilder().setSum(sum).build();

        } catch (IOException ioe) {
            ResponseConverter.setControllerException(controller, ioe);
        } finally {
            if (scanner != null) {
                try {
                    scanner.close();
                } catch (IOException ignored) {}
            }
        }
        done.run(response);
    }
}
    Configuration conf = HBaseConfiguration.create();
// Use below code for HBase version 1.x.x or above.
Connection connection = ConnectionFactory.createConnection(conf);
TableName tableName = TableName.valueOf("users");
Table table = connection.getTable(tableName);

//Use below code HBase version 0.98.xx or below.
//HConnection connection = HConnectionManager.createConnection(conf);
//HTableInterface table = connection.getTable("users");

final SumRequest request = SumRequest.newBuilder().setFamily("salaryDet").setColumn("gross")
                            .build();
try {
Map<byte[], Long> results = table.CoprocessorService (SumService.class, null, null,
new Batch.Call<SumService, Long>() {
    @Override
        public Long call(SumService aggregate) throws IOException {
BlockingRpcCallback rpcCallback = new BlockingRpcCallback();
            aggregate.getSum(null, request, rpcCallback);
            SumResponse response = rpcCallback.get();
            return response.hasSum() ? response.getSum() : 0L;
        }
    });
    for (Long sum : results.values()) {
        System.out.println("Sum = " + sum);
    }
} catch (ServiceException e) {
e.printStackTrace();
} catch (Throwable e) {
    e.printStackTrace();
}

  4. Load the Coprocessor.

  5. Write a client code to call the Coprocessor.

90. Guidelines For Deploying A Coprocessor

Bundling Coprocessors

You can bundle all classes for a coprocessor into a single JAR on the RegionServer’s classpath, for easy deployment. Otherwise, place all dependencies on the RegionServer’s classpath so that they can be loaded during RegionServer start-up. The classpath for a RegionServer is set in the RegionServer’s hbase-env.sh file.

Automating Deployment

You can use a tool such as Puppet, Chef, or Ansible to ship the JAR for the coprocessor to the required location on your RegionServers' filesystems and restart each RegionServer, to automate coprocessor deployment. Details for such set-ups are out of scope of this document.

Updating a Coprocessor

Deploying a new version of a given coprocessor is not as simple as disabling it, replacing the JAR, and re-enabling the coprocessor. This is because you cannot reload a class in a JVM unless you delete all the current references to it. Since the current JVM has reference to the existing coprocessor, you must restart the JVM, by restarting the RegionServer, in order to replace it. This behavior is not expected to change.

Coprocessor Logging

The Coprocessor framework does not provide an API for logging beyond standard Java logging.

Coprocessor Configuration

If you do not want to load coprocessors from the HBase Shell, you can add their configuration properties to hbase-site.xml. In Using HBase Shell, two arguments are set: arg1=1,arg2=2. These could have been added to hbase-site.xml as follows:

<property>
  <name>arg1</name>
  <value>1</value>
</property>
<property>
  <name>arg2</name>
  <value>2</value>
</property>

Then you can read the configuration using code like the following:

Configuration conf = HBaseConfiguration.create();
// Use below code for HBase version 1.x.x or above.
Connection connection = ConnectionFactory.createConnection(conf);
TableName tableName = TableName.valueOf("users");
Table table = connection.getTable(tableName);

//Use below code HBase version 0.98.xx or below.
//HConnection connection = HConnectionManager.createConnection(conf);
//HTableInterface table = connection.getTable("users");

Get get = new Get(Bytes.toBytes("admin"));
Result result = table.get(get);
for (Cell c : result.rawCells()) {
    System.out.println(Bytes.toString(CellUtil.cloneRow(c))
        + "==> " + Bytes.toString(CellUtil.cloneFamily(c))
        + "{" + Bytes.toString(CellUtil.cloneQualifier(c))
        + ":" + Bytes.toLong(CellUtil.cloneValue(c)) + "}");
}
Scan scan = new Scan();
ResultScanner scanner = table.getScanner(scan);
for (Result res : scanner) {
    for (Cell c : res.rawCells()) {
        System.out.println(Bytes.toString(CellUtil.cloneRow(c))
        + " ==> " + Bytes.toString(CellUtil.cloneFamily(c))
        + " {" + Bytes.toString(CellUtil.cloneQualifier(c))
        + ":" + Bytes.toLong(CellUtil.cloneValue(c))
        + "}");
    }
}

91. Monitor Time Spent in Coprocessors

HBase 0.98.5 introduced the ability to monitor some statistics relating to the amount of time spent executing a given Coprocessor. You can see these statistics via the HBase Metrics framework (see HBase Metrics or the Web UI for a given Region Server, via the Coprocessor Metrics tab. These statistics are valuable for debugging and benchmarking the performance impact of a given Coprocessor on your cluster. Tracked statistics include min, max, average, and 90th, 95th, and 99th percentile. All times are shown in milliseconds. The statistics are calculated over Coprocessor execution samples recorded during the reporting interval, which is 10 seconds by default. The metrics sampling rate as described in HBase Metrics.

coprocessor stats
Figure 4. Coprocessor Metrics UI

Apache HBase Performance Tuning

92. Operating System

92.1. Memory

RAM, RAM, RAM. Don’t starve HBase.

92.2. 64-bit

Use a 64-bit platform (and 64-bit JVM).

92.3. Swapping

Watch out for swapping. Set swappiness to 0.

92.4. CPU

Make sure you have set up your Hadoop to use native, hardware checksumming. See link:[hadoop.native.lib].

93. Network

Perhaps the most important factor in avoiding network issues degrading Hadoop and HBase performance is the switching hardware that is used, decisions made early in the scope of the project can cause major problems when you double or triple the size of your cluster (or more).

Important items to consider:

  • Switching capacity of the device

  • Number of systems connected

  • Uplink capacity

93.1. Single Switch

The single most important factor in this configuration is that the switching capacity of the hardware is capable of handling the traffic which can be generated by all systems connected to the switch. Some lower priced commodity hardware can have a slower switching capacity than could be utilized by a full switch.

93.2. Multiple Switches

Multiple switches are a potential pitfall in the architecture. The most common configuration of lower priced hardware is a simple 1Gbps uplink from one switch to another. This often overlooked pinch point can easily become a bottleneck for cluster communication. Especially with MapReduce jobs that are both reading and writing a lot of data the communication across this uplink could be saturated.

Mitigation of this issue is fairly simple and can be accomplished in multiple ways:

  • Use appropriate hardware for the scale of the cluster which you’re attempting to build.

  • Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 port

  • Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch bandwidth.

93.3. Multiple Racks

Multiple rack configurations carry the same potential issues as multiple switches, and can suffer performance degradation from two main areas:

  • Poor switch capacity performance

  • Insufficient uplink to another rack

If the switches in your rack have appropriate switching capacity to handle all the hosts at full speed, the next most likely issue will be caused by homing more of your cluster across racks. The easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded uplink to other racks. The downside of this method however, is in the overhead of ports that could potentially be used. An example of this is, creating an 8Gbps port channel from rack A to rack B, using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few however can mean you’re not getting the most out of your cluster.

Using 10Gbe links between racks will greatly increase performance, and assuming your switches support a 10Gbe uplink or allow for an expansion card will allow you to save your ports for machines as opposed to uplinks.

93.4. Network Interfaces

Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in Case Study #1 (Performance Issue On A Single Node).

93.5. Network Consistency and Partition Tolerance

The CAP Theorem states that a distributed system can maintain two out of the following three characteristics: - *C*onsistency — all nodes see the same data. - *A*vailability — every request receives a response about whether it succeeded or failed. - *P*artition tolerance — the system continues to operate even if some of its components become unavailable to the others.

HBase favors consistency and partition tolerance, where a decision has to be made. Coda Hale explains why partition tolerance is so important, in http://codahale.com/you-cant-sacrifice-partition-tolerance/.

Robert Yokota used an automated testing framework called Jepson to test HBase’s partition tolerance in the face of network partitions, using techniques modeled after Aphyr’s Call Me Maybe series. The results, available as a blog post and an addendum, show that HBase performs correctly.

94. Java

94.1. The Garbage Collector and Apache HBase

94.1.1. Long GC pauses

In his presentation, Avoiding Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon describes two cases of stop-the-world garbage collections common in HBase, especially during loading; CMS failure modes and old generation heap fragmentation brought.

To address the first, start the CMS earlier than default by adding -XX:CMSInitiatingOccupancyFraction and setting it down from defaults. Start at 60 or 70 percent (The lower you bring down the threshold, the more GCing is done, the more CPU used). To address the second fragmentation issue, Todd added an experimental facility, (MSLAB), that must be explicitly enabled in Apache HBase 0.90.x (It’s defaulted to be on in Apache 0.92.x HBase). Set hbase.hregion.memstore.mslab.enabled to true in your Configuration. See the cited slides for background and detail. The latest JVMs do better regards fragmentation so make sure you are running a recent release. Read down in the message, Identifying concurrent mode failures caused by fragmentation. Be aware that when enabled, each MemStore instance will occupy at least an MSLAB instance of memory. If you have thousands of regions or lots of regions each with many column families, this allocation of MSLAB may be responsible for a good portion of your heap allocation and in an extreme case cause you to OOME. Disable MSLAB in this case, or lower the amount of memory it uses or float less regions per server.

If you have a write-heavy workload, check out HBASE-8163 MemStoreChunkPool: An improvement for JAVA GC when using MSLAB. It describes configurations to lower the amount of young GC during write-heavy loadings. If you do not have HBASE-8163 installed, and you are trying to improve your young GC times, one trick to consider — courtesy of our Liang Xie — is to set the GC config -XX:PretenureSizeThreshold in hbase-env.sh to be just smaller than the size of hbase.hregion.memstore.mslab.chunksize so MSLAB allocations happen in the tenured space directly rather than first in the young gen. You’d do this because these MSLAB allocations are going to likely make it to the old gen anyways and rather than pay the price of a copies between s0 and s1 in eden space followed by the copy up from young to old gen after the MSLABs have achieved sufficient tenure, save a bit of YGC churn and allocate in the old gen directly.

Other sources of long GCs can be the JVM itself logging. See Eliminating Large JVM GC Pauses Caused by Background IO Traffic

For more information about GC logs, see JVM Garbage Collection Logs.

Consider also enabling the off-heap Block Cache. This has been shown to mitigate GC pause times. See Block Cache

95. HBase Configurations

See Recommended Configurations.

95.1. Managing Compactions

For larger systems, managing link:[compactions and splits] may be something you want to consider.

95.2. hbase.regionserver.handler.count

See [hbase.regionserver.handler.count].

95.3. hfile.block.cache.size

See [hfile.block.cache.size]. A memory setting for the RegionServer process.

95.4. Prefetch Option for Blockcache

HBASE-9857 adds a new option to prefetch HFile contents when opening the BlockCache, if a Column family or RegionServer property is set. This option is available for HBase 0.98.3 and later. The purpose is to warm the BlockCache as rapidly as possible after the cache is opened, using in-memory table data, and not counting the prefetching as cache misses. This is great for fast reads, but is not a good idea if the data to be preloaded will not fit into the BlockCache. It is useful for tuning the IO impact of prefetching versus the time before all data blocks are in cache.

To enable prefetching on a given column family, you can use HBase Shell or use the API.

Example 54. Enable Prefetch Using HBase Shell
hbase> create 'MyTable', { NAME => 'myCF', PREFETCH_BLOCKS_ON_OPEN => 'true' }
Example 55. Enable Prefetch Using the API
// ...
HTableDescriptor tableDesc = new HTableDescriptor("myTable");
HColumnDescriptor cfDesc = new HColumnDescriptor("myCF");
cfDesc.setPrefetchBlocksOnOpen(true);
tableDesc.addFamily(cfDesc);
// ...

See the API documentation for CacheConfig.

To see prefetch in operation, enable TRACE level logging on org.apache.hadoop.hbase.io.hfile.HFileReaderImpl in hbase-2.0+ or on org.apache.hadoop.hbase.io.hfile.HFileReaderV2 in earlier versions, hbase-1.x, of HBase.

95.5. hbase.regionserver.global.memstore.size

See [hbase.regionserver.global.memstore.size]. This memory setting is often adjusted for the RegionServer process depending on needs.

95.6. hbase.regionserver.global.memstore.size.lower.limit

See [hbase.regionserver.global.memstore.size.lower.limit]. This memory setting is often adjusted for the RegionServer process depending on needs.

95.7. hbase.hstore.blockingStoreFiles

See [hbase.hstore.blockingStoreFiles]. If there is blocking in the RegionServer logs, increasing this can help.

95.8. hbase.hregion.memstore.block.multiplier

See [hbase.hregion.memstore.block.multiplier]. If there is enough RAM, increasing this can help.

95.9. hbase.regionserver.checksum.verify

Have HBase write the checksum into the datablock and save having to do the checksum seek whenever you read.

See [hbase.regionserver.checksum.verify], [hbase.hstore.bytes.per.checksum] and [hbase.hstore.checksum.algorithm]. For more information see the release note on HBASE-5074 support checksums in HBase block cache.

95.10. Tuning callQueue Options

HBASE-11355 introduces several callQueue tuning mechanisms which can increase performance. See the JIRA for some benchmarking information.

To increase the number of callqueues, set hbase.ipc.server.num.callqueue to a value greater than 1. To split the callqueue into separate read and write queues, set hbase.ipc.server.callqueue.read.ratio to a value between 0 and 1. This factor weights the queues toward writes (if below .5) or reads (if above .5). Another way to say this is that the factor determines what percentage of the split queues are used for reads. The following examples illustrate some of the possibilities. Note that you always have at least one write queue, no matter what setting you use.

  • The default value of 0 does not split the queue.

  • A value of .3 uses 30% of the queues for reading and 60% for writing. Given a value of 10 for hbase.ipc.server.num.callqueue, 3 queues would be used for reads and 7 for writes.

  • A value of .5 uses the same number of read queues and write queues. Given a value of 10 for hbase.ipc.server.num.callqueue, 5 queues would be used for reads and 5 for writes.

  • A value of .6 uses 60% of the queues for reading and 30% for reading. Given a value of 10 for hbase.ipc.server.num.callqueue, 7 queues would be used for reads and 3 for writes.

  • A value of 1.0 uses one queue to process write requests, and all other queues process read requests. A value higher than 1.0 has the same effect as a value of 1.0. Given a value of 10 for hbase.ipc.server.num.callqueue, 9 queues would be used for reads and 1 for writes.

You can also split the read queues so that separate queues are used for short reads (from Get operations) and long reads (from Scan operations), by setting the hbase.ipc.server.callqueue.scan.ratio option. This option is a factor between 0 and 1, which determine the ratio of read queues used for Gets and Scans. More queues are used for Gets if the value is below .5 and more are used for scans if the value is above .5. No matter what setting you use, at least one read queue is used for Get operations.

  • A value of 0 does not split the read queue.

  • A value of .3 uses 60% of the read queues for Gets and 30% for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 7 would be used for Gets and 3 for Scans.

  • A value of .5 uses half the read queues for Gets and half for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 5 would be used for Gets and 5 for Scans.

  • A value of .6 uses 30% of the read queues for Gets and 60% for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 3 would be used for Gets and 7 for Scans.

  • A value of 1.0 uses all but one of the read queues for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of`.5` for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 1 would be used for Gets and 9 for Scans.

You can use the new option hbase.ipc.server.callqueue.handler.factor to programmatically tune the number of queues:

  • A value of 0 uses a single shared queue between all the handlers.

  • A value of 1 uses a separate queue for each handler.

  • A value between 0 and 1 tunes the number of queues against the number of handlers. For instance, a value of .5 shares one queue between each two handlers.

    Having more queues, such as in a situation where you have one queue per handler, reduces contention when adding a task to a queue or selecting it from a queue. The trade-off is that if you have some queues with long-running tasks, a handler may end up waiting to execute from that queue rather than processing another queue which has waiting tasks.

For these values to take effect on a given RegionServer, the RegionServer must be restarted. These parameters are intended for testing purposes and should be used carefully.

96. ZooKeeper

See ZooKeeper for information on configuring ZooKeeper, and see the part about having a dedicated disk.

97. Schema Design

97.1. Number of Column Families

See On the number of column families.

97.2. Key and Attribute Lengths

See Try to minimize row and column sizes. See also However…​ for compression caveats.

97.3. Table RegionSize

The regionsize can be set on a per-table basis via setFileSize on HTableDescriptor in the event where certain tables require different regionsizes than the configured default regionsize.

See Determining region count and size for more information.

97.4. Bloom Filters

A Bloom filter, named for its creator, Burton Howard Bloom, is a data structure which is designed to predict whether a given element is a member of a set of data. A positive result from a Bloom filter is not always accurate, but a negative result is guaranteed to be accurate. Bloom filters are designed to be "accurate enough" for sets of data which are so large that conventional hashing mechanisms would be impractical. For more information about Bloom filters in general, refer to http://en.wikipedia.org/wiki/Bloom_filter.

In terms of HBase, Bloom filters provide a lightweight in-memory structure to reduce the number of disk reads for a given Get operation (Bloom filters do not work with Scans) to only the StoreFiles likely to contain the desired Row. The potential performance gain increases with the number of parallel reads.

The Bloom filters themselves are stored in the metadata of each HFile and never need to be updated. When an HFile is opened because a region is deployed to a RegionServer, the Bloom filter is loaded into memory.

HBase includes some tuning mechanisms for folding the Bloom filter to reduce the size and keep the false positive rate within a desired range.

Bloom filters were introduced in HBASE-1200. Since HBase 0.96, row-based Bloom filters are enabled by default. (HBASE-8450)

For more information on Bloom filters in relation to HBase, see Bloom Filters for more information, or the following Quora discussion: How are bloom filters used in HBase?.

97.4.1. When To Use Bloom Filters

Since HBase 0.96, row-based Bloom filters are enabled by default. You may choose to disable them or to change some tables to use row+column Bloom filters, depending on the characteristics of your data and how it is loaded into HBase.

To determine whether Bloom filters could have a positive impact, check the value of blockCacheHitRatio in the RegionServer metrics. If Bloom filters are enabled, the value of blockCacheHitRatio should increase, because the Bloom filter is filtering out blocks that are definitely not needed.

You can choose to enable Bloom filters for a row or for a row+column combination. If you generally scan entire rows, the row+column combination will not provide any benefit. A row-based Bloom filter can operate on a row+column Get, but not the other way around. However, if you have a large number of column-level Puts, such that a row may be present in every StoreFile, a row-based filter will always return a positive result and provide no benefit. Unless you have one column per row, row+column Bloom filters require more space, in order to store more keys. Bloom filters work best when the size of each data entry is at least a few kilobytes in size.

Overhead will be reduced when your data is stored in a few larger StoreFiles, to avoid extra disk IO during low-level scans to find a specific row.

Bloom filters need to be rebuilt upon deletion, so may not be appropriate in environments with a large number of deletions.

97.4.2. Enabling Bloom Filters

Bloom filters are enabled on a Column Family. You can do this by using the setBloomFilterType method of HColumnDescriptor or using the HBase API. Valid values are NONE, ROW (default), or ROWCOL. See When To Use Bloom Filters for more information on ROW versus ROWCOL. See also the API documentation for HColumnDescriptor.

The following example creates a table and enables a ROWCOL Bloom filter on the colfam1 column family.

hbase> create 'mytable',{NAME => 'colfam1', BLOOMFILTER => 'ROWCOL'}

97.4.3. Configuring Server-Wide Behavior of Bloom Filters

You can configure the following settings in the hbase-site.xml.

Parameter Default Description

io.storefile.bloom.enabled

yes

Set to no to kill bloom filters server-wide if something goes wrong

io.storefile.bloom.error.rate

.01

The average false positive rate for bloom filters. Folding is used to maintain the false positive rate. Expressed as a decimal representation of a percentage.

io.storefile.bloom.max.fold

7

The guaranteed maximum fold rate. Changing this setting should not be necessary and is not recommended.

io.storefile.bloom.max.keys

128000000

For default (single-block) Bloom filters, this specifies the maximum number of keys.

io.storefile.delete.family.bloom.enabled

true

Master switch to enable Delete Family Bloom filters and store them in the StoreFile.

io.storefile.bloom.block.size

131072

Target Bloom block size. Bloom filter blocks of approximately this size are interleaved with data blocks.

hfile.block.bloom.cacheonwrite

false

Enables cache-on-write for inline blocks of a compound Bloom filter.

97.5. ColumnFamily BlockSize

The blocksize can be configured for each ColumnFamily in a table, and defaults to 64k. Larger cell values require larger blocksizes. There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting indexes should be roughly halved).

See HColumnDescriptor and Storefor more information.

97.6. In-Memory ColumnFamilies

ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any other ColumnFamily. In-memory blocks have the highest priority in the Block Cache, but it is not a guarantee that the entire table will be in memory.

See HColumnDescriptor for more information.

97.7. Compression

Production systems should use compression with their ColumnFamily definitions. See Compression and Data Block Encoding In HBase for more information.

97.7.1. However…​

Compression deflates data on disk. When it’s in-memory (e.g., in the MemStore) or on the wire (e.g., transferring between RegionServer and Client) it’s inflated. So while using ColumnFamily compression is a best practice, but it’s not going to completely eliminate the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.

See Try to minimize row and column sizes on for schema design tips, and KeyValue for more information on HBase stores data internally.

98. HBase General Patterns

98.1. Constants

When people get started with HBase they have a tendency to write code that looks like this:

Get get = new Get(rowkey);
Result r = table.get(get);
byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns current version of value

But especially when inside loops (and MapReduce jobs), converting the columnFamily and column-names to byte-arrays repeatedly is surprisingly expensive. It’s better to use constants for the byte-arrays, like this:

public static final byte[] CF = "cf".getBytes();
public static final byte[] ATTR = "attr".getBytes();
...
Get get = new Get(rowkey);
Result r = table.get(get);
byte[] b = r.getValue(CF, ATTR);  // returns current version of value

99. Writing to HBase

99.1. Batch Loading

Use the bulk load tool if you can. See Bulk Loading. Otherwise, pay attention to the below.

99.2. Table Creation: Pre-Creating Regions

Tables in HBase are initially created with one region by default. For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty regions. Be somewhat conservative in this, because too-many regions can actually degrade performance.

There are two different approaches to pre-creating splits using the HBase API. The first approach is to rely on the default Admin strategy (which is implemented in Bytes.split)…​

byte[] startKey = ...;      // your lowest key
byte[] endKey = ...;        // your highest key
int numberOfRegions = ...;  // # of regions to create
admin.createTable(table, startKey, endKey, numberOfRegions);

And the other approach, using the HBase API, is to define the splits yourself…​

byte[][] splits = ...;   // create your own splits
admin.createTable(table, splits);

You can achieve a similar effect using the HBase Shell to create tables by specifying split options.

# create table with specific split points
hbase>create 't1','f1',SPLITS => ['\x10\x00', '\x20\x00', '\x30\x00', '\x40\x00']

# create table with four regions based on random bytes keys
hbase>create 't2','f1', { NUMREGIONS => 4 , SPLITALGO => 'UniformSplit' }

# create table with five regions based on hex keys
create 't3','f1', { NUMREGIONS => 5, SPLITALGO => 'HexStringSplit' }

See Relationship Between RowKeys and Region Splits for issues related to understanding your keyspace and pre-creating regions. See manual region splitting decisions for discussion on manually pre-splitting regions. See Pre-splitting tables with the HBase Shell for more details of using the HBase Shell to pre-split tables.

99.3. Table Creation: Deferred Log Flush

The default behavior for Puts using the Write Ahead Log (WAL) is that WAL edits will be written immediately. If deferred log flush is used, WAL edits are kept in memory until the flush period. The benefit is aggregated and asynchronous WAL- writes, but the potential downside is that if the RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using WAL at all with Puts.

Deferred log flush can be configured on tables via HTableDescriptor. The default value of hbase.regionserver.optionallogflushinterval is 1000ms.

99.4. HBase Client: AutoFlush

When performing a lot of Puts, make sure that setAutoFlush is set to false on your Table instance. Otherwise, the Puts will be sent one at a time to the RegionServer. Puts added via table.add(Put) and table.add( <List> Put) wind up in the same write buffer. If autoFlush = false, these messages are not sent until the write-buffer is filled. To explicitly flush the messages, call flushCommits. Calling close on the Table instance will invoke flushCommits.

99.5. HBase Client: Turn off WAL on Puts

A frequent request is to disable the WAL to increase performance of Puts. This is only appropriate for bulk loads, as it puts your data at risk by removing the protection of the WAL in the event of a region server crash. Bulk loads can be re-run in the event of a crash, with little risk of data loss.

If you disable the WAL for anything other than bulk loads, your data is at risk.

In general, it is best to use WAL for Puts, and where loading throughput is a concern to use bulk loading techniques instead. For normal Puts, you are not likely to see a performance improvement which would outweigh the risk. To disable the WAL, see Disabling the WAL.

99.6. HBase Client: Group Puts by RegionServer

In addition to using the writeBuffer, grouping Put`s by RegionServer can reduce the number of client RPC calls per writeBuffer flush. There is a utility `HTableUtil currently on MASTER that does this, but you can either copy that or implement your own version for those still on 0.90.x or earlier.

99.7. MapReduce: Skip The Reducer

When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other Reducers that will most likely be off-node. It’s far more efficient to just write directly to HBase.

For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result). This is a different processing problem than from the above case.

99.8. Anti-Pattern: One Hot Region

If all your data is being written to one region at a time, then re-read the section on processing timeseries data.

Also, if you are pre-splitting regions and all your data is still winding up in a single region even though your keys aren’t monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a variety of reasons that regions may appear "well split" but won’t work with your data. As the HBase client communicates directly with the RegionServers, this can be obtained via Table.getRegionLocation.

See Table Creation: Pre-Creating Regions, as well as HBase Configurations

100. Reading from HBase

The mailing list can help if you are having performance issues. For example, here is a good general thread on what to look at addressing read-time issues: HBase Random Read latency > 100ms

100.1. Scan Caching

If HBase is used as an input source for a MapReduce job, for example, make sure that the input Scan instance to the MapReduce job has setCaching set to something greater than the default (which is 1). Using the default value means that the map-task will make call back to the region-server for every record processed. Setting this value to 500, for example, will transfer 500 rows at a time to the client to be processed. There is a cost/benefit to have the cache value be large because it costs more in memory for both client and RegionServer, so bigger isn’t always better.

100.1.1. Scan Caching in MapReduce Jobs

Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., UnknownScannerException) in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the next set of data. This problem can occur because there is non-trivial processing occurring per row. If you process rows quickly, set caching higher. If you process rows more slowly (e.g., lots of transformations per row, writes), then set caching lower.

Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the processing that is often performed in MapReduce jobs tends to exacerbate this issue.

100.2. Scan Attribute Selection

Whenever a Scan is used to process large numbers of rows (and especially when used as a MapReduce source), be aware of which attributes are selected. If scan.addFamily is called then all of the attributes in the specified ColumnFamily will be returned to the client. If only a small number of the available attributes are to be processed, then only those attributes should be specified in the input scan because attribute over-selection is a non-trivial performance penalty over large datasets.

100.3. Avoid scan seeks

When columns are selected explicitly with scan.addColumn, HBase will schedule seek operations to seek between the selected columns. When rows have few columns and each column has only a few versions this can be inefficient. A seek operation is generally slower if does not seek at least past 5-10 columns/versions or 512-1024 bytes.

In order to opportunistically look ahead a few columns/versions to see if the next column/version can be found that way before a seek operation is scheduled, a new attribute Scan.HINT_LOOKAHEAD can be set on the Scan object. The following code instructs the RegionServer to attempt two iterations of next before a seek is scheduled:

Scan scan = new Scan();
scan.addColumn(...);
scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2));
table.getScanner(scan);

100.4. MapReduce - Input Splits

For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to have the same Input Split (i.e., the RegionServer serving the data), see the Troubleshooting Case Study in Case Study #1 (Performance Issue On A Single Node).

100.5. Close ResultScanners

This isn’t so much about improving performance but rather avoiding performance problems. If you forget to close ResultScanners you can cause problems on the RegionServers. Always have ResultScanner processing enclosed in try/catch blocks.

Scan scan = new Scan();
// set attrs...
ResultScanner rs = table.getScanner(scan);
try {
  for (Result r = rs.next(); r != null; r = rs.next()) {
  // process result...
} finally {
  rs.close();  // always close the ResultScanner!
}
table.close();

100.6. Block Cache

Scan instances can be set to use the block cache in the RegionServer via the setCacheBlocks method. For input Scans to MapReduce jobs, this should be false. For frequently accessed rows, it is advisable to use the block cache.

Cache more data by moving your Block Cache off-heap. See Off-heap Block Cache

100.7. Optimal Loading of Row Keys

When performing a table scan where only the row keys are needed (no families, qualifiers, values or timestamps), add a FilterList with a MUST_PASS_ALL operator to the scanner using setFilter. The filter list should include both a FirstKeyOnlyFilter and a KeyOnlyFilter. Using this filter combination will result in a worst case scenario of a RegionServer reading a single value from disk and minimal network traffic to the client for a single row.

100.8. Concurrency: Monitor Data Spread

When performing a high number of concurrent reads, monitor the data spread of the target tables. If the target table(s) have too few regions then the reads could likely be served from too few nodes.

See Table Creation: Pre-Creating Regions, as well as HBase Configurations

100.9. Bloom Filters

Enabling Bloom Filters can save your having to go to disk and can help improve read latencies.

Bloom filters were developed over in HBase-1200 Add bloomfilters. For description of the development process — why static blooms rather than dynamic — and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the Development Process section of the document BloomFilters in HBase attached to HBASE-1200. The bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2 is a rewrite from scratch though again it starts with the one-lab work.

See also Bloom Filters.

100.9.1. Bloom StoreFile footprint

Bloom filters add an entry to the StoreFile general FileInfo data structure and then two extra entries to the StoreFile metadata section.

BloomFilter in the StoreFile``FileInfo data structure

FileInfo has a BLOOM_FILTER_TYPE entry which is set to NONE, ROW or ROWCOL.

BloomFilter entries in StoreFile metadata

BLOOM_FILTER_META holds Bloom Size, Hash Function used, etc. It’s small in size and is cached on StoreFile.Reader load

BLOOM_FILTER_DATA is the actual bloomfilter data. Obtained on-demand. Stored in the LRU cache, if it is enabled (It’s enabled by default).

100.9.2. Bloom Filter Configuration

io.storefile.bloom.enabled global kill switch

io.storefile.bloom.enabled in Configuration serves as the kill switch in case something goes wrong. Default = true.

io.storefile.bloom.error.rate

io.storefile.bloom.error.rate = average false positive rate. Default = 1%. Decrease rate by ½ (e.g. to .5%) == +1 bit per bloom entry.

io.storefile.bloom.max.fold

io.storefile.bloom.max.fold = guaranteed minimum fold rate. Most people should leave this alone. Default = 7, or can collapse to at least 1/128th of original size. See the Development Process section of the document BloomFilters in HBase for more on what this option means.

100.10. Hedged Reads

Hedged reads are a feature of HDFS, introduced in HDFS-5776. Normally, a single thread is spawned for each read request. However, if hedged reads are enabled, the client waits some configurable amount of time, and if the read does not return, the client spawns a second read request, against a different block replica of the same data. Whichever read returns first is used, and the other read request is discarded. Hedged reads can be helpful for times where a rare slow read is caused by a transient error such as a failing disk or flaky network connection.

Because an HBase RegionServer is a HDFS client, you can enable hedged reads in HBase, by adding the following properties to the RegionServer’s hbase-site.xml and tuning the values to suit your environment.

Configuration for Hedged Reads

  • dfs.client.hedged.read.threadpool.size - the number of threads dedicated to servicing hedged reads. If this is set to 0 (the default), hedged reads are disabled.

  • dfs.client.hedged.read.threshold.millis - the number of milliseconds to wait before spawning a second read thread.

Example 56. Hedged Reads Configuration Example
<property>
  <name>dfs.client.hedged.read.threadpool.size</name>
  <value>20</value>  <!-- 20 threads -->
</property>
<property>
  <name>dfs.client.hedged.read.threshold.millis</name>
  <value>10</value>  <!-- 10 milliseconds -->
</property>

Use the following metrics to tune the settings for hedged reads on your cluster. See HBase Metrics for more information.

Metrics for Hedged Reads

  • hedgedReadOps - the number of times hedged read threads have been triggered. This could indicate that read requests are often slow, or that hedged reads are triggered too quickly.

  • hedgeReadOpsWin - the number of times the hedged read thread was faster than the original thread. This could indicate that a given RegionServer is having trouble servicing requests.

101. Deleting from HBase

101.1. Using HBase Tables as Queues

HBase tables are sometimes used as queues. In this case, special care must be taken to regularly perform major compactions on tables used in this manner. As is documented in Data Model, marking rows as deleted creates additional StoreFiles which then need to be processed on reads. Tombstones only get cleaned up with major compactions.

See also Compaction and Admin.majorCompact.

101.2. Delete RPC Behavior

Be aware that Table.delete(Delete) doesn’t use the writeBuffer. It will execute an RegionServer RPC with each invocation. For a large number of deletes, consider Table.delete(List).

See hbase.client.Delete.

102. HDFS

Because HBase runs on HDFS it is important to understand how it works and how it affects HBase.

102.1. Current Issues With Low-Latency Reads

The original use-case for HDFS was batch processing. As such, there low-latency reads were historically not a priority. With the increased adoption of Apache HBase this is changing, and several improvements are already in development. See the Umbrella Jira Ticket for HDFS Improvements for HBase.

102.2. Leveraging local data

Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via HDFS-2246, it is possible for the DFSClient to take a "short circuit" and read directly from the disk instead of going through the DataNode when the data is local. What this means for HBase is that the RegionServers can read directly off their machine’s disks instead of having to open a socket to talk to the DataNode, the former being generally much faster. See JD’s Performance Talk. Also see HBase, mail # dev - read short circuit thread for more discussion around short circuit reads.

To enable "short circuit" reads, it will depend on your version of Hadoop. The original shortcircuit read patch was much improved upon in Hadoop 2 in HDFS-347. See http://blog.cloudera.com/blog/2013/08/how-improved-short-circuit-local-reads-bring-better-performance-and-security-to-hadoop/ for details on the difference between the old and new implementations. See Hadoop shortcircuit reads configuration page for how to enable the latter, better version of shortcircuit. For example, here is a minimal config. enabling short-circuit reads added to hbase-site.xml:

<property>
  <name>dfs.client.read.shortcircuit</name>
  <value>true</value>
  <description>
    This configuration parameter turns on short-circuit local reads.
  </description>
</property>
<property>
  <name>dfs.domain.socket.path</name>
  <value>/home/stack/sockets/short_circuit_read_socket_PORT</value>
  <description>
    Optional.  This is a path to a UNIX domain socket that will be used for
    communication between the DataNode and local HDFS clients.
    If the string "_PORT" is present in this path, it will be replaced by the
    TCP port of the DataNode.
  </description>
</property>

Be careful about permissions for the directory that hosts the shared domain socket; dfsclient will complain if open to other than the hbase user.

If you are running on an old Hadoop, one that is without HDFS-347 but that has HDFS-2246, you must set two configurations. First, the hdfs-site.xml needs to be amended. Set the property dfs.block.local-path-access.user to be the only user that can use the shortcut. This has to be the user that started HBase. Then in hbase-site.xml, set dfs.client.read.shortcircuit to be true

Services — at least the HBase RegionServers — will need to be restarted in order to pick up the new configurations.

dfs.client.read.shortcircuit.buffer.size

The default for this value is too high when running on a highly trafficked HBase. In HBase, if this value has not been set, we set it down from the default of 1M to 128k (Since HBase 0.98.0 and 0.96.1). See HBASE-8143 HBase on Hadoop 2 with local short circuit reads (ssr) causes OOM). The Hadoop DFSClient in HBase will allocate a direct byte buffer of this size for each block it has open; given HBase keeps its HDFS files open all the time, this can add up quickly.

102.3. Performance Comparisons of HBase vs. HDFS

A fairly common question on the dist-list is why HBase isn’t as performant as HDFS files in a batch context (e.g., as a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues, returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this processing context. There is room for improvement and this gap will, over time, be reduced, but HDFS will always be faster in this use-case.

103. Amazon EC2

Performance questions are common on Amazon EC2 environments because it is a shared environment. You will not see the same throughput as a dedicated server. In terms of running tests on EC2, run them several times for the same reason (i.e., it’s a shared environment and you don’t know what else is happening on the server).

If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that because EC2 issues are practically a separate class of performance issues.

104. Collocating HBase and MapReduce

It is often recommended to have different clusters for HBase and MapReduce. A better qualification of this is: don’t collocate an HBase that serves live requests with a heavy MR workload. OLTP and OLAP-optimized systems have conflicting requirements and one will lose to the other, usually the former. For example, short latency-sensitive disk reads will have to wait in line behind longer reads that are trying to squeeze out as much throughput as possible. MR jobs that write to HBase will also generate flushes and compactions, which will in turn invalidate blocks in the Block Cache.

If you need to process the data from your live HBase cluster in MR, you can ship the deltas with CopyTable or use replication to get the new data in real time on the OLAP cluster. In the worst case, if you really need to collocate both, set MR to use less Map and Reduce slots than you’d normally configure, possibly just one.

When HBase is used for OLAP operations, it’s preferable to set it up in a hardened way like configuring the ZooKeeper session timeout higher and giving more memory to the MemStores (the argument being that the Block Cache won’t be used much since the workloads are usually long scans).

105. Case Studies

For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies.

Troubleshooting and Debugging Apache HBase

106. General Guidelines

Always start with the master log (TODO: Which lines?). Normally it’s just printing the same lines over and over again. If not, then there’s an issue. Google or search-hadoop.com should return some hits for those exceptions you’re seeing.

An error rarely comes alone in Apache HBase, usually when something gets screwed up what will follow may be hundreds of exceptions and stack traces coming from all over the place. The best way to approach this type of problem is to walk the log up to where it all began, for example one trick with RegionServers is that they will print some metrics when aborting so grepping for Dump should get you around the start of the problem.

RegionServer suicides are 'normal', as this is what they do when something goes wrong. For example, if ulimit and max transfer threads (the two most important initial settings, see [ulimit] and dfs.datanode.max.transfer.threads ) aren’t changed, it will make it impossible at some point for DataNodes to create new threads that from the HBase point of view is seen as if HDFS was gone. Think about what would happen if your MySQL database was suddenly unable to access files on your local file system, well it’s the same with HBase and HDFS. Another very common reason to see RegionServers committing seppuku is when they enter prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout. For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Long GC pauses above.

107. Logs

The key process logs are as follows…​ (replace <user> with the user that started the service, and <hostname> for the machine name)

NameNode: $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log

DataNode: $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log

JobTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log

TaskTracker: $HADOOP_HOME/logs/hadoop-<user>-tasktracker-<hostname>.log

HMaster: $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log

RegionServer: $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log

ZooKeeper: TODO

107.1. Log Locations

For stand-alone deployments the logs are obviously going to be on a single machine, however this is a development configuration only. Production deployments need to run on a cluster.

107.1.1. NameNode

The NameNode log is on the NameNode server. The HBase Master is typically run on the NameNode server, and well as ZooKeeper.

For smaller clusters the JobTracker/ResourceManager is typically run on the NameNode server as well.

107.1.2. DataNode

Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.

Additionally, each DataNode server will also have a TaskTracker/NodeManager log for MapReduce task execution.

107.2. Log Levels

107.2.1. Enabling RPC-level logging

Enabling the RPC-level logging on a RegionServer can often give insight on timings at the server. Once enabled, the amount of log spewed is voluminous. It is not recommended that you leave this logging on for more than short bursts of time. To enable RPC-level logging, browse to the RegionServer UI and click on Log Level. Set the log level to DEBUG for the package org.apache.hadoop.ipc (That’s right, for hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log. Analyze.

To disable, set the logging level back to INFO level.

107.3. JVM Garbage Collection Logs

HBase is memory intensive, and using the default GC you can see long pauses in all threads including the Juliet Pause aka "GC of Death". To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.

To enable, in hbase-env.sh, uncomment one of the below lines :

# This enables basic gc logging to the .out file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"

# This enables basic gc logging to its own file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"

# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"

# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR.

At this point you should see logs like so:

64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
64898.953: [CMS-concurrent-mark-start]
64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]

In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This pauses the entire VM, all threads for that period of time.

The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k. Later on in this cycle we see:

64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs]
64901.445: [CMS-concurrent-preclean-start]
64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs]
64901.578: [CMS-concurrent-abortable-preclean-start]
64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs]
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
64901.621: [CMS-concurrent-sweep-start]

The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But this is a concurrent 2.4 seconds, Java has not been paused at any point in time.

There are a few more minor GCs, then there is a pause at the 2nd last line:

64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]

The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.

At this point the sweep starts, and you can watch the heap size go down:

64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
...  lines removed ...
64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs]

At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).

The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.

This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if it’s too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.

Add the below line in hbase-env.sh:

export SERVER_GC_OPTS="$SERVER_GC_OPTS -XX:NewSize=64m -XX:MaxNewSize=64m"

Similarly, to enable GC logging for client processes, uncomment one of the below lines in hbase-env.sh:

# This enables basic gc logging to the .out file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"

# This enables basic gc logging to its own file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"

# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"

# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR .

For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Long GC pauses above.

108. Resources

108.1. search-hadoop.com

search-hadoop.com indexes all the mailing lists and is great for historical searches. Search here first when you have an issue as its more than likely someone has already had your problem.

108.2. Mailing Lists

Ask a question on the Apache HBase mailing lists. The 'dev' mailing list is aimed at the community of developers actually building Apache HBase and for features currently under development, and 'user' is generally used for questions on released versions of Apache HBase. Before going to the mailing list, make sure your question has not already been answered by searching the mailing list archives first. Use search-hadoop.com. Take some time crafting your question. See Getting Answers for ideas on crafting good questions. A quality question that includes all context and exhibits evidence the author has tried to find answers in the manual and out on lists is more likely to get a prompt response.

108.3. IRC

#hbase on irc.freenode.net

108.4. JIRA

JIRA is also really helpful when looking for Hadoop/HBase-specific issues.

109. Tools

109.1. Builtin Tools

109.1.1. Master Web Interface

The Master starts a web-interface on port 16010 by default. (Up to and including 0.98 this was port 60010)

The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally, the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap). The Master web UI allows navigation to each RegionServer’s web UI.

109.1.2. RegionServer Web Interface

RegionServers starts a web-interface on port 16030 by default. (Up to an including 0.98 this was port 60030)

The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).

See HBase Metrics for more information in metric definitions.

109.1.3. zkcli

zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke:

./hbase zkcli -server host:port <cmd> <args>

The commands (and arguments) are:

  connect host:port
  get path [watch]
  ls path [watch]
  set path data [version]
  delquota [-n|-b] path
  quit
  printwatches on|off
  create [-s] [-e] path data acl
  stat path [watch]
  close
  ls2 path [watch]
  history
  listquota path
  setAcl path acl
  getAcl path
  sync path
  redo cmdno
  addauth scheme auth
  delete path [version]
  setquota -n|-b val path

109.2. External Tools

109.2.1. tail

tail is the command line tool that lets you look at the end of a file. Add the -f option and it will refresh when new data is available. It’s useful when you are wondering what’s happening, for example, when a cluster is taking a long time to shutdown or startup as you can just fire a new terminal and tail the master log (and maybe a few RegionServers).

