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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@hbase.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 not work 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.

2. Quick Start - Standalone HBase

This section describes the setup of a single-node standalone HBase. A standalone instance has all HBase daemons — the Master, RegionServers, and ZooKeeper — running in a single JVM persisting to the local filesystem. It is our most basic deploy profile. We will show 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.

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 in Standalone Mode

  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. 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-4.0.0-alpha-1-SNAPSHOT-bin.tar.gz
$ cd hbase-4.0.0-alpha-1-SNAPSHOT/

  3. You must set the JAVA_HOME environment variable before starting HBase. To make this easier, HBase lets you set it within the conf/hbase-env.sh file. You must locate where Java is installed on your machine, and one way to find this is by using the whereis java command. Once you have the location, edit the conf/hbase-env.sh file and uncomment the line starting with #export JAVA_HOME=, and then set it to your Java installation path.

    Example extract from hbase-env.sh where JAVA_HOME is set
    # Set environment variables here.
# The java implementation to use.
export JAVA_HOME=/usr/jdk64/jdk1.8.0_112

  4. 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. Go to http://localhost:16010 to view the HBase Web UI.

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 confirm your table exists

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

=> ["test"]

    Now use the describe command to see details, including configuration defaults

    hbase(main):003:0> describe 'test'
Table test is ENABLED
test
COLUMN FAMILIES DESCRIPTION
{NAME => 'cf', VERSIONS => '1', EVICT_BLOCKS_ON_CLOSE => 'false', NEW_VERSION_BEHAVIOR => 'false', KEEP_DELETED_CELLS => 'FALSE', CACHE_DATA_ON_WRITE =>
'false', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', MIN_VERSIONS => '0', REPLICATION_SCOPE => '0', BLOOMFILTER => 'ROW', CACHE_INDEX_ON_WRITE => 'f
alse', IN_MEMORY => 'false', CACHE_BLOOMS_ON_WRITE => 'false', PREFETCH_BLOCKS_ON_OPEN => 'false', COMPRESSION => 'NONE', BLOCKCACHE => 'true', BLOCKSIZE
 => '65536'}
1 row(s)
Took 0.9998 seconds

  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.

The above has shown you how to start and stop a standalone instance of HBase. In the next sections we give a quick overview of other modes of hbase deploy.

2.3. Pseudo-Distributed for Local Testing

After working your way through quickstart standalone mode, 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: in standalone mode all daemons ran in one jvm process/instance. 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, add a configuration for hbase.rootdir, pointing 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.

    Finally, remove existing configuration for hbase.tmp.dir and hbase.unsafe.stream.capability.enforce,

  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 two ports (16000 and 16010 by default). The port offset is added to these ports, so using an offset of 2, the backup HMaster would use ports 16002 and 16012. The following command starts 3 backup servers using ports 16002/16012, 16003/16013, and 16005/16015.

    $ ./bin/local-master-backup.sh start 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. Since HBase version 1.1.0, HMaster doesn’t use region server ports, this leaves 10 ports (16020 to 16029 and 16030 to 16039) to be used for RegionServers. For supporting additional RegionServers, set environment variables HBASE_RS_BASE_PORT and HBASE_RS_INFO_BASE_PORT to appropriate values before running script local-regionservers.sh. e.g. With values 16200 and 16300 for base ports, 99 additional RegionServers can be supported, on a server. The following command starts four additional RegionServers, running on sequential ports starting at 16022/16032 (base ports 16020/16030 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. Fully Distributed for Production

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, Pseudo-Distributed for Local Testing, 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, you should not be prompted for a password when you SSH from node-a to either of the other nodes using the same username.

  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 section. 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.

    node-a jps Output
    $ jps
20355 Jps
20071 HQuorumPeer
20137 HMaster
    node-b jps Output
    $ jps
15930 HRegionServer
16194 Jps
15838 HQuorumPeer
16010 HMaster
    node-c 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 section.

  4. Browse to the Web UI.

    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/ 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 you have configured, things will not be very resilient. You can still test the behavior of the primary Master or a RegionServer by killing the associated 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. 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 hbase-common/src/main/resources/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.

log4j2.xml

Configuration file for HBase logging via log4j2.

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 contents of the conf/ directory to all nodes of the cluster. HBase will not do this for you. Use a configuration management tool for managing and copying the configuration files to your nodes. For most configurations, a restart is needed for servers to pick up changes. Dynamic configuration is an exception to this, to be described later below.