109.2.2. top

top is probably one of the most important tools when first trying to see what’s running on a machine and how the resources are consumed. Here’s an example from production system:

top - 14:46:59 up 39 days, 11:55,  1 user,  load average: 3.75, 3.57, 3.84
Tasks: 309 total,   1 running, 308 sleeping,   0 stopped,   0 zombie
Cpu(s):  4.5%us,  1.6%sy,  0.0%ni, 91.7%id,  1.4%wa,  0.1%hi,  0.6%si,  0.0%st
Mem:  24414432k total, 24296956k used,   117476k free,     7196k buffers
Swap: 16008732k total,        14348k used, 15994384k free, 11106908k cached

  PID USER          PR  NI  VIRT  RES  SHR S %CPU %MEM        TIME+  COMMAND
15558 hadoop        18  -2 3292m 2.4g 3556 S   79 10.4   6523:52 java
13268 hadoop        18  -2 8967m 8.2g 4104 S   21 35.1   5170:30 java
 8895 hadoop        18  -2 1581m 497m 3420 S   11  2.1   4002:32 java
…

Here we can see that the system load average during the last five minutes is 3.75, which very roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes. In general, the perfect utilization equals to the number of cores, under that number the machine is under utilized and over that the machine is over utilized. This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001.

Apart from load, we can see that the system is using almost all its available RAM but most of it is used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high numbers would indicate swapping activity which is the nemesis of performance of Java systems. Another way to detect swapping is when the load average goes through the roof (although this could also be caused by things like a dying disk, among others).

The list of processes isn’t super useful by default, all we know is that 3 java processes are using about 111% of the CPUs. To know which is which, simply type c and each line will be expanded. Typing 1 will give you the detail of how each CPU is used instead of the average for all of them like shown here.

109.2.3. jps

jps is shipped with every JDK and gives the java process ids for the current user (if root, then it gives the ids for all users). Example:

hadoop@sv4borg12:~$ jps
1322 TaskTracker
17789 HRegionServer
27862 Child
1158 DataNode
25115 HQuorumPeer
2950 Jps
19750 ThriftServer
18776 jmx

In order, we see a:

  • Hadoop TaskTracker, manages the local Childs

  • HBase RegionServer, serves regions

  • Child, its MapReduce task, cannot tell which type exactly

  • Hadoop TaskTracker, manages the local Childs

  • Hadoop DataNode, serves blocks

  • HQuorumPeer, a ZooKeeper ensemble member

  • Jps, well…​ it’s the current process

  • ThriftServer, it’s a special one will be running only if thrift was started

  • jmx, this is a local process that’s part of our monitoring platform ( poorly named maybe). You probably don’t have that.

You can then do stuff like checking out the full command line that started the process:

hadoop@sv4borg12:~$ ps aux | grep HRegionServer
hadoop   17789  155 35.2 9067824 8604364 ?     S&lt;l  Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start

109.2.4. jstack

jstack is one of the most important tools when trying to figure out what a java process is doing apart from looking at the logs. It has to be used in conjunction with jps in order to give it a process id. It shows a list of threads, each one has a name, and they appear in the order that they were created (so the top ones are the most recent threads). Here are a few example:

The main thread of a RegionServer waiting for something to do from the master:

"regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
java.lang.Thread.State: TIMED_WAITING (parking)
    at sun.misc.Unsafe.park(Native Method)
    - parking to wait for  <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
    at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
    at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
    at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
    at java.lang.Thread.run(Thread.java:619)

The MemStore flusher thread that is currently flushing to a file:

"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:485)
    at org.apache.hadoop.ipc.Client.call(Client.java:803)
    - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
    at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
    at $Proxy1.complete(Unknown Source)
    at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
    at java.lang.reflect.Method.invoke(Method.java:597)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
    at $Proxy1.complete(Unknown Source)
    at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
    - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
    at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
    at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
    at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
    at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
    at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
    at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
    - locked <0x00007f16d00e6f08> (a java.lang.Object)
    at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
    at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
    at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
    at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
    at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
    at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)

A handler thread that’s waiting for stuff to do (like put, delete, scan, etc.):

"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
   java.lang.Thread.State: WAITING (parking)
          at sun.misc.Unsafe.park(Native Method)
          - parking to wait for  <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
          at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
          at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
          at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
          at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)

And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a scanner in order to read the last value):

"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
   java.lang.Thread.State: RUNNABLE
          at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56)
          at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79)
          at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
          at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209)
          at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
          at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
          at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
          at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
          at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
          at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
          at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
          at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
          at java.lang.reflect.Method.invoke(Method.java:597)
          at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
          at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)

A thread that receives data from HDFS:

"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
   java.lang.Thread.State: RUNNABLE
          at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
          at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
          at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
          at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
          - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
          - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
          - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
          at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
          at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
          at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)
          at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155)
          at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128)
          at java.io.FilterInputStream.read(FilterInputStream.java:116)
          at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304)
          at java.io.BufferedInputStream.fill(BufferedInputStream.java:218)
          at java.io.BufferedInputStream.read(BufferedInputStream.java:237)
          - locked <0x00007f1808539178> (a java.io.BufferedInputStream)
          at java.io.DataInputStream.readInt(DataInputStream.java:370)
          at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569)
          at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477)

And here is a master trying to recover a lease after a RegionServer died:

"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70]
--
   java.lang.Thread.State: WAITING (on object monitor)
          at java.lang.Object.wait(Native Method)
          at java.lang.Object.wait(Object.java:485)
          at org.apache.hadoop.ipc.Client.call(Client.java:726)
          - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call)
          at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
          at $Proxy1.recoverBlock(Unknown Source)
          at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636)
          at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java:2832)
          at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529)
          at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186)
          at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530)
          at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619)
          at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322)
          at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210)
          at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648)
          at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572)
          at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503)

109.2.5. OpenTSDB

OpenTSDB is an excellent alternative to Ganglia as it uses Apache HBase to store all the time series and doesn’t have to downsample. Monitoring your own HBase cluster that hosts OpenTSDB is a good exercise.

Here’s an example of a cluster that’s suffering from hundreds of compactions launched almost all around the same time, which severely affects the IO performance: (TODO: insert graph plotting compactionQueueSize)

It’s a good practice to build dashboards with all the important graphs per machine and per cluster so that debugging issues can be done with a single quick look. For example, at StumbleUpon there’s one dashboard per cluster with the most important metrics from both the OS and Apache HBase. You can then go down at the machine level and get even more detailed metrics.

109.2.6. clusterssh+top

clusterssh+top, it’s like a poor man’s monitoring system and it can be quite useful when you have only a few machines as it’s very easy to setup. Starting clusterssh will give you one terminal per machine and another terminal in which whatever you type will be retyped in every window. This means that you can type top once and it will start it for all of your machines at the same time giving you full view of the current state of your cluster. You can also tail all the logs at the same time, edit files, etc.

110. Client

For more information on the HBase client, see client.

110.1. Missed Scan Results Due To Mismatch Of hbase.client.scanner.max.result.size Between Client and Server

If either the client or server version is lower than 0.98.11/1.0.0 and the server has a smaller value for hbase.client.scanner.max.result.size than the client, scan requests that reach the server’s hbase.client.scanner.max.result.size are likely to miss data. In particular, 0.98.11 defaults hbase.client.scanner.max.result.size to 2 MB but other versions default to larger values. For this reason, be very careful using 0.98.11 servers with any other client version.

110.2. ScannerTimeoutException or UnknownScannerException

This is thrown if the time between RPC calls from the client to RegionServer exceeds the scan timeout. For example, if Scan.setCaching is set to 500, then there will be an RPC call to fetch the next batch of rows every 500 .next() calls on the ResultScanner because data is being transferred in blocks of 500 rows to the client. Reducing the setCaching value may be an option, but setting this value too low makes for inefficient processing on numbers of rows.

See Scan Caching.

110.3. Performance Differences in Thrift and Java APIs

Poor performance, or even ScannerTimeoutExceptions, can occur if Scan.setCaching is too high, as discussed in ScannerTimeoutException or UnknownScannerException. If the Thrift client uses the wrong caching settings for a given workload, performance can suffer compared to the Java API. To set caching for a given scan in the Thrift client, use the scannerGetList(scannerId, numRows) method, where numRows is an integer representing the number of rows to cache. In one case, it was found that reducing the cache for Thrift scans from 1000 to 100 increased performance to near parity with the Java API given the same queries.

See also Jesse Andersen’s blog post about using Scans with Thrift.

110.4. LeaseException when calling Scanner.next

In some situations clients that fetch data from a RegionServer get a LeaseException instead of the usual ScannerTimeoutException or UnknownScannerException. Usually the source of the exception is org.apache.hadoop.hbase.regionserver.Leases.removeLease(Leases.java:230) (line number may vary). It tends to happen in the context of a slow/freezing RegionServer#next call. It can be prevented by having hbase.rpc.timeout > hbase.regionserver.lease.period. Harsh J investigated the issue as part of the mailing list thread HBase, mail # user - Lease does not exist exceptions

110.5. Shell or client application throws lots of scary exceptions during normal operation

Since 0.20.0 the default log level for `org.apache.hadoop.hbase.*`is DEBUG.

On your clients, edit $HBASE_HOME/conf/log4j.properties and change this: log4j.logger.org.apache.hadoop.hbase=DEBUG to this: log4j.logger.org.apache.hadoop.hbase=INFO, or even log4j.logger.org.apache.hadoop.hbase=WARN.

110.6. Long Client Pauses With Compression

This is a fairly frequent question on the Apache HBase dist-list. The scenario is that a client is typically inserting a lot of data into a relatively un-optimized HBase cluster. Compression can exacerbate the pauses, although it is not the source of the problem.

See Table Creation: Pre-Creating Regions on the pattern for pre-creating regions and confirm that the table isn’t starting with a single region.

See HBase Configurations for cluster configuration, particularly hbase.hstore.blockingStoreFiles, hbase.hregion.memstore.block.multiplier, MAX_FILESIZE (region size), and MEMSTORE_FLUSHSIZE.

A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes blocked on the MemStores which are blocked by the flusher thread which is blocked because there are too many files to compact because the compactor is given too many small files to compact and has to compact the same data repeatedly. This situation can occur even with minor compactions. Compounding this situation, Apache HBase doesn’t compress data in memory. Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which results in a smaller StoreFile. The upside is that more data is packed into the same region, but performance is achieved by being able to write larger files - which is why HBase waits until the flushsize before writing a new StoreFile. And smaller StoreFiles become targets for compaction. Without compression the files are much bigger and don’t need as much compaction, however this is at the expense of I/O.

For additional information, see this thread on Long client pauses with compression.

110.7. Secure Client Connect ([Caused by GSSException: No valid credentials provided…​])

You may encounter the following error:

Secure Client Connect ([Caused by GSSException: No valid credentials provided
        (Mechanism level: Request is a replay (34) V PROCESS_TGS)])

This issue is caused by bugs in the MIT Kerberos replay_cache component, #1201 and #5924. These bugs caused the old version of krb5-server to erroneously block subsequent requests sent from a Principal. This caused krb5-server to block the connections sent from one Client (one HTable instance with multi-threading connection instances for each RegionServer); Messages, such as Request is a replay (34), are logged in the client log You can ignore the messages, because HTable will retry 5 * 10 (50) times for each failed connection by default. HTable will throw IOException if any connection to the RegionServer fails after the retries, so that the user client code for HTable instance can handle it further. NOTE: HTable is deprecated in HBase 1.0, in favor of Table.

Alternatively, update krb5-server to a version which solves these issues, such as krb5-server-1.10.3. See JIRA HBASE-10379 for more details.

110.8. ZooKeeper Client Connection Errors

Errors like this…​

11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
 unexpected error, closing socket connection and attempting reconnect
 java.net.ConnectException: Connection refused: no further information
        at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
        at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
        at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to
 server localhost/127.0.0.1:2181
 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null,
 unexpected error, closing socket connection and attempting reconnect
 java.net.ConnectException: Connection refused: no further information
        at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
        at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source)
        at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078)
 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to
 server localhost/127.0.0.1:2181

…​are either due to ZooKeeper being down, or unreachable due to network issues.

The utility zkcli may help investigate ZooKeeper issues.

110.9. Client running out of memory though heap size seems to be stable (but the off-heap/direct heap keeps growing)

You are likely running into the issue that is described and worked through in the mail thread HBase, mail # user - Suspected memory leak and continued over in HBase, mail # dev - FeedbackRe: Suspected memory leak. A workaround is passing your client-side JVM a reasonable value for -XX:MaxDirectMemorySize. By default, the MaxDirectMemorySize is equal to your -Xmx max heapsize setting (if -Xmx is set). Try setting it to something smaller (for example, one user had success setting it to 1g when they had a client-side heap of 12g). If you set it too small, it will bring on FullGCs so keep it a bit hefty. You want to make this setting client-side only especially if you are running the new experimental server-side off-heap cache since this feature depends on being able to use big direct buffers (You may have to keep separate client-side and server-side config dirs).

110.10. Client Slowdown When Calling Admin Methods (flush, compact, etc.)

This is a client issue fixed by HBASE-5073 in 0.90.6. There was a ZooKeeper leak in the client and the client was getting pummeled by ZooKeeper events with each additional invocation of the admin API.

110.11. Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided(Mechanism level: Failed to find any Kerberos tgt)])

There can be several causes that produce this symptom.

First, check that you have a valid Kerberos ticket. One is required in order to set up communication with a secure Apache HBase cluster. Examine the ticket currently in the credential cache, if any, by running the klist command line utility. If no ticket is listed, you must obtain a ticket by running the kinit command with either a keytab specified, or by interactively entering a password for the desired principal.

Then, consult the Java Security Guide troubleshooting section. The most common problem addressed there is resolved by setting javax.security.auth.useSubjectCredsOnly system property value to false.

Because of a change in the format in which MIT Kerberos writes its credentials cache, there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If you have this problematic combination of components in your environment, to work around this problem, first log in with kinit and then immediately refresh the credential cache with kinit -R. The refresh will rewrite the credential cache without the problematic formatting.

Finally, depending on your Kerberos configuration, you may need to install the Java Cryptography Extension, or JCE. Insure the JCE jars are on the classpath on both server and client systems.

You may also need to download the unlimited strength JCE policy files. Uncompress and extract the downloaded file, and install the policy jars into <java-home>/lib/security.

111. MapReduce

111.1. You Think You’re On The Cluster, But You’re Actually Local

This following stacktrace happened using ImportTsv, but things like this can happen on any job with a mis-configuration.

    WARN mapred.LocalJobRunner: job_local_0001
java.lang.IllegalArgumentException: Can't read partitions file
       at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111)
       at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62)
       at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117)
       at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560)
       at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639)
       at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323)
       at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)
Caused by: java.io.FileNotFoundException: File _partition.lst does not exist.
       at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383)
       at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251)
       at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776)
       at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424)
       at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419)
       at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296)

…​see the critical portion of the stack? It’s…​

at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210)

LocalJobRunner means the job is running locally, not on the cluster.

To solve this problem, you should run your MR job with your HADOOP_CLASSPATH set to include the HBase dependencies. The "hbase classpath" utility can be used to do this easily. For example (substitute VERSION with your HBase version):

HADOOP_CLASSPATH=`hbase classpath` hadoop jar $HBASE_HOME/hbase-server-VERSION.jar rowcounter usertable

See http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpathfor more information on HBase MapReduce jobs and classpaths.

111.2. Launching a job, you get java.lang.IllegalAccessError: com/google/protobuf/HBaseZeroCopyByteString or class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString

See HBASE-10304 Running an hbase job jar: IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString and HBASE-11118 non environment variable solution for "IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString". The issue can also show up when trying to run spark jobs. See HBASE-10877 HBase non-retriable exception list should be expanded.

112. NameNode

For more information on the NameNode, see HDFS.

112.1. HDFS Utilization of Tables and Regions

To determine how much space HBase is using on HDFS use the hadoop shell commands from the NameNode. For example…​

hadoop fs -dus /hbase/

…​returns the summarized disk utilization for all HBase objects.

hadoop fs -dus /hbase/myTable

…​returns the summarized disk utilization for the HBase table 'myTable'.

hadoop fs -du /hbase/myTable

…​returns a list of the regions under the HBase table 'myTable' and their disk utilization.

For more information on HDFS shell commands, see the HDFS FileSystem Shell documentation.

112.2. Browsing HDFS for HBase Objects

Sometimes it will be necessary to explore the HBase objects that exist on HDFS. These objects could include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc. The easiest way to do this is with the NameNode web application that runs on port 50070. The NameNode web application will provide links to the all the DataNodes in the cluster so that they can be browsed seamlessly.

The HDFS directory structure of HBase tables in the cluster is…​

/hbase
    /<Table>                    (Tables in the cluster)
        /<Region>               (Regions for the table)
            /<ColumnFamily>     (ColumnFamilies for the Region for the table)
                /<StoreFile>    (StoreFiles for the ColumnFamily for the Regions for the table)

The HDFS directory structure of HBase WAL is..

/hbase
    /.logs
        /<RegionServer>    (RegionServers)
            /<WAL>         (WAL files for the RegionServer)

See the HDFS User Guide for other non-shell diagnostic utilities like fsck.

112.2.1. Zero size WALs with data in them

Problem: when getting a listing of all the files in a RegionServer’s .logs directory, one file has a size of 0 but it contains data.

Answer: It’s an HDFS quirk. A file that’s currently being written to will appear to have a size of 0 but once it’s closed it will show its true size

112.2.2. Use Cases

Two common use-cases for querying HDFS for HBase objects is research the degree of uncompaction of a table. If there are a large number of StoreFiles for each ColumnFamily it could indicate the need for a major compaction. Additionally, after a major compaction if the resulting StoreFile is "small" it could indicate the need for a reduction of ColumnFamilies for the table.

112.3. Unexpected Filesystem Growth

If you see an unexpected spike in filesystem usage by HBase, two possible culprits are snapshots and WALs.

Snapshots

When you create a snapshot, HBase retains everything it needs to recreate the table’s state at that time of the snapshot. This includes deleted cells or expired versions. For this reason, your snapshot usage pattern should be well-planned, and you should prune snapshots that you no longer need. Snapshots are stored in /hbase/.snapshots, and archives needed to restore snapshots are stored in /hbase/.archive/<_tablename_>/<_region_>/<_column_family_>/.

*Do not* manage snapshots or archives manually via HDFS. HBase provides APIs and
HBase Shell commands for managing them. For more information, see <<ops.snapshots>>.
WAL

Write-ahead logs (WALs) are stored in subdirectories of the HBase root directory, typically /hbase/, depending on their status. Already-processed WALs are stored in /hbase/oldWALs/ and corrupt WALs are stored in /hbase/.corrupt/ for examination. If the size of one of these subdirectories is growing, examine the HBase server logs to find the root cause for why WALs are not being processed correctly.

If you use replication and /hbase/oldWALs/ is using more space than you expect, remember that WALs are saved when replication is disabled, as long as there are peers.

Do not manage WALs manually via HDFS.

113. Network

113.1. Network Spikes

If you are seeing periodic network spikes you might want to check the compactionQueues to see if major compactions are happening.

See Managed Compactions for more information on managing compactions.

113.2. Loopback IP

HBase expects the loopback IP Address to be 127.0.0.1. See the Getting Started section on Loopback IP - HBase 0.94.x and earlier.

113.3. Network Interfaces

Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in Case Studies.

114. RegionServer

For more information on the RegionServers, see RegionServer.

114.1. Startup Errors

114.1.1. Master Starts, But RegionServers Do Not

The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost and resolves to the master’s own localhost.

The RegionServers are erroneously informing the Master that their IP addresses are 127.0.0.1.

Modify /etc/hosts on the region servers, from…​

# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1               fully.qualified.regionservername regionservername  localhost.localdomain localhost
::1             localhost6.localdomain6 localhost6

... to (removing the master node’s name from localhost)…​

# Do not remove the following line, or various programs
# that require network functionality will fail.
127.0.0.1               localhost.localdomain localhost
::1             localhost6.localdomain6 localhost6

114.1.2. Compression Link Errors

Since compression algorithms such as LZO need to be installed and configured on each cluster this is a frequent source of startup error. If you see messages like this…​

11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library
java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path
        at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734)
        at java.lang.Runtime.loadLibrary0(Runtime.java:823)
        at java.lang.System.loadLibrary(System.java:1028)

... then there is a path issue with the compression libraries. See the Configuration section on link:[LZO compression configuration].

114.2. Runtime Errors

114.2.1. RegionServer Hanging

Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump, does it look like threads are BLOCKED but no one holds the lock all are blocked on? See HBASE 3622 Deadlock in HBaseServer (JVM bug?). Adding -XX:+UseMembar to the HBase HBASE_OPTS in conf/hbase-env.sh may fix it.

114.2.2. java.io.IOException…​(Too many open files)

If you see log messages like this…​

2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode:
Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files
        at java.io.UnixFileSystem.createFileExclusively(Native Method)
        at java.io.File.createNewFile(File.java:883)

... see the Getting Started section on link:[ulimit and nproc configuration].

114.2.3. xceiverCount 258 exceeds the limit of concurrent xcievers 256

This typically shows up in the DataNode logs.

See the Getting Started section on link:[xceivers configuration].

114.2.4. System instability, and the presence of "java.lang.OutOfMemoryError: unable to createnew native thread in exceptions" HDFS DataNode logs or that of any system daemon

See the Getting Started section on ulimit and nproc configuration. The default on recent Linux distributions is 1024 - which is far too low for HBase.

114.2.5. DFS instability and/or RegionServer lease timeouts

If you see warning messages like this…​

2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000
2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000
2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying

... or see full GC compactions then you may be experiencing full GC’s.

114.2.6. "No live nodes contain current block" and/or YouAreDeadException

These errors can happen either when running out of OS file handles or in periods of severe network problems where the nodes are unreachable.

See the Getting Started section on ulimit and nproc configuration and check your network.

114.2.7. ZooKeeper SessionExpired events

Master or RegionServers shutting down with messages like those in the logs:

WARN org.apache.zookeeper.ClientCnxn: Exception
closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec
java.io.IOException: TIMED OUT
       at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906)
WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000
INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT
INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT]
INFO org.apache.zookeeper.ClientCnxn: Server connection successful
WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e
java.io.IOException: Session Expired
       at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589)
       at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709)
       at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945)
ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired

The JVM is doing a long running garbage collecting which is pausing every threads (aka "stop the world"). Since the RegionServer’s local ZooKeeper client cannot send heartbeats, the session times out. By design, we shut down any node that isn’t able to contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that may already be assigned elsewhere.

  • Make sure you give plenty of RAM (in hbase-env.sh), the default of 1GB won’t be able to sustain long running imports.

  • Make sure you don’t swap, the JVM never behaves well under swapping.

  • Make sure you are not CPU starving the RegionServer thread. For example, if you are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you are probably starving the RegionServer enough to create longer garbage collection pauses.

  • Increase the ZooKeeper session timeout

If you wish to increase the session timeout, add the following to your hbase-site.xml to increase the timeout from the default of 60 seconds to 120 seconds.

<property>
  <name>zookeeper.session.timeout</name>
  <value>1200000</value>
</property>
<property>
  <name>hbase.zookeeper.property.tickTime</name>
  <value>6000</value>
</property>

Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least that amount of time to be transferred to another RegionServer. For a production system serving live requests, we would instead recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having less garbage to collect per machine).

If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading.

See ZooKeeper, The Cluster Canary for other general information about ZooKeeper troubleshooting.

114.2.8. NotServingRegionException

This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is returned back to the client and then the client goes back to hbase:meta to find the new location of the moved region.

However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong.

114.2.9. Regions listed by domain name, then IP

Fix your DNS. In versions of Apache HBase before 0.92.x, reverse DNS needs to give same answer as forward lookup. See HBASE 3431 RegionServer is not using the name given it by the master; double entry in master listing of servers for gory details.

114.2.10. Logs flooded with '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Gotbrand-new compressor' messages

We are not using the native versions of compression libraries. See HBASE-1900 Put back native support when hadoop 0.21 is released. Copy the native libs from hadoop under HBase lib dir or symlink them into place and the message should go away.

114.2.11. Server handler X on 60020 caught: java.nio.channels.ClosedChannelException

If you see this type of message it means that the region server was trying to read/send data from/to a client but it already went away. Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce job is killed or fails) or if the client receives a SocketTimeoutException. It’s harmless, but you should consider digging in a bit more if you aren’t doing something to trigger them.

114.3. Snapshot Errors Due to Reverse DNS

Several operations within HBase, including snapshots, rely on properly configured reverse DNS. Some environments, such as Amazon EC2, have trouble with reverse DNS. If you see errors like the following on your RegionServers, check your reverse DNS configuration:

2013-05-01 00:04:56,356 DEBUG org.apache.hadoop.hbase.procedure.Subprocedure: Subprocedure 'backup1'
coordinator notified of 'acquire', waiting on 'reached' or 'abort' from coordinator.

In general, the hostname reported by the RegionServer needs to be the same as the hostname the Master is trying to reach. You can see a hostname mismatch by looking for the following type of message in the RegionServer’s logs at start-up.

2013-05-01 00:03:00,614 INFO org.apache.hadoop.hbase.regionserver.HRegionServer: Master passed us hostname
to use. Was=myhost-1234, Now=ip-10-55-88-99.ec2.internal

114.4. Shutdown Errors

115. Master

For more information on the Master, see master.

115.1. Startup Errors

115.1.1. Master says that you need to run the HBase migrations script

Upon running that, the HBase migrations script says no files in root directory.

HBase expects the root directory to either not exist, or to have already been initialized by HBase running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will occur. Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself.

115.1.2. Packet len6080218 is out of range!

If you have many regions on your cluster and you see an error like that reported above in this sections title in your logs, see HBASE-4246 Cluster with too many regions cannot withstand some master failover scenarios.

115.2. Shutdown Errors

116. ZooKeeper

116.1. Startup Errors

116.1.1. Could not find my address: xyz in list of ZooKeeper quorum servers

A ZooKeeper server wasn’t able to start, throws that error. xyz is the name of your server.

This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that machine isn’t able to find itself in the hbase.zookeeper.quorum configuration.

Use the hostname presented in the error message instead of the value you used. If you have a DNS server, you can set hbase.zookeeper.dns.interface and hbase.zookeeper.dns.nameserver in hbase-site.xml to make sure it resolves to the correct FQDN.

116.2. ZooKeeper, The Cluster Canary

ZooKeeper is the cluster’s "canary in the mineshaft". It’ll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster.

See the ZooKeeper Operating Environment Troubleshooting page. It has suggestions and tools for checking disk and networking performance; i.e. the operating environment your ZooKeeper and HBase are running in.

Additionally, the utility zkcli may help investigate ZooKeeper issues.

117. Amazon EC2

117.1. ZooKeeper does not seem to work on Amazon EC2

HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in the Master and/or RegionServer logs:

  2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting
  connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181
  2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception
  closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861
  java.net.ConnectException: Connection refused

Security group policy is blocking the ZooKeeper port on a public address. Use the internal EC2 host names when configuring the ZooKeeper quorum peer list.

117.2. Instability on Amazon EC2

Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list. Search for old threads using Search Hadoop

117.3. Remote Java Connection into EC2 Cluster Not Working

See Andrew’s answer here, up on the user list: Remote Java client connection into EC2 instance.

118. HBase and Hadoop version issues

118.1. NoClassDefFoundError when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x)

Apache HBase 0.90.x does not ship with hadoop-0.20.205.x, etc. To make it run, you need to replace the hadoop jars that Apache HBase shipped with in its lib directory with those of the Hadoop you want to run HBase on. If even after replacing Hadoop jars you get the below exception:

sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration
sv4r6s38:       at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<init>(DefaultMetricsSystem.java:37)
sv4r6s38:       at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<clinit>(DefaultMetricsSystem.java:34)
sv4r6s38:       at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229)
sv4r6s38:       at org.apache.hadoop.security.KerberosName.<clinit>(KerberosName.java:83)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202)
sv4r6s38:       at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177)

you need to copy under hbase/lib, the commons-configuration-X.jar you find in your Hadoop’s lib directory. That should fix the above complaint.

118.2. …​cannot communicate with client version…​

If you see something like the following in your logs ... 2012-09-24 10:20:52,168 FATAL org.apache.hadoop.hbase.master.HMaster: Unhandled exception. Starting shutdown. org.apache.hadoop.ipc.RemoteException: Server IPC version 7 cannot communicate with client version 4 ... …​are you trying to talk to an Hadoop 2.0.x from an HBase that has an Hadoop 1.0.x client? Use the HBase built against Hadoop 2.0 or rebuild your HBase passing the -Dhadoop.profile=2.0 attribute to Maven (See Building against various hadoop versions. for more).

119. IPC Configuration Conflicts with Hadoop

If the Hadoop configuration is loaded after the HBase configuration, and you have configured custom IPC settings in both HBase and Hadoop, the Hadoop values may overwrite the HBase values. There is normally no need to change these settings for HBase, so this problem is an edge case. However, HBASE-11492 renames these settings for HBase to remove the chance of a conflict. Each of the setting names have been prefixed with hbase., as shown in the following table. No action is required related to these changes unless you are already experiencing a conflict.

These changes were backported to HBase 0.98.x and apply to all newer versions.

Pre-0.98.x 0.98-x And Newer

ipc.server.listen.queue.size

hbase.ipc.server.listen.queue.size

ipc.server.max.callqueue.size

hbase.ipc.server.max.callqueue.size

ipc.server.callqueue.handler.factor

hbase.ipc.server.callqueue.handler.factor

ipc.server.callqueue.read.share

hbase.ipc.server.callqueue.read.share

ipc.server.callqueue.type

hbase.ipc.server.callqueue.type

ipc.server.queue.max.call.delay

hbase.ipc.server.queue.max.call.delay

ipc.server.max.callqueue.length

hbase.ipc.server.max.callqueue.length

ipc.server.read.threadpool.size

hbase.ipc.server.read.threadpool.size

ipc.server.tcpkeepalive

hbase.ipc.server.tcpkeepalive

ipc.server.tcpnodelay

hbase.ipc.server.tcpnodelay

ipc.client.call.purge.timeout

hbase.ipc.client.call.purge.timeout

ipc.client.connection.maxidletime

hbase.ipc.client.connection.maxidletime

ipc.client.idlethreshold

hbase.ipc.client.idlethreshold

ipc.client.kill.max

hbase.ipc.client.kill.max

ipc.server.scan.vtime.weight

hbase.ipc.server.scan.vtime.weight

120. HBase and HDFS

General configuration guidance for Apache HDFS is out of the scope of this guide. Refer to the documentation available at http://hadoop.apache.org/ for extensive information about configuring HDFS. This section deals with HDFS in terms of HBase.

In most cases, HBase stores its data in Apache HDFS. This includes the HFiles containing the data, as well as the write-ahead logs (WALs) which store data before it is written to the HFiles and protect against RegionServer crashes. HDFS provides reliability and protection to data in HBase because it is distributed. To operate with the most efficiency, HBase needs data to be available locally. Therefore, it is a good practice to run an HDFS DataNode on each RegionServer.

Important Information and Guidelines for HBase and HDFS
HBase is a client of HDFS.

HBase is an HDFS client, using the HDFS DFSClient class, and references to this class appear in HBase logs with other HDFS client log messages.

Configuration is necessary in multiple places.

Some HDFS configurations relating to HBase need to be done at the HDFS (server) side. Others must be done within HBase (at the client side). Other settings need to be set at both the server and client side.

Write errors which affect HBase may be logged in the HDFS logs rather than HBase logs.

When writing, HDFS pipelines communications from one DataNode to another. HBase communicates to both the HDFS NameNode and DataNode, using the HDFS client classes. Communication problems between DataNodes are logged in the HDFS logs, not the HBase logs.

HBase communicates with HDFS using two different ports.

HBase communicates with DataNodes using the ipc.Client interface and the DataNode class. References to these will appear in HBase logs. Each of these communication channels use a different port (50010 and 50020 by default). The ports are configured in the HDFS configuration, via the dfs.datanode.address and dfs.datanode.ipc.address parameters.

Errors may be logged in HBase, HDFS, or both.

When troubleshooting HDFS issues in HBase, check logs in both places for errors.

HDFS takes a while to mark a node as dead. You can configure HDFS to avoid using stale DataNodes.

By default, HDFS does not mark a node as dead until it is unreachable for 630 seconds. In Hadoop 1.1 and Hadoop 2.x, this can be alleviated by enabling checks for stale DataNodes, though this check is disabled by default. You can enable the check for reads and writes separately, via dfs.namenode.avoid.read.stale.datanode and dfs.namenode.avoid.write.stale.datanode settings. A stale DataNode is one that has not been reachable for dfs.namenode.stale.datanode.interval (default is 30 seconds). Stale datanodes are avoided, and marked as the last possible target for a read or write operation. For configuration details, see the HDFS documentation.

Settings for HDFS retries and timeouts are important to HBase.

You can configure settings for various retries and timeouts. Always refer to the HDFS documentation for current recommendations and defaults. Some of the settings important to HBase are listed here. Defaults are current as of Hadoop 2.3. Check the Hadoop documentation for the most current values and recommendations.

The HBase Balancer and HDFS Balancer are incompatible

The HDFS balancer attempts to spread HDFS blocks evenly among DataNodes. HBase relies on compactions to restore locality after a region split or failure. These two types of balancing do not work well together.

In the past, the generally accepted advice was to turn off the HDFS load balancer and rely on the HBase balancer, since the HDFS balancer would degrade locality. This advice is still valid if your HDFS version is lower than 2.7.1.

HDFS-6133 provides the ability to exclude a given directory from the HDFS load balancer, by setting the dfs.datanode.block-pinning.enabled property to true in your HDFS configuration and running the following hdfs command:

$ sudo -u hdfs hdfs balancer -exclude /hbase
HDFS-6133 is available in HDFS 2.7.0 and higher, but HBase does not support running on HDFS 2.7.0, so you must be using HDFS 2.7.1 or higher to use this feature with HBase.
Connection Timeouts

Connection timeouts occur between the client (HBASE) and the HDFS DataNode. They may occur when establishing a connection, attempting to read, or attempting to write. The two settings below are used in combination, and affect connections between the DFSClient and the DataNode, the ipc.cClient and the DataNode, and communication between two DataNodes.

dfs.client.socket-timeout (default: 60000)

The amount of time before a client connection times out when establishing a connection or reading. The value is expressed in milliseconds, so the default is 60 seconds.

dfs.datanode.socket.write.timeout (default: 480000)

The amount of time before a write operation times out. The default is 8 minutes, expressed as milliseconds.

Typical Error Logs

The following types of errors are often seen in the logs.

INFO HDFS.DFSClient: Failed to connect to /xxx50010, add to deadNodes and continue java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel to be ready for connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/region-server-1:50010]:: All DataNodes for a block are dead, and recovery is not possible. Here is the sequence of events that leads to this error:

INFO org.apache.hadoop.HDFS.DFSClient: Exception in createBlockOutputStream java.net.SocketTimeoutException: 69000 millis timeout while waiting for channel to be ready for connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/ xxx:50010]:: This type of error indicates a write issue. In this case, the master wants to split the log. It does not have a local DataNodes so it tries to connect to a remote DataNode, but the DataNode is dead.

121. Running unit or integration tests

121.1. Runtime exceptions from MiniDFSCluster when running tests

If you see something like the following

...
java.lang.NullPointerException: null
at org.apache.hadoop.hdfs.MiniDFSCluster.startDataNodes
at org.apache.hadoop.hdfs.MiniDFSCluster.<init>
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniDFSCluster
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
...

or

...
java.io.IOException: Shutting down
at org.apache.hadoop.hbase.MiniHBaseCluster.init
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.MiniHBaseCluster.<init>
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniHBaseCluster
at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster
...

... then try issuing the command umask 022 before launching tests. This is a workaround for HDFS-2556

122. Case Studies

For Performance and Troubleshooting Case Studies, see Apache HBase Case Studies.

123. Cryptographic Features

123.1. sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD

This problem manifests as exceptions ultimately caused by:

Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD
  at sun.security.pkcs11.wrapper.PKCS11.C_DecryptUpdate(Native Method)
  at sun.security.pkcs11.P11Cipher.implDoFinal(P11Cipher.java:795)

This problem appears to affect some versions of OpenJDK 7 shipped by some Linux vendors. NSS is configured as the default provider. If the host has an x86_64 architecture, depending on if the vendor packages contain the defect, the NSS provider will not function correctly.

To work around this problem, find the JRE home directory and edit the file lib/security/java.security. Edit the file to comment out the line:

security.provider.1=sun.security.pkcs11.SunPKCS11 ${java.home}/lib/security/nss.cfg

Then renumber the remaining providers accordingly.

124. Operating System Specific Issues

124.1. Page Allocation Failure

This issue is known to affect CentOS 6.2 and possibly CentOS 6.5. It may also affect some versions of Red Hat Enterprise Linux, according to https://bugzilla.redhat.com/show_bug.cgi?id=770545.

Some users have reported seeing the following error:

kernel: java: page allocation failure. order:4, mode:0x20

Raising the value of min_free_kbytes was reported to fix this problem. This parameter is set to a percentage of the amount of RAM on your system, and is described in more detail at http://www.centos.org/docs/5/html/5.1/Deployment_Guide/s3-proc-sys-vm.html.

To find the current value on your system, run the following command:

[user@host]# cat /proc/sys/vm/min_free_kbytes

Next, raise the value. Try doubling, then quadrupling the value. Note that setting the value too low or too high could have detrimental effects on your system. Consult your operating system vendor for specific recommendations.

Use the following command to modify the value of min_free_kbytes, substituting <value> with your intended value:

[user@host]# echo <value> > /proc/sys/vm/min_free_kbytes

125. JDK Issues

125.1. NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet

If you see this in your logs:

Caused by: java.lang.NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet()Ljava/util/concurrent/ConcurrentHashMap$KeySetView;
  at org.apache.hadoop.hbase.master.ServerManager.findServerWithSameHostnamePortWithLock(ServerManager.java:393)
  at org.apache.hadoop.hbase.master.ServerManager.checkAndRecordNewServer(ServerManager.java:307)
  at org.apache.hadoop.hbase.master.ServerManager.regionServerStartup(ServerManager.java:244)
  at org.apache.hadoop.hbase.master.MasterRpcServices.regionServerStartup(MasterRpcServices.java:304)
  at org.apache.hadoop.hbase.protobuf.generated.RegionServerStatusProtos$RegionServerStatusService$2.callBlockingMethod(RegionServerStatusProtos.java:7910)
  at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2020)
  ... 4 more

then check if you compiled with jdk8 and tried to run it on jdk7. If so, this won’t work. Run on jdk8 or recompile with jdk7. See HBASE-10607 JDK8 NoSuchMethodError involving ConcurrentHashMap.keySet if running on JRE 7.

Apache HBase Case Studies

126. Overview

This chapter will describe a variety of performance and troubleshooting case studies that can provide a useful blueprint on diagnosing Apache HBase cluster issues.

For more information on Performance and Troubleshooting, see Apache HBase Performance Tuning and Troubleshooting and Debugging Apache HBase.

127. Schema Design

See the schema design case studies here: Schema Design Case Studies

128. Performance/Troubleshooting

128.1. Case Study #1 (Performance Issue On A Single Node)

128.1.1. Scenario

Following a scheduled reboot, one data node began exhibiting unusual behavior. Routine MapReduce jobs run against HBase tables which regularly completed in five or six minutes began taking 30 or 40 minutes to finish. These jobs were consistently found to be waiting on map and reduce tasks assigned to the troubled data node (e.g., the slow map tasks all had the same Input Split). The situation came to a head during a distributed copy, when the copy was severely prolonged by the lagging node.

128.1.2. Hardware

Datanodes:

  • Two 12-core processors

  • Six Enterprise SATA disks

  • 24GB of RAM

  • Two bonded gigabit NICs

Network:

  • 10 Gigabit top-of-rack switches

  • 20 Gigabit bonded interconnects between racks.

128.1.3. Hypotheses

HBase "Hot Spot" Region

We hypothesized that we were experiencing a familiar point of pain: a "hot spot" region in an HBase table, where uneven key-space distribution can funnel a huge number of requests to a single HBase region, bombarding the RegionServer process and cause slow response time. Examination of the HBase Master status page showed that the number of HBase requests to the troubled node was almost zero. Further, examination of the HBase logs showed that there were no region splits, compactions, or other region transitions in progress. This effectively ruled out a "hot spot" as the root cause of the observed slowness.

HBase Region With Non-Local Data

Our next hypothesis was that one of the MapReduce tasks was requesting data from HBase that was not local to the DataNode, thus forcing HDFS to request data blocks from other servers over the network. Examination of the DataNode logs showed that there were very few blocks being requested over the network, indicating that the HBase region was correctly assigned, and that the majority of the necessary data was located on the node. This ruled out the possibility of non-local data causing a slowdown.

Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk

After concluding that the Hadoop and HBase were not likely to be the culprits, we moved on to troubleshooting the DataNode’s hardware. Java, by design, will periodically scan its entire memory space to do garbage collection. If system memory is heavily overcommitted, the Linux kernel may enter a vicious cycle, using up all of its resources swapping Java heap back and forth from disk to RAM as Java tries to run garbage collection. Further, a failing hard disk will often retry reads and/or writes many times before giving up and returning an error. This can manifest as high iowait, as running processes wait for reads and writes to complete. Finally, a disk nearing the upper edge of its performance envelope will begin to cause iowait as it informs the kernel that it cannot accept any more data, and the kernel queues incoming data into the dirty write pool in memory. However, using vmstat(1) and free(1), we could see that no swap was being used, and the amount of disk IO was only a few kilobytes per second.

Slowness Due To High Processor Usage

Next, we checked to see whether the system was performing slowly simply due to very high computational load. top(1) showed that the system load was higher than normal, but vmstat(1) and mpstat(1) showed that the amount of processor being used for actual computation was low.

Network Saturation (The Winner)

Since neither the disks nor the processors were being utilized heavily, we moved on to the performance of the network interfaces. The DataNode had two gigabit ethernet adapters, bonded to form an active-standby interface. ifconfig(8) showed some unusual anomalies, namely interface errors, overruns, framing errors. While not unheard of, these kinds of errors are exceedingly rare on modern hardware which is operating as it should:

$ /sbin/ifconfig bond0
bond0  Link encap:Ethernet  HWaddr 00:00:00:00:00:00
inet addr:10.x.x.x  Bcast:10.x.x.255  Mask:255.255.255.0
UP BROADCAST RUNNING MASTER MULTICAST  MTU:1500  Metric:1
RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6          <--- Look Here! Errors!
TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0
collisions:0 txqueuelen:0
RX bytes:2416328868676 (2.4 TB)  TX bytes:3464991094001 (3.4 TB)

These errors immediately lead us to suspect that one or more of the ethernet interfaces might have negotiated the wrong line speed. This was confirmed both by running an ICMP ping from an external host and observing round-trip-time in excess of 700ms, and by running ethtool(8) on the members of the bond interface and discovering that the active interface was operating at 100Mbs/, full duplex.