4. Basic Prerequisites

This section lists required services and some required system configuration.

Java

HBase runs on the Java Virtual Machine, thus all HBase deployments require a JVM runtime.

The following table summarizes the recommendations of the HBase community with respect to running on various Java versions. The symbol indicates a base level of testing and willingness to help diagnose and address issues you might run into; these are the expected deployment combinations. An entry of means that there may be challenges with this combination, and you should look for more information before deciding to pursue this as your deployment strategy. The means this combination does not work; either an older Java version is considered deprecated by the HBase community, or this combination is known to not work. For combinations of newer JDK with older HBase releases, it’s likely there are known compatibility issues that cannot be addressed under our compatibility guarantees, making the combination impossible. In some cases, specific guidance on limitations (e.g. whether compiling / unit tests work, specific operational issues, etc) are also noted. Assume any combination not listed here is considered .

Long-Term Support JDKs are Recommended

HBase recommends downstream users rely only on JDK releases that are marked as Long-Term Supported (LTS), either from the OpenJDK project or vendors. At the time of this writing, the following JDK releases are NOT LTS releases and are NOT tested or advocated for use by the Apache HBase community: JDK9, JDK10, JDK12, JDK13, and JDK14. Community discussion around this decision is recorded on HBASE-20264.
HotSpot vs. OpenJ9

At this time, all testing performed by the Apache HBase project runs on the HotSpot variant of the JVM. When selecting your JDK distribution, please take this into consideration.
Table 2. Java support by release line
HBase Version JDK 6 JDK 7 JDK 8 JDK 11 JDK 17

HBase 2.6

HBase 2.5

*

HBase 2.4

HBase 2.3

*

HBase 2.0-2.2

HBase 1.2+

HBase 1.0-1.1

HBase 0.98

HBase 0.94

A Note on JDK11/JDK17 *

Preliminary support for JDK11 is introduced with HBase 2.3.0, and for JDK17 is introduced with HBase 2.5.x. We will compile and run test suites with JDK11/17 in pre commit checks and nightly checks. We will mark the support as as long as we have run some ITs with the JDK version and also there are users in the community use the JDK version in real production clusters.

For JDK11/JDK17 support in HBase, please refer to HBASE-22972 and HBASE-26038

For JDK11/JDK17 support in Hadoop, which may also affect HBase, please refer to HADOOP-15338 and HADOOP-17177
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.

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.

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.

Example 1. 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

Running production systems on Windows machines is not recommended.

4.1. Hadoop

The following table summarizes the versions of Hadoop supported with each version of HBase. Older versions not appearing in this table are considered unsupported and likely missing necessary features, while newer versions are untested but may be suitable.

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 3.x is recommended.

Comparing to Hadoop 1.x, Hadoop 2.x is faster and includes features, such as short-circuit reads (see Leveraging local data), 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 does not support running with earlier versions of Hadoop. See the table below for requirements specific to different HBase versions.

Today, Hadoop 3.x is recommended as the last Hadoop 2.x release 2.10.2 was released years ago, and there is no release for Hadoop 2.x for a very long time, although the Hadoop community does not officially EOL Hadoop 2.x yet.

Use the following legend to interpret these tables:

  • = Tested to be fully-functional

  • = Known to not be fully-functional, or there are CVEs so we drop the support in newer minor releases

  • = Not tested, may/may-not function

Table 3. Hadoop version support matrix for active release lines
HBase-2.5.x HBase-2.6.x

Hadoop-2.10.[0-1]

Hadoop-2.10.2+

Hadoop-3.1.0

Hadoop-3.1.1+

Hadoop-3.2.[0-2]

Hadoop-3.2.3+

Hadoop-3.3.[0-1]

Hadoop-3.3.[2-4]

Hadoop-3.3.5+

Table 4. Hadoop version support matrix for EOM 2.3+ release lines
HBase-2.3.x HBase-2.4.x

Hadoop-2.10.x

Hadoop-3.1.0

Hadoop-3.1.1+

Hadoop-3.2.x

Hadoop-3.3.x

Table 5. Hadoop version support matrix for EOM 2.x release lines
HBase-2.0.x HBase-2.1.x HBase-2.2.x

Hadoop-2.6.1+

Hadoop-2.7.[0-6]

Hadoop-2.7.7+

Hadoop-2.8.[0-2]