$ sudo ethtool eth0
Settings for eth0:
Supported ports: [ TP ]
Supported link modes:   10baseT/Half 10baseT/Full
                       100baseT/Half 100baseT/Full
                       1000baseT/Full
Supports auto-negotiation: Yes
Advertised link modes:  10baseT/Half 10baseT/Full
                       100baseT/Half 100baseT/Full
                       1000baseT/Full
Advertised pause frame use: No
Advertised auto-negotiation: Yes
Link partner advertised link modes:  Not reported
Link partner advertised pause frame use: No
Link partner advertised auto-negotiation: No
Speed: 100Mb/s                                     <--- Look Here!  Should say 1000Mb/s!
Duplex: Full
Port: Twisted Pair
PHYAD: 1
Transceiver: internal
Auto-negotiation: on
MDI-X: Unknown
Supports Wake-on: umbg
Wake-on: g
Current message level: 0x00000003 (3)
Link detected: yes

In normal operation, the ICMP ping round trip time should be around 20ms, and the interface speed and duplex should read, "1000MB/s", and, "Full", respectively.

128.1.4. Resolution

After determining that the active ethernet adapter was at the incorrect speed, we used the ifenslave(8) command to make the standby interface the active interface, which yielded an immediate improvement in MapReduce performance, and a 10 times improvement in network throughput:

On the next trip to the datacenter, we determined that the line speed issue was ultimately caused by a bad network cable, which was replaced.

128.2. Case Study #2 (Performance Research 2012)

Investigation results of a self-described "we’re not sure what’s wrong, but it seems slow" problem. http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html

128.3. Case Study #3 (Performance Research 2010))

Investigation results of general cluster performance from 2010. Although this research is on an older version of the codebase, this writeup is still very useful in terms of approach. http://hstack.org/hbase-performance-testing/

128.4. Case Study #4 (max.transfer.threads Config)

Case study of configuring max.transfer.threads (previously known as xcievers) and diagnosing errors from misconfigurations. http://www.larsgeorge.com/2012/03/hadoop-hbase-and-xceivers.html

See also dfs.datanode.max.transfer.threads .

Apache HBase Operational Management

This chapter will cover operational tools and practices required of a running Apache HBase cluster. The subject of operations is related to the topics of Troubleshooting and Debugging Apache HBase, Apache HBase Performance Tuning, and Apache HBase Configuration but is a distinct topic in itself.

129. HBase Tools and Utilities

HBase provides several tools for administration, analysis, and debugging of your cluster. The entry-point to most of these tools is the bin/hbase command, though some tools are available in the dev-support/ directory.

To see usage instructions for bin/hbase command, run it with no arguments, or with the -h argument. These are the usage instructions for HBase 0.98.x. Some commands, such as version, pe, ltt, clean, are not available in previous versions.

$ bin/hbase
Usage: hbase [<options>] <command> [<args>]
Options:
  --config DIR    Configuration direction to use. Default: ./conf
  --hosts HOSTS   Override the list in 'regionservers' file

Commands:
Some commands take arguments. Pass no args or -h for usage.
  shell           Run the HBase shell
  hbck            Run the hbase 'fsck' tool
  wal             Write-ahead-log analyzer
  hfile           Store file analyzer
  zkcli           Run the ZooKeeper shell
  upgrade         Upgrade hbase
  master          Run an HBase HMaster node
  regionserver    Run an HBase HRegionServer node
  zookeeper       Run a ZooKeeper server
  rest            Run an HBase REST server
  thrift          Run the HBase Thrift server
  thrift2         Run the HBase Thrift2 server
  clean           Run the HBase clean up script
  classpath       Dump hbase CLASSPATH
  mapredcp        Dump CLASSPATH entries required by mapreduce
  pe              Run PerformanceEvaluation
  ltt             Run LoadTestTool
  version         Print the version
  CLASSNAME       Run the class named CLASSNAME

Some of the tools and utilities below are Java classes which are passed directly to the bin/hbase command, as referred to in the last line of the usage instructions. Others, such as hbase shell (The Apache HBase Shell), hbase upgrade (Upgrading), and hbase thrift (Thrift API and Filter Language), are documented elsewhere in this guide.

129.1. Canary

There is a Canary class can help users to canary-test the HBase cluster status, with every column-family for every regions or RegionServer’s granularity. To see the usage, use the --help parameter.

$ ${HBASE_HOME}/bin/hbase canary -help

Usage: bin/hbase org.apache.hadoop.hbase.tool.Canary [opts] [table1 [table2]...] | [regionserver1 [regionserver2]..]
 where [opts] are:
   -help          Show this help and exit.
   -regionserver  replace the table argument to regionserver,
      which means to enable regionserver mode
   -daemon        Continuous check at defined intervals.
   -interval <N>  Interval between checks (sec)
   -e             Use region/regionserver as regular expression
      which means the region/regionserver is regular expression pattern
   -f <B>         stop whole program if first error occurs, default is true
   -t <N>         timeout for a check, default is 600000 (milliseconds)
   -writeSniffing enable the write sniffing in canary
   -treatFailureAsError treats read / write failure as error
   -writeTable    The table used for write sniffing. Default is hbase:canary
   -D<configProperty>=<value> assigning or override the configuration params

This tool will return non zero error codes to user for collaborating with other monitoring tools, such as Nagios. The error code definitions are:

private static final int USAGE_EXIT_CODE = 1;
private static final int INIT_ERROR_EXIT_CODE = 2;
private static final int TIMEOUT_ERROR_EXIT_CODE = 3;
private static final int ERROR_EXIT_CODE = 4;

Here are some examples based on the following given case. There are two Table objects called test-01 and test-02, they have two column family cf1 and cf2 respectively, and deployed on the 3 RegionServers. see following table.

RegionServer test-01 test-02

rs1

r1

r2

rs2

r2

rs3

r2

r1

Following are some examples based on the previous given case.

129.1.1. Canary test for every column family (store) of every region of every table

$ ${HBASE_HOME}/bin/hbase canary

3/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf1 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf2 in 2ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf1 in 4ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf2 in 1ms
...
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf1 in 5ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf2 in 3ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf1 in 31ms
13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf2 in 8ms

So you can see, table test-01 has two regions and two column families, so the Canary tool will pick 4 small piece of data from 4 (2 region * 2 store) different stores. This is a default behavior of the this tool does.

129.1.2. Canary test for every column family (store) of every region of specific table(s)

You can also test one or more specific tables.

$ ${HBASE_HOME}/bin/hbase canary test-01 test-02

129.1.3. Canary test with RegionServer granularity

This will pick one small piece of data from each RegionServer, and can also put your RegionServer name as input options for canary-test specific RegionServer.

$ ${HBASE_HOME}/bin/hbase canary -regionserver

13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs2 in 72ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-02 on region server:rs3 in 34ms
13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs1 in 56ms

129.1.4. Canary test with regular expression pattern

This will test both table test-01 and test-02.

$ ${HBASE_HOME}/bin/hbase canary -e test-0[1-2]

129.1.5. Run canary test as daemon mode

Run repeatedly with interval defined in option -interval whose default value is 6 seconds. This daemon will stop itself and return non-zero error code if any error occurs, due to the default value of option -f is true.

$ ${HBASE_HOME}/bin/hbase canary -daemon

Run repeatedly with internal 5 seconds and will not stop itself even if errors occur in the test.

$ ${HBASE_HOME}/bin/hbase canary -daemon -interval 50000 -f false

129.1.6. Force timeout if canary test stuck

In some cases the request is stuck and no response is sent back to the client. This can happen with dead RegionServers which the master has not yet noticed. Because of this we provide a timeout option to kill the canary test and return a non-zero error code. This run sets the timeout value to 60 seconds, the default value is 600 seconds.

$ ${HBASE_HOME}/bin/hbase canary -t 600000

129.1.7. Enable write sniffing in canary

By default, the canary tool only check the read operations, it’s hard to find the problem in the write path. To enable the write sniffing, you can run canary with the -writeSniffing option. When the write sniffing is enabled, the canary tool will create an hbase table and make sure the regions of the table distributed on all region servers. In each sniffing period, the canary will try to put data to these regions to check the write availability of each region server.

$ ${HBASE_HOME}/bin/hbase canary -writeSniffing

The default write table is hbase:canary and can be specified by the option -writeTable.

$ ${HBASE_HOME}/bin/hbase canary -writeSniffing -writeTable ns:canary

The default value size of each put is 10 bytes and you can set it by the config key: hbase.canary.write.value.size.

129.1.8. Treat read / write failure as error

By default, the canary tool only logs read failure, due to e.g. RetriesExhaustedException, while returning normal exit code. To treat read / write failure as error, you can run canary with the -treatFailureAsError option. When enabled, read / write failure would result in error exit code.

$ ${HBASE_HOME}/bin/hbase canary --treatFailureAsError

129.1.9. Running Canary in a Kerberos-enabled Cluster

To run Canary in a Kerberos-enabled cluster, configure the following two properties in hbase-site.xml:

  • hbase.client.keytab.file

  • hbase.client.kerberos.principal

Kerberos credentials are refreshed every 30 seconds when Canary runs in daemon mode.

To configure the DNS interface for the client, configure the following optional properties in hbase-site.xml.

  • hbase.client.dns.interface

  • hbase.client.dns.nameserver

Example 57. Canary in a Kerberos-Enabled Cluster

This example shows each of the properties with valid values.

<property>
  <name>hbase.client.kerberos.principal</name>
  <value>hbase/_HOST@YOUR-REALM.COM</value>
</property>
<property>
  <name>hbase.client.keytab.file</name>
  <value>/etc/hbase/conf/keytab.krb5</value>
</property>
<!-- optional params -->
property>
  <name>hbase.client.dns.interface</name>
  <value>default</value>
</property>
<property>
  <name>hbase.client.dns.nameserver</name>
  <value>default</value>
</property>

129.2. Health Checker

You can configure HBase to run a script periodically and if it fails N times (configurable), have the server exit. See HBASE-7351 Periodic health check script for configurations and detail.

129.3. Driver

Several frequently-accessed utilities are provided as Driver classes, and executed by the bin/hbase command. These utilities represent MapReduce jobs which run on your cluster. They are run in the following way, replacing UtilityName with the utility you want to run. This command assumes you have set the environment variable HBASE_HOME to the directory where HBase is unpacked on your server.

${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.mapreduce.UtilityName

The following utilities are available:

LoadIncrementalHFiles

Complete a bulk data load.

CopyTable

Export a table from the local cluster to a peer cluster.

Export

Write table data to HDFS.

Import

Import data written by a previous Export operation.

ImportTsv

Import data in TSV format.

RowCounter

Count rows in an HBase table.

CellCounter

Count cells in an HBase table.

replication.VerifyReplication

Compare the data from tables in two different clusters. WARNING: It doesn’t work for incrementColumnValues’d cells since the timestamp is changed. Note that this command is in a different package than the others.

Each command except RowCounter and CellCounter accept a single --help argument to print usage instructions.

129.4. HBase hbck

To run hbck against your HBase cluster run $./bin/hbase hbck. At the end of the command’s output it prints OK or INCONSISTENCY. If your cluster reports inconsistencies, pass -details to see more detail emitted. If inconsistencies, run hbck a few times because the inconsistency may be transient (e.g. cluster is starting up or a region is splitting). Passing -fix may correct the inconsistency (This is an experimental feature).

For more information, see hbck In Depth.

129.5. HFile Tool

See HFile Tool.

129.6. WAL Tools

129.6.1. FSHLog tool

The main method on FSHLog offers manual split and dump facilities. Pass it WALs or the product of a split, the content of the recovered.edits. directory.

You can get a textual dump of a WAL file content by doing the following:

 $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012

The return code will be non-zero if there are any issues with the file so you can test wholesomeness of file by redirecting STDOUT to /dev/null and testing the program return.

Similarly you can force a split of a log file directory by doing:

 $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/
WAL Pretty Printer

The WAL Pretty Printer is a tool with configurable options to print the contents of a WAL. You can invoke it via the HBase cli with the 'wal' command.

 $ ./bin/hbase wal hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012
WAL Printing in older versions of HBase

Prior to version 2.0, the WAL Pretty Printer was called the HLogPrettyPrinter, after an internal name for HBase’s write ahead log. In those versions, you can print the contents of a WAL using the same configuration as above, but with the 'hlog' command.

 $ ./bin/hbase hlog hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012

129.7. Compression Tool

See compression.test.

129.8. CopyTable

CopyTable is a utility that can copy part or of all of a table, either to the same cluster or another cluster. The target table must first exist. The usage is as follows:

$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
/bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help
Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename>

Options:
 rs.class     hbase.regionserver.class of the peer cluster,
              specify if different from current cluster
 rs.impl      hbase.regionserver.impl of the peer cluster,
 startrow     the start row
 stoprow      the stop row
 starttime    beginning of the time range (unixtime in millis)
              without endtime means from starttime to forever
 endtime      end of the time range.  Ignored if no starttime specified.
 versions     number of cell versions to copy
 new.name     new table's name
 peer.adr     Address of the peer cluster given in the format
              hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
 families     comma-separated list of families to copy
              To copy from cf1 to cf2, give sourceCfName:destCfName.
              To keep the same name, just give "cfName"
 all.cells    also copy delete markers and deleted cells

Args:
 tablename    Name of the table to copy

Examples:
 To copy 'TestTable' to a cluster that uses replication for a 1 hour window:
 $ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289 --endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase --families=myOldCf:myNewCf,cf2,cf3 TestTable

For performance consider the following general options:
  It is recommended that you set the following to >=100. A higher value uses more memory but
  decreases the round trip time to the server and may increase performance.
    -Dhbase.client.scanner.caching=100
  The following should always be set to false, to prevent writing data twice, which may produce
  inaccurate results.
    -Dmapred.map.tasks.speculative.execution=false
Scanner Caching

Caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.
Versions

By default, CopyTable utility only copies the latest version of row cells unless --versions=n is explicitly specified in the command.

See Jonathan Hsieh’s Online HBase Backups with CopyTable blog post for more on CopyTable.

129.9. Export

Export is a utility that will dump the contents of table to HDFS in a sequence file. Invoke via:

$ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]]
To see usage instructions, run the command with no options. Available options include specifying column families and applying filters during the export.

By default, the Export tool only exports the newest version of a given cell, regardless of the number of versions stored. To export more than one version, replace <versions> with the desired number of versions.

Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.

129.10. Import

Import is a utility that will load data that has been exported back into HBase. Invoke via:

$ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>
To see usage instructions, run the command with no options.

To import 0.94 exported files in a 0.96 cluster or onwards, you need to set system property "hbase.import.version" when running the import command as below:

$ bin/hbase -Dhbase.import.version=0.94 org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir>

129.11. ImportTsv

ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct usages: loading data from TSV format in HDFS into HBase via Puts, and preparing StoreFiles to be loaded via the completebulkload.

To load data via Puts (i.e., non-bulk loading):

$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir>

To generate StoreFiles for bulk-loading:

$ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir>

These generated StoreFiles can be loaded into HBase via completebulkload.

129.11.1. ImportTsv Options

Running ImportTsv with no arguments prints brief usage information:

Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir>

Imports the given input directory of TSV data into the specified table.

The column names of the TSV data must be specified using the -Dimporttsv.columns
option. This option takes the form of comma-separated column names, where each
column name is either a simple column family, or a columnfamily:qualifier. The special
column name HBASE_ROW_KEY is used to designate that this column should be used
as the row key for each imported record. You must specify exactly one column
to be the row key, and you must specify a column name for every column that exists in the
input data.

By default importtsv will load data directly into HBase. To instead generate
HFiles of data to prepare for a bulk data load, pass the option:
  -Dimporttsv.bulk.output=/path/for/output
  Note: the target table will be created with default column family descriptors if it does not already exist.

Other options that may be specified with -D include:
  -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line
  '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs
  -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import
  -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper

129.11.2. ImportTsv Example

For example, assume that we are loading data into a table called 'datatsv' with a ColumnFamily called 'd' with two columns "c1" and "c2".

Assume that an input file exists as follows:

row1	c1	c2
row2	c1	c2
row3	c1	c2
row4	c1	c2
row5	c1	c2
row6	c1	c2
row7	c1	c2
row8	c1	c2
row9	c1	c2
row10	c1	c2

For ImportTsv to use this input file, the command line needs to look like this:

 HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile

... and in this example the first column is the rowkey, which is why the HBASE_ROW_KEY is used. The second and third columns in the file will be imported as "d:c1" and "d:c2", respectively.

129.11.3. ImportTsv Warning

If you have preparing a lot of data for bulk loading, make sure the target HBase table is pre-split appropriately.

129.11.4. See Also

For more information about bulk-loading HFiles into HBase, see arch.bulk.load

129.12. CompleteBulkLoad

The completebulkload utility will move generated StoreFiles into an HBase table. This utility is often used in conjunction with output from importtsv.

There are two ways to invoke this utility, with explicit classname and via the driver:

Explicit Classname
$ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles <hdfs://storefileoutput> <tablename>
Driver
HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename>

129.12.1. CompleteBulkLoad Warning

Data generated via MapReduce is often created with file permissions that are not compatible with the running HBase process. Assuming you’re running HDFS with permissions enabled, those permissions will need to be updated before you run CompleteBulkLoad.

For more information about bulk-loading HFiles into HBase, see arch.bulk.load.

129.13. WALPlayer

WALPlayer is a utility to replay WAL files into HBase.

The WAL can be replayed for a set of tables or all tables, and a timerange can be provided (in milliseconds). The WAL is filtered to this set of tables. The output can optionally be mapped to another set of tables.

WALPlayer can also generate HFiles for later bulk importing, in that case only a single table and no mapping can be specified.

Invoke via:

$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]>

For example:

$ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2

WALPlayer, by default, runs as a mapreduce job. To NOT run WALPlayer as a mapreduce job on your cluster, force it to run all in the local process by adding the flags -Dmapreduce.jobtracker.address=local on the command line.

129.14. RowCounter and CellCounter

RowCounter is a mapreduce job to count all the rows of a table. This is a good utility to use as a sanity check to ensure that HBase can read all the blocks of a table if there are any concerns of metadata inconsistency. It will run the mapreduce all in a single process but it will run faster if you have a MapReduce cluster in place for it to exploit. It is also possible to limit the time range of data to be scanned by using the --starttime=[starttime] and --endtime=[endtime] flags.

$ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...]

RowCounter only counts one version per cell.

Note: caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.

HBase ships another diagnostic mapreduce job called CellCounter. Like RowCounter, it gathers more fine-grained statistics about your table. The statistics gathered by RowCounter are more fine-grained and include:

  • Total number of rows in the table.

  • Total number of CFs across all rows.

  • Total qualifiers across all rows.

  • Total occurrence of each CF.

  • Total occurrence of each qualifier.

  • Total number of versions of each qualifier.

The program allows you to limit the scope of the run. Provide a row regex or prefix to limit the rows to analyze. Specify a time range to scan the table by using the --starttime=[starttime] and --endtime=[endtime] flags.

Use hbase.mapreduce.scan.column.family to specify scanning a single column family.

$ bin/hbase org.apache.hadoop.hbase.mapreduce.CellCounter <tablename> <outputDir> [regex or prefix]

Note: just like RowCounter, caching for the input Scan is configured via hbase.client.scanner.caching in the job configuration.

129.15. mlockall

It is possible to optionally pin your servers in physical memory making them less likely to be swapped out in oversubscribed environments by having the servers call mlockall on startup. See HBASE-4391 Add ability to start RS as root and call mlockall for how to build the optional library and have it run on startup.

129.16. Offline Compaction Tool

See the usage for the CompactionTool. Run it like:

$ ./bin/hbase org.apache.hadoop.hbase.regionserver.CompactionTool

129.17. hbase clean

The hbase clean command cleans HBase data from ZooKeeper, HDFS, or both. It is appropriate to use for testing. Run it with no options for usage instructions. The hbase clean command was introduced in HBase 0.98.

$ bin/hbase clean
Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll)
Options:
        --cleanZk   cleans hbase related data from zookeeper.
        --cleanHdfs cleans hbase related data from hdfs.
        --cleanAll  cleans hbase related data from both zookeeper and hdfs.

129.18. hbase pe

The hbase pe command is a shortcut provided to run the org.apache.hadoop.hbase.PerformanceEvaluation tool, which is used for testing. The hbase pe command was introduced in HBase 0.98.4.

The PerformanceEvaluation tool accepts many different options and commands. For usage instructions, run the command with no options.

To run PerformanceEvaluation prior to HBase 0.98.4, issue the command hbase org.apache.hadoop.hbase.PerformanceEvaluation.

The PerformanceEvaluation tool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLs and visibility labels, multiget support for RPC calls, increased sampling sizes, an option to randomly sleep during testing, and ability to "warm up" the cluster before testing starts.

129.19. hbase ltt

The hbase ltt command is a shortcut provided to run the org.apache.hadoop.hbase.util.LoadTestTool utility, which is used for testing. The hbase ltt command was introduced in HBase 0.98.4.

You must specify either -write or -update-read as the first option. For general usage instructions, pass the -h option.

To run LoadTestTool prior to HBase 0.98.4, issue the command hbase org.apache.hadoop.hbase.util.LoadTestTool.

The LoadTestTool has received many updates in recent HBase releases, including support for namespaces, support for tags, cell-level ACLS and visibility labels, testing security-related features, ability to specify the number of regions per server, tests for multi-get RPC calls, and tests relating to replication.

130. Region Management

130.1. Major Compaction

Major compactions can be requested via the HBase shell or Admin.majorCompact.

Note: major compactions do NOT do region merges. See compaction for more information about compactions.

130.2. Merge

Merge is a utility that can merge adjoining regions in the same table (see org.apache.hadoop.hbase.util.Merge).

$ bin/hbase org.apache.hadoop.hbase.util.Merge <tablename> <region1> <region2>

If you feel you have too many regions and want to consolidate them, Merge is the utility you need. Merge must run be done when the cluster is down. See the O’Reilly HBase Book for an example of usage.

You will need to pass 3 parameters to this application. The first one is the table name. The second one is the fully qualified name of the first region to merge, like "table_name,\x0A,1342956111995.7cef47f192318ba7ccc75b1bbf27a82b.". The third one is the fully qualified name for the second region to merge.

Additionally, there is a Ruby script attached to HBASE-1621 for region merging.

131. Node Management

131.1. Node Decommission

You can stop an individual RegionServer by running the following script in the HBase directory on the particular node:

$ ./bin/hbase-daemon.sh stop regionserver

The RegionServer will first close all regions and then shut itself down. On shutdown, the RegionServer’s ephemeral node in ZooKeeper will expire. The master will notice the RegionServer gone and will treat it as a 'crashed' server; it will reassign the nodes the RegionServer was carrying.

Disable the Load Balancer before Decommissioning a node

If the load balancer runs while a node is shutting down, then there could be contention between the Load Balancer and the Master’s recovery of the just decommissioned RegionServer. Avoid any problems by disabling the balancer first. See lb below.
Kill Node Tool

In hbase-2.0, in the bin directory, we added a script named considerAsDead.sh that can be used to kill a regionserver. Hardware issues could be detected by specialized monitoring tools before the zookeeper timeout has expired. considerAsDead.sh is a simple function to mark a RegionServer as dead. It deletes all the znodes of the server, starting the recovery process. Plug in the script into your monitoring/fault detection tools to initiate faster failover. Be careful how you use this disruptive tool. Copy the script if you need to make use of it in a version of hbase previous to hbase-2.0.

A downside to the above stop of a RegionServer is that regions could be offline for a good period of time. Regions are closed in order. If many regions on the server, the first region to close may not be back online until all regions close and after the master notices the RegionServer’s znode gone. In Apache HBase 0.90.2, we added facility for having a node gradually shed its load and then shutdown itself down. Apache HBase 0.90.2 added the graceful_stop.sh script. Here is its usage:

$ ./bin/graceful_stop.sh
Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--rest] &hostname>
 thrift      If we should stop/start thrift before/after the hbase stop/start
 rest        If we should stop/start rest before/after the hbase stop/start
 restart     If we should restart after graceful stop
 reload      Move offloaded regions back on to the stopped server
 debug       Move offloaded regions back on to the stopped server
 hostname    Hostname of server we are to stop

To decommission a loaded RegionServer, run the following: $ ./bin/graceful_stop.sh HOSTNAME where HOSTNAME is the host carrying the RegionServer you would decommission.

On HOSTNAME

The HOSTNAME passed to graceful_stop.sh must match the hostname that hbase is using to identify RegionServers. Check the list of RegionServers in the master UI for how HBase is referring to servers. It’s usually hostname but can also be FQDN. Whatever HBase is using, this is what you should pass the graceful_stop.sh decommission script. If you pass IPs, the script is not yet smart enough to make a hostname (or FQDN) of it and so it will fail when it checks if server is currently running; the graceful unloading of regions will not run.

The graceful_stop.sh script will move the regions off the decommissioned RegionServer one at a time to minimize region churn. It will verify the region deployed in the new location before it will moves the next region and so on until the decommissioned server is carrying zero regions. At this point, the graceful_stop.sh tells the RegionServer stop. The master will at this point notice the RegionServer gone but all regions will have already been redeployed and because the RegionServer went down cleanly, there will be no WAL logs to split.

Load Balancer

It is assumed that the Region Load Balancer is disabled while the graceful_stop script runs (otherwise the balancer and the decommission script will end up fighting over region deployments). Use the shell to disable the balancer:

hbase(main):001:0> balance_switch false
true
0 row(s) in 0.3590 seconds

This turns the balancer OFF. To reenable, do:

hbase(main):001:0> balance_switch true
false
0 row(s) in 0.3590 seconds

The graceful_stop will check the balancer and if enabled, will turn it off before it goes to work. If it exits prematurely because of error, it will not have reset the balancer. Hence, it is better to manage the balancer apart from graceful_stop reenabling it after you are done w/ graceful_stop.

131.1.1. Decommissioning several Regions Servers concurrently

If you have a large cluster, you may want to decommission more than one machine at a time by gracefully stopping multiple RegionServers concurrently. To gracefully drain multiple regionservers at the same time, RegionServers can be put into a "draining" state. This is done by marking a RegionServer as a draining node by creating an entry in ZooKeeper under the hbase_root/draining znode. This znode has format name,port,startcode just like the regionserver entries under hbase_root/rs znode.

Without this facility, decommissioning multiple nodes may be non-optimal because regions that are being drained from one region server may be moved to other regionservers that are also draining. Marking RegionServers to be in the draining state prevents this from happening. See this blog post for more details.

131.1.2. Bad or Failing Disk

It is good having dfs.datanode.failed.volumes.tolerated set if you have a decent number of disks per machine for the case where a disk plain dies. But usually disks do the "John Wayne" — i.e. take a while to go down spewing errors in dmesg — or for some reason, run much slower than their companions. In this case you want to decommission the disk. You have two options. You can decommission the datanode or, less disruptive in that only the bad disks data will be rereplicated, can stop the datanode, unmount the bad volume (You can’t umount a volume while the datanode is using it), and then restart the datanode (presuming you have set dfs.datanode.failed.volumes.tolerated > 0). The regionserver will throw some errors in its logs as it recalibrates where to get its data from — it will likely roll its WAL log too — but in general but for some latency spikes, it should keep on chugging.

Short Circuit Reads

If you are doing short-circuit reads, you will have to move the regions off the regionserver before you stop the datanode; when short-circuiting reading, though chmod’d so regionserver cannot have access, because it already has the files open, it will be able to keep reading the file blocks from the bad disk even though the datanode is down. Move the regions back after you restart the datanode.

131.2. Rolling Restart

Some cluster configuration changes require either the entire cluster, or the RegionServers, to be restarted in order to pick up the changes. In addition, rolling restarts are supported for upgrading to a minor or maintenance release, and to a major release if at all possible. See the release notes for release you want to upgrade to, to find out about limitations to the ability to perform a rolling upgrade.

There are multiple ways to restart your cluster nodes, depending on your situation. These methods are detailed below.

131.2.1. Using the rolling-restart.sh Script

HBase ships with a script, bin/rolling-restart.sh, that allows you to perform rolling restarts on the entire cluster, the master only, or the RegionServers only. The script is provided as a template for your own script, and is not explicitly tested. It requires password-less SSH login to be configured and assumes that you have deployed using a tarball. The script requires you to set some environment variables before running it. Examine the script and modify it to suit your needs.

Example 58. rolling-restart.sh General Usage
$ ./bin/rolling-restart.sh --help
Usage: rolling-restart.sh [--config <hbase-confdir>] [--rs-only] [--master-only] [--graceful] [--maxthreads xx]
Rolling Restart on RegionServers Only

To perform a rolling restart on the RegionServers only, use the --rs-only option. This might be necessary if you need to reboot the individual RegionServer or if you make a configuration change that only affects RegionServers and not the other HBase processes.

Rolling Restart on Masters Only

To perform a rolling restart on the active and backup Masters, use the --master-only option. You might use this if you know that your configuration change only affects the Master and not the RegionServers, or if you need to restart the server where the active Master is running.

Graceful Restart

If you specify the --graceful option, RegionServers are restarted using the bin/graceful_stop.sh script, which moves regions off a RegionServer before restarting it. This is safer, but can delay the restart.

Limiting the Number of Threads

To limit the rolling restart to using only a specific number of threads, use the --maxthreads option.

131.2.2. Manual Rolling Restart

To retain more control over the process, you may wish to manually do a rolling restart across your cluster. This uses the graceful-stop.sh command decommission. In this method, you can restart each RegionServer individually and then move its old regions back into place, retaining locality. If you also need to restart the Master, you need to do it separately, and restart the Master before restarting the RegionServers using this method. The following is an example of such a command. You may need to tailor it to your environment. This script does a rolling restart of RegionServers only. It disables the load balancer before moving the regions.

$ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &;

Monitor the output of the /tmp/log.txt file to follow the progress of the script.

131.2.3. Logic for Crafting Your Own Rolling Restart Script

Use the following guidelines if you want to create your own rolling restart script.

  1. Extract the new release, verify its configuration, and synchronize it to all nodes of your cluster using rsync, scp, or another secure synchronization mechanism.

  2. Use the hbck utility to ensure that the cluster is consistent.

    $ ./bin/hbck

    Perform repairs if required. See hbck for details.

  3. Restart the master first. You may need to modify these commands if your new HBase directory is different from the old one, such as for an upgrade.

    $ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master

  4. Gracefully restart each RegionServer, using a script such as the following, from the Master.

    $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i; done &> /tmp/log.txt &

    If you are running Thrift or REST servers, pass the --thrift or --rest options. For other available options, run the bin/graceful-stop.sh --help command.

    It is important to drain HBase regions slowly when restarting multiple RegionServers. Otherwise, multiple regions go offline simultaneously and must be reassigned to other nodes, which may also go offline soon. This can negatively affect performance. You can inject delays into the script above, for instance, by adding a Shell command such as sleep. To wait for 5 minutes between each RegionServer restart, modify the above script to the following:

    $ for i in `cat conf/regionservers|sort`; do ./bin/graceful_stop.sh --restart --reload --debug $i & sleep 5m; done &> /tmp/log.txt &

  5. Restart the Master again, to clear out the dead servers list and re-enable the load balancer.

  6. Run the hbck utility again, to be sure the cluster is consistent.

131.3. Adding a New Node

Adding a new regionserver in HBase is essentially free, you simply start it like this: $ ./bin/hbase-daemon.sh start regionserver and it will register itself with the master. Ideally you also started a DataNode on the same machine so that the RS can eventually start to have local files. If you rely on ssh to start your daemons, don’t forget to add the new hostname in conf/regionservers on the master.

At this point the region server isn’t serving data because no regions have moved to it yet. If the balancer is enabled, it will start moving regions to the new RS. On a small/medium cluster this can have a very adverse effect on latency as a lot of regions will be offline at the same time. It is thus recommended to disable the balancer the same way it’s done when decommissioning a node and move the regions manually (or even better, using a script that moves them one by one).

The moved regions will all have 0% locality and won’t have any blocks in cache so the region server will have to use the network to serve requests. Apart from resulting in higher latency, it may also be able to use all of your network card’s capacity. For practical purposes, consider that a standard 1GigE NIC won’t be able to read much more than 100MB/s. In this case, or if you are in a OLAP environment and require having locality, then it is recommended to major compact the moved regions.

132. HBase Metrics

HBase emits metrics which adhere to the Hadoop metrics API. Starting with HBase 0.95[5], HBase is configured to emit a default set of metrics with a default sampling period of every 10 seconds. You can use HBase metrics in conjunction with Ganglia. You can also filter which metrics are emitted and extend the metrics framework to capture custom metrics appropriate for your environment.

132.1. Metric Setup

For HBase 0.95 and newer, HBase ships with a default metrics configuration, or sink. This includes a wide variety of individual metrics, and emits them every 10 seconds by default. To configure metrics for a given region server, edit the conf/hadoop-metrics2-hbase.properties file. Restart the region server for the changes to take effect.

To change the sampling rate for the default sink, edit the line beginning with *.period. To filter which metrics are emitted or to extend the metrics framework, see http://hadoop.apache.org/docs/current/api/org/apache/hadoop/metrics2/package-summary.html

HBase Metrics and Ganglia

By default, HBase emits a large number of metrics per region server. Ganglia may have difficulty processing all these metrics. Consider increasing the capacity of the Ganglia server or reducing the number of metrics emitted by HBase. See Metrics Filtering.

132.2. Disabling Metrics

To disable metrics for a region server, edit the conf/hadoop-metrics2-hbase.properties file and comment out any uncommented lines. Restart the region server for the changes to take effect.

132.3. Discovering Available Metrics

Rather than listing each metric which HBase emits by default, you can browse through the available metrics, either as a JSON output or via JMX. Different metrics are exposed for the Master process and each region server process.

Procedure: Access a JSON Output of Available Metrics

  1. After starting HBase, access the region server’s web UI, at http://REGIONSERVER_HOSTNAME:60030 by default (or port 16030 in HBase 1.0+).

  2. Click the Metrics Dump link near the top. The metrics for the region server are presented as a dump of the JMX bean in JSON format. This will dump out all metrics names and their values. To include metrics descriptions in the listing — this can be useful when you are exploring what is available — add a query string of ?description=true so your URL becomes http://REGIONSERVER_HOSTNAME:60030/jmx?description=true. Not all beans and attributes have descriptions.

  3. To view metrics for the Master, connect to the Master’s web UI instead (defaults to http://localhost:60010 or port 16010 in HBase 1.0+) and click its Metrics Dump link. To include metrics descriptions in the listing — this can be useful when you are exploring what is available — add a query string of ?description=true so your URL becomes http://REGIONSERVER_HOSTNAME:60010/jmx?description=true. Not all beans and attributes have descriptions.

You can use many different tools to view JMX content by browsing MBeans. This procedure uses jvisualvm, which is an application usually available in the JDK.

Procedure: Browse the JMX Output of Available Metrics

  1. Start HBase, if it is not already running.

  2. Run the command jvisualvm command on a host with a GUI display. You can launch it from the command line or another method appropriate for your operating system.

  3. Be sure the VisualVM-MBeans plugin is installed. Browse to Tools → Plugins. Click Installed and check whether the plugin is listed. If not, click Available Plugins, select it, and click Install. When finished, click Close.

  4. To view details for a given HBase process, double-click the process in the Local sub-tree in the left-hand panel. A detailed view opens in the right-hand panel. Click the MBeans tab which appears as a tab in the top of the right-hand panel.

  5. To access the HBase metrics, navigate to the appropriate sub-bean:

  6. The name of each metric and its current value is displayed in the Attributes tab. For a view which includes more details, including the description of each attribute, click the Metadata tab.

132.4. Units of Measure for Metrics

Different metrics are expressed in different units, as appropriate. Often, the unit of measure is in the name (as in the metric shippedKBs). Otherwise, use the following guidelines. When in doubt, you may need to examine the source for a given metric.

  • Metrics that refer to a point in time are usually expressed as a timestamp.

  • Metrics that refer to an age (such as ageOfLastShippedOp) are usually expressed in milliseconds.

  • Metrics that refer to memory sizes are in bytes.

  • Sizes of queues (such as sizeOfLogQueue) are expressed as the number of items in the queue. Determine the size by multiplying by the block size (default is 64 MB in HDFS).

  • Metrics that refer to things like the number of a given type of operations (such as logEditsRead) are expressed as an integer.

132.5. Most Important Master Metrics

Note: Counts are usually over the last metrics reporting interval.

hbase.master.numRegionServers

Number of live regionservers

hbase.master.numDeadRegionServers

Number of dead regionservers

hbase.master.ritCount

The number of regions in transition

hbase.master.ritCountOverThreshold

The number of regions that have been in transition longer than a threshold time (default: 60 seconds)

hbase.master.ritOldestAge

The age of the longest region in transition, in milliseconds

132.6. Most Important RegionServer Metrics

Note: Counts are usually over the last metrics reporting interval.

hbase.regionserver.regionCount

The number of regions hosted by the regionserver

hbase.regionserver.storeFileCount

The number of store files on disk currently managed by the regionserver

hbase.regionserver.storeFileSize

Aggregate size of the store files on disk

hbase.regionserver.hlogFileCount

The number of write ahead logs not yet archived

hbase.regionserver.totalRequestCount

The total number of requests received

hbase.regionserver.readRequestCount

The number of read requests received

hbase.regionserver.writeRequestCount

The number of write requests received

hbase.regionserver.numOpenConnections

The number of open connections at the RPC layer

hbase.regionserver.numActiveHandler

The number of RPC handlers actively servicing requests

hbase.regionserver.numCallsInGeneralQueue

The number of currently enqueued user requests

hbase.regionserver.numCallsInReplicationQueue

The number of currently enqueued operations received from replication

hbase.regionserver.numCallsInPriorityQueue

The number of currently enqueued priority (internal housekeeping) requests

hbase.regionserver.flushQueueLength

Current depth of the memstore flush queue. If increasing, we are falling behind with clearing memstores out to HDFS.

hbase.regionserver.updatesBlockedTime

Number of milliseconds updates have been blocked so the memstore can be flushed

hbase.regionserver.compactionQueueLength

Current depth of the compaction request queue. If increasing, we are falling behind with storefile compaction.

hbase.regionserver.blockCacheHitCount

The number of block cache hits

hbase.regionserver.blockCacheMissCount

The number of block cache misses

hbase.regionserver.blockCacheExpressHitPercent

The percent of the time that requests with the cache turned on hit the cache

hbase.regionserver.percentFilesLocal

Percent of store file data that can be read from the local DataNode, 0-100

hbase.regionserver.<op>_<measure>

Operation latencies, where <op> is one of Append, Delete, Mutate, Get, Replay, Increment; and where <measure> is one of min, max, mean, median, 75th_percentile, 95th_percentile, 99th_percentile

hbase.regionserver.slow<op>Count

The number of operations we thought were slow, where <op> is one of the list above

hbase.regionserver.GcTimeMillis

Time spent in garbage collection, in milliseconds

hbase.regionserver.GcTimeMillisParNew

Time spent in garbage collection of the young generation, in milliseconds

hbase.regionserver.GcTimeMillisConcurrentMarkSweep

Time spent in garbage collection of the old generation, in milliseconds

hbase.regionserver.authenticationSuccesses

Number of client connections where authentication succeeded

hbase.regionserver.authenticationFailures

Number of client connection authentication failures

hbase.regionserver.mutationsWithoutWALCount

Count of writes submitted with a flag indicating they should bypass the write ahead log

133. HBase Monitoring

133.1. Overview

The following metrics are arguably the most important to monitor for each RegionServer for "macro monitoring", preferably with a system like OpenTSDB. If your cluster is having performance issues it’s likely that you’ll see something unusual with this group.

HBase
OS

  • IO Wait

  • User CPU

Java

  • GC

For more information on HBase metrics, see hbase metrics.

133.2. Slow Query Log

The HBase slow query log consists of parseable JSON structures describing the properties of those client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or produced too much output. The thresholds for "too long to run" and "too much output" are configurable, as described below. The output is produced inline in the main region server logs so that it is easy to discover further details from context with other logged events. It is also prepended with identifying tags (responseTooSlow), (responseTooLarge), (operationTooSlow), and (operationTooLarge) in order to enable easy filtering with grep, in case the user desires to see only slow queries.

133.2.1. Configuration

There are two configuration knobs that can be used to adjust the thresholds for when queries are logged.

  • hbase.ipc.warn.response.time Maximum number of milliseconds that a query can be run without being logged. Defaults to 10000, or 10 seconds. Can be set to -1 to disable logging by time.

  • hbase.ipc.warn.response.size Maximum byte size of response that a query can return without being logged. Defaults to 100 megabytes. Can be set to -1 to disable logging by size.

133.2.2. Metrics

The slow query log exposes to metrics to JMX.

  • hadoop.regionserver_rpc_slowResponse a global metric reflecting the durations of all responses that triggered logging.

  • hadoop.regionserver_rpc_methodName.aboveOneSec A metric reflecting the durations of all responses that lasted for more than one second.

133.2.3. Output

The output is tagged with operation e.g. (operationTooSlow) if the call was a client operation, such as a Put, Get, or Delete, which we expose detailed fingerprint information for. If not, it is tagged (responseTooSlow) and still produces parseable JSON output, but with less verbose information solely regarding its duration and size in the RPC itself. TooLarge is substituted for TooSlow if the response size triggered the logging, with TooLarge appearing even in the case that both size and duration triggered logging.

133.2.4. Example

2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":1315501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1","vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timestamp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f98764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:33623","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","responsesize":0,"method":"multiPut"}

Note that everything inside the "tables" structure is output produced by MultiPut’s fingerprint, while the rest of the information is RPC-specific, such as processing time and client IP/port. Other client operations follow the same pattern and the same general structure, with necessary differences due to the nature of the individual operations. In the case that the call is not a client operation, that detailed fingerprint information will be completely absent.

This particular example, for example, would indicate that the likely cause of slowness is simply a very large (on the order of 100MB) multiput, as we can tell by the "vlen," or value length, fields of each put in the multiPut.

133.3. Block Cache Monitoring

Starting with HBase 0.98, the HBase Web UI includes the ability to monitor and report on the performance of the block cache. To view the block cache reports, click . Following are a few examples of the reporting capabilities.

bc basic
Figure 5. Basic Info
bc config
Figure 6. Config
bc stats
Figure 7. Stats
bc l1
Figure 8. L1 and L2

This is not an exhaustive list of all the screens and reports available. Have a look in the Web UI.

134. Cluster Replication

This information was previously available at Cluster Replication.

HBase provides a cluster replication mechanism which allows you to keep one cluster’s state synchronized with that of another cluster, using the write-ahead log (WAL) of the source cluster to propagate the changes. Some use cases for cluster replication include:

  • Backup and disaster recovery

  • Data aggregation

  • Geographic data distribution

  • Online data ingestion combined with offline data analytics

Replication is enabled at the granularity of the column family. Before enabling replication for a column family, create the table and all column families to be replicated, on the destination cluster.

134.1. Replication Overview

Cluster replication uses a source-push methodology. An HBase cluster can be a source (also called master or active, meaning that it is the originator of new data), a destination (also called slave or passive, meaning that it receives data via replication), or can fulfill both roles at once. Replication is asynchronous, and the goal of replication is eventual consistency. When the source receives an edit to a column family with replication enabled, that edit is propagated to all destination clusters using the WAL for that for that column family on the RegionServer managing the relevant region.

When data is replicated from one cluster to another, the original source of the data is tracked via a cluster ID which is part of the metadata. In HBase 0.96 and newer (HBASE-7709), all clusters which have already consumed the data are also tracked. This prevents replication loops.

The WALs for each region server must be kept in HDFS as long as they are needed to replicate data to any slave cluster. Each region server reads from the oldest log it needs to replicate and keeps track of its progress processing WALs inside ZooKeeper to simplify failure recovery. The position marker which indicates a slave cluster’s progress, as well as the queue of WALs to process, may be different for every slave cluster.

The clusters participating in replication can be of different sizes. The master cluster relies on randomization to attempt to balance the stream of replication on the slave clusters. It is expected that the slave cluster has storage capacity to hold the replicated data, as well as any data it is responsible for ingesting. If a slave cluster does run out of room, or is inaccessible for other reasons, it throws an error and the master retains the WAL and retries the replication at intervals.

Consistency Across Replicated Clusters

How your application builds on top of the HBase API matters when replication is in play. HBase’s replication system provides at-least-once delivery of client edits for an enabled column family to each configured destination cluster. In the event of failure to reach a given destination, the replication system will retry sending edits in a way that might repeat a given message. Further more, there is not a guaranteed order of delivery for client edits. In the event of a RegionServer failing, recovery of the replication queue happens independent of recovery of the individual regions that server was previously handling. This means that it is possible for the not-yet-replicated edits to be serviced by a RegionServer that is currently slower to replicate than the one that handles edits from after the failure.

The combination of these two properties (at-least-once delivery and the lack of message ordering) means that some destination clusters may end up in a different state if your application makes use of operations that are not idempotent, e.g. Increments.
Terminology Changes

Previously, terms such as master-master, master-slave, and cyclical were used to describe replication relationships in HBase. These terms added confusion, and have been abandoned in favor of discussions about cluster topologies appropriate for different scenarios.
Cluster Topologies

  • A central source cluster might propagate changes out to multiple destination clusters, for failover or due to geographic distribution.

  • A source cluster might push changes to a destination cluster, which might also push its own changes back to the original cluster.

  • Many different low-latency clusters might push changes to one centralized cluster for backup or resource-intensive data analytics jobs. The processed data might then be replicated back to the low-latency clusters.

Multiple levels of replication may be chained together to suit your organization’s needs. The following diagram shows a hypothetical scenario. Use the arrows to follow the data paths.

hbase replication diagram
Figure 9. Example of a Complex Cluster Replication Configuration

HBase replication borrows many concepts from the statement-based replication design used by MySQL. Instead of SQL statements, entire WALEdits (consisting of multiple cell inserts coming from Put and Delete operations on the clients) are replicated in order to maintain atomicity.

134.2. Managing and Configuring Cluster Replication

Cluster Configuration Overview

  1. Configure and start the source and destination clusters. Create tables with the same names and column families on both the source and destination clusters, so that the destination cluster knows where to store data it will receive.

  2. All hosts in the source and destination clusters should be reachable to each other.

  3. If both clusters use the same ZooKeeper cluster, you must use a different zookeeper.znode.parent, because they cannot write in the same folder.

  4. Check to be sure that replication has not been disabled. hbase.replication defaults to true.

  5. On the source cluster, in HBase Shell, add the destination cluster as a peer, using the add_peer command.

  6. On the source cluster, in HBase Shell, enable the table replication, using the enable_table_replication command.

  7. Check the logs to see if replication is taking place. If so, you will see messages like the following, coming from the ReplicationSource.

LOG.info("Replicating "+clusterId + " -> " + peerClusterId);
Cluster Management Commands
add_peer <ID> <CLUSTER_KEY>

Adds a replication relationship between two clusters.

  • ID — a unique string, which must not contain a hyphen.

  • CLUSTER_KEY: composed using the following template, with appropriate place-holders: hbase.zookeeper.quorum:hbase.zookeeper.property.clientPort:zookeeper.znode.parent

list_peers

list all replication relationships known by this cluster

enable_peer <ID>

Enable a previously-disabled replication relationship

disable_peer <ID>

Disable a replication relationship. HBase will no longer send edits to that peer cluster, but it still keeps track of all the new WALs that it will need to replicate if and when it is re-enabled. WALs are retained when enabling or disabling replication as long as peers exist.

remove_peer <ID>

Disable and remove a replication relationship. HBase will no longer send edits to that peer cluster or keep track of WALs.

enable_table_replication <TABLE_NAME>

Enable the table replication switch for all its column families. If the table is not found in the destination cluster then it will create one with the same name and column families.

disable_table_replication <TABLE_NAME>

Disable the table replication switch for all its column families.

134.3. Verifying Replicated Data

The VerifyReplication MapReduce job, which is included in HBase, performs a systematic comparison of replicated data between two different clusters. Run the VerifyReplication job on the master cluster, supplying it with the peer ID and table name to use for validation. You can limit the verification further by specifying a time range or specific families. The job’s short name is verifyrep. To run the job, use a command like the following:

+

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` "${HADOOP_HOME}/bin/hadoop" jar "${HBASE_HOME}/hbase-server-VERSION.jar" verifyrep --starttime=<timestamp> --endtime=<timestamp> --families=<myFam> <ID> <tableName>

+ The VerifyReplication command prints out GOODROWS and BADROWS counters to indicate rows that did and did not replicate correctly.

134.4. Detailed Information About Cluster Replication

replication overview
Figure 10. Replication Architecture Overview

134.4.1. Life of a WAL Edit

A single WAL edit goes through several steps in order to be replicated to a slave cluster.

  1. An HBase client uses a Put or Delete operation to manipulate data in HBase.

  2. The region server writes the request to the WAL in a way allows it to be replayed if it is not written successfully.

  3. If the changed cell corresponds to a column family that is scoped for replication, the edit is added to the queue for replication.

  4. In a separate thread, the edit is read from the log, as part of a batch process. Only the KeyValues that are eligible for replication are kept. Replicable KeyValues are part of a column family whose schema is scoped GLOBAL, are not part of a catalog such as hbase:meta, did not originate from the target slave cluster, and have not already been consumed by the target slave cluster.

  5. The edit is tagged with the master’s UUID and added to a buffer. When the buffer is filled, or the reader reaches the end of the file, the buffer is sent to a random region server on the slave cluster.

  6. The region server reads the edits sequentially and separates them into buffers, one buffer per table. After all edits are read, each buffer is flushed using Table, HBase’s normal client. The master’s UUID and the UUIDs of slaves which have already consumed the data are preserved in the edits they are applied, in order to prevent replication loops.

  7. In the master, the offset for the WAL that is currently being replicated is registered in ZooKeeper.

  8. The first three steps, where the edit is inserted, are identical.

  9. Again in a separate thread, the region server reads, filters, and edits the log edits in the same way as above. The slave region server does not answer the RPC call.

  10. The master sleeps and tries again a configurable number of times.

  11. If the slave region server is still not available, the master selects a new subset of region server to replicate to, and tries again to send the buffer of edits.

  12. Meanwhile, the WALs are rolled and stored in a queue in ZooKeeper. Logs that are archived by their region server, by moving them from the region server’s log directory to a central log directory, will update their paths in the in-memory queue of the replicating thread.

  13. When the slave cluster is finally available, the buffer is applied in the same way as during normal processing. The master region server will then replicate the backlog of logs that accumulated during the outage.

Spreading Queue Failover Load

When replication is active, a subset of region servers in the source cluster is responsible for shipping edits to the sink. This responsibility must be failed over like all other region server functions should a process or node crash. The following configuration settings are recommended for maintaining an even distribution of replication activity over the remaining live servers in the source cluster:

  • Set replication.source.maxretriesmultiplier to 300.

  • Set replication.source.sleepforretries to 1 (1 second). This value, combined with the value of replication.source.maxretriesmultiplier, causes the retry cycle to last about 5 minutes.

  • Set replication.sleep.before.failover to 30000 (30 seconds) in the source cluster site configuration.

Preserving Tags During Replication

By default, the codec used for replication between clusters strips tags, such as cell-level ACLs, from cells. To prevent the tags from being stripped, you can use a different codec which does not strip them. Configure hbase.replication.rpc.codec to use org.apache.hadoop.hbase.codec.KeyValueCodecWithTags, on both the source and sink RegionServers involved in the replication. This option was introduced in HBASE-10322.

134.4.2. Replication Internals

Replication State in ZooKeeper

HBase replication maintains its state in ZooKeeper. By default, the state is contained in the base node /hbase/replication. This node contains two child nodes, the Peers znode and the RS znode.

The Peers Znode

The peers znode is stored in /hbase/replication/peers by default. It consists of a list of all peer replication clusters, along with the status of each of them. The value of each peer is its cluster key, which is provided in the HBase Shell. The cluster key contains a list of ZooKeeper nodes in the cluster’s quorum, the client port for the ZooKeeper quorum, and the base znode for HBase in HDFS on that cluster.

The RS Znode

The rs znode contains a list of WAL logs which need to be replicated. This list is divided into a set of queues organized by region server and the peer cluster the region server is shipping the logs to. The rs znode has one child znode for each region server in the cluster. The child znode name is the region server’s hostname, client port, and start code. This list includes both live and dead region servers.

134.4.3. Choosing Region Servers to Replicate To

When a master cluster region server initiates a replication source to a slave cluster, it first connects to the slave’s ZooKeeper ensemble using the provided cluster key . It then scans the rs/ directory to discover all the available sinks (region servers that are accepting incoming streams of edits to replicate) and randomly chooses a subset of them using a configured ratio which has a default value of 10%. For example, if a slave cluster has 150 machines, 15 will be chosen as potential recipient for edits that this master cluster region server sends. Because this selection is performed by each master region server, the probability that all slave region servers are used is very high, and this method works for clusters of any size. For example, a master cluster of 10 machines replicating to a slave cluster of 5 machines with a ratio of 10% causes the master cluster region servers to choose one machine each at random.

A ZooKeeper watcher is placed on the ${zookeeper.znode.parent}/rs node of the slave cluster by each of the master cluster’s region servers. This watch is used to monitor changes in the composition of the slave cluster. When nodes are removed from the slave cluster, or if nodes go down or come back up, the master cluster’s region servers will respond by selecting a new pool of slave region servers to replicate to.

134.4.4. Keeping Track of Logs

Each master cluster region server has its own znode in the replication znodes hierarchy. It contains one znode per peer cluster (if 5 slave clusters, 5 znodes are created), and each of these contain a queue of WALs to process. Each of these queues will track the WALs created by that region server, but they can differ in size. For example, if one slave cluster becomes unavailable for some time, the WALs should not be deleted, so they need to stay in the queue while the others are processed. See rs.failover.details for an example.

When a source is instantiated, it contains the current WAL that the region server is writing to. During log rolling, the new file is added to the queue of each slave cluster’s znode just before it is made available. This ensures that all the sources are aware that a new log exists before the region server is able to append edits into it, but this operations is now more expensive. The queue items are discarded when the replication thread cannot read more entries from a file (because it reached the end of the last block) and there are other files in the queue. This means that if a source is up to date and replicates from the log that the region server writes to, reading up to the "end" of the current file will not delete the item in the queue.

A log can be archived if it is no longer used or if the number of logs exceeds hbase.regionserver.maxlogs because the insertion rate is faster than regions are flushed. When a log is archived, the source threads are notified that the path for that log changed. If a particular source has already finished with an archived log, it will just ignore the message. If the log is in the queue, the path will be updated in memory. If the log is currently being replicated, the change will be done atomically so that the reader doesn’t attempt to open the file when has already been moved. Because moving a file is a NameNode operation , if the reader is currently reading the log, it won’t generate any exception.

134.4.5. Reading, Filtering and Sending Edits

By default, a source attempts to read from a WAL and ship log entries to a sink as quickly as possible. Speed is limited by the filtering of log entries Only KeyValues that are scoped GLOBAL and that do not belong to catalog tables will be retained. Speed is also limited by total size of the list of edits to replicate per slave, which is limited to 64 MB by default. With this configuration, a master cluster region server with three slaves would use at most 192 MB to store data to replicate. This does not account for the data which was filtered but not garbage collected.

Once the maximum size of edits has been buffered or the reader reaches the end of the WAL, the source thread stops reading and chooses at random a sink to replicate to (from the list that was generated by keeping only a subset of slave region servers). It directly issues a RPC to the chosen region server and waits for the method to return. If the RPC was successful, the source determines whether the current file has been emptied or it contains more data which needs to be read. If the file has been emptied, the source deletes the znode in the queue. Otherwise, it registers the new offset in the log’s znode. If the RPC threw an exception, the source will retry 10 times before trying to find a different sink.

134.4.6. Cleaning Logs

If replication is not enabled, the master’s log-cleaning thread deletes old logs using a configured TTL. This TTL-based method does not work well with replication, because archived logs which have exceeded their TTL may still be in a queue. The default behavior is augmented so that if a log is past its TTL, the cleaning thread looks up every queue until it finds the log, while caching queues it has found. If the log is not found in any queues, the log will be deleted. The next time the cleaning process needs to look for a log, it starts by using its cached list.

WALs are saved when replication is enabled or disabled as long as peers exist.

134.4.7. Region Server Failover

When no region servers are failing, keeping track of the logs in ZooKeeper adds no value. Unfortunately, region servers do fail, and since ZooKeeper is highly available, it is useful for managing the transfer of the queues in the event of a failure.

Each of the master cluster region servers keeps a watcher on every other region server, in order to be notified when one dies (just as the master does). When a failure happens, they all race to create a znode called lock inside the dead region server’s znode that contains its queues. The region server that creates it successfully then transfers all the queues to its own znode, one at a time since ZooKeeper does not support renaming queues. After queues are all transferred, they are deleted from the old location. The znodes that were recovered are renamed with the ID of the slave cluster appended with the name of the dead server.

Next, the master cluster region server creates one new source thread per copied queue, and each of the source threads follows the read/filter/ship pattern. The main difference is that those queues will never receive new data, since they do not belong to their new region server. When the reader hits the end of the last log, the queue’s znode is deleted and the master cluster region server closes that replication source.

Given a master cluster with 3 region servers replicating to a single slave with id 2, the following hierarchy represents what the znodes layout could be at some point in time. The region servers' znodes all contain a peers znode which contains a single queue. The znode names in the queues represent the actual file names on HDFS in the form address,port.timestamp.

/hbase/replication/rs/
  1.1.1.1,60020,123456780/
    2/
      1.1.1.1,60020.1234  (Contains a position)
      1.1.1.1,60020.1265
  1.1.1.2,60020,123456790/
    2/
      1.1.1.2,60020.1214  (Contains a position)
      1.1.1.2,60020.1248
      1.1.1.2,60020.1312
  1.1.1.3,60020,    123456630/
    2/
      1.1.1.3,60020.1280  (Contains a position)

Assume that 1.1.1.2 loses its ZooKeeper session. The survivors will race to create a lock, and, arbitrarily, 1.1.1.3 wins. It will then start transferring all the queues to its local peers znode by appending the name of the dead server. Right before 1.1.1.3 is able to clean up the old znodes, the layout will look like the following:

/hbase/replication/rs/
  1.1.1.1,60020,123456780/
    2/
      1.1.1.1,60020.1234  (Contains a position)
      1.1.1.1,60020.1265
  1.1.1.2,60020,123456790/
    lock
    2/
      1.1.1.2,60020.1214  (Contains a position)
      1.1.1.2,60020.1248
      1.1.1.2,60020.1312
  1.1.1.3,60020,123456630/
    2/
      1.1.1.3,60020.1280  (Contains a position)

    2-1.1.1.2,60020,123456790/
      1.1.1.2,60020.1214  (Contains a position)
      1.1.1.2,60020.1248
      1.1.1.2,60020.1312

Some time later, but before 1.1.1.3 is able to finish replicating the last WAL from 1.1.1.2, it dies too. Some new logs were also created in the normal queues. The last region server will then try to lock 1.1.1.3’s znode and will begin transferring all the queues. The new layout will be:

/hbase/replication/rs/
  1.1.1.1,60020,123456780/
    2/
      1.1.1.1,60020.1378  (Contains a position)

    2-1.1.1.3,60020,123456630/
      1.1.1.3,60020.1325  (Contains a position)
      1.1.1.3,60020.1401

    2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/
      1.1.1.2,60020.1312  (Contains a position)
  1.1.1.3,60020,123456630/
    lock
    2/
      1.1.1.3,60020.1325  (Contains a position)
      1.1.1.3,60020.1401

    2-1.1.1.2,60020,123456790/
      1.1.1.2,60020.1312  (Contains a position)

134.5. Replication Metrics

The following metrics are exposed at the global region server level and at the peer level:

source.sizeOfLogQueue

number of WALs to process (excludes the one which is being processed) at the Replication source

source.shippedOps

number of mutations shipped

source.logEditsRead

number of mutations read from WALs at the replication source

source.ageOfLastShippedOp

age of last batch that was shipped by the replication source

source.completedLogs

The number of write-ahead-log files that have completed their acknowledged sending to the peer associated with this source. Increments to this metric are a part of normal operation of HBase replication.

source.completedRecoverQueues

The number of recovery queues this source has completed sending to the associated peer. Increments to this metric are a part of normal recovery of HBase replication in the face of failed Region Servers.

source.uncleanlyClosedLogs

The number of write-ahead-log files the replication system considered completed after reaching the end of readable entries in the face of an uncleanly closed file.

source.ignoredUncleanlyClosedLogContentsInBytes

When a write-ahead-log file is not closed cleanly, there will likely be some entry that has been partially serialized. This metric contains the number of bytes of such entries the HBase replication system believes were remaining at the end of files skipped in the face of an uncleanly closed file. Those bytes should either be in different file or represent a client write that was not acknowledged.

source.restartedLogReading

The number of times the HBase replication system detected that it failed to correctly parse a cleanly closed write-ahead-log file. In this circumstance, the system replays the entire log from the beginning, ensuring that no edits fail to be acknowledged by the associated peer. Increments to this metric indicate that the HBase replication system is having difficulty correctly handling failures in the underlying distributed storage system. No dataloss should occur, but you should check Region Server log files for details of the failures.

source.repeatedLogFileBytes

When the HBase replication system determines that it needs to replay a given write-ahead-log file, this metric is incremented by the number of bytes the replication system believes had already been acknowledged by the associated peer prior to starting over.

source.closedLogsWithUnknownFileLength

Incremented when the HBase replication system believes it is at the end of a write-ahead-log file but it can not determine the length of that file in the underlying distributed storage system. Could indicate dataloss since the replication system is unable to determine if the end of readable entries lines up with the expected end of the file. You should check Region Server log files for details of the failures.

134.6. Replication Configuration Options

Option Description Default

zookeeper.znode.parent

The name of the base ZooKeeper znode used for HBase

/hbase

zookeeper.znode.replication

The name of the base znode used for replication

replication

zookeeper.znode.replication.peers

The name of the peer znode

peers

zookeeper.znode.replication.peers.state

The name of peer-state znode

peer-state

zookeeper.znode.replication.rs

The name of the rs znode

rs

replication.sleep.before.failover

How many milliseconds a worker should sleep before attempting to replicate a dead region server’s WAL queues.

replication.executor.workers

The number of region servers a given region server should attempt to failover simultaneously.

1

134.7. Monitoring Replication Status

You can use the HBase Shell command status 'replication' to monitor the replication status on your cluster. The command has three variations: * status 'replication' — prints the status of each source and its sinks, sorted by hostname. * status 'replication', 'source' — prints the status for each replication source, sorted by hostname. * status 'replication', 'sink' — prints the status for each replication sink, sorted by hostname.

135. Running Multiple Workloads On a Single Cluster

HBase provides the following mechanisms for managing the performance of a cluster handling multiple workloads: . Quotas . Request Queues . Multiple-Typed Queues

135.1. Quotas

HBASE-11598 introduces quotas, which allow you to throttle requests based on the following limits:

  1. The number or size of requests(read, write, or read+write) in a given timeframe

  2. The number of tables allowed in a namespace

These limits can be enforced for a specified user, table, or namespace.

Enabling Quotas

Quotas are disabled by default. To enable the feature, set the hbase.quota.enabled property to true in hbase-site.xml file for all cluster nodes.

General Quota Syntax

  1. THROTTLE_TYPE can be expressed as READ, WRITE, or the default type(read + write).

  2. Timeframes can be expressed in the following units: sec, min, hour, day

  3. Request sizes can be expressed in the following units: B (bytes), K (kilobytes), M (megabytes), G (gigabytes), T (terabytes), P (petabytes)

  4. Numbers of requests are expressed as an integer followed by the string req

  5. Limits relating to time are expressed as req/time or size/time. For instance 10req/day or 100P/hour.

  6. Numbers of tables or regions are expressed as integers.

Setting Request Quotas

You can set quota rules ahead of time, or you can change the throttle at runtime. The change will propagate after the quota refresh period has expired. This expiration period defaults to 5 minutes. To change it, modify the hbase.quota.refresh.period property in hbase-site.xml. This property is expressed in milliseconds and defaults to 300000.

# Limit user u1 to 10 requests per second
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => '10req/sec'

# Limit user u1 to 10 read requests per second
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => READ, USER => 'u1', LIMIT => '10req/sec'

# Limit user u1 to 10 M per day everywhere
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => '10M/day'

# Limit user u1 to 10 M write size per sec
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => WRITE, USER => 'u1', LIMIT => '10M/sec'

# Limit user u1 to 5k per minute on table t2
hbase> set_quota TYPE => THROTTLE, USER => 'u1', TABLE => 't2', LIMIT => '5K/min'

# Limit user u1 to 10 read requests per sec on table t2
hbase> set_quota TYPE => THROTTLE, THROTTLE_TYPE => READ, USER => 'u1', TABLE => 't2', LIMIT => '10req/sec'

# Remove an existing limit from user u1 on namespace ns2
hbase> set_quota TYPE => THROTTLE, USER => 'u1', NAMESPACE => 'ns2', LIMIT => NONE

# Limit all users to 10 requests per hour on namespace ns1
hbase> set_quota TYPE => THROTTLE, NAMESPACE => 'ns1', LIMIT => '10req/hour'

# Limit all users to 10 T per hour on table t1
hbase> set_quota TYPE => THROTTLE, TABLE => 't1', LIMIT => '10T/hour'

# Remove all existing limits from user u1
hbase> set_quota TYPE => THROTTLE, USER => 'u1', LIMIT => NONE

# List all quotas for user u1 in namespace ns2
hbase> list_quotas USER => 'u1, NAMESPACE => 'ns2'

# List all quotas for namespace ns2
hbase> list_quotas NAMESPACE => 'ns2'

# List all quotas for table t1
hbase> list_quotas TABLE => 't1'

# list all quotas
hbase> list_quotas

You can also place a global limit and exclude a user or a table from the limit by applying the GLOBAL_BYPASS property.

hbase> set_quota NAMESPACE => 'ns1', LIMIT => '100req/min'               # a per-namespace request limit
hbase> set_quota USER => 'u1', GLOBAL_BYPASS => true                     # user u1 is not affected by the limit
Setting Namespace Quotas

You can specify the maximum number of tables or regions allowed in a given namespace, either when you create the namespace or by altering an existing namespace, by setting the hbase.namespace.quota.maxtables property on the namespace.

Limiting Tables Per Namespace
# Create a namespace with a max of 5 tables
hbase> create_namespace 'ns1', {'hbase.namespace.quota.maxtables'=>'5'}

# Alter an existing namespace to have a max of 8 tables
hbase> alter_namespace 'ns2', {METHOD => 'set', 'hbase.namespace.quota.maxtables'=>'8'}

# Show quota information for a namespace
hbase> describe_namespace 'ns2'

# Alter an existing namespace to remove a quota
hbase> alter_namespace 'ns2', {METHOD => 'unset', NAME=>'hbase.namespace.quota.maxtables'}
Limiting Regions Per Namespace
# Create a namespace with a max of 10 regions
hbase> create_namespace 'ns1', {'hbase.namespace.quota.maxregions'=>'10'

# Show quota information for a namespace
hbase> describe_namespace 'ns1'

# Alter an existing namespace to have a max of 20 tables
hbase> alter_namespace 'ns2', {METHOD => 'set', 'hbase.namespace.quota.maxregions'=>'20'}

# Alter an existing namespace to remove a quota
hbase> alter_namespace 'ns2', {METHOD => 'unset', NAME=> 'hbase.namespace.quota.maxregions'}

135.2. Request Queues

If no throttling policy is configured, when the RegionServer receives multiple requests, they are now placed into a queue waiting for a free execution slot (HBASE-6721). The simplest queue is a FIFO queue, where each request waits for all previous requests in the queue to finish before running. Fast or interactive queries can get stuck behind large requests.

If you are able to guess how long a request will take, you can reorder requests by pushing the long requests to the end of the queue and allowing short requests to preempt them. Eventually, you must still execute the large requests and prioritize the new requests behind them. The short requests will be newer, so the result is not terrible, but still suboptimal compared to a mechanism which allows large requests to be split into multiple smaller ones.

HBASE-10993 introduces such a system for deprioritizing long-running scanners. There are two types of queues, fifo and deadline. To configure the type of queue used, configure the hbase.ipc.server.callqueue.type property in hbase-site.xml. There is no way to estimate how long each request may take, so de-prioritization only affects scans, and is based on the number of “next” calls a scan request has made. An assumption is made that when you are doing a full table scan, your job is not likely to be interactive, so if there are concurrent requests, you can delay long-running scans up to a limit tunable by setting the hbase.ipc.server.queue.max.call.delay property. The slope of the delay is calculated by a simple square root of (numNextCall * weight) where the weight is configurable by setting the hbase.ipc.server.scan.vtime.weight property.

135.3. Multiple-Typed Queues

You can also prioritize or deprioritize different kinds of requests by configuring a specified number of dedicated handlers and queues. You can segregate the scan requests in a single queue with a single handler, and all the other available queues can service short Get requests.

You can adjust the IPC queues and handlers based on the type of workload, using static tuning options. This approach is an interim first step that will eventually allow you to change the settings at runtime, and to dynamically adjust values based on the load.

Multiple Queues

To avoid contention and separate different kinds of requests, configure the hbase.ipc.server.callqueue.handler.factor property, which allows you to increase the number of queues and control how many handlers can share the same queue., allows admins to increase the number of queues and decide how many handlers share the same queue.

Using more queues reduces contention when adding a task to a queue or selecting it from a queue. You can even configure one queue per handler. The trade-off is that if some queues contain long-running tasks, a handler may need to wait to execute from that queue rather than stealing from another queue which has waiting tasks.

Read and Write Queues

With multiple queues, you can now divide read and write requests, giving more priority (more queues) to one or the other type. Use the hbase.ipc.server.callqueue.read.ratio property to choose to serve more reads or more writes.

Get and Scan Queues

Similar to the read/write split, you can split gets and scans by tuning the hbase.ipc.server.callqueue.scan.ratio property to give more priority to gets or to scans. A scan ratio of 0.1 will give more queue/handlers to the incoming gets, which means that more gets can be processed at the same time and that fewer scans can be executed at the same time. A value of 0.9 will give more queue/handlers to scans, so the number of scans executed will increase and the number of gets will decrease.

136. HBase Backup

There are two broad strategies for performing HBase backups: backing up with a full cluster shutdown, and backing up on a live cluster. Each approach has pros and cons.

For additional information, see HBase Backup Options over on the Sematext Blog.

136.1. Full Shutdown Backup

Some environments can tolerate a periodic full shutdown of their HBase cluster, for example if it is being used a back-end analytic capacity and not serving front-end web-pages. The benefits are that the NameNode/Master are RegionServers are down, so there is no chance of missing any in-flight changes to either StoreFiles or metadata. The obvious con is that the cluster is down. The steps include:

136.1.1. Stop HBase

136.1.2. Distcp

Distcp could be used to either copy the contents of the HBase directory in HDFS to either the same cluster in another directory, or to a different cluster.

Note: Distcp works in this situation because the cluster is down and there are no in-flight edits to files. Distcp-ing of files in the HBase directory is not generally recommended on a live cluster.

136.1.3. Restore (if needed)

The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory via distcp. The act of copying these files creates new HDFS metadata, which is why a restore of the NameNode edits from the time of the HBase backup isn’t required for this kind of restore, because it’s a restore (via distcp) of a specific HDFS directory (i.e., the HBase part) not the entire HDFS file-system.

136.2. Live Cluster Backup - Replication

This approach assumes that there is a second cluster. See the HBase page on replication for more information.

136.3. Live Cluster Backup - CopyTable

The copytable utility could either be used to copy data from one table to another on the same cluster, or to copy data to another table on another cluster.

Since the cluster is up, there is a risk that edits could be missed in the copy process.

136.4. Live Cluster Backup - Export

The export approach dumps the content of a table to HDFS on the same cluster. To restore the data, the import utility would be used.

Since the cluster is up, there is a risk that edits could be missed in the export process.

137. HBase Snapshots

HBase Snapshots allow you to take a snapshot of a table without too much impact on Region Servers. Snapshot, Clone and restore operations don’t involve data copying. Also, Exporting the snapshot to another cluster doesn’t have impact on the Region Servers.

Prior to version 0.94.6, the only way to backup or to clone a table is to use CopyTable/ExportTable, or to copy all the hfiles in HDFS after disabling the table. The disadvantages of these methods are that you can degrade region server performance (Copy/Export Table) or you need to disable the table, that means no reads or writes; and this is usually unacceptable.

137.1. Configuration

To turn on the snapshot support just set the hbase.snapshot.enabled property to true. (Snapshots are enabled by default in 0.95+ and off by default in 0.94.6+)

  <property>
    <name>hbase.snapshot.enabled</name>
    <value>true</value>
  </property>

137.2. Take a Snapshot

You can take a snapshot of a table regardless of whether it is enabled or disabled. The snapshot operation doesn’t involve any data copying.

$ ./bin/hbase shell
hbase> snapshot 'myTable', 'myTableSnapshot-122112'
Take a Snapshot Without Flushing

The default behavior is to perform a flush of data in memory before the snapshot is taken. This means that data in memory is included in the snapshot. In most cases, this is the desired behavior. However, if your set-up can tolerate data in memory being excluded from the snapshot, you can use the SKIP_FLUSH option of the snapshot command to disable and flushing while taking the snapshot.

hbase> snapshot 'mytable', 'snapshot123', {SKIP_FLUSH => true}
There is no way to determine or predict whether a very concurrent insert or update will be included in a given snapshot, whether flushing is enabled or disabled. A snapshot is only a representation of a table during a window of time. The amount of time the snapshot operation will take to reach each Region Server may vary from a few seconds to a minute, depending on the resource load and speed of the hardware or network, among other factors. There is also no way to know whether a given insert or update is in memory or has been flushed.

137.3. Listing Snapshots

List all snapshots taken (by printing the names and relative information).

$ ./bin/hbase shell
hbase> list_snapshots

137.4. Deleting Snapshots

You can remove a snapshot, and the files retained for that snapshot will be removed if no longer needed.

$ ./bin/hbase shell
hbase> delete_snapshot 'myTableSnapshot-122112'

137.5. Clone a table from snapshot

From a snapshot you can create a new table (clone operation) with the same data that you had when the snapshot was taken. The clone operation, doesn’t involve data copies, and a change to the cloned table doesn’t impact the snapshot or the original table.

$ ./bin/hbase shell
hbase> clone_snapshot 'myTableSnapshot-122112', 'myNewTestTable'

137.6. Restore a snapshot

The restore operation requires the table to be disabled, and the table will be restored to the state at the time when the snapshot was taken, changing both data and schema if required.

$ ./bin/hbase shell
hbase> disable 'myTable'
hbase> restore_snapshot 'myTableSnapshot-122112'
Since Replication works at log level and snapshots at file-system level, after a restore, the replicas will be in a different state from the master. If you want to use restore, you need to stop replication and redo the bootstrap.

In case of partial data-loss due to misbehaving client, instead of a full restore that requires the table to be disabled, you can clone the table from the snapshot and use a Map-Reduce job to copy the data that you need, from the clone to the main one.

137.7. Snapshots operations and ACLs

If you are using security with the AccessController Coprocessor (See hbase.accesscontrol.configuration), only a global administrator can take, clone, or restore a snapshot, and these actions do not capture the ACL rights. This means that restoring a table preserves the ACL rights of the existing table, while cloning a table creates a new table that has no ACL rights until the administrator adds them.

137.8. Export to another cluster

The ExportSnapshot tool copies all the data related to a snapshot (hfiles, logs, snapshot metadata) to another cluster. The tool executes a Map-Reduce job, similar to distcp, to copy files between the two clusters, and since it works at file-system level the hbase cluster does not have to be online.

To copy a snapshot called MySnapshot to an HBase cluster srv2 (hdfs:///srv2:8082/hbase) using 16 mappers:

$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16
Limiting Bandwidth Consumption

You can limit the bandwidth consumption when exporting a snapshot, by specifying the -bandwidth parameter, which expects an integer representing megabytes per second. The following example limits the above example to 200 MB/sec.

$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16 -bandwidth 200

137.9. Storing Snapshots in an Amazon S3 Bucket

You can store and retrieve snapshots from Amazon S3, using the following procedure.

You can also store snapshots in Microsoft Azure Blob Storage. See Storing Snapshots in Microsoft Azure Blob Storage.
Prerequisites

  • You must be using HBase 1.0 or higher and Hadoop 2.6.1 or higher, which is the first configuration that uses the Amazon AWS SDK.

  • You must use the s3a:// protocol to connect to Amazon S3. The older s3n:// and s3:// protocols have various limitations and do not use the Amazon AWS SDK.

  • The s3a:// URI must be configured and available on the server where you run the commands to export and restore the snapshot.

After you have fulfilled the prerequisites, take the snapshot like you normally would. Afterward, you can export it using the org.apache.hadoop.hbase.snapshot.ExportSnapshot command like the one below, substituting your own s3a:// path in the copy-from or copy-to directive and substituting or modifying other options as required:

$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \
    -snapshot MySnapshot \
    -copy-from hdfs://srv2:8082/hbase \
    -copy-to s3a://<bucket>/<namespace>/hbase \
    -chuser MyUser \
    -chgroup MyGroup \
    -chmod 700 \
    -mappers 16
$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \
    -snapshot MySnapshot
    -copy-from s3a://<bucket>/<namespace>/hbase \
    -copy-to hdfs://srv2:8082/hbase \
    -chuser MyUser \
    -chgroup MyGroup \
    -chmod 700 \
    -mappers 16

You can also use the org.apache.hadoop.hbase.snapshot.SnapshotInfo utility with the s3a:// path by including the -remote-dir option.

$ hbase org.apache.hadoop.hbase.snapshot.SnapshotInfo \
    -remote-dir s3a://<bucket>/<namespace>/hbase \
    -list-snapshots

138. Storing Snapshots in Microsoft Azure Blob Storage

You can store snapshots in Microsoft Azure Blog Storage using the same techniques as in Storing Snapshots in an Amazon S3 Bucket.

Prerequisites

After you meet the prerequisites, follow the instructions in Storing Snapshots in an Amazon S3 Bucket, replacingthe protocol specifier with wasb:// or wasbs://.

139. Capacity Planning and Region Sizing

There are several considerations when planning the capacity for an HBase cluster and performing the initial configuration. Start with a solid understanding of how HBase handles data internally.

139.1. Node count and hardware/VM configuration

139.1.1. Physical data size

Physical data size on disk is distinct from logical size of your data and is affected by the following:

  • Increased by HBase overhead

  • See keyvalue and keysize. At least 24 bytes per key-value (cell), can be more. Small keys/values means more relative overhead.

  • KeyValue instances are aggregated into blocks, which are indexed. Indexes also have to be stored. Blocksize is configurable on a per-ColumnFamily basis. See regions.arch.

  • Decreased by compression and data block encoding, depending on data. See also this thread. You might want to test what compression and encoding (if any) make sense for your data.

  • Increased by size of region server wal (usually fixed and negligible - less than half of RS memory size, per RS).

  • Increased by HDFS replication - usually x3.

Aside from the disk space necessary to store the data, one RS may not be able to serve arbitrarily large amounts of data due to some practical limits on region count and size (see ops.capacity.regions).

139.1.2. Read/Write throughput

Number of nodes can also be driven by required throughput for reads and/or writes. The throughput one can get per node depends a lot on data (esp. key/value sizes) and request patterns, as well as node and system configuration. Planning should be done for peak load if it is likely that the load would be the main driver of the increase of the node count. PerformanceEvaluation and ycsb tools can be used to test single node or a test cluster.

For write, usually 5-15Mb/s per RS can be expected, since every region server has only one active WAL. There’s no good estimate for reads, as it depends vastly on data, requests, and cache hit rate. perf.casestudy might be helpful.

139.1.3. JVM GC limitations

RS cannot currently utilize very large heap due to cost of GC. There’s also no good way of running multiple RS-es per server (other than running several VMs per machine). Thus, ~20-24Gb or less memory dedicated to one RS is recommended. GC tuning is required for large heap sizes. See gcpause, trouble.log.gc and elsewhere (TODO: where?)

139.2. Determining region count and size

Generally less regions makes for a smoother running cluster (you can always manually split the big regions later (if necessary) to spread the data, or request load, over the cluster); 20-200 regions per RS is a reasonable range. The number of regions cannot be configured directly (unless you go for fully disable.splitting); adjust the region size to achieve the target region size given table size.

When configuring regions for multiple tables, note that most region settings can be set on a per-table basis via HTableDescriptor, as well as shell commands. These settings will override the ones in hbase-site.xml. That is useful if your tables have different workloads/use cases.

Also note that in the discussion of region sizes here, HDFS replication factor is not (and should not be) taken into account, whereas other factors ops.capacity.nodes.datasize should be. So, if your data is compressed and replicated 3 ways by HDFS, "9 Gb region" means 9 Gb of compressed data. HDFS replication factor only affects your disk usage and is invisible to most HBase code.

139.2.1. Viewing the Current Number of Regions

You can view the current number of regions for a given table using the HMaster UI. In the Tables section, the number of online regions for each table is listed in the Online Regions column. This total only includes the in-memory state and does not include disabled or offline regions. If you do not want to use the HMaster UI, you can determine the number of regions by counting the number of subdirectories of the /hbase/<table>/ subdirectories in HDFS, or by running the bin/hbase hbck command. Each of these methods may return a slightly different number, depending on the status of each region.

139.2.2. Number of regions per RS - upper bound

In production scenarios, where you have a lot of data, you are normally concerned with the maximum number of regions you can have per server. too many regions has technical discussion on the subject. Basically, the maximum number of regions is mostly determined by memstore memory usage. Each region has its own memstores; these grow up to a configurable size; usually in 128-256 MB range, see hbase.hregion.memstore.flush.size. One memstore exists per column family (so there’s only one per region if there’s one CF in the table). The RS dedicates some fraction of total memory to its memstores (see hbase.regionserver.global.memstore.size). If this memory is exceeded (too much memstore usage), it can cause undesirable consequences such as unresponsive server or compaction storms. A good starting point for the number of regions per RS (assuming one table) is:

((RS memory) * (total memstore fraction)) / ((memstore size)*(# column families))

This formula is pseudo-code. Here are two formulas using the actual tunable parameters, first for HBase 0.98+ and second for HBase 0.94.x.

HBase 0.98.x
((RS Xmx) * hbase.regionserver.global.memstore.size) / (hbase.hregion.memstore.flush.size * (# column families))
HBase 0.94.x
((RS Xmx) * hbase.regionserver.global.memstore.upperLimit) / (hbase.hregion.memstore.flush.size * (# column families))+

If a given RegionServer has 16 GB of RAM, with default settings, the formula works out to 16384*0.4/128 ~ 51 regions per RS is a starting point. The formula can be extended to multiple tables; if they all have the same configuration, just use the total number of families.

This number can be adjusted; the formula above assumes all your regions are filled at approximately the same rate. If only a fraction of your regions are going to be actively written to, you can divide the result by that fraction to get a larger region count. Then, even if all regions are written to, all region memstores are not filled evenly, and eventually jitter appears even if they are (due to limited number of concurrent flushes). Thus, one can have as many as 2-3 times more regions than the starting point; however, increased numbers carry increased risk.

For write-heavy workload, memstore fraction can be increased in configuration at the expense of block cache; this will also allow one to have more regions.

139.2.3. Number of regions per RS - lower bound

HBase scales by having regions across many servers. Thus if you have 2 regions for 16GB data, on a 20 node machine your data will be concentrated on just a few machines - nearly the entire cluster will be idle. This really can’t be stressed enough, since a common problem is loading 200MB data into HBase and then wondering why your awesome 10 node cluster isn’t doing anything.

On the other hand, if you have a very large amount of data, you may also want to go for a larger number of regions to avoid having regions that are too large.

139.2.4. Maximum region size

For large tables in production scenarios, maximum region size is mostly limited by compactions - very large compactions, esp. major, can degrade cluster performance. Currently, the recommended maximum region size is 10-20Gb, and 5-10Gb is optimal. For older 0.90.x codebase, the upper-bound of regionsize is about 4Gb, with a default of 256Mb.

The size at which the region is split into two is generally configured via hbase.hregion.max.filesize; for details, see arch.region.splits.

If you cannot estimate the size of your tables well, when starting off, it’s probably best to stick to the default region size, perhaps going smaller for hot tables (or manually split hot regions to spread the load over the cluster), or go with larger region sizes if your cell sizes tend to be largish (100k and up).

In HBase 0.98, experimental stripe compactions feature was added that would allow for larger regions, especially for log data. See ops.stripe.

139.2.5. Total data size per region server

According to above numbers for region size and number of regions per region server, in an optimistic estimate 10 GB x 100 regions per RS will give up to 1TB served per region server, which is in line with some of the reported multi-PB use cases. However, it is important to think about the data vs cache size ratio at the RS level. With 1TB of data per server and 10 GB block cache, only 1% of the data will be cached, which may barely cover all block indices.

139.3. Initial configuration and tuning

First, see important configurations. Note that some configurations, more than others, depend on specific scenarios. Pay special attention to:

  • hbase.regionserver.handler.count - request handler thread count, vital for high-throughput workloads.

  • config.wals - the blocking number of WAL files depends on your memstore configuration and should be set accordingly to prevent potential blocking when doing high volume of writes.

Then, there are some considerations when setting up your cluster and tables.

139.3.1. Compactions

Depending on read/write volume and latency requirements, optimal compaction settings may be different. See compaction for some details.

When provisioning for large data sizes, however, it’s good to keep in mind that compactions can affect write throughput. Thus, for write-intensive workloads, you may opt for less frequent compactions and more store files per regions. Minimum number of files for compactions (hbase.hstore.compaction.min) can be set to higher value; hbase.hstore.blockingStoreFiles should also be increased, as more files might accumulate in such case. You may also consider manually managing compactions: managed.compactions

139.3.2. Pre-splitting the table

Based on the target number of the regions per RS (see ops.capacity.regions.count) and number of RSes, one can pre-split the table at creation time. This would both avoid some costly splitting as the table starts to fill up, and ensure that the table starts out already distributed across many servers.