Hadoop-2.8.[3-4]

Hadoop-2.8.5+

Hadoop-2.9.[0-1]

Hadoop-2.9.2+

Hadoop-3.0.[0-2]

Hadoop-3.0.3+

Hadoop-3.1.0

Hadoop-3.1.1+

Table 6. Hadoop version support matrix for EOM 1.5+ release lines
HBase-1.5.x HBase-1.6.x HBase-1.7.x

Hadoop-2.7.7+

Hadoop-2.8.[0-4]

Hadoop-2.8.5+

Hadoop-2.9.[0-1]

Hadoop-2.9.2+

Hadoop-2.10.x

Table 7. Hadoop version support matrix for EOM 1.x release lines
HBase-1.0.x (Hadoop 1.x is NOT supported) HBase-1.1.x HBase-1.2.x HBase-1.3.x HBase-1.4.x

Hadoop-2.4.x

Hadoop-2.5.x

Hadoop-2.6.0

Hadoop-2.6.1+

Hadoop-2.7.0

Hadoop-2.7.1+

Table 8. Hadoop version support matrix for EOM pre-1.0 release lines
HBase-0.92.x HBase-0.94.x HBase-0.96.x HBase-0.98.x (Support for Hadoop 1.1+ is deprecated.)

Hadoop-0.20.205

Hadoop-0.22.x

Hadoop-1.0.x

Hadoop-1.1.x

Hadoop-0.23.x

Hadoop-2.0.x-alpha

Hadoop-2.1.0-beta

Hadoop-2.2.0

Hadoop-2.3.x

Hadoop-2.4.x

Hadoop-2.5.x

Hadoop 2.y.0 Releases

Starting around the time of Hadoop version 2.7.0, the Hadoop PMC got into the habit of calling out new minor releases on their major version 2 release line as not stable / production ready. As such, HBase expressly advises downstream users to avoid running on top of these releases. Note that additionally the 2.8.1 release was given the same caveat by the Hadoop PMC. For reference, see the release announcements for Apache Hadoop 2.7.0, Apache Hadoop 2.8.0, Apache Hadoop 2.8.1, and Apache Hadoop 2.9.0.
Hadoop 3.1.0 Release

The Hadoop PMC called out the 3.1.0 release as not stable / production ready. As such, HBase expressly advises downstream users to avoid running on top of this release. For reference, see the release announcement for Hadoop 3.1.0.
Replace the Hadoop Bundled With HBase!

Because HBase depends on Hadoop, it bundles Hadoop jars under its lib directory. The bundled jars are ONLY for use in stand-alone 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 jars found in the HBase lib directory with the equivalent hadoop jars from the version you are running on your cluster to avoid version mismatch issues. Make sure you replace the jars under HBase across your whole cluster. Hadoop version mismatch issues have various manifestations. Check for mismatch if HBase appears hung.

4.1.1. 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

An Apache ZooKeeper quorum is required. The exact version depends on your version of HBase, though the minimum ZooKeeper version is 3.4.x due to the useMulti feature made default in 1.0.0 (see HBASE-16598).

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.

5.1.1. Standalone HBase over HDFS

A sometimes useful variation on standalone hbase has all daemons running inside the one JVM but rather than persist to the local filesystem, instead they persist to an HDFS instance.

You might consider this profile when you are intent on a simple deploy profile, the loading is light, but the data must persist across node comings and goings. Writing to HDFS where data is replicated ensures the latter.

To configure this standalone variant, edit your hbase-site.xml setting hbase.rootdir to point at a directory in your HDFS instance but then set hbase.cluster.distributed to false. For example:

<configuration>
  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://namenode.example.org:8020/hbase</value>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>false</value>
  </property>
</configuration>

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 HBase configuration for testing and prototyping purposes only. Do not use this configuration for production or for performance evaluation.

5.3. Fully-distributed

By default, HBase runs in stand-alone mode. Both stand-alone mode and pseudo-distributed mode are provided for the purposes of small-scale testing. For a production environment, distributed mode is advised. 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 2. 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.

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. Some configurations would only appear in source code; the only way to identify these changes are through code review.

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.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.property.clientPort config. and pass it into zookeeper constructor as the connectString parameter.