If the table is expected to grow large enough to justify that, at least one region per RS should be created. It is not recommended to split immediately into the full target number of regions (e.g. 50 * number of RSes), but a low intermediate value can be chosen. For multiple tables, it is recommended to be conservative with presplitting (e.g. pre-split 1 region per RS at most), especially if you don’t know how much each table will grow. If you split too much, you may end up with too many regions, with some tables having too many small regions.

For pre-splitting howto, see manual region splitting decisions and precreate.regions.

140. Table Rename

In versions 0.90.x of hbase and earlier, we had a simple script that would rename the hdfs table directory and then do an edit of the hbase:meta table replacing all mentions of the old table name with the new. The script was called ./bin/rename_table.rb. The script was deprecated and removed mostly because it was unmaintained and the operation performed by the script was brutal.

As of hbase 0.94.x, you can use the snapshot facility renaming a table. Here is how you would do it using the hbase shell:

hbase shell> disable 'tableName'
hbase shell> snapshot 'tableName', 'tableSnapshot'
hbase shell> clone_snapshot 'tableSnapshot', 'newTableName'
hbase shell> delete_snapshot 'tableSnapshot'
hbase shell> drop 'tableName'

or in code it would be as follows:

void rename(Admin admin, String oldTableName, TableName newTableName) {
  String snapshotName = randomName();
  admin.disableTable(oldTableName);
  admin.snapshot(snapshotName, oldTableName);
  admin.cloneSnapshot(snapshotName, newTableName);
  admin.deleteSnapshot(snapshotName);
  admin.deleteTable(oldTableName);
}

Building and Developing Apache HBase

This chapter contains information and guidelines for building and releasing HBase code and documentation. Being familiar with these guidelines will help the HBase committers to use your contributions more easily.

141. Getting Involved

Apache HBase gets better only when people contribute! If you are looking to contribute to Apache HBase, look for issues in JIRA tagged with the label 'beginner'. These are issues HBase contributors have deemed worthy but not of immediate priority and a good way to ramp on HBase internals. See What label is used for issues that are good on ramps for new contributors? from the dev mailing list for background.

Before you get started submitting code to HBase, please refer to developing.

As Apache HBase is an Apache Software Foundation project, see asf for more information about how the ASF functions.

141.1. Mailing Lists

Sign up for the dev-list and the user-list. See the mailing lists page. Posing questions - and helping to answer other people’s questions - is encouraged! There are varying levels of experience on both lists so patience and politeness are encouraged (and please stay on topic.)

141.2. Internet Relay Chat (IRC)

For real-time questions and discussions, use the #hbase IRC channel on the FreeNode IRC network. FreeNode offers a web-based client, but most people prefer a native client, and several clients are available for each operating system.

141.3. Jira

Check for existing issues in Jira. If it’s either a new feature request, enhancement, or a bug, file a ticket.

To check for existing issues which you can tackle as a beginner, search for issues in JIRA tagged with the label 'beginner'.

  • .JIRA PrioritiesBlocker: Should only be used if the issue WILL cause data loss or cluster instability reliably.

  • Critical: The issue described can cause data loss or cluster instability in some cases.

  • Major: Important but not tragic issues, like updates to the client API that will add a lot of much-needed functionality or significant bugs that need to be fixed but that don’t cause data loss.

  • Minor: Useful enhancements and annoying but not damaging bugs.

  • Trivial: Useful enhancements but generally cosmetic.

Example 59. Code Blocks in Jira Comments

A commonly used macro in Jira is {code}. Everything inside the tags is preformatted, as in this example.

{code}
code snippet
{code}

142. Apache HBase Repositories

There are two different repositories for Apache HBase: Subversion (SVN) and Git. GIT is our repository of record for all but the Apache HBase website. We used to be on SVN. We migrated. See Migrate Apache HBase SVN Repos to Git. See Source Code Management page for contributor and committer links or search for HBase on the Apache Git page.

143. IDEs

143.1. Eclipse

143.1.1. Code Formatting

Under the dev-support/ folder, you will find hbase_eclipse_formatter.xml. We encourage you to have this formatter in place in eclipse when editing HBase code.

Procedure: Load the HBase Formatter Into Eclipse

  1. Open the menu item.

  2. In Preferences, Go to Java→Code Style→Formatter.

  3. Click Import and browse to the location of the hbase_eclipse_formatter.xml file, which is in the dev-support/ directory. Click Apply.

  4. Still in Preferences, click . Be sure the following options are selected:

    • Perform the selected actions on save

    • Format source code

    • Format edited lines

      Click Apply. Close all dialog boxes and return to the main window.

In addition to the automatic formatting, make sure you follow the style guidelines explained in common.patch.feedback

Also, no @author tags - that’s a rule. Quality Javadoc comments are appreciated. And include the Apache license.

143.1.2. Eclipse Git Plugin

If you cloned the project via git, download and install the Git plugin (EGit). Attach to your local git repo (via the Git Repositories window) and you’ll be able to see file revision history, generate patches, etc.

143.1.3. HBase Project Setup in Eclipse using m2eclipse

The easiest way is to use the m2eclipse plugin for Eclipse. Eclipse Indigo or newer includes m2eclipse, or you can download it from http://www.eclipse.org/m2e/. It provides Maven integration for Eclipse, and even lets you use the direct Maven commands from within Eclipse to compile and test your project.

To import the project, click and select the HBase root directory. m2eclipse locates all the hbase modules for you.

If you install m2eclipse and import HBase in your workspace, do the following to fix your eclipse Build Path.

  1. Remove target folder

  2. Add target/generated-jamon and target/generated-sources/java folders.

  3. Remove from your Build Path the exclusions on the src/main/resources and src/test/resources to avoid error message in the console, such as the following:

    Failed to execute goal
org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase:
'An Ant BuildException has occurred: Replace: source file .../target/classes/hbase-default.xml
doesn't exist

    This will also reduce the eclipse build cycles and make your life easier when developing.

143.1.4. HBase Project Setup in Eclipse Using the Command Line

Instead of using m2eclipse, you can generate the Eclipse files from the command line.

  1. First, run the following command, which builds HBase. You only need to do this once.

    mvn clean install -DskipTests

  2. Close Eclipse, and execute the following command from the terminal, in your local HBase project directory, to generate new .project and .classpath files.

    mvn eclipse:eclipse

  3. Reopen Eclipse and import the .project file in the HBase directory to a workspace.

143.1.5. Maven Classpath Variable

The $M2_REPO classpath variable needs to be set up for the project. This needs to be set to your local Maven repository, which is usually ~/.m2/repository

If this classpath variable is not configured, you will see compile errors in Eclipse like this:

Description	Resource	Path	Location	Type
The project cannot be built until build path errors are resolved	hbase		Unknown	Java Problem
Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase'	hbase		Build path	Build Path Problem
Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in project 'hbase'	hbase		Build path	Build Path Problem
Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-java-2.3.0.jar' in project 'hbase'	hbase		Build path	Build Path Problem Unbound classpath variable:

143.1.6. Eclipse Known Issues

Eclipse will currently complain about Bytes.java. It is not possible to turn these errors off.

Description	Resource	Path	Location	Type
Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar	Bytes.java	/hbase/src/main/java/org/apache/hadoop/hbase/util	line 1061	Java Problem
Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar	Bytes.java	/hbase/src/main/java/org/apache/hadoop/hbase/util	line 1064	Java Problem
Access restriction: The method getLong(Object, long) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar	Bytes.java	/hbase/src/main/java/org/apache/hadoop/hbase/util	line 1111	Java Problem

143.1.7. Eclipse - More Information

For additional information on setting up Eclipse for HBase development on Windows, see Michael Morello’s blog on the topic.

143.2. IntelliJ IDEA

You can set up IntelliJ IDEA for similar functionality as Eclipse. Follow these steps.

  1. Select

  2. You do not need to select a profile. Be sure Maven project required is selected, and click Next.

  3. Select the location for the JDK.

Using the HBase Formatter in IntelliJ IDEA

Using the Eclipse Code Formatter plugin for IntelliJ IDEA, you can import the HBase code formatter described in eclipse.code.formatting.

143.3. Other IDEs

It would be useful to mirror the eclipse set-up instructions for other IDEs. If you would like to assist, please have a look at HBASE-11704.

144. Building Apache HBase

144.1. Basic Compile

HBase is compiled using Maven. You must use at least Maven 3.0.4. To check your Maven version, run the command mvn -version.

JDK Version Requirements

Starting with HBase 1.0 you must use Java 7 or later to build from source code. See java for more complete information about supported JDK versions.

144.1.1. Maven Build Commands

All commands are executed from the local HBase project directory.

Package

The simplest command to compile HBase from its java source code is to use the package target, which builds JARs with the compiled files.

mvn package -DskipTests

Or, to clean up before compiling:

mvn clean package -DskipTests

With Eclipse set up as explained above in eclipse, you can also use the Build command in Eclipse. To create the full installable HBase package takes a little bit more work, so read on.

Compile

The compile target does not create the JARs with the compiled files.

mvn compile
mvn clean compile
Install

To install the JARs in your ~/.m2/ directory, use the install target.

mvn install
mvn clean install
mvn clean install -DskipTests

144.1.2. Running all or individual Unit Tests

See the hbase.unittests.cmds section in hbase.unittests

144.1.3. Building against various hadoop versions.

As of 0.96, Apache HBase supports building against Apache Hadoop versions: 1.0.3, 2.0.0-alpha and 3.0.0-SNAPSHOT. By default, in 0.96 and earlier, we will build with Hadoop-1.0.x. As of 0.98, Hadoop 1.x is deprecated and Hadoop 2.x is the default. To change the version to build against, add a hadoop.profile property when you invoke mvn:

mvn -Dhadoop.profile=1.0 ...

The above will build against whatever explicit hadoop 1.x version we have in our pom.xml as our '1.0' version. Tests may not all pass so you may need to pass -DskipTests unless you are inclined to fix the failing tests.

'dependencyManagement.dependencies.dependency.artifactId' fororg.apache.hbase:${compat.module}:test-jar with value '${compat.module}'does not match a valid id pattern

You will see ERRORs like the above title if you pass the default profile; e.g. if you pass hadoop.profile=1.1 when building 0.96 or hadoop.profile=2.0 when building hadoop 0.98; just drop the hadoop.profile stipulation in this case to get your build to run again. This seems to be a maven peculiarity that is probably fixable but we’ve not spent the time trying to figure it.

Similarly, for 3.0, you would just replace the profile value. Note that Hadoop-3.0.0-SNAPSHOT does not currently have a deployed maven artifact - you will need to build and install your own in your local maven repository if you want to run against this profile.

In earlier versions of Apache HBase, you can build against older versions of Apache Hadoop, notably, Hadoop 0.22.x and 0.23.x. If you are running, for example HBase-0.94 and wanted to build against Hadoop 0.23.x, you would run with:

mvn -Dhadoop.profile=22 ...

144.1.4. Build Protobuf

You may need to change the protobuf definitions that reside in the hbase-protocol module or other modules.

Previous to hbase-2.0.0, protobuf definition files were sprinkled across all hbase modules but now all to do with protobuf must reside in the hbase-protocol module; we are trying to contain our protobuf use so we can freely change versions without upsetting any downstream project use of protobuf.

The protobuf files are located in hbase-protocol/src/main/protobuf. For the change to be effective, you will need to regenerate the classes. You can use maven profile compile-protobuf to do this.

mvn compile -Pcompile-protobuf

You may also want to define protoc.path for the protoc binary, using the following command:

mvn compile -Pcompile-protobuf -Dprotoc.path=/opt/local/bin/protoc

Read the hbase-protocol/README.txt for more details.

144.1.5. Build Thrift

You may need to change the thrift definitions that reside in the hbase-thrift module or other modules.

The thrift files are located in hbase-thrift/src/main/resources. For the change to be effective, you will need to regenerate the classes. You can use maven profile compile-thrift to do this.

mvn compile -Pcompile-thrift

You may also want to define thrift.path for the thrift binary, using the following command:

                  mvn compile -Pcompile-thrift -Dthrift.path=/opt/local/bin/thrift

144.1.6. Build a Tarball

You can build a tarball without going through the release process described in releasing, by running the following command:

mvn -DskipTests clean install && mvn -DskipTests package assembly:single

The distribution tarball is built in hbase-assembly/target/hbase-<version>-bin.tar.gz.

You can install or deploy the tarball by having the assembly:single goal before install or deploy in the maven command:

mvn -DskipTests package assembly:single install
mvn -DskipTests package assembly:single deploy

144.1.7. Build Gotchas

If you see Unable to find resource 'VM_global_library.vm', ignore it. It’s not an error. It is officially ugly though.

145. Releasing Apache HBase

Building against HBase 1.x

HBase 1.x requires Java 7 to build. See java for Java requirements per HBase release.

145.1. Building against HBase 0.96-0.98

HBase 0.96.x will run on Hadoop 1.x or Hadoop 2.x. HBase 0.98 still runs on both, but HBase 0.98 deprecates use of Hadoop 1. HBase 1.x will not run on Hadoop 1. In the following procedures, we make a distinction between HBase 1.x builds and the awkward process involved building HBase 0.96/0.98 for either Hadoop 1 or Hadoop 2 targets.

You must choose which Hadoop to build against. It is not possible to build a single HBase binary that runs against both Hadoop 1 and Hadoop 2. Hadoop is included in the build, because it is needed to run HBase in standalone mode. Therefore, the set of modules included in the tarball changes, depending on the build target. To determine which HBase you have, look at the HBase version. The Hadoop version is embedded within it.

Maven, our build system, natively does not allow a single product to be built against different dependencies. Also, Maven cannot change the set of included modules and write out the correct pom.xml files with appropriate dependencies, even using two build targets, one for Hadoop 1 and another for Hadoop 2. A prerequisite step is required, which takes as input the current pom.xml_s and generates Hadoop 1 or Hadoop 2 versions using a script in the _dev-tools/ directory, called generate-hadoopX-poms.sh where X is either 1 or 2. You then reference these generated poms when you build. For now, just be aware of the difference between HBase 1.x builds and those of HBase 0.96-0.98. This difference is important to the build instructions.

Example 60. Example ~/.m2/settings.xml File

Publishing to maven requires you sign the artifacts you want to upload. For the build to sign them for you, you a properly configured settings.xml in your local repository under .m2, such as the following.

<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
                      http://maven.apache.org/xsd/settings-1.0.0.xsd">
  <servers>
    <!- To publish a snapshot of some part of Maven -->
    <server>
      <id>apache.snapshots.https</id>
      <username>YOUR_APACHE_ID
      </username>
      <password>YOUR_APACHE_PASSWORD
      </password>
    </server>
    <!-- To publish a website using Maven -->
    <!-- To stage a release of some part of Maven -->
    <server>
      <id>apache.releases.https</id>
      <username>YOUR_APACHE_ID
      </username>
      <password>YOUR_APACHE_PASSWORD
      </password>
    </server>
  </servers>
  <profiles>
    <profile>
      <id>apache-release</id>
      <properties>
    <gpg.keyname>YOUR_KEYNAME</gpg.keyname>
    <!--Keyname is something like this ... 00A5F21E... do gpg --list-keys to find it-->
    <gpg.passphrase>YOUR_KEY_PASSWORD
    </gpg.passphrase>
      </properties>
    </profile>
  </profiles>
</settings>

145.2. Making a Release Candidate

These instructions are for building HBase 1.0.x. For building earlier versions, e.g. 0.98.x, the process is different. See this section under the respective release documentation folders.
Point Releases

If you are making a point release (for example to quickly address a critical incompatibility or security problem) off of a release branch instead of a development branch, the tagging instructions are slightly different. I’ll prefix those special steps with Point Release Only.

Before You Begin

Before you make a release candidate, do a practice run by deploying a snapshot. Before you start, check to be sure recent builds have been passing for the branch from where you are going to take your release. You should also have tried recent branch tips out on a cluster under load, perhaps by running the hbase-it integration test suite for a few hours to 'burn in' the near-candidate bits.

Point Release Only

At this point you should tag the previous release branch (ex: 0.96.1) with the new point release tag (e.g. 0.96.1.1 tag). Any commits with changes for the point release should go against the new tag.

The Hadoop How To Release wiki page is used as a model for most of the instructions below. Although it now stale, it may have more detail on particular sections, so it is worth review especially if you get stuck.

Specifying the Heap Space for Maven on OSX

On OSX, you may run into OutOfMemoryErrors building, particularly building the site and documentation. Up the heap and permgen space for Maven by setting the MAVEN_OPTS variable. You can prefix the variable to the Maven command, as in the following example:

MAVEN_OPTS="-Xmx4g -XX:MaxPermSize=256m" mvn package

You could also set this in an environment variable or alias in your shell.
The script dev-support/make_rc.sh automates many of these steps. It does not do the modification of the CHANGES.txt for the release, the close of the staging repository in Apache Maven (human intervention is needed here), the checking of the produced artifacts to ensure they are 'good' — e.g. extracting the produced tarballs, verifying that they look right, then starting HBase and checking that everything is running correctly, then the signing and pushing of the tarballs to people.apache.org. The script handles everything else, and comes in handy.
Procedure: Release Procedure

  1. Update the CHANGES.txt file and the POM files.

    Update CHANGES.txt with the changes since the last release. Make sure the URL to the JIRA points to the proper location which lists fixes for this release. Adjust the version in all the POM files appropriately. If you are making a release candidate, you must remove the -SNAPSHOT label from all versions in all pom.xml files. If you are running this receipe to publish a snapshot, you must keep the -SNAPSHOT suffix on the hbase version. The Versions Maven Plugin can be of use here. To set a version in all the many poms of the hbase multi-module project, use a command like the following:

    $ mvn clean org.codehaus.mojo:versions-maven-plugin:1.3.1:set -DnewVersion=0.96.0

    Make sure all versions in poms are changed! Checkin the CHANGES.txt and any version changes.

  2. Update the documentation.

    Update the documentation under src/main/asciidoc. This usually involves copying the latest from master and making version-particular adjustments to suit this release candidate version.

  3. Build the source tarball.

    Now, build the source tarball. This tarball is Hadoop-version-independent. It is just the pure source code and documentation without a particular hadoop taint, etc. Add the -Prelease profile when building. It checks files for licenses and will fail the build if unlicensed files are present.

    $ mvn clean install -DskipTests assembly:single -Dassembly.file=hbase-assembly/src/main/assembly/src.xml -Prelease

    Extract the tarball and make sure it looks good. A good test for the src tarball being 'complete' is to see if you can build new tarballs from this source bundle. If the source tarball is good, save it off to a version directory, a directory somewhere where you are collecting all of the tarballs you will publish as part of the release candidate. For example if you were building an hbase-0.96.0 release candidate, you might call the directory hbase-0.96.0RC0. Later you will publish this directory as our release candidate.

  4. Build the binary tarball.

    Next, build the binary tarball. Add the -Prelease profile when building. It checks files for licenses and will fail the build if unlicensed files are present. Do it in two steps.

    • First install into the local repository

      $ mvn clean install -DskipTests -Prelease

    • Next, generate documentation and assemble the tarball.

      $ mvn install -DskipTests site assembly:single -Prelease

    Otherwise, the build complains that hbase modules are not in the maven repository when you try to do it all in one step, especially on a fresh repository. It seems that you need the install goal in both steps.

    + Extract the generated tarball and check it out. Look at the documentation, see if it runs, etc. If good, copy the tarball to the above mentioned version directory.

  5. Create a new tag.

    Point Release Only

    The following step that creates a new tag can be skipped since you’ve already created the point release tag

    Tag the release at this point since it looks good. If you find an issue later, you can delete the tag and start over. Release needs to be tagged for the next step.

  6. Deploy to the Maven Repository.

    Next, deploy HBase to the Apache Maven repository, using the apache-release profile instead of the release profile when running the mvn deploy command. This profile invokes the Apache pom referenced by our pom files, and also signs your artifacts published to Maven, as long as the settings.xml is configured correctly, as described in Example ~/.m2/settings.xml File.

    $ mvn deploy -DskipTests -Papache-release -Prelease

    This command copies all artifacts up to a temporary staging Apache mvn repository in an 'open' state. More work needs to be done on these maven artifacts to make them generally available.

    We do not release HBase tarball to the Apache Maven repository. To avoid deploying the tarball, do not include the assembly:single goal in your mvn deploy command. Check the deployed artifacts as described in the next section.

  7. Make the Release Candidate available.

    The artifacts are in the maven repository in the staging area in the 'open' state. While in this 'open' state you can check out what you’ve published to make sure all is good. To do this, log in to Apache’s Nexus at repository.apache.org using your Apache ID. Find your artifacts in the staging repository. Click on 'Staging Repositories' and look for a new one ending in "hbase" with a status of 'Open', select it. Use the tree view to expand the list of repository contents and inspect if the artifacts you expect are present. Check the POMs. As long as the staging repo is open you can re-upload if something is missing or built incorrectly.

    If something is seriously wrong and you would like to back out the upload, you can use the 'Drop' button to drop and delete the staging repository.

    If it checks out, close the repo using the 'Close' button. The repository must be closed before a public URL to it becomes available. It may take a few minutes for the repository to close. Once complete you’ll see a public URL to the repository in the Nexus UI. You may also receive an email with the URL. Provide the URL to the temporary staging repository in the email that announces the release candidate. (Folks will need to add this repo URL to their local poms or to their local settings.xml file to pull the published release candidate artifacts.)

    When the release vote concludes successfully, return here and click the 'Release' button to release the artifacts to central. The release process will automatically drop and delete the staging repository.

    hbase-downstreamer

    See the hbase-downstreamer test for a simple example of a project that is downstream of HBase an depends on it. Check it out and run its simple test to make sure maven artifacts are properly deployed to the maven repository. Be sure to edit the pom to point to the proper staging repository. Make sure you are pulling from the repository when tests run and that you are not getting from your local repository, by either passing the -U flag or deleting your local repo content and check maven is pulling from remote out of the staging repository.

    See Publishing Maven Artifacts for some pointers on this maven staging process.

    We no longer publish using the maven release plugin. Instead we do mvn deploy. It seems to give us a backdoor to maven release publishing. If there is no -SNAPSHOT on the version string, then we are 'deployed' to the apache maven repository staging directory from which we can publish URLs for candidates and later, if they pass, publish as release (if a -SNAPSHOT on the version string, deploy will put the artifacts up into apache snapshot repos).

    If the HBase version ends in -SNAPSHOT, the artifacts go elsewhere. They are put into the Apache snapshots repository directly and are immediately available. Making a SNAPSHOT release, this is what you want to happen.

  8. If you used the make_rc.sh script instead of doing the above manually, do your sanity checks now.

    At this stage, you have two tarballs in your 'version directory' and a set of artifacts in a staging area of the maven repository, in the 'closed' state. These are publicly accessible in a temporary staging repository whose URL you should have gotten in an email. The above mentioned script, make_rc.sh does all of the above for you minus the check of the artifacts built, the closing of the staging repository up in maven, and the tagging of the release. If you run the script, do your checks at this stage verifying the src and bin tarballs and checking what is up in staging using hbase-downstreamer project. Tag before you start the build. You can always delete it if the build goes haywire.

  9. Sign, fingerprint and then 'stage' your release candiate version directory via svnpubsub by committing your directory to The 'dev' distribution directory (See comments on HBASE-10554 Please delete old releases from mirroring system but in essence it is an svn checkout of https://dist.apache.org/repos/dist/dev/hbase — releases are at https://dist.apache.org/repos/dist/release/hbase). In the version directory run the following commands:

    $ for i in *.tar.gz; do echo $i; gpg --print-mds $i > $i.mds ; done
$ for i in *.tar.gz; do echo $i; gpg --print-md MD5 $i > $i.md5 ; done
$ for i in *.tar.gz; do echo $i; gpg --print-md SHA512 $i > $i.sha ; done
$ for i in *.tar.gz; do echo $i; gpg --armor --output $i.asc --detach-sig $i  ; done
$ cd ..
# Presuming our 'version directory' is named 0.96.0RC0, copy it to the svn checkout of the dist dev dir
# in this case named hbase.dist.dev.svn
$ cd /Users/stack/checkouts/hbase.dist.dev.svn
$ svn info
Path: .
Working Copy Root Path: /Users/stack/checkouts/hbase.dist.dev.svn
URL: https://dist.apache.org/repos/dist/dev/hbase
Repository Root: https://dist.apache.org/repos/dist
Repository UUID: 0d268c88-bc11-4956-87df-91683dc98e59
Revision: 15087
Node Kind: directory
Schedule: normal
Last Changed Author: ndimiduk
Last Changed Rev: 15045
Last Changed Date: 2016-08-28 11:13:36 -0700 (Sun, 28 Aug 2016)
$ mv 0.96.0RC0 /Users/stack/checkouts/hbase.dist.dev.svn
$ svn add 0.96.0RC0
$ svn commit ...

    Ensure it actually gets published by checking https://dist.apache.org/repos/dist/dev/hbase/.

Announce the release candidate on the mailing list and call a vote.

145.3. Publishing a SNAPSHOT to maven

Make sure your settings.xml is set up properly (see Example ~/.m2/settings.xml File). Make sure the hbase version includes -SNAPSHOT as a suffix. Following is an example of publishing SNAPSHOTS of a release that had an hbase version of 0.96.0 in its poms.

 $ mvn clean install -DskipTests  javadoc:aggregate site assembly:single -Prelease
 $ mvn -DskipTests  deploy -Papache-release

The make_rc.sh script mentioned above (see maven.release) can help you publish SNAPSHOTS. Make sure your hbase.version has a -SNAPSHOT suffix before running the script. It will put a snapshot up into the apache snapshot repository for you.

146. Voting on Release Candidates

Everyone is encouraged to try and vote on HBase release candidates. Only the votes of PMC members are binding. PMC members, please read this WIP doc on policy voting for a release candidate, Release Policy. [quote]_Before casting 1 binding votes, individuals are required to download the signed source code package onto their own hardware, compile it as provided, and test the resulting executable on their own platform, along with also validating cryptographic signatures and verifying that the package meets the requirements of the ASF policy on releases._ Regards the latter, run +mvn apache-rat:check to verify all files are suitably licensed. See HBase, mail # dev - On recent discussion clarifying ASF release policy. for how we arrived at this process.

147. Generating the HBase Reference Guide

The manual is marked up using Asciidoc. We then use the Asciidoctor maven plugin to transform the markup to html. This plugin is run when you specify the site goal as in when you run mvn site. See appendix contributing to documentation for more information on building the documentation.

148. Updating hbase.apache.org

148.1. Contributing to hbase.apache.org

See appendix contributing to documentation for more information on contributing to the documentation or website.

148.2. Publishing hbase.apache.org

See Publishing the HBase Website and Documentation for instructions on publishing the website and documentation.

149. Tests

Developers, at a minimum, should familiarize themselves with the unit test detail; unit tests in HBase have a character not usually seen in other projects.

This information is about unit tests for HBase itself. For developing unit tests for your HBase applications, see unit.tests.

149.1. Apache HBase Modules

As of 0.96, Apache HBase is split into multiple modules. This creates "interesting" rules for how and where tests are written. If you are writing code for hbase-server, see hbase.unittests for how to write your tests. These tests can spin up a minicluster and will need to be categorized. For any other module, for example hbase-common, the tests must be strict unit tests and just test the class under test - no use of the HBaseTestingUtility or minicluster is allowed (or even possible given the dependency tree).

149.1.1. Testing the HBase Shell

The HBase shell and its tests are predominantly written in jruby. In order to make these tests run as a part of the standard build, there is a single JUnit test, TestShell, that takes care of loading the jruby implemented tests and running them. You can run all of these tests from the top level with:

      mvn clean test -Dtest=TestShell

Alternatively, you may limit the shell tests that run using the system variable shell.test. This value should specify the ruby literal equivalent of a particular test case by name. For example, the tests that cover the shell commands for altering tables are contained in the test case AdminAlterTableTest and you can run them with:

      mvn clean test -Dtest=TestShell -Dshell.test=/AdminAlterTableTest/

You may also use a Ruby Regular Expression literal (in the /pattern/ style) to select a set of test cases. You can run all of the HBase admin related tests, including both the normal administration and the security administration, with the command:

      mvn clean test -Dtest=TestShell -Dshell.test=/.*Admin.*Test/

In the event of a test failure, you can see details by examining the XML version of the surefire report results

      vim hbase-shell/target/surefire-reports/TEST-org.apache.hadoop.hbase.client.TestShell.xml

149.1.2. Running Tests in other Modules

If the module you are developing in has no other dependencies on other HBase modules, then you can cd into that module and just run:

mvn test

which will just run the tests IN THAT MODULE. If there are other dependencies on other modules, then you will have run the command from the ROOT HBASE DIRECTORY. This will run the tests in the other modules, unless you specify to skip the tests in that module. For instance, to skip the tests in the hbase-server module, you would run:

mvn clean test -PskipServerTests

from the top level directory to run all the tests in modules other than hbase-server. Note that you can specify to skip tests in multiple modules as well as just for a single module. For example, to skip the tests in hbase-server and hbase-common, you would run:

mvn clean test -PskipServerTests -PskipCommonTests

Also, keep in mind that if you are running tests in the hbase-server module you will need to apply the maven profiles discussed in hbase.unittests.cmds to get the tests to run properly.

149.2. Unit Tests

Apache HBase test cases are subdivided into four categories: small, medium, large, and integration with corresponding JUnit categories: SmallTests, MediumTests, LargeTests, IntegrationTests. JUnit categories are denoted using java annotations and look like this in your unit test code.

...
@Category(SmallTests.class)
public class TestHRegionInfo {
  @Test
  public void testCreateHRegionInfoName() throws Exception {
    // ...
  }
}

The above example shows how to mark a test case as belonging to the small category. All test cases in HBase should have a categorization.

The first three categories, small, medium, and large, are for test cases which run when you type $ mvn test. In other words, these three categorizations are for HBase unit tests. The integration category is not for unit tests, but for integration tests. These are run when you invoke $ mvn verify. Integration tests are described in integration.tests.

HBase uses a patched maven surefire plugin and maven profiles to implement its unit test characterizations.

Keep reading to figure which annotation of the set small, medium, and large to put on your new HBase test case.

Categorizing Tests
Small Tests

Small test cases are executed in a shared JVM and individual test cases should run in 15 seconds or less; i.e. a junit test fixture, a java object made up of test methods, should finish in under 15 seconds. These test cases can not use mini cluster. These are run as part of patch pre-commit.

Medium Tests

Medium test cases are executed in separate JVM and individual test case should run in 50 seconds or less. Together, they should take less than 30 minutes, and are quite stable in their results. These test cases can use a mini cluster. These are run as part of patch pre-commit.

Large Tests

Large test cases are everything else. They are typically large-scale tests, regression tests for specific bugs, timeout tests, performance tests. They are executed before a commit on the pre-integration machines. They can be run on the developer machine as well.

Integration Tests

Integration tests are system level tests. See integration.tests for more info.

149.3. Running tests

149.3.1. Default: small and medium category tests

Running mvn test will execute all small tests in a single JVM (no fork) and then medium tests in a separate JVM for each test instance. Medium tests are NOT executed if there is an error in a small test. Large tests are NOT executed. There is one report for small tests, and one report for medium tests if they are executed.

149.3.2. Running all tests

Running mvn test -P runAllTests will execute small tests in a single JVM then medium and large tests in a separate JVM for each test. Medium and large tests are NOT executed if there is an error in a small test. Large tests are NOT executed if there is an error in a small or medium test. There is one report for small tests, and one report for medium and large tests if they are executed.

149.3.3. Running a single test or all tests in a package

To run an individual test, e.g. MyTest, rum mvn test -Dtest=MyTest You can also pass multiple, individual tests as a comma-delimited list:

mvn test  -Dtest=MyTest1,MyTest2,MyTest3

You can also pass a package, which will run all tests under the package:

mvn test '-Dtest=org.apache.hadoop.hbase.client.*'

When -Dtest is specified, the localTests profile will be used. It will use the official release of maven surefire, rather than our custom surefire plugin, and the old connector (The HBase build uses a patched version of the maven surefire plugin). Each junit test is executed in a separate JVM (A fork per test class). There is no parallelization when tests are running in this mode. You will see a new message at the end of the -report: "[INFO] Tests are skipped". It’s harmless. However, you need to make sure the sum of Tests run: in the Results: section of test reports matching the number of tests you specified because no error will be reported when a non-existent test case is specified.

149.3.4. Other test invocation permutations

Running mvn test -P runSmallTests will execute "small" tests only, using a single JVM.

Running mvn test -P runMediumTests will execute "medium" tests only, launching a new JVM for each test-class.

Running mvn test -P runLargeTests will execute "large" tests only, launching a new JVM for each test-class.

For convenience, you can run mvn test -P runDevTests to execute both small and medium tests, using a single JVM.

149.3.5. Running tests faster

By default, $ mvn test -P runAllTests runs 5 tests in parallel. It can be increased on a developer’s machine. Allowing that you can have 2 tests in parallel per core, and you need about 2GB of memory per test (at the extreme), if you have an 8 core, 24GB box, you can have 16 tests in parallel. but the memory available limits it to 12 (24/2), To run all tests with 12 tests in parallel, do this: mvn test -P runAllTests -Dsurefire.secondPartForkCount=12. If using a version earlier than 2.0, do: +mvn test -P runAllTests -Dsurefire.secondPartThreadCount=12 +. To increase the speed, you can as well use a ramdisk. You will need 2GB of memory to run all tests. You will also need to delete the files between two test run. The typical way to configure a ramdisk on Linux is:

$ sudo mkdir /ram2G
sudo mount -t tmpfs -o size=2048M tmpfs /ram2G

You can then use it to run all HBase tests on 2.0 with the command:

mvn test
                        -P runAllTests -Dsurefire.secondPartForkCount=12
                        -Dtest.build.data.basedirectory=/ram2G

On earlier versions, use:

mvn test
                        -P runAllTests -Dsurefire.secondPartThreadCount=12
                        -Dtest.build.data.basedirectory=/ram2G

149.3.6. hbasetests.sh

It’s also possible to use the script hbasetests.sh. This script runs the medium and large tests in parallel with two maven instances, and provides a single report. This script does not use the hbase version of surefire so no parallelization is being done other than the two maven instances the script sets up. It must be executed from the directory which contains the pom.xml.

For example running ./dev-support/hbasetests.sh will execute small and medium tests. Running ./dev-support/hbasetests.sh runAllTests will execute all tests. Running ./dev-support/hbasetests.sh replayFailed will rerun the failed tests a second time, in a separate jvm and without parallelisation.

149.3.7. Test Resource Checker

A custom Maven SureFire plugin listener checks a number of resources before and after each HBase unit test runs and logs its findings at the end of the test output files which can be found in target/surefire-reports per Maven module (Tests write test reports named for the test class into this directory. Check the *-out.txt files). The resources counted are the number of threads, the number of file descriptors, etc. If the number has increased, it adds a LEAK? comment in the logs. As you can have an HBase instance running in the background, some threads can be deleted/created without any specific action in the test. However, if the test does not work as expected, or if the test should not impact these resources, it’s worth checking these log lines ...hbase.ResourceChecker(157): before... and ...hbase.ResourceChecker(157): after.... For example:

2012-09-26 09:22:15,315 INFO [pool-1-thread-1]
hbase.ResourceChecker(157): after:
regionserver.TestColumnSeeking#testReseeking Thread=65 (was 65),
OpenFileDescriptor=107 (was 107), MaxFileDescriptor=10240 (was 10240),
ConnectionCount=1 (was 1)

149.4. Writing Tests

149.4.1. General rules

  • As much as possible, tests should be written as category small tests.

  • All tests must be written to support parallel execution on the same machine, hence they should not use shared resources as fixed ports or fixed file names.

  • Tests should not overlog. More than 100 lines/second makes the logs complex to read and use i/o that are hence not available for the other tests.

  • Tests can be written with HBaseTestingUtility. This class offers helper functions to create a temp directory and do the cleanup, or to start a cluster.

149.4.2. Categories and execution time

  • All tests must be categorized, if not they could be skipped.

  • All tests should be written to be as fast as possible.

  • See <<hbase.unittests,hbase.unittests> for test case categories and corresponding timeouts. This should ensure a good parallelization for people using it, and ease the analysis when the test fails.

149.4.3. Sleeps in tests

Whenever possible, tests should not use Thread.sleep, but rather waiting for the real event they need. This is faster and clearer for the reader. Tests should not do a Thread.sleep without testing an ending condition. This allows understanding what the test is waiting for. Moreover, the test will work whatever the machine performance is. Sleep should be minimal to be as fast as possible. Waiting for a variable should be done in a 40ms sleep loop. Waiting for a socket operation should be done in a 200 ms sleep loop.

149.4.4. Tests using a cluster

Tests using a HRegion do not have to start a cluster: A region can use the local file system. Start/stopping a cluster cost around 10 seconds. They should not be started per test method but per test class. Started cluster must be shutdown using HBaseTestingUtility#shutdownMiniCluster, which cleans the directories. As most as possible, tests should use the default settings for the cluster. When they don’t, they should document it. This will allow to share the cluster later.

149.4.5. Tests Skeleton Code

Here is a test skeleton code with Categorization and a Category-based timeout rule to copy and paste and use as basis for test contribution.

/**
 * Describe what this testcase tests. Talk about resources initialized in @BeforeClass (before
 * any test is run) and before each test is run, etc.
 */
// Specify the category as explained in <<hbase.unittests,hbase.unittests>>.
@Category(SmallTests.class)
public class TestExample {
  // Replace the TestExample.class in the below with the name of your test fixture class.
  private static final Log LOG = LogFactory.getLog(TestExample.class);

  // Handy test rule that allows you subsequently get the name of the current method. See
  // down in 'testExampleFoo()' where we use it to log current test's name.
  @Rule public TestName testName = new TestName();

  // CategoryBasedTimeout.forClass(<testcase>) decides the timeout based on the category
  // (small/medium/large) of the testcase. @ClassRule requires that the full testcase runs within
  // this timeout irrespective of individual test methods' times.
  @ClassRule
  public static TestRule timeout = CategoryBasedTimeout.forClass(TestExample.class);

  @Before
  public void setUp() throws Exception {
  }

  @After
  public void tearDown() throws Exception {
  }

  @Test
  public void testExampleFoo() {
    LOG.info("Running test " + testName.getMethodName());
  }
}

149.5. Integration Tests

HBase integration/system tests are tests that are beyond HBase unit tests. They are generally long-lasting, sizeable (the test can be asked to 1M rows or 1B rows), targetable (they can take configuration that will point them at the ready-made cluster they are to run against; integration tests do not include cluster start/stop code), and verifying success, integration tests rely on public APIs only; they do not attempt to examine server internals asserting success/fail. Integration tests are what you would run when you need to more elaborate proofing of a release candidate beyond what unit tests can do. They are not generally run on the Apache Continuous Integration build server, however, some sites opt to run integration tests as a part of their continuous testing on an actual cluster.

Integration tests currently live under the src/test directory in the hbase-it submodule and will match the regex: */IntegrationTest.java. All integration tests are also annotated with @Category(IntegrationTests.class).

Integration tests can be run in two modes: using a mini cluster, or against an actual distributed cluster. Maven failsafe is used to run the tests using the mini cluster. IntegrationTestsDriver class is used for executing the tests against a distributed cluster. Integration tests SHOULD NOT assume that they are running against a mini cluster, and SHOULD NOT use private API’s to access cluster state. To interact with the distributed or mini cluster uniformly, IntegrationTestingUtility, and HBaseCluster classes, and public client API’s can be used.

On a distributed cluster, integration tests that use ChaosMonkey or otherwise manipulate services thru cluster manager (e.g. restart regionservers) use SSH to do it. To run these, test process should be able to run commands on remote end, so ssh should be configured accordingly (for example, if HBase runs under hbase user in your cluster, you can set up passwordless ssh for that user and run the test also under it). To facilitate that, hbase.it.clustermanager.ssh.user, hbase.it.clustermanager.ssh.opts and hbase.it.clustermanager.ssh.cmd configuration settings can be used. "User" is the remote user that cluster manager should use to perform ssh commands. "Opts" contains additional options that are passed to SSH (for example, "-i /tmp/my-key"). Finally, if you have some custom environment setup, "cmd" is the override format for the entire tunnel (ssh) command. The default string is {/usr/bin/ssh %1$s %2$s%3$s%4$s "%5$s"} and is a good starting point. This is a standard Java format string with 5 arguments that is used to execute the remote command. The argument 1 (%1$s) is SSH options set the via opts setting or via environment variable, 2 is SSH user name, 3 is "@" if username is set or "" otherwise, 4 is the target host name, and 5 is the logical command to execute (that may include single quotes, so don’t use them). For example, if you run the tests under non-hbase user and want to ssh as that user and change to hbase on remote machine, you can use:

/usr/bin/ssh %1$s %2$s%3$s%4$s "su hbase - -c \"%5$s\""

That way, to kill RS (for example) integration tests may run:

{/usr/bin/ssh some-hostname "su hbase - -c \"ps aux | ... | kill ...\""}

The command is logged in the test logs, so you can verify it is correct for your environment.

To disable the running of Integration Tests, pass the following profile on the command line -PskipIntegrationTests. For example,

$ mvn clean install test -Dtest=TestZooKeeper  -PskipIntegrationTests

149.5.1. Running integration tests against mini cluster

HBase 0.92 added a verify maven target. Invoking it, for example by doing mvn verify, will run all the phases up to and including the verify phase via the maven failsafe plugin, running all the above mentioned HBase unit tests as well as tests that are in the HBase integration test group. After you have completed mvn install -DskipTests You can run just the integration tests by invoking:

cd hbase-it
mvn verify

If you just want to run the integration tests in top-level, you need to run two commands. First: mvn failsafe:integration-test This actually runs ALL the integration tests.

This command will always output BUILD SUCCESS even if there are test failures.

At this point, you could grep the output by hand looking for failed tests. However, maven will do this for us; just use: mvn failsafe:verify The above command basically looks at all the test results (so don’t remove the 'target' directory) for test failures and reports the results.

Running a subset of Integration tests

This is very similar to how you specify running a subset of unit tests (see above), but use the property it.test instead of test. To just run IntegrationTestClassXYZ.java, use: mvn failsafe:integration-test -Dit.test=IntegrationTestClassXYZ The next thing you might want to do is run groups of integration tests, say all integration tests that are named IntegrationTestClassX*.java: mvn failsafe:integration-test -Dit.test=*ClassX* This runs everything that is an integration test that matches ClassX. This means anything matching: "*/IntegrationTest*ClassX". You can also run multiple groups of integration tests using comma-delimited lists (similar to unit tests). Using a list of matches still supports full regex matching for each of the groups. This would look something like: mvn failsafe:integration-test -Dit.test=*ClassX*, *ClassY

149.5.2. Running integration tests against distributed cluster

If you have an already-setup HBase cluster, you can launch the integration tests by invoking the class IntegrationTestsDriver. You may have to run test-compile first. The configuration will be picked by the bin/hbase script.

mvn test-compile

Then launch the tests with:

bin/hbase [--config config_dir] org.apache.hadoop.hbase.IntegrationTestsDriver

Pass -h to get usage on this sweet tool. Running the IntegrationTestsDriver without any argument will launch tests found under hbase-it/src/test, having @Category(IntegrationTests.class) annotation, and a name starting with IntegrationTests. See the usage, by passing -h, to see how to filter test classes. You can pass a regex which is checked against the full class name; so, part of class name can be used. IntegrationTestsDriver uses Junit to run the tests. Currently there is no support for running integration tests against a distributed cluster using maven (see HBASE-6201).

The tests interact with the distributed cluster by using the methods in the DistributedHBaseCluster (implementing HBaseCluster) class, which in turn uses a pluggable ClusterManager. Concrete implementations provide actual functionality for carrying out deployment-specific and environment-dependent tasks (SSH, etc). The default ClusterManager is HBaseClusterManager, which uses SSH to remotely execute start/stop/kill/signal commands, and assumes some posix commands (ps, etc). Also assumes the user running the test has enough "power" to start/stop servers on the remote machines. By default, it picks up HBASE_SSH_OPTS, HBASE_HOME, HBASE_CONF_DIR from the env, and uses bin/hbase-daemon.sh to carry out the actions. Currently tarball deployments, deployments which uses hbase-daemons.sh, and Apache Ambari deployments are supported. /etc/init.d/ scripts are not supported for now, but it can be easily added. For other deployment options, a ClusterManager can be implemented and plugged in.

149.5.3. Destructive integration / system tests (ChaosMonkey)

HBase 0.96 introduced a tool named ChaosMonkey, modeled after same-named tool by Netflix’s Chaos Monkey tool. ChaosMonkey simulates real-world faults in a running cluster by killing or disconnecting random servers, or injecting other failures into the environment. You can use ChaosMonkey as a stand-alone tool to run a policy while other tests are running. In some environments, ChaosMonkey is always running, in order to constantly check that high availability and fault tolerance are working as expected.

ChaosMonkey defines Actions and Policies.

Actions

Actions are predefined sequences of events, such as the following:

  • Restart active master (sleep 5 sec)

  • Restart random regionserver (sleep 5 sec)

  • Restart random regionserver (sleep 60 sec)

  • Restart META regionserver (sleep 5 sec)

  • Restart ROOT regionserver (sleep 5 sec)

  • Batch restart of 50% of regionservers (sleep 5 sec)

  • Rolling restart of 100% of regionservers (sleep 5 sec)

Policies

A policy is a strategy for executing one or more actions. The default policy executes a random action every minute based on predefined action weights. A given policy will be executed until ChaosMonkey is interrupted.

Most ChaosMonkey actions are configured to have reasonable defaults, so you can run ChaosMonkey against an existing cluster without any additional configuration. The following example runs ChaosMonkey with the default configuration:

$ bin/hbase org.apache.hadoop.hbase.util.ChaosMonkey

12/11/19 23:21:57 INFO util.ChaosMonkey: Using ChaosMonkey Policy: class org.apache.hadoop.hbase.util.ChaosMonkey$PeriodicRandomActionPolicy, period:60000
12/11/19 23:21:57 INFO util.ChaosMonkey: Sleeping for 26953 to add jitter
12/11/19 23:22:24 INFO util.ChaosMonkey: Performing action: Restart active master
12/11/19 23:22:24 INFO util.ChaosMonkey: Killing master:master.example.com,60000,1353367210440
12/11/19 23:22:24 INFO hbase.HBaseCluster: Aborting Master: master.example.com,60000,1353367210440
12/11/19 23:22:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:master.example.com
12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
12/11/19 23:22:25 INFO hbase.HBaseCluster: Waiting service:master to stop: master.example.com,60000,1353367210440
12/11/19 23:22:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:master.example.com
12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
12/11/19 23:22:25 INFO util.ChaosMonkey: Killed master server:master.example.com,60000,1353367210440
12/11/19 23:22:25 INFO util.ChaosMonkey: Sleeping for:5000
12/11/19 23:22:30 INFO util.ChaosMonkey: Starting master:master.example.com
12/11/19 23:22:30 INFO hbase.HBaseCluster: Starting Master on: master.example.com
12/11/19 23:22:30 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start master , hostname:master.example.com
12/11/19 23:22:31 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting master, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-master-master.example.com.out
....
12/11/19 23:22:33 INFO util.ChaosMonkey: Started master: master.example.com,60000,1353367210440
12/11/19 23:22:33 INFO util.ChaosMonkey: Sleeping for:51321
12/11/19 23:23:24 INFO util.ChaosMonkey: Performing action: Restart random region server
12/11/19 23:23:24 INFO util.ChaosMonkey: Killing region server:rs3.example.com,60020,1353367027826
12/11/19 23:23:24 INFO hbase.HBaseCluster: Aborting RS: rs3.example.com,60020,1353367027826
12/11/19 23:23:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:rs3.example.com
12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
12/11/19 23:23:25 INFO hbase.HBaseCluster: Waiting service:regionserver to stop: rs3.example.com,60020,1353367027826
12/11/19 23:23:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:rs3.example.com
12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:
12/11/19 23:23:25 INFO util.ChaosMonkey: Killed region server:rs3.example.com,60020,1353367027826. Reported num of rs:6
12/11/19 23:23:25 INFO util.ChaosMonkey: Sleeping for:60000
12/11/19 23:24:25 INFO util.ChaosMonkey: Starting region server:rs3.example.com
12/11/19 23:24:25 INFO hbase.HBaseCluster: Starting RS on: rs3.example.com
12/11/19 23:24:25 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start regionserver , hostname:rs3.example.com
12/11/19 23:24:26 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting regionserver, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-regionserver-rs3.example.com.out

12/11/19 23:24:27 INFO util.ChaosMonkey: Started region server:rs3.example.com,60020,1353367027826. Reported num of rs:6

The output indicates that ChaosMonkey started the default PeriodicRandomActionPolicy policy, which is configured with all the available actions. It chose to run RestartActiveMaster and RestartRandomRs actions.

149.5.4. Available Policies

HBase ships with several ChaosMonkey policies, available in the hbase/hbase-it/src/test/java/org/apache/hadoop/hbase/chaos/policies/ directory.

149.5.5. Configuring Individual ChaosMonkey Actions

Since HBase version 1.0.0 (HBASE-11348), ChaosMonkey integration tests can be configured per test run. Create a Java properties file in the HBase classpath and pass it to ChaosMonkey using the -monkeyProps configuration flag. Configurable properties, along with their default values if applicable, are listed in the org.apache.hadoop.hbase.chaos.factories.MonkeyConstants class. For properties that have defaults, you can override them by including them in your properties file.

The following example uses a properties file called monkey.properties.

$ bin/hbase org.apache.hadoop.hbase.IntegrationTestIngest -m slowDeterministic -monkeyProps monkey.properties

The above command will start the integration tests and chaos monkey passing the properties file monkey.properties. Here is an example chaos monkey file:

Example ChaosMonkey Properties File
sdm.action1.period=120000
sdm.action2.period=40000
move.regions.sleep.time=80000
move.regions.max.time=1000000
move.regions.sleep.time=80000
batch.restart.rs.ratio=0.4f

HBase 1.0.2 and newer adds the ability to restart HBase’s underlying ZooKeeper quorum or HDFS nodes. To use these actions, you need to configure some new properties, which have no reasonable defaults because they are deployment-specific, in your ChaosMonkey properties file, which may be hbase-site.xml or a different properties file.

<property>
  <name>hbase.it.clustermanager.hadoop.home</name>
  <value>$HADOOP_HOME</value>
</property>
<property>
  <name>hbase.it.clustermanager.zookeeper.home</name>
  <value>$ZOOKEEPER_HOME</value>
</property>
<property>
  <name>hbase.it.clustermanager.hbase.user</name>
  <value>hbase</value>
</property>
<property>
  <name>hbase.it.clustermanager.hadoop.hdfs.user</name>
  <value>hdfs</value>
</property>
<property>
  <name>hbase.it.clustermanager.zookeeper.user</name>
  <value>zookeeper</value>
</property>

150. Developer Guidelines

150.1. Codelines

Most development is done on the master branch, which is named master in the Git repository. Previously, HBase used Subversion, in which the master branch was called TRUNK. Branches exist for minor releases, and important features and bug fixes are often back-ported.

150.2. Release Managers

Each maintained release branch has a release manager, who volunteers to coordinate new features and bug fixes are backported to that release. The release managers are committers. If you would like your feature or bug fix to be included in a given release, communicate with that release manager. If this list goes out of date or you can’t reach the listed person, reach out to someone else on the list.

End-of-life releases are not included in this list.
Table 18. Release Managers
Release Release Manager

0.94

Lars Hofhansl

0.98

Andrew Purtell

1.0

Enis Soztutar

1.1

Nick Dimiduk

1.2

Sean Busbey

1.3

Mikhail Antonov

150.3. Code Standards

See eclipse.code.formatting and common.patch.feedback.

150.3.1. Interface Classifications

Interfaces are classified both by audience and by stability level. These labels appear at the head of a class. The conventions followed by HBase are inherited by its parent project, Hadoop.

The following interface classifications are commonly used:

InterfaceAudience
@InterfaceAudience.Public

APIs for users and HBase applications. These APIs will be deprecated through major versions of HBase.

@InterfaceAudience.Private

APIs for HBase internals developers. No guarantees on compatibility or availability in future versions. Private interfaces do not need an @InterfaceStability classification.

@InterfaceAudience.LimitedPrivate(HBaseInterfaceAudience.COPROC)

APIs for HBase coprocessor writers. As of HBase 0.92/0.94/0.96/0.98 this api is still unstable. No guarantees on compatibility with future versions.

No @InterfaceAudience Classification

Packages without an @InterfaceAudience label are considered private. Mark your new packages if publicly accessible.

Excluding Non-Public Interfaces from API Documentation

Only interfaces classified @InterfaceAudience.Public should be included in API documentation (Javadoc). Committers must add new package excludes ExcludePackageNames section of the pom.xml for new packages which do not contain public classes.
@InterfaceStability

@InterfaceStability is important for packages marked @InterfaceAudience.Public.

@InterfaceStability.Stable

Public packages marked as stable cannot be changed without a deprecation path or a very good reason.

@InterfaceStability.Unstable

Public packages marked as unstable can be changed without a deprecation path.

@InterfaceStability.Evolving

Public packages marked as evolving may be changed, but it is discouraged.

No @InterfaceStability Label

Public classes with no @InterfaceStability label are discouraged, and should be considered implicitly unstable.

If you are unclear about how to mark packages, ask on the development list.

150.3.2. Code Formatting Conventions

Please adhere to the following guidelines so that your patches can be reviewed more quickly. These guidelines have been developed based upon common feedback on patches from new contributors.

See the Code Conventions for the Java Programming Language for more information on coding conventions in Java.

Space Invaders

Do not use extra spaces around brackets. Use the second style, rather than the first.

if ( foo.equals( bar ) ) {     // don't do this
if (foo.equals(bar)) {
foo = barArray[ i ];     // don't do this
foo = barArray[i];
Auto Generated Code

Auto-generated code in Eclipse often uses bad variable names such as arg0. Use more informative variable names. Use code like the second example here.

 public void readFields(DataInput arg0) throws IOException {    // don't do this
   foo = arg0.readUTF();                                       // don't do this
 public void readFields(DataInput di) throws IOException {
   foo = di.readUTF();
Long Lines

Keep lines less than 100 characters. You can configure your IDE to do this automatically.

Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3, argument4, argument5, argument6, argument7, argument8, argument9);  // don't do this
Bar bar = foo.veryLongMethodWithManyArguments(
 argument1, argument2, argument3,argument4, argument5, argument6, argument7, argument8, argument9);
Trailing Spaces

Trailing spaces are a common problem. Be sure there is a line break after the end of your code, and avoid lines with nothing but whitespace. This makes diffs more meaningful. You can configure your IDE to help with this.

Bar bar = foo.getBar();     <--- imagine there is an extra space(s) after the semicolon.
API Documentation (Javadoc)

This is also a very common feedback item. Don’t forget Javadoc!

Javadoc warnings are checked during precommit. If the precommit tool gives you a '-1', please fix the javadoc issue. Your patch won’t be committed if it adds such warnings.

Findbugs

Findbugs is used to detect common bugs pattern. It is checked during the precommit build by Apache’s Jenkins. If errors are found, please fix them. You can run findbugs locally with mvn findbugs:findbugs, which will generate the findbugs files locally. Sometimes, you may have to write code smarter than findbugs. You can annotate your code to tell findbugs you know what you’re doing, by annotating your class with the following annotation:

@edu.umd.cs.findbugs.annotations.SuppressWarnings(
value="HE_EQUALS_USE_HASHCODE",
justification="I know what I'm doing")

It is important to use the Apache-licensed version of the annotations. That generally means using annotations in the edu.umd.cs.findbugs.annotations package so that we can rely on the cleanroom reimplementation rather than annotations in the javax.annotations package.

Javadoc - Useless Defaults

Don’t just leave the @param arguments the way your IDE generated them.:

  /**
   *
   * @param bar             <---- don't do this!!!!
   * @return                <---- or this!!!!
   */
  public Foo getFoo(Bar bar);

Either add something descriptive to the @param and @return lines, or just remove them. The preference is to add something descriptive and useful.

One Thing At A Time, Folks

If you submit a patch for one thing, don’t do auto-reformatting or unrelated reformatting of code on a completely different area of code.

Likewise, don’t add unrelated cleanup or refactorings outside the scope of your Jira.

Ambigious Unit Tests

Make sure that you’re clear about what you are testing in your unit tests and why.

Implementing Writable
Applies pre-0.96 only

In 0.96, HBase moved to protocol buffers (protobufs). The below section on Writables applies to 0.94.x and previous, not to 0.96 and beyond.

Every class returned by RegionServers must implement the Writable interface. If you are creating a new class that needs to implement this interface, do not forget the default constructor.

150.3.3. Garbage-Collection Conserving Guidelines

The following guidelines were borrowed from http://engineering.linkedin.com/performance/linkedin-feed-faster-less-jvm-garbage. Keep them in mind to keep preventable garbage collection to a minimum. Have a look at the blog post for some great examples of how to refactor your code according to these guidelines.

  • Be careful with Iterators

  • Estimate the size of a collection when initializing

  • Defer expression evaluation

  • Compile the regex patterns in advance

  • Cache it if you can

  • String Interns are useful but dangerous

150.4. Invariants

We don’t have many but what we have we list below. All are subject to challenge of course but until then, please hold to the rules of the road.

150.4.1. No permanent state in ZooKeeper

ZooKeeper state should transient (treat it like memory). If ZooKeeper state is deleted, hbase should be able to recover and essentially be in the same state.

  • .Exceptions: There are currently a few exceptions that we need to fix around whether a table is enabled or disabled.

  • Replication data is currently stored only in ZooKeeper. Deleting ZooKeeper data related to replication may cause replication to be disabled. Do not delete the replication tree, /hbase/replication/.

    Replication may be disrupted and data loss may occur if you delete the replication tree (/hbase/replication/) from ZooKeeper. Follow progress on this issue at HBASE-10295.

150.5. Running In-Situ

If you are developing Apache HBase, frequently it is useful to test your changes against a more-real cluster than what you find in unit tests. In this case, HBase can be run directly from the source in local-mode. All you need to do is run:

${HBASE_HOME}/bin/start-hbase.sh

This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your machine.

Keep in mind that you will need to have installed HBase into your local maven repository for the in-situ cluster to work properly. That is, you will need to run:

mvn clean install -DskipTests

to ensure that maven can find the correct classpath and dependencies. Generally, the above command is just a good thing to try running first, if maven is acting oddly.

150.6. Adding Metrics

After adding a new feature a developer might want to add metrics. HBase exposes metrics using the Hadoop Metrics 2 system, so adding a new metric involves exposing that metric to the hadoop system. Unfortunately the API of metrics2 changed from hadoop 1 to hadoop 2. In order to get around this a set of interfaces and implementations have to be loaded at runtime. To get an in-depth look at the reasoning and structure of these classes you can read the blog post located here. To add a metric to an existing MBean follow the short guide below:

150.6.1. Add Metric name and Function to Hadoop Compat Interface.

Inside of the source interface the corresponds to where the metrics are generated (eg MetricsMasterSource for things coming from HMaster) create new static strings for metric name and description. Then add a new method that will be called to add new reading.

150.6.2. Add the Implementation to Both Hadoop 1 and Hadoop 2 Compat modules.

Inside of the implementation of the source (eg. MetricsMasterSourceImpl in the above example) create a new histogram, counter, gauge, or stat in the init method. Then in the method that was added to the interface wire up the parameter passed in to the histogram.

Now add tests that make sure the data is correctly exported to the metrics 2 system. For this the MetricsAssertHelper is provided.

150.7. Git Best Practices

Use the correct method to create patches.

See submitting.patches.

Avoid git merges.

Use git pull --rebase or git fetch followed by git rebase.

Do not use git push --force.

If the push does not work, fix the problem or ask for help.

Please contribute to this document if you think of other Git best practices.

150.7.1. rebase_all_git_branches.sh

The dev-support/rebase_all_git_branches.sh script is provided to help keep your Git repository clean. Use the -h parameter to get usage instructions. The script automatically refreshes your tracking branches, attempts an automatic rebase of each local branch against its remote branch, and gives you the option to delete any branch which represents a closed HBASE- JIRA. The script has one optional configuration option, the location of your Git directory. You can set a default by editing the script. Otherwise, you can pass the git directory manually by using the -d parameter, followed by an absolute or relative directory name, or even '.' for the current working directory. The script checks the directory for sub-directory called .git/, before proceeding.

150.8. Submitting Patches

HBase moved to GIT from SVN. Until we develop our own documentation for how to contribute patches in our new GIT context, caveat the fact that we have a different branching model and that we don’t currently do the merge practice described in the following, the accumulo doc on how to contribute and develop after our move to GIT is worth a read. See also git.best.practices.

If you are new to submitting patches to open source or new to submitting patches to Apache, start by reading the On Contributing Patches page from Apache Commons Project. It provides a nice overview that applies equally to the Apache HBase Project.

150.8.1. Create Patch

Use dev-support/submit-patch.py to create patches and optionally, upload to jira and update reviews on Review Board. Patch name is formatted as (JIRA).(branch name).(patch number).patch to follow Yetus' naming rules. Use -h flag to know detailed usage information. Most useful options are:

  1. -b BRANCH, --branch BRANCH : Specify base branch for generating the diff. If not specified, tracking branch is used. If there is no tracking branch, error will be thrown.

  2. -jid JIRA_ID, --jira-id JIRA_ID : Jira id of the issue. If set, we deduce next patch version from attachments in the jira and also upload the new patch. Script will ask for jira username/password for authentication. If not set, patch is named <branch>.patch.

The script builds a new patch, and uses REST API to upload it to the jira (if --jira-id is specified) and update the review on ReviewBoard (if --skip-review-board not specified). Remote links in the jira are used to figure out if a review request already exists. If no review request is present, then creates a new one and populates all required fields using jira summary, patch description, etc. Also adds this review’s link to the jira.

Authentication

Since attaching patches on JIRA and creating/changing review request on ReviewBoard requires a logged in user, the script will prompt you for username and password. To avoid the hassle every time, set up ~/.apache-creds with login details and encrypt it by following the steps in footer of script’s help message.

Python dependencies

To install required python dependencies, execute pip install -r dev-support/python-requirements.txt from the master branch.

Patching Workflow

  • Always patch against the master branch first, even if you want to patch in another branch. HBase committers always apply patches first to the master branch, and backport if necessary.

  • Submit one single patch for a fix. If necessary, squash local commits to merge local commits into a single one first. See this Stack Overflow question for more information about squashing commits.

  • Patch name should be as follows to adhere to Yetus' naming convention.

    (JIRA).(branch name).(patch number).patch

    For eg. HBASE-11625.master.001.patch, HBASE-XXXXX.branch-1.2.0005.patch, etc.

  • To submit a patch, first create it using one of the methods in patching.methods. Next, attach the patch to the JIRA (one patch for the whole fix), using the dialog. Next, click the Patch Available button, which triggers the Hudson job which checks the patch for validity.

    Please understand that not every patch may get committed, and that feedback will likely be provided on the patch.

  • If your patch is longer than a single screen, also attach a Review Board to the case. See reviewboard.

  • If you need to revise your patch, leave the previous patch file(s) attached to the JIRA, and upload the new one, following the naming conventions in submitting.patches.create. Cancel the Patch Available flag and then re-trigger it, by toggling the Patch Available button in JIRA. JIRA sorts attached files by the time they were attached, and has no problem with multiple attachments with the same name. However, at times it is easier to increment patch number in the patch name.

Methods to Create Patches
Eclipse

Select the menu item.

Git

git format-patch is preferred:

  • It preserves the committer and commit message.

  • It handles binary files by default, whereas git diff ignores them unless you use the --binary option. Use git rebase -i first, to combine (squash) smaller commits into a single larger one.

Subversion

Make sure you review eclipse.code.formatting and common.patch.feedback for code style. If your patch was generated incorrectly or your code does not adhere to the code formatting guidelines, you may be asked to redo some work.

150.8.2. Unit Tests

Yes, please. Please try to include unit tests with every code patch (and especially new classes and large changes). Make sure unit tests pass locally before submitting the patch.

Also, see mockito.

If you are creating a new unit test class, notice how other unit test classes have classification/sizing annotations at the top and a static method on the end. Be sure to include these in any new unit test files you generate. See hbase.tests for more on how the annotations work.

150.8.3. Integration Tests

Significant new features should provide an integration test in addition to unit tests, suitable for exercising the new feature at different points in its configuration space.

150.8.4. ReviewBoard

Patches larger than one screen, or patches that will be tricky to review, should go through ReviewBoard.

Procedure: Use ReviewBoard

  1. Register for an account if you don’t already have one. It does not use the credentials from issues.apache.org. Log in.

  2. Click New Review Request.

  3. Choose the hbase-git repository. Click Choose File to select the diff and optionally a parent diff. Click Create Review Request.

  4. Fill in the fields as required. At the minimum, fill in the Summary and choose hbase as the Review Group. If you fill in the Bugs field, the review board links back to the relevant JIRA. The more fields you fill in, the better. Click Publish to make your review request public. An email will be sent to everyone in the hbase group, to review the patch.

  5. Back in your JIRA, click , and paste in the URL of your ReviewBoard request. This attaches the ReviewBoard to the JIRA, for easy access.

  6. To cancel the request, click .

For more information on how to use ReviewBoard, see the ReviewBoard documentation.

150.8.5. Guide for HBase Committers

New committers

New committers are encouraged to first read Apache’s generic committer documentation:

Review

HBase committers should, as often as possible, attempt to review patches submitted by others. Ideally every submitted patch will get reviewed by a committer within a few days. If a committer reviews a patch they have not authored, and believe it to be of sufficient quality, then they can commit the patch, otherwise the patch should be cancelled with a clear explanation for why it was rejected.

The list of submitted patches is in the HBase Review Queue, which is ordered by time of last modification. Committers should scan the list from top to bottom, looking for patches that they feel qualified to review and possibly commit.

For non-trivial changes, it is required to get another committer to review your own patches before commit. Use the Submit Patch button in JIRA, just like other contributors, and then wait for a +1 response from another committer before committing.

Reject

Patches which do not adhere to the guidelines in HowToContribute and to the code review checklist should be rejected. Committers should always be polite to contributors and try to instruct and encourage them to contribute better patches. If a committer wishes to improve an unacceptable patch, then it should first be rejected, and a new patch should be attached by the committer for review.

Commit

Committers commit patches to the Apache HBase GIT repository.

Before you commit!!!!

Make sure your local configuration is correct, especially your identity and email. Examine the output of the $ git config --list command and be sure it is correct. See this GitHub article, Set Up Git if you need pointers.

When you commit a patch, please:

  1. Include the Jira issue id in the commit message, along with a short description of the change and the name of the contributor if it is not you. Be sure to get the issue ID right, as this causes Jira to link to the change in Git (use the issue’s "All" tab to see these).

  2. Commit the patch to a new branch based off master or other intended branch. It’s a good idea to call this branch by the JIRA ID. Then check out the relevant target branch where you want to commit, make sure your local branch has all remote changes, by doing a git pull --rebase or another similar command, cherry-pick the change into each relevant branch (such as master), and do git push <remote-server> <remote-branch>.

    If you do not have all remote changes, the push will fail. If the push fails for any reason, fix the problem or ask for help. Do not do a git push --force.

    Before you can commit a patch, you need to determine how the patch was created. The instructions and preferences around the way to create patches have changed, and there will be a transition period.

    • .Determine How a Patch Was Created: If the first few lines of the patch look like the headers of an email, with a From, Date, and Subject, it was created using git format-patch. This is the preference, because you can reuse the submitter’s commit message. If the commit message is not appropriate, you can still use the commit, then run the command git rebase -i origin/master, and squash and reword as appropriate.

    • If the first line of the patch looks similar to the following, it was created using git diff without --no-prefix. This is acceptable too. Notice the a and b in front of the file names. This is the indication that the patch was not created with --no-prefix.

      diff --git a/src/main/asciidoc/_chapters/developer.adoc b/src/main/asciidoc/_chapters/developer.adoc

    • If the first line of the patch looks similar to the following (without the a and b), the patch was created with git diff --no-prefix and you need to add -p0 to the git apply command below.

      diff --git src/main/asciidoc/_chapters/developer.adoc src/main/asciidoc/_chapters/developer.adoc
    Example 61. Example of Committing a Patch

    One thing you will notice with these examples is that there are a lot of git pull commands. The only command that actually writes anything to the remote repository is git push, and you need to make absolutely sure you have the correct versions of everything and don’t have any conflicts before pushing. The extra git pull commands are usually redundant, but better safe than sorry.

    The first example shows how to apply a patch that was generated with git format-patch and apply it to the master and branch-1 branches.

    The directive to use git format-patch rather than git diff, and not to use --no-prefix, is a new one. See the second example for how to apply a patch created with git diff, and educate the person who created the patch.

    $ git checkout -b HBASE-XXXX
$ git am ~/Downloads/HBASE-XXXX-v2.patch
$ git checkout master
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase          # Better safe than sorry
$ git push origin master
$ git checkout branch-1
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary
$ git pull --rebase          # Better safe than sorry
$ git push origin branch-1
$ git branch -D HBASE-XXXX

    This example shows how to commit a patch that was created using git diff without --no-prefix. If the patch was created with --no-prefix, add -p0 to the git apply command.

    $ git apply ~/Downloads/HBASE-XXXX-v2.patch
$ git commit -m "HBASE-XXXX Really Good Code Fix (Joe Schmo)" -a # This extra step is needed for patches created with 'git diff'
$ git checkout master
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase          # Better safe than sorry
$ git push origin master
$ git checkout branch-1
$ git pull --rebase
$ git cherry-pick <sha-from-commit>
# Resolve conflicts if necessary or ask the submitter to do it
$ git pull --rebase           # Better safe than sorry
$ git push origin branch-1
$ git branch -D HBASE-XXXX

  3. Resolve the issue as fixed, thanking the contributor. Always set the "Fix Version" at this point, but please only set a single fix version for each branch where the change was committed, the earliest release in that branch in which the change will appear.

Commit Message Format

The commit message should contain the JIRA ID and a description of what the patch does. The preferred commit message format is:

<jira-id> <jira-title> (<contributor-name-if-not-commit-author>)
HBASE-12345 Fix All The Things (jane@example.com)

If the contributor used git format-patch to generate the patch, their commit message is in their patch and you can use that, but be sure the JIRA ID is at the front of the commit message, even if the contributor left it out.

Add Amending-Author when a conflict cherrypick backporting

We’ve established the practice of committing to master and then cherry picking back to branches whenever possible. When there is a minor conflict we can fix it up and just proceed with the commit. The resulting commit retains the original author. When the amending author is different from the original committer, add notice of this at the end of the commit message as: Amending-Author: Author <committer&apache> See discussion at HBase, mail # dev - [DISCUSSION Best practice when amending commits cherry picked from master to branch].

Committers are responsible for making sure commits do not break the build or tests

If a committer commits a patch, it is their responsibility to make sure it passes the test suite. It is helpful if contributors keep an eye out that their patch does not break the hbase build and/or tests, but ultimately, a contributor cannot be expected to be aware of all the particular vagaries and interconnections that occur in a project like HBase. A committer should.

Patching Etiquette

In the thread HBase, mail # dev - ANNOUNCEMENT: Git Migration In Progress (WAS ⇒ Re: Git Migration), it was agreed on the following patch flow

  1. Develop and commit the patch against master first.

  2. Try to cherry-pick the patch when backporting if possible.

  3. If this does not work, manually commit the patch to the branch.

Merge Commits

Avoid merge commits, as they create problems in the git history.

Committing Documentation

See appendix contributing to documentation.

150.8.6. Dialog

Committers should hang out in the #hbase room on irc.freenode.net for real-time discussions. However any substantive discussion (as with any off-list project-related discussion) should be re-iterated in Jira or on the developer list.

150.8.7. Do not edit JIRA comments

Misspellings and/or bad grammar is preferable to the disruption a JIRA comment edit causes: See the discussion at Re:(HBASE-451) Remove HTableDescriptor from HRegionInfo

150.9. Development of HBase-related Maven archetypes

The development of HBase-related Maven archetypes was begun with HBASE-14876. For an overview of the hbase-archetypes infrastructure and instructions for developing new HBase-related Maven archetypes, please see hbase/hbase-archetypes/README.md.

Unit Testing HBase Applications

This chapter discusses unit testing your HBase application using JUnit, Mockito, MRUnit, and HBaseTestingUtility. Much of the information comes from a community blog post about testing HBase applications. For information on unit tests for HBase itself, see hbase.tests.

151. JUnit

HBase uses JUnit 4 for unit tests

This example will add unit tests to the following example class:

public class MyHBaseDAO {

    public static void insertRecord(Table.getTable(table), HBaseTestObj obj)
    throws Exception {
        Put put = createPut(obj);
        table.put(put);
    }

    private static Put createPut(HBaseTestObj obj) {
        Put put = new Put(Bytes.toBytes(obj.getRowKey()));
        put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1"),
                    Bytes.toBytes(obj.getData1()));
        put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2"),
                    Bytes.toBytes(obj.getData2()));
        return put;
    }
}

The first step is to add JUnit dependencies to your Maven POM file:

<dependency>
    <groupId>junit</groupId>
    <artifactId>junit</artifactId>
    <version>4.11</version>
    <scope>test</scope>
</dependency>

Next, add some unit tests to your code. Tests are annotated with @Test. Here, the unit tests are in bold.

public class TestMyHbaseDAOData {
  @Test
  public void testCreatePut() throws Exception {
  HBaseTestObj obj = new HBaseTestObj();
  obj.setRowKey("ROWKEY-1");
  obj.setData1("DATA-1");
  obj.setData2("DATA-2");
  Put put = MyHBaseDAO.createPut(obj);
  assertEquals(obj.getRowKey(), Bytes.toString(put.getRow()));
  assertEquals(obj.getData1(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")).get(0).getValue()));
  assertEquals(obj.getData2(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")).get(0).getValue()));
  }
}

These tests ensure that your createPut method creates, populates, and returns a Put object with expected values. Of course, JUnit can do much more than this. For an introduction to JUnit, see https://github.com/junit-team/junit/wiki/Getting-started.

152. Mockito

Mockito is a mocking framework. It goes further than JUnit by allowing you to test the interactions between objects without having to replicate the entire environment. You can read more about Mockito at its project site, https://code.google.com/p/mockito/.

You can use Mockito to do unit testing on smaller units. For instance, you can mock a org.apache.hadoop.hbase.Server instance or a org.apache.hadoop.hbase.master.MasterServices interface reference rather than a full-blown org.apache.hadoop.hbase.master.HMaster.

This example builds upon the example code in unit.tests, to test the insertRecord method.

First, add a dependency for Mockito to your Maven POM file.

<dependency>
    <groupId>org.mockito</groupId>
    <artifactId>mockito-all</artifactId>
    <version>1.9.5</version>
    <scope>test</scope>
</dependency>

Next, add a @RunWith annotation to your test class, to direct it to use Mockito.

@RunWith(MockitoJUnitRunner.class)
public class TestMyHBaseDAO{
  @Mock
  Configuration config = HBaseConfiguration.create();
  @Mock
  Connection connection = ConnectionFactory.createConnection(config);
  @Mock
  private Table table;
  @Captor
  private ArgumentCaptor putCaptor;

  @Test
  public void testInsertRecord() throws Exception {
    //return mock table when getTable is called
    when(connection.getTable(TableName.valueOf("tablename")).thenReturn(table);
    //create test object and make a call to the DAO that needs testing
    HBaseTestObj obj = new HBaseTestObj();
    obj.setRowKey("ROWKEY-1");
    obj.setData1("DATA-1");
    obj.setData2("DATA-2");
    MyHBaseDAO.insertRecord(table, obj);
    verify(table).put(putCaptor.capture());
    Put put = putCaptor.getValue();

    assertEquals(Bytes.toString(put.getRow()), obj.getRowKey());
    assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")));
    assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")));
    assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-1")).get(0).getValue()), "DATA-1");
    assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-2")).get(0).getValue()), "DATA-2");
  }
}

This code populates HBaseTestObj with ROWKEY-1'', DATA-1'', ``DATA-2'' as values. It then inserts the record into the mocked table. The Put that the DAO would have inserted is captured, and values are tested to verify that they are what you expected them to be.

The key here is to manage Connection and Table instance creation outside the DAO. This allows you to mock them cleanly and test Puts as shown above. Similarly, you can now expand into other operations such as Get, Scan, or Delete.

153. MRUnit

Apache MRUnit is a library that allows you to unit-test MapReduce jobs. You can use it to test HBase jobs in the same way as other MapReduce jobs.

Given a MapReduce job that writes to an HBase table called MyTest, which has one column family called CF, the reducer of such a job could look like the following:

public class MyReducer extends TableReducer<Text, Text, ImmutableBytesWritable> {
   public static final byte[] CF = "CF".getBytes();
   public static final byte[] QUALIFIER = "CQ-1".getBytes();
   public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
     //bunch of processing to extract data to be inserted, in our case, let's say we are simply
     //appending all the records we receive from the mapper for this particular
     //key and insert one record into HBase
     StringBuffer data = new StringBuffer();
     Put put = new Put(Bytes.toBytes(key.toString()));
     for (Text val : values) {
         data = data.append(val);
     }
     put.add(CF, QUALIFIER, Bytes.toBytes(data.toString()));
     //write to HBase
     context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())), put);
   }
 }

To test this code, the first step is to add a dependency to MRUnit to your Maven POM file.

<dependency>
   <groupId>org.apache.mrunit</groupId>
   <artifactId>mrunit</artifactId>
   <version>1.0.0 </version>
   <scope>test</scope>
</dependency>

Next, use the ReducerDriver provided by MRUnit, in your Reducer job.

public class MyReducerTest {
    ReduceDriver<Text, Text, ImmutableBytesWritable, Writable> reduceDriver;
    byte[] CF = "CF".getBytes();
    byte[] QUALIFIER = "CQ-1".getBytes();

    @Before
    public void setUp() {
      MyReducer reducer = new MyReducer();
      reduceDriver = ReduceDriver.newReduceDriver(reducer);
    }

   @Test
   public void testHBaseInsert() throws IOException {
      String strKey = "RowKey-1", strValue = "DATA", strValue1 = "DATA1",
strValue2 = "DATA2";
      List<Text> list = new ArrayList<Text>();
      list.add(new Text(strValue));
      list.add(new Text(strValue1));
      list.add(new Text(strValue2));
      //since in our case all that the reducer is doing is appending the records that the mapper
      //sends it, we should get the following back
      String expectedOutput = strValue + strValue1 + strValue2;
     //Setup Input, mimic what mapper would have passed
      //to the reducer and run test
      reduceDriver.withInput(new Text(strKey), list);
      //run the reducer and get its output
      List<Pair<ImmutableBytesWritable, Writable>> result = reduceDriver.run();

      //extract key from result and verify
      assertEquals(Bytes.toString(result.get(0).getFirst().get()), strKey);

      //extract value for CF/QUALIFIER and verify
      Put a = (Put)result.get(0).getSecond();
      String c = Bytes.toString(a.get(CF, QUALIFIER).get(0).getValue());
      assertEquals(expectedOutput,c );
   }

}

Your MRUnit test verifies that the output is as expected, the Put that is inserted into HBase has the correct value, and the ColumnFamily and ColumnQualifier have the correct values.

MRUnit includes a MapperDriver to test mapping jobs, and you can use MRUnit to test other operations, including reading from HBase, processing data, or writing to HDFS,

154. Integration Testing with an HBase Mini-Cluster

HBase ships with HBaseTestingUtility, which makes it easy to write integration tests using a mini-cluster. The first step is to add some dependencies to your Maven POM file. Check the versions to be sure they are appropriate.

<properties>
  <hbase.version>2.0.0-SNAPSHOT</hbase.version>
</properties>

<dependencies>
  <dependency>
    <groupId>org.apache.hbase</groupId>
    <artifactId>hbase-testing-util</artifactId>
    <version>${hbase.version}</version>
    <scope>test</scope>
  </dependency>
</dependencies>

This code represents an integration test for the MyDAO insert shown in unit.tests.

public class MyHBaseIntegrationTest {
    private static HBaseTestingUtility utility;
    byte[] CF = "CF".getBytes();
    byte[] CQ1 = "CQ-1".getBytes();
    byte[] CQ2 = "CQ-2".getBytes();

    @Before
    public void setup() throws Exception {
            utility = new HBaseTestingUtility();
            utility.startMiniCluster();
    }

    @Test
        public void testInsert() throws Exception {
                HTableInterface table = utility.createTable(Bytes.toBytes("MyTest"), CF);
                HBaseTestObj obj = new HBaseTestObj();
                obj.setRowKey("ROWKEY-1");
                obj.setData1("DATA-1");
                obj.setData2("DATA-2");
                MyHBaseDAO.insertRecord(table, obj);
                Get get1 = new Get(Bytes.toBytes(obj.getRowKey()));
                get1.addColumn(CF, CQ1);
                Result result1 = table.get(get1);
                assertEquals(Bytes.toString(result1.getRow()), obj.getRowKey());
                assertEquals(Bytes.toString(result1.value()), obj.getData1());
                Get get2 = new Get(Bytes.toBytes(obj.getRowKey()));
                get2.addColumn(CF, CQ2);
                Result result2 = table.get(get2);
                assertEquals(Bytes.toString(result2.getRow()), obj.getRowKey());
                assertEquals(Bytes.toString(result2.value()), obj.getData2());
    }
}

This code creates an HBase mini-cluster and starts it. Next, it creates a table called MyTest with one column family, CF. A record is inserted, a Get is performed from the same table, and the insertion is verified.

Starting the mini-cluster takes about 20-30 seconds, but that should be appropriate for integration testing.

To use an HBase mini-cluster on Microsoft Windows, you need to use a Cygwin environment.

See the paper at HBase Case-Study: Using HBaseTestingUtility for Local Testing and Development (2010) for more information about HBaseTestingUtility.

ZooKeeper

A distributed Apache HBase installation depends on a running ZooKeeper cluster. All participating nodes and clients need to be able to access the running ZooKeeper ensemble. Apache HBase by default manages a ZooKeeper "cluster" for you. It will start and stop the ZooKeeper ensemble as part of the HBase start/stop process. You can also manage the ZooKeeper ensemble independent of HBase and just point HBase at the cluster it should use. To toggle HBase management of ZooKeeper, use the HBASE_MANAGES_ZK variable in conf/hbase-env.sh. This variable, which defaults to true, tells HBase whether to start/stop the ZooKeeper ensemble servers as part of HBase start/stop.

When HBase manages the ZooKeeper ensemble, you can specify ZooKeeper configuration directly in conf/hbase-site.xml. A ZooKeeper configuration option can be set as a property in the HBase hbase-site.xml XML configuration file by prefacing the ZooKeeper option name with hbase.zookeeper.property. For example, the clientPort setting in ZooKeeper can be changed by setting the hbase.zookeeper.property.clientPort property. For all default values used by HBase, including ZooKeeper configuration, see hbase default configurations. Look for the hbase.zookeeper.property prefix. For the full list of ZooKeeper configurations, see ZooKeeper’s zoo.cfg. HBase does not ship with a zoo.cfg so you will need to browse the conf directory in an appropriate ZooKeeper download.

You must at least list the ensemble servers in hbase-site.xml using the hbase.zookeeper.quorum property. This property defaults to a single ensemble member at localhost which is not suitable for a fully distributed HBase. (It binds to the local machine only and remote clients will not be able to connect).

How many ZooKeepers should I run?

You can run a ZooKeeper ensemble that comprises 1 node only but in production it is recommended that you run a ZooKeeper ensemble of 3, 5 or 7 machines; the more members an ensemble has, the more tolerant the ensemble is of host failures. Also, run an odd number of machines. In ZooKeeper, an even number of peers is supported, but it is normally not used because an even sized ensemble requires, proportionally, more peers to form a quorum than an odd sized ensemble requires. For example, an ensemble with 4 peers requires 3 to form a quorum, while an ensemble with 5 also requires 3 to form a quorum. Thus, an ensemble of 5 allows 2 peers to fail, and thus is more fault tolerant than the ensemble of 4, which allows only 1 down peer.

Give each ZooKeeper server around 1GB of RAM, and if possible, its own dedicated disk (A dedicated disk is the best thing you can do to ensure a performant ZooKeeper ensemble). For very heavily loaded clusters, run ZooKeeper servers on separate machines from RegionServers (DataNodes and TaskTrackers).

For example, to have HBase manage a ZooKeeper quorum on nodes rs{1,2,3,4,5}.example.com, bound to port 2222 (the default is 2181) ensure HBASE_MANAGE_ZK is commented out or set to true in conf/hbase-env.sh and then edit conf/hbase-site.xml and set hbase.zookeeper.property.clientPort and hbase.zookeeper.quorum. You should also set hbase.zookeeper.property.dataDir to other than the default as the default has ZooKeeper persist data under /tmp which is often cleared on system restart. In the example below we have ZooKeeper persist to /user/local/zookeeper.

  <configuration>
    ...
    <property>
      <name>hbase.zookeeper.property.clientPort</name>
      <value>2222</value>
      <description>Property from ZooKeeper's config zoo.cfg.
      The port at which the clients will connect.
      </description>
    </property>
    <property>
      <name>hbase.zookeeper.quorum</name>
      <value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com</value>
      <description>Comma separated list of servers in the ZooKeeper Quorum.
      For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com".
      By default this is set to localhost for local and pseudo-distributed modes
      of operation. For a fully-distributed setup, this should be set to a full
      list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh
      this is the list of servers which we will start/stop ZooKeeper on.
      </description>
    </property>
    <property>
      <name>hbase.zookeeper.property.dataDir</name>
      <value>/usr/local/zookeeper</value>
      <description>Property from ZooKeeper's config zoo.cfg.
      The directory where the snapshot is stored.
      </description>
    </property>
    ...
  </configuration>
What version of ZooKeeper should I use?

The newer version, the better. ZooKeeper 3.4.x is required as of HBase 1.0.0
ZooKeeper Maintenance

Be sure to set up the data dir cleaner described under ZooKeeper Maintenance else you could have 'interesting' problems a couple of months in; i.e. zookeeper could start dropping sessions if it has to run through a directory of hundreds of thousands of logs which is wont to do around leader reelection time — a process rare but run on occasion whether because a machine is dropped or happens to hiccup.