Default

127.0.0.1

zookeeper.recovery.retry.maxsleeptime
Description

Max sleep time before retry zookeeper operations in milliseconds, a max time is needed here so that sleep time won’t grow unboundedly

Default

60000

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,org.apache.hadoop.hbase.master.cleaner.TimeToLiveProcedureWALCleaner,org.apache.hadoop.hbase.master.cleaner.TimeToLiveMasterLocalStoreWALCleaner

hbase.master.logcleaner.ttl
Description

How long a WAL remain in the archive ({hbase.rootdir}/oldWALs) directory, after which it will be cleaned by a Master thread. The value is in milliseconds.

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 hfile cleaners in the list as they will be overwritten in hbase-site.xml.

Default

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

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. Config. makes sense when Master is serving Regions (not the default).

Default

true

hbase.master.fileSplitTimeout
Description

Splitting a region, how long to wait on the file-splitting step before aborting the attempt. Default: 600000. This setting used to be known as hbase.regionserver.fileSplitTimeout in hbase-1.x. Split is now run master-side hence the rename (If a 'hbase.master.fileSplitTimeout' setting found, will use it to prime the current 'hbase.master.fileSplitTimeout' Configuration.

Default

600000

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. Too many handlers can be counter-productive. Make it a multiple of CPU count. If mostly read-only, handlers count close to cpu count does well. Start with twice the CPU count and tune from there.

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.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 empty 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 empty in order to honor the old hbase.regionserver.global.memstore.lowerLimit property if present.

Default

none

hbase.systemtables.compacting.memstore.type
Description

Determines the type of memstore to be used for system tables like META, namespace tables etc. By default NONE is the type and hence we use the default memstore for all the system tables. If we need to use compacting memstore for system tables then set this property to BASIC/EAGER

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.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 BusyRegionSplitPolicy, ConstantSizeRegionSplitPolicy, DisabledRegionSplitPolicy, DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy, and SteppingSplitPolicy. DisabledRegionSplitPolicy blocks manual region splitting.

Default

org.apache.hadoop.hbase.regionserver.SteppingSplitPolicy

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 https://zookeeper.apache.org/doc/current/zookeeperProgrammers.html#ch_zkSessions. For example, if an 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 maxSessionTimeout 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 paths are configured with a relative path, so they will all go under this directory unless changed.

Default

/hbase

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 https://zookeeper.apache.org/doc/r3.4.10/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

2888

hbase.zookeeper.leaderport
Description

Port used by ZooKeeper for leader election. See https://zookeeper.apache.org/doc/r3.4.10/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

3888

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 BufferedMutator 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.pause.server.overloaded
Description

Pause time when encountering an exception indicating a server is overloaded, CallQueueTooBigException or CallDroppedException. Set this property to a higher value than hbase.client.pause if you observe frequent CallQueueTooBigException or CallDroppedException from the same RegionServer and the call queue there keeps filling up. This config used to be called hbase.client.pause.cqtbe, which has been deprecated as of 2.5.0.

Default

none

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

15

hbase.client.max.total.tasks
Description

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

Default

100

hbase.client.max.perserver.tasks
Description

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

Default

2

hbase.client.max.perregion.tasks
Description

The maximum number of concurrent mutation tasks 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.perserver.requests.threshold
Description

The max number of concurrent pending requests for one server in all client threads (process level). Exceeding requests will be thrown ServerTooBusyException immediately to prevent user’s threads being occupied and blocked by only one slow region server. If you use a fix number of threads to access HBase in a synchronous way, set this to a suitable value which is related to the number of threads will help you. See https://issues.apache.org/jira/browse/HBASE-16388 for details.

Default

2147483647

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.server.keyvalue.maxsize
Description

Maximum allowed size of an individual cell, inclusive of value and all key components. A value of 0 or less disables the check. The default value is 10MB. This is a safety setting to protect the server from OOM situations.

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.compaction.after.bulkload.enable
Description

Request Compaction after bulkload immediately. If bulkload is continuous, the triggered compactions may increase load, bring about performance side effect.

Default

false

hbase.master.balancer.maxRitPercent
Description

The max percent of regions in transition when balancing. The default value is 1.0. So there are no balancer throttling. If set this config to 0.01, It means that there are at most 1% regions in transition when balancing. Then the cluster’s availability is at least 99% when balancing.

Default

1.0

hbase.balancer.period
Description

Period at which the region balancer runs in the Master, in milliseconds.

Default

300000

hbase.master.oldwals.dir.updater.period
Description

Period at which the oldWALs directory size calculator/updater will run in the Master, in milliseconds.