155. Using existing ZooKeeper ensemble

To point HBase at an existing ZooKeeper cluster, one that is not managed by HBase, set HBASE_MANAGES_ZK in conf/hbase-env.sh to false

  ...
  # Tell HBase whether it should manage its own instance of ZooKeeper or not.
  export HBASE_MANAGES_ZK=false

Next set ensemble locations and client port, if non-standard, in hbase-site.xml.

When HBase manages ZooKeeper, it will start/stop the ZooKeeper servers as a part of the regular start/stop scripts. If you would like to run ZooKeeper yourself, independent of HBase start/stop, you would do the following

${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper

Note that you can use HBase in this manner to spin up a ZooKeeper cluster, unrelated to HBase. Just make sure to set HBASE_MANAGES_ZK to false if you want it to stay up across HBase restarts so that when HBase shuts down, it doesn’t take ZooKeeper down with it.

For more information about running a distinct ZooKeeper cluster, see the ZooKeeper Getting Started Guide. Additionally, see the ZooKeeper Wiki or the ZooKeeper documentation for more information on ZooKeeper sizing.

156. SASL Authentication with ZooKeeper

Newer releases of Apache HBase (>= 0.92) will support connecting to a ZooKeeper Quorum that supports SASL authentication (which is available in ZooKeeper versions 3.4.0 or later).

This describes how to set up HBase to mutually authenticate with a ZooKeeper Quorum. ZooKeeper/HBase mutual authentication (HBASE-2418) is required as part of a complete secure HBase configuration (HBASE-3025). For simplicity of explication, this section ignores additional configuration required (Secure HDFS and Coprocessor configuration). It’s recommended to begin with an HBase-managed ZooKeeper configuration (as opposed to a standalone ZooKeeper quorum) for ease of learning.

156.1. Operating System Prerequisites

You need to have a working Kerberos KDC setup. For each $HOST that will run a ZooKeeper server, you should have a principle zookeeper/$HOST. For each such host, add a service key (using the kadmin or kadmin.local tool’s ktadd command) for zookeeper/$HOST and copy this file to $HOST, and make it readable only to the user that will run zookeeper on $HOST. Note the location of this file, which we will use below as $PATH_TO_ZOOKEEPER_KEYTAB.

Similarly, for each $HOST that will run an HBase server (master or regionserver), you should have a principle: hbase/$HOST. For each host, add a keytab file called hbase.keytab containing a service key for hbase/$HOST, copy this file to $HOST, and make it readable only to the user that will run an HBase service on $HOST. Note the location of this file, which we will use below as $PATH_TO_HBASE_KEYTAB.

Each user who will be an HBase client should also be given a Kerberos principal. This principal should usually have a password assigned to it (as opposed to, as with the HBase servers, a keytab file) which only this user knows. The client’s principal’s maxrenewlife should be set so that it can be renewed enough so that the user can complete their HBase client processes. For example, if a user runs a long-running HBase client process that takes at most 3 days, we might create this user’s principal within kadmin with: addprinc -maxrenewlife 3days. The ZooKeeper client and server libraries manage their own ticket refreshment by running threads that wake up periodically to do the refreshment.

On each host that will run an HBase client (e.g. hbase shell), add the following file to the HBase home directory’s conf directory:

Client {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=false
  useTicketCache=true;
};

We’ll refer to this JAAS configuration file as $CLIENT_CONF below.

156.2. HBase-managed ZooKeeper Configuration

On each node that will run a zookeeper, a master, or a regionserver, create a JAAS configuration file in the conf directory of the node’s HBASE_HOME directory that looks like the following:

Server {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
  storeKey=true
  useTicketCache=false
  principal="zookeeper/$HOST";
};
Client {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  useTicketCache=false
  keyTab="$PATH_TO_HBASE_KEYTAB"
  principal="hbase/$HOST";
};

where the $PATH_TO_HBASE_KEYTAB and $PATH_TO_ZOOKEEPER_KEYTAB files are what you created above, and $HOST is the hostname for that node.

The Server section will be used by the ZooKeeper quorum server, while the Client section will be used by the HBase master and regionservers. The path to this file should be substituted for the text $HBASE_SERVER_CONF in the hbase-env.sh listing below.

The path to this file should be substituted for the text $CLIENT_CONF in the hbase-env.sh listing below.

Modify your hbase-env.sh to include the following:

export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
export HBASE_MANAGES_ZK=true
export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"

where $HBASE_SERVER_CONF and $CLIENT_CONF are the full paths to the JAAS configuration files created above.

Modify your hbase-site.xml on each node that will run zookeeper, master or regionserver to contain:

<configuration>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>$ZK_NODES</value>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.authProvider.1</name>
    <value>org.apache.zookeeper.server.auth.SASLAuthenticationProvider</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.kerberos.removeHostFromPrincipal</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.kerberos.removeRealmFromPrincipal</name>
    <value>true</value>
  </property>
</configuration>

where $ZK_NODES is the comma-separated list of hostnames of the ZooKeeper Quorum hosts.

Start your hbase cluster by running one or more of the following set of commands on the appropriate hosts:

bin/hbase zookeeper start
bin/hbase master start
bin/hbase regionserver start

156.3. External ZooKeeper Configuration

Add a JAAS configuration file that looks like:

Client {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  useTicketCache=false
  keyTab="$PATH_TO_HBASE_KEYTAB"
  principal="hbase/$HOST";
};

where the $PATH_TO_HBASE_KEYTAB is the keytab created above for HBase services to run on this host, and $HOST is the hostname for that node. Put this in the HBase home’s configuration directory. We’ll refer to this file’s full pathname as $HBASE_SERVER_CONF below.

Modify your hbase-env.sh to include the following:

export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF"
export HBASE_MANAGES_ZK=false
export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"
export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF"

Modify your hbase-site.xml on each node that will run a master or regionserver to contain:

<configuration>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>$ZK_NODES</value>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.authProvider.1</name>
    <value>org.apache.zookeeper.server.auth.SASLAuthenticationProvider</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.kerberos.removeHostFromPrincipal</name>
    <value>true</value>
  </property>
  <property>
    <name>hbase.zookeeper.property.kerberos.removeRealmFromPrincipal</name>
    <value>true</value>
  </property>
</configuration>

where $ZK_NODES is the comma-separated list of hostnames of the ZooKeeper Quorum hosts.

Also on each of these hosts, create a JAAS configuration file containing:

Server {
  com.sun.security.auth.module.Krb5LoginModule required
  useKeyTab=true
  keyTab="$PATH_TO_ZOOKEEPER_KEYTAB"
  storeKey=true
  useTicketCache=false
  principal="zookeeper/$HOST";
};

where $HOST is the hostname of each Quorum host. We will refer to the full pathname of this file as $ZK_SERVER_CONF below.

Start your ZooKeepers on each ZooKeeper Quorum host with:

SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start

Start your HBase cluster by running one or more of the following set of commands on the appropriate nodes:

bin/hbase master start
bin/hbase regionserver start

156.4. ZooKeeper Server Authentication Log Output

If the configuration above is successful, you should see something similar to the following in your ZooKeeper server logs:

11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in.
11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port 0.0.0.0/0.0.0.0:2181
11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started.
11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at:        Mon Dec 05 22:43:39 UTC 2011
11/12/05 22:43:39 INFO zookeeper.Login: TGT expires:                  Tue Dec 06 22:43:39 UTC 2011
11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:36:42 UTC 2011
..
11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler:
  Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN;
  authorizationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN.
11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase
11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for authorizationID: hbase

156.5. ZooKeeper Client Authentication Log Output

On the ZooKeeper client side (HBase master or regionserver), you should see something similar to the following:

11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000 watcher=master:60000
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server /10.166.175.249:2181
11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process is 14851@ip-10-166-175-249
11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in.
11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL mechanism.
11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started.
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session
11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at:        Mon Dec 05 22:43:59 UTC 2011
11/12/05 22:43:59 INFO zookeeper.Login: TGT expires:                  Tue Dec 06 22:43:59 UTC 2011
11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:30:37 UTC 2011
11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid = 0x134106594320000, negotiated timeout = 180000

156.6. Configuration from Scratch

This has been tested on the current standard Amazon Linux AMI. First setup KDC and principals as described above. Next checkout code and run a sanity check.

git clone git://git.apache.org/hbase.git
cd hbase
mvn clean test -Dtest=TestZooKeeperACL

Then configure HBase as described above. Manually edit target/cached_classpath.txt (see below):

bin/hbase zookeeper &
bin/hbase master &
bin/hbase regionserver &

156.7. Future improvements

156.7.1. Fix target/cached_classpath.txt

You must override the standard hadoop-core jar file from the target/cached_classpath.txt file with the version containing the HADOOP-7070 fix. You can use the following script to do this:

echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt
mv target/tmp.txt target/cached_classpath.txt

156.7.2. Set JAAS configuration programmatically

This would avoid the need for a separate Hadoop jar that fixes HADOOP-7070.

156.7.3. Elimination of kerberos.removeHostFromPrincipal and`kerberos.removeRealmFromPrincipal`

Community

157. Decisions

Feature Branches

Feature Branches are easy to make. You do not have to be a committer to make one. Just request the name of your branch be added to JIRA up on the developer’s mailing list and a committer will add it for you. Thereafter you can file issues against your feature branch in Apache HBase JIRA. Your code you keep elsewhere — it should be public so it can be observed — and you can update dev mailing list on progress. When the feature is ready for commit, 3 +1s from committers will get your feature merged. See HBase, mail # dev - Thoughts about large feature dev branches

Patch +1 Policy

The below policy is something we put in place 09/2012. It is a suggested policy rather than a hard requirement. We want to try it first to see if it works before we cast it in stone.

Apache HBase is made of components. Components have one or more OWNERs. See the 'Description' field on the components JIRA page for who the current owners are by component.

Patches that fit within the scope of a single Apache HBase component require, at least, a +1 by one of the component’s owners before commit. If owners are absent — busy or otherwise — two +1s by non-owners will suffice.

Patches that span components need at least two +1s before they can be committed, preferably +1s by owners of components touched by the x-component patch (TODO: This needs tightening up but I think fine for first pass).

Any -1 on a patch by anyone vetoes a patch; it cannot be committed until the justification for the -1 is addressed.

How to set fix version in JIRA on issue resolve

Here is how we agreed to set versions in JIRA when we resolve an issue. If master is going to be 0.98.0 then:

  • Commit only to master: Mark with 0.98

  • Commit to 0.95 and master: Mark with 0.98, and 0.95.x

  • Commit to 0.94.x and 0.95, and master: Mark with 0.98, 0.95.x, and 0.94.x

  • Commit to 89-fb: Mark with 89-fb.

  • Commit site fixes: no version

Policy on when to set a RESOLVED JIRA as CLOSED

We agreed that for issues that list multiple releases in their Fix Version/s field, CLOSE the issue on the release of any of the versions listed; subsequent change to the issue must happen in a new JIRA.

Only transient state in ZooKeeper!

You should be able to kill the data in zookeeper and hbase should ride over it recreating the zk content as it goes. This is an old adage around these parts. We just made note of it now. We also are currently in violation of this basic tenet — replication at least keeps permanent state in zk — but we are working to undo this breaking of a golden rule.

158. Community Roles

Component Owner/Lieutenant

Component owners are listed in the description field on this Apache HBase JIRA components page. The owners are listed in the 'Description' field rather than in the 'Component Lead' field because the latter only allows us list one individual whereas it is encouraged that components have multiple owners.

Owners or component lieutenants are volunteers who are (usually, but not necessarily) expert in their component domain and may have an agenda on how they think their Apache HBase component should evolve.

  1. Owners will try and review patches that land within their component’s scope.

  2. If applicable, if an owner has an agenda, they will publish their goals or the design toward which they are driving their component

If you would like to be volunteer as a component owner, just write the dev list and we’ll sign you up. Owners do not need to be committers.

159. Commit Message format

We agreed to the following Git commit message format:

HBASE-xxxxx <title>. (<contributor>)

If the person making the commit is the contributor, leave off the '(<contributor>)' element.

Appendix

Appendix A: Contributing to Documentation

The Apache HBase project welcomes contributions to all aspects of the project, including the documentation.

In HBase, documentation includes the following areas, and probably some others:

  • The HBase Reference Guide (this book)

  • The HBase website

  • API documentation

  • Command-line utility output and help text

  • Web UI strings, explicit help text, context-sensitive strings, and others

  • Log messages

  • Comments in source files, configuration files, and others

  • Localization of any of the above into target languages other than English

No matter which area you want to help out with, the first step is almost always to download (typically by cloning the Git repository) and familiarize yourself with the HBase source code. For information on downloading and building the source, see developer.

A.1. Contributing to Documentation or Other Strings

If you spot an error in a string in a UI, utility, script, log message, or elsewhere, or you think something could be made more clear, or you think text needs to be added where it doesn’t currently exist, the first step is to file a JIRA. Be sure to set the component to Documentation in addition any other involved components. Most components have one or more default owners, who monitor new issues which come into those queues. Regardless of whether you feel able to fix the bug, you should still file bugs where you see them.

If you want to try your hand at fixing your newly-filed bug, assign it to yourself. You will need to clone the HBase Git repository to your local system and work on the issue there. When you have developed a potential fix, submit it for review. If it addresses the issue and is seen as an improvement, one of the HBase committers will commit it to one or more branches, as appropriate.

Procedure: Suggested Work flow for Submitting Patches

This procedure goes into more detail than Git pros will need, but is included in this appendix so that people unfamiliar with Git can feel confident contributing to HBase while they learn.

  1. If you have not already done so, clone the Git repository locally. You only need to do this once.

  2. Fairly often, pull remote changes into your local repository by using the git pull command, while your tracking branch is checked out.

  3. For each issue you work on, create a new branch. One convention that works well for naming the branches is to name a given branch the same as the JIRA it relates to:

    $ git checkout -b HBASE-123456

  4. Make your suggested changes on your branch, committing your changes to your local repository often. If you need to switch to working on a different issue, remember to check out the appropriate branch.

  5. When you are ready to submit your patch, first be sure that HBase builds cleanly and behaves as expected in your modified branch.

  6. If you have made documentation changes, be sure the documentation and website builds by running mvn clean site.

  7. If it takes you several days or weeks to implement your fix, or you know that the area of the code you are working in has had a lot of changes lately, make sure you rebase your branch against the remote master and take care of any conflicts before submitting your patch.

    $ git checkout HBASE-123456
$ git rebase origin/master

  8. Generate your patch against the remote master. Run the following command from the top level of your git repository (usually called hbase):

    $ git format-patch --stdout origin/master > HBASE-123456.patch

    The name of the patch should contain the JIRA ID.

  9. Look over the patch file to be sure that you did not change any additional files by accident and that there are no other surprises.

  10. When you are satisfied, attach the patch to the JIRA and click the Patch Available button. A reviewer will review your patch.

  11. If you need to submit a new version of the patch, leave the old one on the JIRA and add a version number to the name of the new patch.

  12. After a change has been committed, there is no need to keep your local branch around.

A.2. Editing the HBase Website

The source for the HBase website is in the HBase source, in the src/main/site/ directory. Within this directory, source for the individual pages is in the xdocs/ directory, and images referenced in those pages are in the resources/images/ directory. This directory also stores images used in the HBase Reference Guide.

The website’s pages are written in an HTML-like XML dialect called xdoc, which has a reference guide at http://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html. You can edit these files in a plain-text editor, an IDE, or an XML editor such as XML Mind XML Editor (XXE) or Oxygen XML Author.

To preview your changes, build the website using the mvn clean site -DskipTests command. The HTML output resides in the target/site/ directory. When you are satisfied with your changes, follow the procedure in submit doc patch procedure to submit your patch.

A.3. Publishing the HBase Website and Documentation

HBase uses the ASF’s gitpubsub mechanism. . After generating the website and documentation artifacts using mvn clean site site:stage, check out the asf-site repository.

  1. Remove previously-generated content using the following command:

    rm -rf rm -rf *apidocs* *xref* *book* *.html *.pdf* css js
    Do not remove the 0.94/ directory. To regenerate them, you must check out the 0.94 branch and run mvn clean site site:stage from there, and then copy the artifacts to the 0.94/ directory of the asf-site branch.

  2. Copy the contents of target/staging to the branch.

  3. Add and commit your changes, and submit a patch for review.

A.4. HBase Reference Guide Style Guide and Cheat Sheet

The HBase Reference Guide is written in Asciidoc and built using AsciiDoctor. The following cheat sheet is included for your reference. More nuanced and comprehensive documentation is available at http://asciidoctor.org/docs/user-manual/.

Table 19. AsciiDoc Cheat Sheet
Element Type Desired Rendering How to do it

A paragraph

a paragraph

Just type some text with a blank line at the top and bottom.

Add line breaks within a paragraph without adding blank lines

Manual line breaks

This will break + at the plus sign. Or prefix the whole paragraph with a line containing '[%hardbreaks]'

Give a title to anything

Colored italic bold differently-sized text

In-Line Code or commands

monospace

`text`

In-line literal content (things to be typed exactly as shown)

bold mono

*`typethis`*

In-line replaceable content (things to substitute with your own values)

bold italic mono

*_typesomething_*

Code blocks with highlighting

monospace, highlighted, preserve space

 
[source,java]
----
  myAwesomeCode() {
}
----

Code block included from a separate file

included just as though it were part of the main file

 
[source,ruby]
----
include\::path/to/app.rb[]
----

Include only part of a separate file

Similar to Javadoc

Filenames, directory names, new terms

italic

_hbase-default.xml_

External naked URLs

A link with the URL as link text

 
link:http://www.google.com

External URLs with text

A link with arbitrary link text

 
link:http://www.google.com[Google]

Create an internal anchor to cross-reference

not rendered

 
[[anchor_name]]

Cross-reference an existing anchor using its default title

an internal hyperlink using the element title if available, otherwise using the anchor name

 
<<anchor_name>>

Cross-reference an existing anchor using custom text

an internal hyperlink using arbitrary text

 
<<anchor_name,Anchor Text>>

A block image

The image with alt text

 
image::sunset.jpg[Alt Text]

(put the image in the src/main/site/resources/images directory)

An inline image

The image with alt text, as part of the text flow

 
image:sunset.jpg [Alt Text]

(only one colon)

Link to a remote image

show an image hosted elsewhere

 
image::http://inkscape.org/doc/examples/tux.svg[Tux,250,350]

(or image:)

Add dimensions or a URL to the image

depends

inside the brackets after the alt text, specify width, height and/or link="http://my_link.com"

A footnote

subscript link which takes you to the footnote

 
Some text.footnote:[The footnote text.]

A note or warning with no title

The admonition image followed by the admonition

 
NOTE:My note here
WARNING:My warning here

A complex note

The note has a title and/or multiple paragraphs and/or code blocks or lists, etc

 
.The Title
[NOTE]
====
Here is the note text. Everything until the second set of four equals signs is part of the note.
----
some source code
----
====

Bullet lists

bullet lists

Numbered lists

numbered list

Checklists

Checked or unchecked boxes

Checked:

- [*]

Unchecked:

- [ ]

Multiple levels of lists

bulleted or numbered or combo

 
. Numbered (1), at top level
* Bullet (2), nested under 1
* Bullet (3), nested under 1
. Numbered (4), at top level
* Bullet (5), nested under 4
** Bullet (6), nested under 5
- [x] Checked (7), at top level

Labelled lists / variablelists

a list item title or summary followed by content

 
Title:: content

Title::
  content

Sidebars, quotes, or other blocks of text

a block of text, formatted differently from the default

Delimited using different delimiters, see http://asciidoctor.org/docs/user-manual/#built-in-blocks-summary. Some of the examples above use delimiters like ...., ----,====.

[example]
====
This is an example block.
====

[source]
----
This is a source block.
----

[note]
====
This is a note block.
====

[quote]
____
This is a quote block.
____

If you want to insert literal Asciidoc content that keeps being interpreted, when in doubt, use eight dots as the delimiter at the top and bottom.

Nested Sections

chapter, section, sub-section, etc

 
= Book (or chapter if the chapter can be built alone, see the leveloffset info below)

== Chapter (or section if the chapter is standalone)

=== Section (or subsection, etc)

==== Subsection

and so on up to 6 levels (think carefully about going deeper than 4 levels, maybe you can just titled paragraphs or lists instead). Note that you can include a book inside another book by adding the :leveloffset:+1 macro directive directly before your include, and resetting it to 0 directly after. See the book.adoc source for examples, as this is how this guide handles chapters. Don’t do it for prefaces, glossaries, appendixes, or other special types of chapters.

Include one file from another

Content is included as though it were inline

 
include::[/path/to/file.adoc]

For plenty of examples. see book.adoc.

A table

a table

See http://asciidoctor.org/docs/user-manual/#tables. Generally rows are separated by newlines and columns by pipes

Comment out a single line

A line is skipped during rendering

// This line won’t show up

Comment out a block

A section of the file is skipped during rendering

 
////
Nothing between the slashes will show up.
////

Highlight text for review

text shows up with yellow background

 
Test between #hash marks# is highlighted yellow.

A.5. Auto-Generated Content

Some parts of the HBase Reference Guide, most notably config.files, are generated automatically, so that this area of the documentation stays in sync with the code. This is done by means of an XSLT transform, which you can examine in the source at src/main/xslt/configuration_to_asciidoc_chapter.xsl. This transforms the hbase-common/src/main/resources/hbase-default.xml file into an Asciidoc output which can be included in the Reference Guide.

Sometimes, it is necessary to add configuration parameters or modify their descriptions. Make the modifications to the source file, and they will be included in the Reference Guide when it is rebuilt.

It is possible that other types of content can and will be automatically generated from HBase source files in the future.

A.6. Images in the HBase Reference Guide

You can include images in the HBase Reference Guide. It is important to include an image title if possible, and alternate text always. This allows screen readers to navigate to the image and also provides alternative text for the image. The following is an example of an image with a title and alternate text. Notice the double colon.

.My Image Title
image::sunset.jpg[Alt Text]

Here is an example of an inline image with alternate text. Notice the single colon. Inline images cannot have titles. They are generally small images like GUI buttons.

image:sunset.jpg[Alt Text]

When doing a local build, save the image to the src/main/site/resources/images/ directory. When you link to the image, do not include the directory portion of the path. The image will be copied to the appropriate target location during the build of the output.

When you submit a patch which includes adding an image to the HBase Reference Guide, attach the image to the JIRA. If the committer asks where the image should be committed, it should go into the above directory.

A.7. Adding a New Chapter to the HBase Reference Guide

If you want to add a new chapter to the HBase Reference Guide, the easiest way is to copy an existing chapter file, rename it, and change the ID (in double brackets) and title. Chapters are located in the src/main/asciidoc/_chapters/ directory.

Delete the existing content and create the new content. Then open the src/main/asciidoc/book.adoc file, which is the main file for the HBase Reference Guide, and copy an existing include element to include your new chapter in the appropriate location. Be sure to add your new file to your Git repository before creating your patch.

When in doubt, check to see how other files have been included.

A.8. Common Documentation Issues

The following documentation issues come up often. Some of these are preferences, but others can create mysterious build errors or other problems.

  1. Isolate Changes for Easy Diff Review.

    Be careful with pretty-printing or re-formatting an entire XML file, even if the formatting has degraded over time. If you need to reformat a file, do that in a separate JIRA where you do not change any content. Be careful because some XML editors do a bulk-reformat when you open a new file, especially if you use GUI mode in the editor.

  2. Syntax Highlighting

    The HBase Reference Guide uses coderay for syntax highlighting. To enable syntax highlighting for a given code listing, use the following type of syntax:

    [source,xml]
----
<name>My Name</name>
----

    Several syntax types are supported. The most interesting ones for the HBase Reference Guide are java, xml, sql, and bash.

Appendix B: FAQ

B.1. General

When should I use HBase?

See Overview in the Architecture chapter.

Are there other HBase FAQs?

See the FAQ that is up on the wiki, HBase Wiki FAQ.

Does HBase support SQL?

Not really. SQL-ish support for HBase via Hive is in development, however Hive is based on MapReduce which is not generally suitable for low-latency requests. See the Data Model section for examples on the HBase client.

How can I find examples of NoSQL/HBase?

See the link to the BigTable paper in Other Information About HBase, as well as the other papers.

What is the history of HBase?

See hbase.history.

B.2. Upgrading

How do I upgrade Maven-managed projects from HBase 0.94 to HBase 0.96+?

In HBase 0.96, the project moved to a modular structure. Adjust your project’s dependencies to rely upon the hbase-client module or another module as appropriate, rather than a single JAR. You can model your Maven dependency after one of the following, depending on your targeted version of HBase. See Section 3.5, “Upgrading from 0.94.x to 0.96.x” or Section 3.3, “Upgrading from 0.96.x to 0.98.x” for more information.

Maven Dependency for HBase 0.98
<dependency>
  <groupId>org.apache.hbase</groupId>
  <artifactId>hbase-client</artifactId>
  <version>0.98.5-hadoop2</version>
</dependency>
Maven Dependency for HBase 0.96
<dependency>
  <groupId>org.apache.hbase</groupId>
  <artifactId>hbase-client</artifactId>
  <version>0.96.2-hadoop2</version>
</dependency>
Maven Dependency for HBase 0.94
<dependency>
  <groupId>org.apache.hbase</groupId>
  <artifactId>hbase</artifactId>
  <version>0.94.3</version>
</dependency>

B.3. Architecture

How does HBase handle Region-RegionServer assignment and locality?

See Regions.

B.4. Configuration

How can I get started with my first cluster?

See Quick Start - Standalone HBase.

Where can I learn about the rest of the configuration options?

See Apache HBase Configuration.

B.5. Schema Design / Data Access

How should I design my schema in HBase?

See Data Model and HBase and Schema Design.

How can I store (fill in the blank) in HBase?

See Supported Datatypes.

How can I handle secondary indexes in HBase?

See Secondary Indexes and Alternate Query Paths.

Can I change a table’s rowkeys?

This is a very common question. You can’t. See Immutability of Rowkeys.

What APIs does HBase support?

See Data Model, Client, and Apache HBase External APIs.

B.6. MapReduce

How can I use MapReduce with HBase?

See HBase and MapReduce.

B.7. Performance and Troubleshooting

How can I improve HBase cluster performance?

See Apache HBase Performance Tuning.

How can I troubleshoot my HBase cluster?

See Troubleshooting and Debugging Apache HBase.

B.8. Amazon EC2

I am running HBase on Amazon EC2 and…​

EC2 issues are a special case. See Amazon EC2 and Amazon EC2.

B.9. Operations

How do I manage my HBase cluster?

See Apache HBase Operational Management.

How do I back up my HBase cluster?

See HBase Backup.

B.10. HBase in Action

Where can I find interesting videos and presentations on HBase?

See Other Information About HBase.

Appendix C: hbck In Depth

HBaseFsck (hbck) is a tool for checking for region consistency and table integrity problems and repairing a corrupted HBase. It works in two basic modes — a read-only inconsistency identifying mode and a multi-phase read-write repair mode.

C.1. Running hbck to identify inconsistencies

To check to see if your HBase cluster has corruptions, run hbck against your HBase cluster:

$ ./bin/hbase hbck

At the end of the commands output it prints OK or tells you the number of INCONSISTENCIES present. You may also want to run hbck a few times because some inconsistencies can be transient (e.g. cluster is starting up or a region is splitting). Operationally you may want to run hbck regularly and setup alert (e.g. via nagios) if it repeatedly reports inconsistencies . A run of hbck will report a list of inconsistencies along with a brief description of the regions and tables affected. The using the -details option will report more details including a representative listing of all the splits present in all the tables.

$ ./bin/hbase hbck -details

If you just want to know if some tables are corrupted, you can limit hbck to identify inconsistencies in only specific tables. For example the following command would only attempt to check table TableFoo and TableBar. The benefit is that hbck will run in less time.

$ ./bin/hbase hbck TableFoo TableBar

C.2. Inconsistencies

If after several runs, inconsistencies continue to be reported, you may have encountered a corruption. These should be rare, but in the event they occur newer versions of HBase include the hbck tool enabled with automatic repair options.

There are two invariants that when violated create inconsistencies in HBase:

  • HBase’s region consistency invariant is satisfied if every region is assigned and deployed on exactly one region server, and all places where this state kept is in accordance.

  • HBase’s table integrity invariant is satisfied if for each table, every possible row key resolves to exactly one region.

Repairs generally work in three phases — a read-only information gathering phase that identifies inconsistencies, a table integrity repair phase that restores the table integrity invariant, and then finally a region consistency repair phase that restores the region consistency invariant. Starting from version 0.90.0, hbck could detect region consistency problems report on a subset of possible table integrity problems. It also included the ability to automatically fix the most common inconsistency, region assignment and deployment consistency problems. This repair could be done by using the -fix command line option. These problems close regions if they are open on the wrong server or on multiple region servers and also assigns regions to region servers if they are not open.

Starting from HBase versions 0.90.7, 0.92.2 and 0.94.0, several new command line options are introduced to aid repairing a corrupted HBase. This hbck sometimes goes by the nickname ``uberhbck''. Each particular version of uber hbck is compatible with the HBase’s of the same major version (0.90.7 uberhbck can repair a 0.90.4). However, versions ⇐0.90.6 and versions ⇐0.92.1 may require restarting the master or failing over to a backup master.

C.3. Localized repairs

When repairing a corrupted HBase, it is best to repair the lowest risk inconsistencies first. These are generally region consistency repairs — localized single region repairs, that only modify in-memory data, ephemeral zookeeper data, or patch holes in the META table. Region consistency requires that the HBase instance has the state of the region’s data in HDFS (.regioninfo files), the region’s row in the hbase:meta table., and region’s deployment/assignments on region servers and the master in accordance. Options for repairing region consistency include:

  • -fixAssignments (equivalent to the 0.90 -fix option) repairs unassigned, incorrectly assigned or multiply assigned regions.

  • -fixMeta which removes meta rows when corresponding regions are not present in HDFS and adds new meta rows if they regions are present in HDFS while not in META. To fix deployment and assignment problems you can run this command:

$ ./bin/hbase hbck -fixAssignments

To fix deployment and assignment problems as well as repairing incorrect meta rows you can run this command:

$ ./bin/hbase hbck -fixAssignments -fixMeta

There are a few classes of table integrity problems that are low risk repairs. The first two are degenerate (startkey == endkey) regions and backwards regions (startkey > endkey). These are automatically handled by sidelining the data to a temporary directory (/hbck/xxxx). The third low-risk class is hdfs region holes. This can be repaired by using the:

  • -fixHdfsHoles option for fabricating new empty regions on the file system. If holes are detected you can use -fixHdfsHoles and should include -fixMeta and -fixAssignments to make the new region consistent.

$ ./bin/hbase hbck -fixAssignments -fixMeta -fixHdfsHoles

Since this is a common operation, we’ve added a the -repairHoles flag that is equivalent to the previous command:

$ ./bin/hbase hbck -repairHoles

If inconsistencies still remain after these steps, you most likely have table integrity problems related to orphaned or overlapping regions.

C.4. Region Overlap Repairs

Table integrity problems can require repairs that deal with overlaps. This is a riskier operation because it requires modifications to the file system, requires some decision making, and may require some manual steps. For these repairs it is best to analyze the output of a hbck -details run so that you isolate repairs attempts only upon problems the checks identify. Because this is riskier, there are safeguard that should be used to limit the scope of the repairs. WARNING: This is a relatively new and have only been tested on online but idle HBase instances (no reads/writes). Use at your own risk in an active production environment! The options for repairing table integrity violations include:

  • -fixHdfsOrphans option for ``adopting'' a region directory that is missing a region metadata file (the .regioninfo file).

  • -fixHdfsOverlaps ability for fixing overlapping regions

When repairing overlapping regions, a region’s data can be modified on the file system in two ways: 1) by merging regions into a larger region or 2) by sidelining regions by moving data to ``sideline'' directory where data could be restored later. Merging a large number of regions is technically correct but could result in an extremely large region that requires series of costly compactions and splitting operations. In these cases, it is probably better to sideline the regions that overlap with the most other regions (likely the largest ranges) so that merges can happen on a more reasonable scale. Since these sidelined regions are already laid out in HBase’s native directory and HFile format, they can be restored by using HBase’s bulk load mechanism. The default safeguard thresholds are conservative. These options let you override the default thresholds and to enable the large region sidelining feature.

  • -maxMerge <n> maximum number of overlapping regions to merge

  • -sidelineBigOverlaps if more than maxMerge regions are overlapping, sideline attempt to sideline the regions overlapping with the most other regions.

  • -maxOverlapsToSideline <n> if sidelining large overlapping regions, sideline at most n regions.

Since often times you would just want to get the tables repaired, you can use this option to turn on all repair options:

  • -repair includes all the region consistency options and only the hole repairing table integrity options.

Finally, there are safeguards to limit repairs to only specific tables. For example the following command would only attempt to check and repair table TableFoo and TableBar.

$ ./bin/hbase hbck -repair TableFoo TableBar

C.4.1. Special cases: Meta is not properly assigned

There are a few special cases that hbck can handle as well. Sometimes the meta table’s only region is inconsistently assigned or deployed. In this case there is a special -fixMetaOnly option that can try to fix meta assignments.

$ ./bin/hbase hbck -fixMetaOnly -fixAssignments

C.4.2. Special cases: HBase version file is missing

HBase’s data on the file system requires a version file in order to start. If this file is missing, you can use the -fixVersionFile option to fabricating a new HBase version file. This assumes that the version of hbck you are running is the appropriate version for the HBase cluster.

C.4.3. Special case: Root and META are corrupt.

The most drastic corruption scenario is the case where the ROOT or META is corrupted and HBase will not start. In this case you can use the OfflineMetaRepair tool create new ROOT and META regions and tables. This tool assumes that HBase is offline. It then marches through the existing HBase home directory, loads as much information from region metadata files (.regioninfo files) as possible from the file system. If the region metadata has proper table integrity, it sidelines the original root and meta table directories, and builds new ones with pointers to the region directories and their data.

$ ./bin/hbase org.apache.hadoop.hbase.util.hbck.OfflineMetaRepair
This tool is not as clever as uberhbck but can be used to bootstrap repairs that uberhbck can complete. If the tool succeeds you should be able to start hbase and run online repairs if necessary.

C.4.4. Special cases: Offline split parent

Once a region is split, the offline parent will be cleaned up automatically. Sometimes, daughter regions are split again before their parents are cleaned up. HBase can clean up parents in the right order. However, there could be some lingering offline split parents sometimes. They are in META, in HDFS, and not deployed. But HBase can’t clean them up. In this case, you can use the -fixSplitParents option to reset them in META to be online and not split. Therefore, hbck can merge them with other regions if fixing overlapping regions option is used.

This option should not normally be used, and it is not in -fixAll.

Appendix D: Access Control Matrix

The following matrix shows the permission set required to perform operations in HBase. Before using the table, read through the information about how to interpret it.

Interpreting the ACL Matrix Table

The following conventions are used in the ACL Matrix table:

D.1. Scopes

Permissions are evaluated starting at the widest scope and working to the narrowest scope.

A scope corresponds to a level of the data model. From broadest to narrowest, the scopes are as follows:

Scopes

  • Global

  • Namespace (NS)

  • Table

  • Column Family (CF)

  • Column Qualifier (CQ)

  • Cell

For instance, a permission granted at table level dominates any grants done at the Column Family, Column Qualifier, or cell level. The user can do what that grant implies at any location in the table. A permission granted at global scope dominates all: the user is always allowed to take that action everywhere.

D.2. Permissions

Possible permissions include the following:

Permissions

  • Superuser - a special user that belongs to group "supergroup" and has unlimited access

  • Admin (A)

  • Create (C)

  • Write (W)

  • Read (R)

  • Execute (X)

For the most part, permissions work in an expected way, with the following caveats:

Having Write permission does not imply Read permission.

It is possible and sometimes desirable for a user to be able to write data that same user cannot read. One such example is a log-writing process.

The hbase:meta table is readable by every user, regardless of the user’s other grants or restrictions.

This is a requirement for HBase to function correctly.

CheckAndPut and CheckAndDelete operations will fail if the user does not have both Write and Read permission.
Increment and Append operations do not require Read access.
The superuser, as the name suggests has permissions to perform all possible operations.
And for the operations marked with *, the checks are done in post hook and only subset of results satisfying access checks are returned back to the user.

The following table is sorted by the interface that provides each operation. In case the table goes out of date, the unit tests which check for accuracy of permissions can be found in hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java, and the access controls themselves can be examined in hbase-server/src/main/java/org/apache/hadoop/hbase/security/access/AccessController.java.

Table 20. ACL Matrix
Interface Operation Permissions

Master

createTable

superuser|global(C)|NS(C)

modifyTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

deleteTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

truncateTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

addColumn

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

modifyColumn

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)|column(A)|column(C)

deleteColumn

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)|column(A)|column(C)

enableTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

disableTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

disableAclTable

Not allowed

move

superuser|global(A)|NS(A)|TableOwner|table(A)

assign

superuser|global(A)|NS(A)|TableOwner|table(A)

unassign

superuser|global(A)|NS(A)|TableOwner|table(A)

regionOffline

superuser|global(A)|NS(A)|TableOwner|table(A)

balance

superuser|global(A)

balanceSwitch

superuser|global(A)

shutdown

superuser|global(A)

stopMaster

superuser|global(A)

snapshot

superuser|global(A)|NS(A)|TableOwner|table(A)

listSnapshot

superuser|global(A)|SnapshotOwner

cloneSnapshot

superuser|global(A)

restoreSnapshot

superuser|global(A)|SnapshotOwner & (NS(A)|TableOwner|table(A))

deleteSnapshot

superuser|global(A)|SnapshotOwner

createNamespace

superuser|global(A)

deleteNamespace

superuser|global(A)

modifyNamespace

superuser|global(A)

getNamespaceDescriptor

superuser|global(A)|NS(A)

listNamespaceDescriptors*

superuser|global(A)|NS(A)

flushTable

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

getTableDescriptors*

superuser|global(A)|global(C)|NS(A)|NS(C)|TableOwner|table(A)|table(C)

getTableNames*

superuser|TableOwner|Any global or table perm

setUserQuota(global level)

superuser|global(A)

setUserQuota(namespace level)

superuser|global(A)

setUserQuota(Table level)

superuser|global(A)|NS(A)|TableOwner|table(A)

setTableQuota

superuser|global(A)|NS(A)|TableOwner|table(A)

setNamespaceQuota

superuser|global(A)

Region

openRegion

superuser|global(A)

closeRegion

superuser|global(A)

flush

superuser|global(A)|global(C)|TableOwner|table(A)|table(C)

split

superuser|global(A)|TableOwner|TableOwner|table(A)

compact

superuser|global(A)|global(C)|TableOwner|table(A)|table(C)

getClosestRowBefore

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

getOp

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

exists

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

put

superuser|global(W)|NS(W)|table(W)|TableOwner|CF(W)|CQ(W)

delete

superuser|global(W)|NS(W)|table(W)|TableOwner|CF(W)|CQ(W)

batchMutate

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

checkAndPut

superuser|global(RW)|NS(RW)|TableOwner|table(RW)|CF(RW)|CQ(RW)

checkAndPutAfterRowLock

superuser|global(R)|NS(R)|TableOwner|Table(R)|CF(R)|CQ(R)

checkAndDelete

superuser|global(RW)|NS(RW)|TableOwner|table(RW)|CF(RW)|CQ(RW)

checkAndDeleteAfterRowLock

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

incrementColumnValue

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

append

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

appendAfterRowLock

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

increment

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

incrementAfterRowLock

superuser|global(W)|NS(W)|TableOwner|table(W)|CF(W)|CQ(W)

scannerOpen

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

scannerNext

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

scannerClose

superuser|global(R)|NS(R)|TableOwner|table(R)|CF(R)|CQ(R)

bulkLoadHFile

superuser|global(C)|TableOwner|table(C)|CF(C)

prepareBulkLoad

superuser|global(C)|TableOwner|table(C)|CF(C)

cleanupBulkLoad

superuser|global(C)|TableOwner|table(C)|CF(C)

Endpoint

invoke

superuser|global(X)|NS(X)|TableOwner|table(X)

AccessController

grant(global level)

global(A)

grant(namespace level)

global(A)|NS(A)

grant(table level)

global(A)|NS(A)|TableOwner|table(A)|CF(A)|CQ(A)

revoke(global level)

global(A)

revoke(namespace level)

global(A)|NS(A)

revoke(table level)

global(A)|NS(A)|TableOwner|table(A)|CF(A)|CQ(A)

getUserPermissions(global level)

global(A)

getUserPermissions(namespace level)

global(A)|NS(A)

getUserPermissions(table level)

global(A)|NS(A)|TableOwner|table(A)|CF(A)|CQ(A)

RegionServer

stopRegionServer

superuser|global(A)

mergeRegions

superuser|global(A)

rollWALWriterRequest

superuser|global(A)

replicateLogEntries

superuser|global(W)

Appendix E: Compression and Data Block Encoding In HBase

Codecs mentioned in this section are for encoding and decoding data blocks or row keys. For information about replication codecs, see cluster.replication.preserving.tags.

Some of the information in this section is pulled from a discussion on the HBase Development mailing list.

HBase supports several different compression algorithms which can be enabled on a ColumnFamily. Data block encoding attempts to limit duplication of information in keys, taking advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys and the schema of a given table. Compressors reduce the size of large, opaque byte arrays in cells, and can significantly reduce the storage space needed to store uncompressed data.

Compressors and data block encoding can be used together on the same ColumnFamily.

Changes Take Effect Upon Compaction

If you change compression or encoding for a ColumnFamily, the changes take effect during compaction.

Some codecs take advantage of capabilities built into Java, such as GZip compression. Others rely on native libraries. Native libraries may be available as part of Hadoop, such as LZ4. In this case, HBase only needs access to the appropriate shared library.

Other codecs, such as Google Snappy, need to be installed first. Some codecs are licensed in ways that conflict with HBase’s license and cannot be shipped as part of HBase.

This section discusses common codecs that are used and tested with HBase. No matter what codec you use, be sure to test that it is installed correctly and is available on all nodes in your cluster. Extra operational steps may be necessary to be sure that codecs are available on newly-deployed nodes. You can use the compression.test utility to check that a given codec is correctly installed.

To configure HBase to use a compressor, see compressor.install. To enable a compressor for a ColumnFamily, see changing.compression. To enable data block encoding for a ColumnFamily, see data.block.encoding.enable.

Block Compressors

  • none

  • Snappy

  • LZO

  • LZ4

  • GZ

Data Block Encoding Types
Prefix

Often, keys are very similar. Specifically, keys often share a common prefix and only differ near the end. For instance, one key might be RowKey:Family:Qualifier0 and the next key might be RowKey:Family:Qualifier1.
In Prefix encoding, an extra column is added which holds the length of the prefix shared between the current key and the previous key. Assuming the first key here is totally different from the key before, its prefix length is 0.

The second key’s prefix length is 23, since they have the first 23 characters in common.

Obviously if the keys tend to have nothing in common, Prefix will not provide much benefit.