Default

300000

hbase.regions.slop
Description

The load balancer can trigger for several reasons. This value controls one of those reasons. Run the balancer if any regionserver has a region count outside the range of average +/- (average * slop) regions. If the value of slop is negative, disable sloppiness checks. The balancer can still run for other reasons, but sloppiness will not be one of them. If the value of slop is 0, run the balancer if any server has a region count more than 1 from the average. If the value of slop is 100, run the balancer if any server has a region count greater than 101 times the average. The default value of this parameter is 0.2, which runs the balancer if any server has a region count less than 80% of the average, or greater than 120% of the average. Note that for the default StochasticLoadBalancer, this does not guarantee any balancing actions will be taken, but only that the balancer will attempt to run.

Default

0.2

hbase.normalizer.period
Description

Period at which the region normalizer runs in the Master, in milliseconds.

Default

300000

hbase.normalizer.split.enabled
Description

Whether to split a region as part of normalization.

Default

true

hbase.normalizer.merge.enabled
Description

Whether to merge a region as part of normalization.

Default

true

hbase.normalizer.merge.min.region.count
Description

The minimum number of regions in a table to consider it for merge normalization.

Default

3

hbase.normalizer.merge.min_region_age.days
Description

The minimum age for a region to be considered for a merge, in days.

Default

3

hbase.normalizer.merge.min_region_size.mb
Description

The minimum size for a region to be considered for a merge, in whole MBs.

Default

1

hbase.normalizer.merge.merge_request_max_number_of_regions
Description

The maximum number of region count in a merge request for merge normalization.

Default

100

hbase.table.normalization.enabled
Description

This config is used to set default behaviour of normalizer at table level. To override this at table level one can set NORMALIZATION_ENABLED at table descriptor level and that property will be honored

Default

false

hbase.server.thread.wakefrequency
Description

In master side, this config is the period used for FS related behaviors: checking if hdfs is out of safe mode, setting or checking hbase.version file, setting or checking hbase.id file. Using default value should be fine. In regionserver side, this config is used in several places: flushing check interval, compaction check interval, wal rolling check interval. Specially, admin can tune flushing and compaction check interval by hbase.regionserver.flush.check.period and hbase.regionserver.compaction.check.period. (in milliseconds)

Default

10000

hbase.regionserver.flush.check.period
Description

It determines the flushing check period of PeriodicFlusher in regionserver. If unset, it uses hbase.server.thread.wakefrequency as default value. (in milliseconds)

Default

${hbase.server.thread.wakefrequency}

hbase.regionserver.compaction.check.period
Description

It determines the compaction check period of CompactionChecker in regionserver. If unset, it uses hbase.server.thread.wakefrequency as default value. (in milliseconds)

Default

${hbase.server.thread.wakefrequency}

hbase.server.versionfile.writeattempts
Description

How many times to retry attempting to write a version file before just aborting. Each attempt is separated 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.min
Description

If FlushLargeStoresPolicy is used and there are multiple column families, then every time that we hit the total memstore limit, we find out all the column families whose memstores exceed a "lower bound" and only flush them while retaining the others in memory. The "lower bound" will be "hbase.hregion.memstore.flush.size / column_family_number" by default unless value of this property is larger than that. If none of the families have their memstore size more than lower bound, all the memstores will be flushed (just as usual).

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.memstore.mslab.chunksize
Description

The maximum byte size of a chunk in the MemStoreLAB. Unit: bytes

Default

2097152

hbase.regionserver.offheap.global.memstore.size
Description

The amount of off-heap memory all MemStores in a RegionServer may use. A value of 0 means that no off-heap memory will be used and all chunks in MSLAB will be HeapByteBuffer, otherwise the non-zero value means how many megabyte of off-heap memory will be used for chunks in MSLAB and all chunks in MSLAB will be DirectByteBuffer. Unit: megabytes.

Default

0

hbase.hregion.memstore.mslab.max.allocation
Description

The maximal size of one allocation in the MemStoreLAB, if the desired byte size exceed this threshold then it will be just allocated from JVM heap rather than MemStoreLAB.

Default

262144

hbase.hregion.max.filesize
Description

Maximum file size. If the sum of the sizes of a region’s HFiles has grown to exceed this value, the region is split in two. There are two choices of how this option works, the first is when any store’s size exceed the threshold then split, and the other is overall region’s size exceed the threshold then split, it can be configed by hbase.hregion.split.overallfiles.