The following image shows a hypothetical ColumnFamily with no data block encoding.

data block no encoding
Figure 11. ColumnFamily with No Encoding

Here is the same data with prefix data encoding.

data block prefix encoding
Figure 12. ColumnFamily with Prefix Encoding
Diff

Diff encoding expands upon Prefix encoding. Instead of considering the key sequentially as a monolithic series of bytes, each key field is split so that each part of the key can be compressed more efficiently.

Two new fields are added: timestamp and type.

If the ColumnFamily is the same as the previous row, it is omitted from the current row.

If the key length, value length or type are the same as the previous row, the field is omitted.

In addition, for increased compression, the timestamp is stored as a Diff from the previous row’s timestamp, rather than being stored in full. Given the two row keys in the Prefix example, and given an exact match on timestamp and the same type, neither the value length, or type needs to be stored for the second row, and the timestamp value for the second row is just 0, rather than a full timestamp.

Diff encoding is disabled by default because writing and scanning are slower but more data is cached.

This image shows the same ColumnFamily from the previous images, with Diff encoding.

data block diff encoding
Figure 13. ColumnFamily with Diff Encoding
Fast Diff

Fast Diff works similar to Diff, but uses a faster implementation. It also adds another field which stores a single bit to track whether the data itself is the same as the previous row. If it is, the data is not stored again.

Fast Diff is the recommended codec to use if you have long keys or many columns.

The data format is nearly identical to Diff encoding, so there is not an image to illustrate it.

Prefix Tree

Prefix tree encoding was introduced as an experimental feature in HBase 0.96. It provides similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides faster random access at a cost of slower encoding speed.

Prefix Tree may be appropriate for applications that have high block cache hit ratios. It introduces new 'tree' fields for the row and column. The row tree field contains a list of offsets/references corresponding to the cells in that row. This allows for a good deal of compression. For more details about Prefix Tree encoding, see HBASE-4676.

It is difficult to graphically illustrate a prefix tree, so no image is included. See the Wikipedia article for Trie for more general information about this data structure.

E.1. Which Compressor or Data Block Encoder To Use

The compression or codec type to use depends on the characteristics of your data. Choosing the wrong type could cause your data to take more space rather than less, and can have performance implications.

In general, you need to weigh your options between smaller size and faster compression/decompression. Following are some general guidelines, expanded from a discussion at Documenting Guidance on compression and codecs.

  • If you have long keys (compared to the values) or many columns, use a prefix encoder. FAST_DIFF is recommended, as more testing is needed for Prefix Tree encoding.

  • If the values are large (and not precompressed, such as images), use a data block compressor.

  • Use GZIP for cold data, which is accessed infrequently. GZIP compression uses more CPU resources than Snappy or LZO, but provides a higher compression ratio.

  • Use Snappy or LZO for hot data, which is accessed frequently. Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high of a compression ratio.

  • In most cases, enabling Snappy or LZO by default is a good choice, because they have a low performance overhead and provide space savings.

  • Before Snappy became available by Google in 2011, LZO was the default. Snappy has similar qualities as LZO but has been shown to perform better.

E.2. Making use of Hadoop Native Libraries in HBase

The Hadoop shared library has a bunch of facility including compression libraries and fast crc’ing — hardware crc’ing if your chipset supports it. To make this facility available to HBase, do the following. HBase/Hadoop will fall back to use alternatives if it cannot find the native library versions — or fail outright if you asking for an explicit compressor and there is no alternative available.

First make sure of your Hadoop. Fix this message if you are seeing it starting Hadoop processes:

16/02/09 22:40:24 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

It means is not properly pointing at its native libraries or the native libs were compiled for another platform. Fix this first.

Then if you see the following in your HBase logs, you know that HBase was unable to locate the Hadoop native libraries:

2014-08-07 09:26:20,139 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

If the libraries loaded successfully, the WARN message does not show. Usually this means you are good to go but read on.

Let’s presume your Hadoop shipped with a native library that suits the platform you are running HBase on. To check if the Hadoop native library is available to HBase, run the following tool (available in Hadoop 2.1 and greater):

$ ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:15:38,717 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Native library checking:
hadoop: false
zlib:   false
snappy: false
lz4:    false
bzip2:  false
2014-08-26 13:15:38,863 INFO  [main] util.ExitUtil: Exiting with status 1

Above shows that the native hadoop library is not available in HBase context.

The above NativeLibraryChecker tool may come back saying all is hunky-dory — i.e. all libs show 'true', that they are available — but follow the below presecription anyways to ensure the native libs are available in HBase context, when it goes to use them.

To fix the above, either copy the Hadoop native libraries local or symlink to them if the Hadoop and HBase stalls are adjacent in the filesystem. You could also point at their location by setting the LD_LIBRARY_PATH environment variable in your hbase-env.sh.

Where the JVM looks to find native libraries is "system dependent" (See java.lang.System#loadLibrary(name)). On linux, by default, is going to look in lib/native/PLATFORM where PLATFORM is the label for the platform your HBase is installed on. On a local linux machine, it seems to be the concatenation of the java properties os.name and os.arch followed by whether 32 or 64 bit. HBase on startup prints out all of the java system properties so find the os.name and os.arch in the log. For example:

...
2014-08-06 15:27:22,853 INFO  [main] zookeeper.ZooKeeper: Client environment:os.name=Linux
2014-08-06 15:27:22,853 INFO  [main] zookeeper.ZooKeeper: Client environment:os.arch=amd64
...

So in this case, the PLATFORM string is Linux-amd64-64. Copying the Hadoop native libraries or symlinking at lib/native/Linux-amd64-64 will ensure they are found. Rolling restart after you have made this change.

Here is an example of how you would set up the symlinks. Let the hadoop and hbase installs be in your home directory. Assume your hadoop native libs are at ~/hadoop/lib/native. Assume you are on a Linux-amd64-64 platform. In this case, you would do the following to link the hadoop native lib so hbase could find them.

...
$ mkdir -p ~/hbaseLinux-amd64-64 -> /home/stack/hadoop/lib/native/lib/native/
$ cd ~/hbase/lib/native/
$ ln -s ~/hadoop/lib/native Linux-amd64-64
$ ls -la
# Linux-amd64-64 -> /home/USER/hadoop/lib/native
...

If you see PureJavaCrc32C in a stack track or if you see something like the below in a perf trace, then native is not working; you are using the java CRC functions rather than native:

  5.02%  perf-53601.map      [.] Lorg/apache/hadoop/util/PureJavaCrc32C;.update

See HBASE-11927 Use Native Hadoop Library for HFile checksum (And flip default from CRC32 to CRC32C), for more on native checksumming support. See in particular the release note for how to check if your hardware to see if your processor has support for hardware CRCs. Or checkout the Apache Checksums in HBase blog post.

Here is example of how to point at the Hadoop libs with LD_LIBRARY_PATH environment variable:

$ LD_LIBRARY_PATH=~/hadoop-2.5.0-SNAPSHOT/lib/native ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:42:49,332 INFO  [main] bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native
2014-08-26 13:42:49,337 INFO  [main] zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /home/stack/hadoop-2.5.0-SNAPSHOT/lib/native/libhadoop.so.1.0.0
zlib:   true /lib64/libz.so.1
snappy: true /usr/lib64/libsnappy.so.1
lz4:    true revision:99
bzip2:  true /lib64/libbz2.so.1

Set in hbase-env.sh the LD_LIBRARY_PATH environment variable when starting your HBase.

E.3. Compressor Configuration, Installation, and Use

E.3.1. Configure HBase For Compressors

Before HBase can use a given compressor, its libraries need to be available. Due to licensing issues, only GZ compression is available to HBase (via native Java libraries) in a default installation. Other compression libraries are available via the shared library bundled with your hadoop. The hadoop native library needs to be findable when HBase starts. See

Compressor Support On the Master

A new configuration setting was introduced in HBase 0.95, to check the Master to determine which data block encoders are installed and configured on it, and assume that the entire cluster is configured the same. This option, hbase.master.check.compression, defaults to true. This prevents the situation described in HBASE-6370, where a table is created or modified to support a codec that a region server does not support, leading to failures that take a long time to occur and are difficult to debug.

If hbase.master.check.compression is enabled, libraries for all desired compressors need to be installed and configured on the Master, even if the Master does not run a region server.

Install GZ Support Via Native Libraries

HBase uses Java’s built-in GZip support unless the native Hadoop libraries are available on the CLASSPATH. The recommended way to add libraries to the CLASSPATH is to set the environment variable HBASE_LIBRARY_PATH for the user running HBase. If native libraries are not available and Java’s GZIP is used, Got brand-new compressor reports will be present in the logs. See brand.new.compressor).

Install LZO Support

HBase cannot ship with LZO because of incompatibility between HBase, which uses an Apache Software License (ASL) and LZO, which uses a GPL license. See the Using LZO Compression wiki page for information on configuring LZO support for HBase.

If you depend upon LZO compression, consider configuring your RegionServers to fail to start if LZO is not available. See hbase.regionserver.codecs.

Configure LZ4 Support

LZ4 support is bundled with Hadoop. Make sure the hadoop shared library (libhadoop.so) is accessible when you start HBase. After configuring your platform (see hadoop.native.lib), you can make a symbolic link from HBase to the native Hadoop libraries. This assumes the two software installs are colocated. For example, if my 'platform' is Linux-amd64-64:

$ cd $HBASE_HOME
$ mkdir lib/native
$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64

Use the compression tool to check that LZ4 is installed on all nodes. Start up (or restart) HBase. Afterward, you can create and alter tables to enable LZ4 as a compression codec.:

hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
Install Snappy Support

HBase does not ship with Snappy support because of licensing issues. You can install Snappy binaries (for instance, by using yum install snappy on CentOS) or build Snappy from source. After installing Snappy, search for the shared library, which will be called libsnappy.so.X where X is a number. If you built from source, copy the shared library to a known location on your system, such as /opt/snappy/lib/.

In addition to the Snappy library, HBase also needs access to the Hadoop shared library, which will be called something like libhadoop.so.X.Y, where X and Y are both numbers. Make note of the location of the Hadoop library, or copy it to the same location as the Snappy library.

The Snappy and Hadoop libraries need to be available on each node of your cluster. See compression.test to find out how to test that this is the case.

See hbase.regionserver.codecs to configure your RegionServers to fail to start if a given compressor is not available.

Each of these library locations need to be added to the environment variable HBASE_LIBRARY_PATH for the operating system user that runs HBase. You need to restart the RegionServer for the changes to take effect.

CompressionTest

You can use the CompressionTest tool to verify that your compressor is available to HBase:

 $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
Enforce Compression Settings On a RegionServer

You can configure a RegionServer so that it will fail to restart if compression is configured incorrectly, by adding the option hbase.regionserver.codecs to the hbase-site.xml, and setting its value to a comma-separated list of codecs that need to be available. For example, if you set this property to lzo,gz, the RegionServer would fail to start if both compressors were not available. This would prevent a new server from being added to the cluster without having codecs configured properly.

E.3.2. Enable Compression On a ColumnFamily

To enable compression for a ColumnFamily, use an alter command. You do not need to re-create the table or copy data. If you are changing codecs, be sure the old codec is still available until all the old StoreFiles have been compacted.

Example 62. Enabling Compression on a ColumnFamily of an Existing Table using HBaseShell
hbase> disable 'test'
hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'}
hbase> enable 'test'
Example 63. Creating a New Table with Compression On a ColumnFamily
hbase> create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' }
Example 64. Verifying a ColumnFamily’s Compression Settings
hbase> describe 'test'
DESCRIPTION                                          ENABLED
 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false
 ', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
 VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS
 => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa
 lse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B
 LOCKCACHE => 'true'}
1 row(s) in 0.1070 seconds

E.3.3. Testing Compression Performance

HBase includes a tool called LoadTestTool which provides mechanisms to test your compression performance. You must specify either -write or -update-read as your first parameter, and if you do not specify another parameter, usage advice is printed for each option.

Example 65. LoadTestTool Usage
$ bin/hbase org.apache.hadoop.hbase.util.LoadTestTool -h
usage: bin/hbase org.apache.hadoop.hbase.util.LoadTestTool <options>
Options:
 -batchupdate                 Whether to use batch as opposed to separate
                              updates for every column in a row
 -bloom <arg>                 Bloom filter type, one of [NONE, ROW, ROWCOL]
 -compression <arg>           Compression type, one of [LZO, GZ, NONE, SNAPPY,
                              LZ4]
 -data_block_encoding <arg>   Encoding algorithm (e.g. prefix compression) to
                              use for data blocks in the test column family, one
                              of [NONE, PREFIX, DIFF, FAST_DIFF, PREFIX_TREE].
 -encryption <arg>            Enables transparent encryption on the test table,
                              one of [AES]
 -generator <arg>             The class which generates load for the tool. Any
                              args for this class can be passed as colon
                              separated after class name
 -h,--help                    Show usage
 -in_memory                   Tries to keep the HFiles of the CF inmemory as far
                              as possible.  Not guaranteed that reads are always
                              served from inmemory
 -init_only                   Initialize the test table only, don't do any
                              loading
 -key_window <arg>            The 'key window' to maintain between reads and
                              writes for concurrent write/read workload. The
                              default is 0.
 -max_read_errors <arg>       The maximum number of read errors to tolerate
                              before terminating all reader threads. The default
                              is 10.
 -multiput                    Whether to use multi-puts as opposed to separate
                              puts for every column in a row
 -num_keys <arg>              The number of keys to read/write
 -num_tables <arg>            A positive integer number. When a number n is
                              speicfied, load test tool  will load n table
                              parallely. -tn parameter value becomes table name
                              prefix. Each table name is in format
                              <tn>_1...<tn>_n
 -read <arg>                  <verify_percent>[:<#threads=20>]
 -regions_per_server <arg>    A positive integer number. When a number n is
                              specified, load test tool will create the test
                              table with n regions per server
 -skip_init                   Skip the initialization; assume test table already
                              exists
 -start_key <arg>             The first key to read/write (a 0-based index). The
                              default value is 0.
 -tn <arg>                    The name of the table to read or write
 -update <arg>                <update_percent>[:<#threads=20>][:<#whether to
                              ignore nonce collisions=0>]
 -write <arg>                 <avg_cols_per_key>:<avg_data_size>[:<#threads=20>]
 -zk <arg>                    ZK quorum as comma-separated host names without
                              port numbers
 -zk_root <arg>               name of parent znode in zookeeper
Example 66. Example Usage of LoadTestTool
$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000
          -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE

160. Enable Data Block Encoding

Codecs are built into HBase so no extra configuration is needed. Codecs are enabled on a table by setting the DATA_BLOCK_ENCODING property. Disable the table before altering its DATA_BLOCK_ENCODING setting. Following is an example using HBase Shell:

Example 67. Enable Data Block Encoding On a Table
hbase>  disable 'test'
hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
Updating all regions with the new schema...
0/1 regions updated.
1/1 regions updated.
Done.
0 row(s) in 2.2820 seconds
hbase> enable 'test'
0 row(s) in 0.1580 seconds
Example 68. Verifying a ColumnFamily’s Data Block Encoding
hbase> describe 'test'
DESCRIPTION                                          ENABLED
 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true
 _DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE =>
 '0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS
 IONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =
 > 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals
 e', BLOCKCACHE => 'true'}
1 row(s) in 0.0650 seconds

Appendix F: SQL over HBase

The following projects offer some support for SQL over HBase.

F.1. Apache Phoenix

Apache Phoenix

F.2. Trafodion

Trafodion: Transactional SQL-on-HBase

Appendix G: YCSB

YCSB: The Yahoo! Cloud Serving Benchmark and HBase

TODO: Describe how YCSB is poor for putting up a decent cluster load.

TODO: Describe setup of YCSB for HBase. In particular, presplit your tables before you start a run. See HBASE-4163 Create Split Strategy for YCSB Benchmark for why and a little shell command for how to do it.

Ted Dunning redid YCSB so it’s mavenized and added facility for verifying workloads. See Ted Dunning’s YCSB.

Appendix H: HFile format

This appendix describes the evolution of the HFile format.

H.1. HBase File Format (version 1)

As we will be discussing changes to the HFile format, it is useful to give a short overview of the original (HFile version 1) format.

H.1.1. Overview of Version 1

An HFile in version 1 format is structured as follows:

HFile Version 1
Figure 14. HFile V1 Format

H.1.2. Block index format in version 1

The block index in version 1 is very straightforward. For each entry, it contains:

  1. Offset (long)

  2. Uncompressed size (int)

  3. Key (a serialized byte array written using Bytes.writeByteArray)

    1. Key length as a variable-length integer (VInt)

    2. Key bytes

The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to the method that reads the block index. One of the limitations of the block index in version 1 is that it does not provide the compressed size of a block, which turns out to be necessary for decompression. Therefore, the HFile reader has to infer this compressed size from the offset difference between blocks. We fix this limitation in version 2, where we store on-disk block size instead of uncompressed size, and get uncompressed size from the block header.

H.2. HBase file format with inline blocks (version 2)

Note: this feature was introduced in HBase 0.92

H.2.1. Motivation

We found it necessary to revise the HFile format after encountering high memory usage and slow startup times caused by large Bloom filters and block indexes in the region server. Bloom filters can get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions. Block indexes can grow as large as 6 GB in aggregate size over the same set of regions. A region is not considered opened until all of its block index data is loaded. Large Bloom filters produce a different performance problem: the first get request that requires a Bloom filter lookup will incur the latency of loading the entire Bloom filter bit array.

To speed up region server startup we break Bloom filters and block indexes into multiple blocks and write those blocks out as they fill up, which also reduces the HFile writer’s memory footprint. In the Bloom filter case, "filling up a block" means accumulating enough keys to efficiently utilize a fixed-size bit array, and in the block index case we accumulate an "index block" of the desired size. Bloom filter blocks and index blocks (we call these "inline blocks") become interspersed with data blocks, and as a side effect we can no longer rely on the difference between block offsets to determine data block length, as it was done in version 1.

HFile is a low-level file format by design, and it should not deal with application-specific details such as Bloom filters, which are handled at StoreFile level. Therefore, we call Bloom filter blocks in an HFile "inline" blocks. We also supply HFile with an interface to write those inline blocks.

Another format modification aimed at reducing the region server startup time is to use a contiguous "load-on-open" section that has to be loaded in memory at the time an HFile is being opened. Currently, as an HFile opens, there are separate seek operations to read the trailer, data/meta indexes, and file info. To read the Bloom filter, there are two more seek operations for its "data" and "meta" portions. In version 2, we seek once to read the trailer and seek again to read everything else we need to open the file from a contiguous block.

H.2.2. Overview of Version 2

The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only writes version 2 HFiles. A version 2 HFile is structured as follows:

HFile Version 2 Structure

HFile Version 2

H.2.3. Unified version 2 block format

In the version 2 every block in the data section contains the following fields:

  1. 8 bytes: Block type, a sequence of bytes equivalent to version 1’s "magic records". Supported block types are:

    1. DATA – data blocks

    2. LEAF_INDEX – leaf-level index blocks in a multi-level-block-index

    3. BLOOM_CHUNK – Bloom filter chunks

    4. META – meta blocks (not used for Bloom filters in version 2 anymore)

    5. INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex

    6. ROOT_INDEX – root>level index blocks in a multi>level block index

    7. FILE_INFO – the ``file info'' block, a small key>value map of metadata

    8. BLOOM_META – a Bloom filter metadata block in the load>on>open section

    9. TRAILER – a fixed>size file trailer. As opposed to the above, this is not an HFile v2 block but a fixed>size (for each HFile version) data structure

    10. INDEX_V1 – this block type is only used for legacy HFile v1 block

  2. Compressed size of the block’s data, not including the header (int).

    Can be used for skipping the current data block when scanning HFile data.

  3. Uncompressed size of the block’s data, not including the header (int)

    This is equal to the compressed size if the compression algorithm is NONE

  4. File offset of the previous block of the same type (long)

    Can be used for seeking to the previous data/index block

  5. Compressed data (or uncompressed data if the compression algorithm is NONE).

The above format of blocks is used in the following HFile sections:

Scanned block section

The section is named so because it contains all data blocks that need to be read when an HFile is scanned sequentially. Also contains leaf block index and Bloom chunk blocks.

Non-scanned block section

This section still contains unified-format v2 blocks but it does not have to be read when doing a sequential scan. This section contains "meta" blocks and intermediate-level index blocks.

We are supporting "meta" blocks in version 2 the same way they were supported in version 1, even though we do not store Bloom filter data in these blocks anymore.

H.2.4. Block index in version 2

There are three types of block indexes in HFile version 2, stored in two different formats (root and non-root):

  1. Data index — version 2 multi-level block index, consisting of:

    1. Version 2 root index, stored in the data block index section of the file

    2. Optionally, version 2 intermediate levels, stored in the non%root format in the data index section of the file. Intermediate levels can only be present if leaf level blocks are present

    3. Optionally, version 2 leaf levels, stored in the non%root format inline with data blocks

  2. Meta index — version 2 root index format only, stored in the meta index section of the file

  3. Bloom index — version 2 root index format only, stored in the ``load-on-open'' section as part of Bloom filter metadata.

H.2.5. Root block index format in version 2

This format applies to:

  1. Root level of the version 2 data index

  2. Entire meta and Bloom indexes in version 2, which are always single-level.

A version 2 root index block is a sequence of entries of the following format, similar to entries of a version 1 block index, but storing on-disk size instead of uncompressed size.

  1. Offset (long)

    This offset may point to a data block or to a deeper>level index block.

  2. On-disk size (int)

  3. Key (a serialized byte array stored using Bytes.writeByteArray)

  4. Key (VInt)

  5. Key bytes

A single-level version 2 block index consists of just a single root index block. To read a root index block of version 2, one needs to know the number of entries. For the data index and the meta index the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound Bloom filter metadata.

For a multi-level block index we also store the following fields in the root index block in the load-on-open section of the HFile, in addition to the data structure described above:

  1. Middle leaf index block offset

  2. Middle leaf block on-disk size (meaning the leaf index block containing the reference to the ``middle'' data block of the file)

  3. The index of the mid-key (defined below) in the middle leaf-level block.

These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits, which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total number of blocks in the HFile is n. This definition is consistent with how the mid-key was determined in HFile version 1, and is reasonable in general, because blocks are likely to be the same size on average, but we don’t have any estimates on individual key/value pair sizes.

When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index block is kept track of. When we finish writing and the total number of leaf-level blocks is determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are computed. When reading the HFile and the mid-key is requested, we retrieve the middle leaf index block (potentially from the block cache) and get the mid-key value from the appropriate position inside that leaf block.

H.2.6. Non-root block index format in version 2

This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block index. Every non-root index block is structured as follows.

  1. numEntries: the number of entries (int).

  2. entryOffsets: the "secondary index" of offsets of entries in the block, to facilitate a quick binary search on the key (numEntries + 1 int values). The last value is the total length of all entries in this index block. For example, in a non-root index block with entry sizes 60, 80, 50 the "secondary index" will contain the following int array: {0, 60, 140, 190}.

  3. Entries. Each entry contains:

  4. Offset of the block referenced by this entry in the file (long)

  5. On>disk size of the referenced block (int)

  6. Key. The length can be calculated from entryOffsets.

H.2.7. Bloom filters in version 2

In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open section of the HFile for quick startup.

  1. A compound Bloom filter.

  2. Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the Bloom filter version number 2

  3. The total byte size of all compound Bloom filter chunks (long)

  4. Number of hash functions (int

  5. Type of hash functions (int)

  6. The total key count inserted into the Bloom filter (long)

  7. The maximum total number of keys in the Bloom filter (long)

  8. The number of chunks (int)

  9. Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using Bytes.writeByteArray

  10. Bloom block index in the version 2 root block index format

H.2.8. File Info format in versions 1 and 2

The file info block is a serialized map from byte arrays to byte arrays, with the following keys, among others. StoreFile-level logic adds more keys to this.

hfile.LASTKEY

The last key of the file (byte array)

hfile.AVG_KEY_LEN

The average key length in the file (int)

hfile.AVG_VALUE_LEN

The average value length in the file (int)

In version 2, we did not change the file format, but we moved the file info to the final section of the file, which can be loaded as one block when the HFile is being opened.

Also, we do not store the comparator in the version 2 file info anymore. Instead, we store it in the fixed file trailer. This is because we need to know the comparator at the time of parsing the load-on-open section of the HFile.

H.2.9. Fixed file trailer format differences between versions 1 and 2

The following table shows common and different fields between fixed file trailers in versions 1 and 2. Note that the size of the trailer is different depending on the version, so it is ``fixed'' only within one version. However, the version is always stored as the last four-byte integer in the file.

Table 21. Differences between HFile Versions 1 and 2

Version 1

Version 2

File info offset (long)

Data index offset (long)

loadOnOpenOffset (long) /The offset of the section that we need to load when opening the file./

Number of data index entries (int)

metaIndexOffset (long) /This field is not being used by the version 1 reader, so we removed it from version 2./

uncompressedDataIndexSize (long) /The total uncompressed size of the whole data block index, including root-level, intermediate-level, and leaf-level blocks./

Number of meta index entries (int)

Total uncompressed bytes (long)

numEntries (int)

numEntries (long)

Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int)

Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int)

The number of levels in the data block index (int)

firstDataBlockOffset (long) /The offset of the first data block. Used when scanning./

lastDataBlockEnd (long) /The offset of the first byte after the last key/value data block. We don’t need to go beyond this offset when scanning./

Version: 1 (int)

Version: 2 (int)

H.2.10. getShortMidpointKey(an optimization for data index block)

Note: this optimization was introduced in HBase 0.95+

HFiles contain many blocks that contain a range of sorted Cells. Each cell has a key. To save IO when reading Cells, the HFile also has an index that maps a Cell’s start key to the offset of the beginning of a particular block. Prior to this optimization, HBase would use the key of the first cell in each data block as the index key.

In HBASE-7845, we generate a new key that is lexicographically larger than the last key of the previous block and lexicographically equal or smaller than the start key of the current block. While actual keys can potentially be very long, this "fake key" or "virtual key" can be much shorter. For example, if the stop key of previous block is "the quick brown fox", the start key of current block is "the who", we could use "the r" as our virtual key in our hfile index.

There are two benefits to this:

  • having shorter keys reduces the hfile index size, (allowing us to keep more indexes in memory), and

  • using something closer to the end key of the previous block allows us to avoid a potential extra IO when the target key lives in between the "virtual key" and the key of the first element in the target block.

This optimization (implemented by the getShortMidpointKey method) is inspired by LevelDB’s ByteWiseComparatorImpl::FindShortestSeparator() and FindShortSuccessor().

H.3. HBase File Format with Security Enhancements (version 3)

Note: this feature was introduced in HBase 0.98

H.3.1. Motivation

Version 3 of HFile makes changes needed to ease management of encryption at rest and cell-level metadata (which in turn is needed for cell-level ACLs and cell-level visibility labels). For more information see hbase.encryption.server, hbase.tags, hbase.accesscontrol.configuration, and hbase.visibility.labels.

H.3.2. Overview

The version of HBase introducing the above features reads HFiles in versions 1, 2, and 3 but only writes version 3 HFiles. Version 3 HFiles are structured the same as version 2 HFiles. For more information see hfilev2.overview.

H.3.3. File Info Block in Version 3

Version 3 added two additional pieces of information to the reserved keys in the file info block.

hfile.MAX_TAGS_LEN

The maximum number of bytes needed to store the serialized tags for any single cell in this hfile (int)

hfile.TAGS_COMPRESSED

Does the block encoder for this hfile compress tags? (boolean). Should only be present if hfile.MAX_TAGS_LEN is also present.

When reading a Version 3 HFile the presence of MAX_TAGS_LEN is used to determine how to deserialize the cells within a data block. Therefore, consumers must read the file’s info block prior to reading any data blocks.

When writing a Version 3 HFile, HBase will always include `MAX_TAGS_LEN ` when flushing the memstore to underlying filesystem and when using prefix tree encoding for data blocks, as described in compression.

When compacting extant files, the default writer will omit MAX_TAGS_LEN if all of the files selected do not themselves contain any cells with tags.

See compaction for details on the compaction file selection algorithm.

H.3.4. Data Blocks in Version 3

Within an HFile, HBase cells are stored in data blocks as a sequence of KeyValues (see hfilev1.overview, or Lars George’s excellent introduction to HBase Storage). In version 3, these KeyValue optionally will include a set of 0 or more tags:

Version 1 & 2, Version 3 without MAX_TAGS_LEN

Version 3 with MAX_TAGS_LEN

Key Length (4 bytes)

Value Length (4 bytes)

Key bytes (variable)

Value bytes (variable)

Tags Length (2 bytes)

Tags bytes (variable)

If the info block for a given HFile contains an entry for MAX_TAGS_LEN each cell will have the length of that cell’s tags included, even if that length is zero. The actual tags are stored as a sequence of tag length (2 bytes), tag type (1 byte), tag bytes (variable). The format an individual tag’s bytes depends on the tag type.

Note that the dependence on the contents of the info block implies that prior to reading any data blocks you must first process a file’s info block. It also implies that prior to writing a data block you must know if the file’s info block will include MAX_TAGS_LEN.

H.3.5. Fixed File Trailer in Version 3

The fixed file trailers written with HFile version 3 are always serialized with protocol buffers. Additionally, it adds an optional field to the version 2 protocol buffer named encryption_key. If HBase is configured to encrypt HFiles this field will store a data encryption key for this particular HFile, encrypted with the current cluster master key using AES. For more information see hbase.encryption.server.

Appendix I: Other Information About HBase

I.1. HBase Videos

Introduction to HBase

HBase and Hadoop, Mixing Real-Time and Batch Processing at StumbleUpon by JD Cryans (Hadoop World 2010).

I.2. HBase Presentations (Slides)

Advanced HBase Schema Design by Lars George (Hadoop World 2011).

Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011).

Getting The Most From Your HBase Install by Ryan Rawson, Jonathan Gray (Hadoop World 2009).

I.3. HBase Papers

BigTable by Google (2006).

HBase and HDFS Locality by Lars George (2010).

No Relation: The Mixed Blessings of Non-Relational Databases by Ian Varley (2009).

I.4. HBase Sites

Cloudera’s HBase Blog has a lot of links to useful HBase information.

  • CAP Confusion is a relevant entry for background information on distributed storage systems.

HBase Wiki has a page with a number of presentations.

HBase RefCard from DZone.

I.5. HBase Books

HBase: The Definitive Guide by Lars George.

I.6. Hadoop Books

Hadoop: The Definitive Guide by Tom White.

Appendix J: HBase History

  • 2006: BigTable paper published by Google.

  • 2006 (end of year): HBase development starts.

  • 2008: HBase becomes Hadoop sub-project.

  • 2010: HBase becomes Apache top-level project.

Appendix K: HBase and the Apache Software Foundation

HBase is a project in the Apache Software Foundation and as such there are responsibilities to the ASF to ensure a healthy project.

K.1. ASF Development Process

See the Apache Development Process page for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing and getting involved, and how open-source works at ASF.

K.2. ASF Board Reporting

Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by the HBase project lead and the committers. See ASF board reporting for more information.

Appendix L: Apache HBase Orca

jumping orca rotated 25percent
Figure 15. Apache HBase Orca

An Orca is the Apache HBase mascot. See NOTICES.txt. Our Orca logo we got here: http://www.vectorfree.com/jumping-orca It is licensed Creative Commons Attribution 3.0. See https://creativecommons.org/licenses/by/3.0/us/ We changed the logo by stripping the colored background, inverting it and then rotating it some.

Appendix M: Enabling Dapper-like Tracing in HBase

HBASE-6449 added support for tracing requests through HBase, using the open source tracing library, HTrace. Setting up tracing is quite simple, however it currently requires some very minor changes to your client code (it would not be very difficult to remove this requirement).

M.1. SpanReceivers

The tracing system works by collecting information in structures called 'Spans'. It is up to you to choose how you want to receive this information by implementing the SpanReceiver interface, which defines one method:

public void receiveSpan(Span span);

This method serves as a callback whenever a span is completed. HTrace allows you to use as many SpanReceivers as you want so you can easily send trace information to multiple destinations.

Configure what SpanReceivers you’d like to us by putting a comma separated list of the fully-qualified class name of classes implementing SpanReceiver in hbase-site.xml property: hbase.trace.spanreceiver.classes.

HTrace includes a LocalFileSpanReceiver that writes all span information to local files in a JSON-based format. The LocalFileSpanReceiver looks in hbase-site.xml for a hbase.local-file-span-receiver.path property with a value describing the name of the file to which nodes should write their span information.

<property>
  <name>hbase.trace.spanreceiver.classes</name>
  <value>org.apache.htrace.impl.LocalFileSpanReceiver</value>
</property>
<property>
  <name>hbase.htrace.local-file-span-receiver.path</name>
  <value>/var/log/hbase/htrace.out</value>
</property>

HTrace also provides ZipkinSpanReceiver which converts spans to Zipkin span format and send them to Zipkin server. In order to use this span receiver, you need to install the jar of htrace-zipkin to your HBase’s classpath on all of the nodes in your cluster.

htrace-zipkin is published to the Maven central repository. You could get the latest version from there or just build it locally (see the HTrace homepage for information on how to do this) and then copy it out to all nodes.

ZipkinSpanReceiver for properties called hbase.htrace.zipkin.collector-hostname and hbase.htrace.zipkin.collector-port in hbase-site.xml with values describing the Zipkin collector server to which span information are sent.

<property>
  <name>hbase.trace.spanreceiver.classes</name>
  <value>org.apache.htrace.impl.ZipkinSpanReceiver</value>
</property>
<property>
  <name>hbase.htrace.zipkin.collector-hostname</name>
  <value>localhost</value>
</property>
<property>
  <name>hbase.htrace.zipkin.collector-port</name>
  <value>9410</value>
</property>

If you do not want to use the included span receivers, you are encouraged to write your own receiver (take a look at LocalFileSpanReceiver for an example). If you think others would benefit from your receiver, file a JIRA with the HTrace project.

161. Client Modifications

In order to turn on tracing in your client code, you must initialize the module sending spans to receiver once per client process.

private SpanReceiverHost spanReceiverHost;

...

  Configuration conf = HBaseConfiguration.create();
  SpanReceiverHost spanReceiverHost = SpanReceiverHost.getInstance(conf);

Then you simply start tracing span before requests you think are interesting, and close it when the request is done. For example, if you wanted to trace all of your get operations, you change this:

Configuration config = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("t1"));
Get get = new Get(Bytes.toBytes("r1"));
Result res = table.get(get);

into:

TraceScope ts = Trace.startSpan("Gets", Sampler.ALWAYS);
try {
  Table table = connection.getTable(TableName.valueOf("t1"));
  Get get = new Get(Bytes.toBytes("r1"));
  Result res = table.get(get);
} finally {
  ts.close();
}

If you wanted to trace half of your 'get' operations, you would pass in:

new ProbabilitySampler(0.5)

in lieu of Sampler.ALWAYS to Trace.startSpan(). See the HTrace README for more information on Samplers.

162. Tracing from HBase Shell

You can use trace command for tracing requests from HBase Shell. trace 'start' command turns on tracing and trace 'stop' command turns off tracing.

hbase(main):001:0> trace 'start'
hbase(main):002:0> put 'test', 'row1', 'f:', 'val1'   # traced commands
hbase(main):003:0> trace 'stop'

trace 'start' and trace 'stop' always returns boolean value representing if or not there is ongoing tracing. As a result, trace 'stop' returns false on success. trace 'status' just returns if or not tracing is turned on.

hbase(main):001:0> trace 'start'
=> true

hbase(main):002:0> trace 'status'
=> true

hbase(main):003:0> trace 'stop'
=> false

hbase(main):004:0> trace 'status'
=> false

Appendix N: 0.95 RPC Specification

In 0.95, all client/server communication is done with protobuf’ed Messages rather than with Hadoop Writables. Our RPC wire format therefore changes. This document describes the client/server request/response protocol and our new RPC wire-format.

For what RPC is like in 0.94 and previous, see Benoît/Tsuna’s Unofficial Hadoop / HBase RPC protocol documentation. For more background on how we arrived at this spec., see HBase RPC: WIP

N.1. Goals

  1. A wire-format we can evolve

  2. A format that does not require our rewriting server core or radically changing its current architecture (for later).

N.2. TODO

  1. List of problems with currently specified format and where we would like to go in a version2, etc. For example, what would we have to change if anything to move server async or to support streaming/chunking?

  2. Diagram on how it works

  3. A grammar that succinctly describes the wire-format. Currently we have these words and the content of the rpc protobuf idl but a grammar for the back and forth would help with groking rpc. Also, a little state machine on client/server interactions would help with understanding (and ensuring correct implementation).

N.3. RPC

The client will send setup information on connection establish. Thereafter, the client invokes methods against the remote server sending a protobuf Message and receiving a protobuf Message in response. Communication is synchronous. All back and forth is preceded by an int that has the total length of the request/response. Optionally, Cells(KeyValues) can be passed outside of protobufs in follow-behind Cell blocks (because we can’t protobuf megabytes of KeyValues or Cells). These CellBlocks are encoded and optionally compressed.

For more detail on the protobufs involved, see the RPC.proto file in master.

N.3.1. Connection Setup

Client initiates connection.

Client

On connection setup, client sends a preamble followed by a connection header.

<preamble>
<MAGIC 4 byte integer> <1 byte RPC Format Version> <1 byte auth type>

We need the auth method spec. here so the connection header is encoded if auth enabled.

E.g.: HBas0x000x50 — 4 bytes of MAGIC — `HBas' — plus one-byte of version, 0 in this case, and one byte, 0x50 (SIMPLE). of an auth type.

<Protobuf ConnectionHeader Message>

Has user info, and ``protocol'', as well as the encoders and compression the client will use sending CellBlocks. CellBlock encoders and compressors are for the life of the connection. CellBlock encoders implement org.apache.hadoop.hbase.codec.Codec. CellBlocks may then also be compressed. Compressors implement org.apache.hadoop.io.compress.CompressionCodec. This protobuf is written using writeDelimited so is prefaced by a pb varint with its serialized length

Server

After client sends preamble and connection header, server does NOT respond if successful connection setup. No response means server is READY to accept requests and to give out response. If the version or authentication in the preamble is not agreeable or the server has trouble parsing the preamble, it will throw a org.apache.hadoop.hbase.ipc.FatalConnectionException explaining the error and will then disconnect. If the client in the connection header — i.e. the protobuf’d Message that comes after the connection preamble — asks for a Service the server does not support or a codec the server does not have, again we throw a FatalConnectionException with explanation.

N.3.2. Request

After a Connection has been set up, client makes requests. Server responds.

A request is made up of a protobuf RequestHeader followed by a protobuf Message parameter. The header includes the method name and optionally, metadata on the optional CellBlock that may be following. The parameter type suits the method being invoked: i.e. if we are doing a getRegionInfo request, the protobuf Message param will be an instance of GetRegionInfoRequest. The response will be a GetRegionInfoResponse. The CellBlock is optionally used ferrying the bulk of the RPC data: i.e. Cells/KeyValues.

Request Parts
<Total Length>

The request is prefaced by an int that holds the total length of what follows.

<Protobuf RequestHeader Message>

Will have call.id, trace.id, and method name, etc. including optional Metadata on the Cell block IFF one is following. Data is protobuf’d inline in this pb Message or optionally comes in the following CellBlock

<Protobuf Param Message>

If the method being invoked is getRegionInfo, if you study the Service descriptor for the client to regionserver protocol, you will find that the request sends a GetRegionInfoRequest protobuf Message param in this position.

<CellBlock>

An encoded and optionally compressed Cell block.

N.3.3. Response

Same as Request, it is a protobuf ResponseHeader followed by a protobuf Message response where the Message response type suits the method invoked. Bulk of the data may come in a following CellBlock.

Response Parts
<Total Length>

The response is prefaced by an int that holds the total length of what follows.

<Protobuf ResponseHeader Message>

Will have call.id, etc. Will include exception if failed processing. Optionally includes metadata on optional, IFF there is a CellBlock following.

<Protobuf Response Message>

Return or may be nothing if exception. If the method being invoked is getRegionInfo, if you study the Service descriptor for the client to regionserver protocol, you will find that the response sends a GetRegionInfoResponse protobuf Message param in this position.

<CellBlock>

An encoded and optionally compressed Cell block.

N.3.4. Exceptions

There are two distinct types. There is the request failed which is encapsulated inside the response header for the response. The connection stays open to receive new requests. The second type, the FatalConnectionException, kills the connection.

Exceptions can carry extra information. See the ExceptionResponse protobuf type. It has a flag to indicate do-no-retry as well as other miscellaneous payload to help improve client responsiveness.

N.3.5. CellBlocks

These are not versioned. Server can do the codec or it cannot. If new version of a codec with say, tighter encoding, then give it a new class name. Codecs will live on the server for all time so old clients can connect.

N.4. Notes

Constraints

In some part, current wire-format — i.e. all requests and responses preceded by a length — has been dictated by current server non-async architecture.

One fat pb request or header+param

We went with pb header followed by pb param making a request and a pb header followed by pb response for now. Doing header+param rather than a single protobuf Message with both header and param content:

  1. Is closer to what we currently have

  2. Having a single fat pb requires extra copying putting the already pb’d param into the body of the fat request pb (and same making result)

  3. We can decide whether to accept the request or not before we read the param; for example, the request might be low priority. As is, we read header+param in one go as server is currently implemented so this is a TODO.

The advantages are minor. If later, fat request has clear advantage, can roll out a v2 later.

N.4.1. RPC Configurations

CellBlock Codecs

To enable a codec other than the default KeyValueCodec, set hbase.client.rpc.codec to the name of the Codec class to use. Codec must implement hbase’s Codec Interface. After connection setup, all passed cellblocks will be sent with this codec. The server will return cellblocks using this same codec as long as the codec is on the servers' CLASSPATH (else you will get UnsupportedCellCodecException).

To change the default codec, set hbase.client.default.rpc.codec.

To disable cellblocks completely and to go pure protobuf, set the default to the empty String and do not specify a codec in your Configuration. So, set hbase.client.default.rpc.codec to the empty string and do not set hbase.client.rpc.codec. This will cause the client to connect to the server with no codec specified. If a server sees no codec, it will return all responses in pure protobuf. Running pure protobuf all the time will be slower than running with cellblocks.

Compression

Uses hadoop’s compression codecs. To enable compressing of passed CellBlocks, set hbase.client.rpc.compressor to the name of the Compressor to use. Compressor must implement Hadoop’s CompressionCodec Interface. After connection setup, all passed cellblocks will be sent compressed. The server will return cellblocks compressed using this same compressor as long as the compressor is on its CLASSPATH (else you will get UnsupportedCompressionCodecException).

1. See 'Source Compatibility' https://blogs.oracle.com/darcy/entry/kinds_of_compatibility
2. See http://docs.oracle.com/javase/specs/jls/se7/html/jls-13.html.
3. Note that this indicates what could break, not that it will break. We will/should add specifics in our release notes.
4. comp_matrix_offline_upgrade_note,Running an offline upgrade tool without rollback might be needed. We will typically only support migrating data from major version X to major version X+1.
5. The Metrics system was redone in HBase 0.96. See Migration to the New Metrics Hotness – Metrics2 by Elliot Clark for detail