Default

10737418240

hbase.hregion.split.overallfiles
Description

If we should sum overall region files size when check to split.

Default

true

hbase.hregion.majorcompaction
Description

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. The default value is 7 days, expressed in milliseconds. If major compactions are causing disruption in your environment, you can configure them to run at off-peak times for your deployment, or disable time-based major compactions by setting this parameter to 0, and run major compactions in a cron job or by another external mechanism.

Default

604800000

hbase.hregion.majorcompaction.jitter
Description

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.

Default

0.50

hbase.hstore.compactionThreshold
Description

If more than or equal to this number of StoreFiles exist in any one Store (one StoreFile is written per flush of MemStore), a compaction is run to rewrite all StoreFiles into a single StoreFile. Larger values delay compaction, but when compaction does occur, it takes longer to complete.

Default

3

hbase.regionserver.compaction.enabled
Description

Enable/disable compactions on by setting true/false. We can further switch compactions dynamically with the compaction_switch shell command.

Default

true

hbase.hstore.flusher.count
Description

The number of flush threads. With fewer threads, the MemStore flushes will be queued. With more threads, the flushes will be executed in parallel, increasing the load on HDFS, and potentially causing more compactions.

Default

2

hbase.hstore.blockingStoreFiles
Description

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

Default

16

hbase.hstore.blockingWaitTime
Description

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

Default

90000

hbase.hstore.compaction.min
Description

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 (empty value here, results in 3 by code logic). In previous versions of HBase, the parameter hbase.hstore.compaction.min was named hbase.hstore.compactionThreshold.

Default

none

hbase.hstore.compaction.max
Description

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
Description

A StoreFile (or a selection of StoreFiles, when using ExploringCompactionPolicy) smaller than this size will always be eligible for minor compaction. HFiles 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 StoreFiles 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 expressed in bytes.

Default

134217728

hbase.hstore.compaction.max.size
Description

A StoreFile (or a selection of StoreFiles, when using ExploringCompactionPolicy) 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: the value of LONG.MAX_VALUE, expressed in bytes.

Default

9223372036854775807

hbase.hstore.compaction.ratio
Description

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 StoreFiles. 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 low value, such as .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. Otherwise, you can lower this value to something like 1.0 to reduce the background cost of writes, and use Bloom filters to control the number of StoreFiles touched during reads. For most cases, the default value is appropriate.

Default

1.2F

hbase.hstore.compaction.ratio.offpeak
Description

Allows you to set a different (by default, more aggressive) ratio for determining whether larger StoreFiles are included in compactions during off-peak hours. Works in the same way as hbase.hstore.compaction.ratio. Only applies if hbase.offpeak.start.hour and hbase.offpeak.end.hour are also enabled.

Default

5.0F

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.offpeak.start.hour
Description

The start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.

Default

-1

hbase.offpeak.end.hour
Description

The end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.

Default

-1

hbase.regionserver.thread.compaction.throttle
Description

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 128MB). The value field assumes that the value of hbase.hregion.memstore.flush.size is unchanged from the default.

Default

2684354560

hbase.regionserver.majorcompaction.pagecache.drop
Description

Specifies whether to drop pages read/written into the system page cache by major compactions. Setting it to true helps prevent major compactions from polluting the page cache, which is almost always required, especially for clusters with low/moderate memory to storage ratio.

Default

true

hbase.regionserver.minorcompaction.pagecache.drop
Description

Specifies whether to drop pages read/written into the system page cache by minor compactions. Setting it to true helps prevent minor compactions from polluting the page cache, which is most beneficial on clusters with low memory to storage ratio or very write heavy clusters. You may want to set it to false under moderate to low write workload when bulk of the reads are on the most recently written data.

Default

true

hbase.hstore.compaction.kv.max
Description

The maximum number of KeyValues to read and then write in a batch when flushing or compacting. Set this lower if you have big KeyValues and problems with Out Of Memory Exceptions Set this higher if you have wide, small rows.

Default

10

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.policy
Description

The eviction policy for the L1 block cache (LRU or TinyLFU).

Default

LRU

hfile.block.cache.size
Description

Percentage of maximum heap (-Xmx setting) to allocate to block cache used by a 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: offheap, file, files, mmap or pmem. If a file or files, set it to file(s):PATH_TO_FILE. mmap means the content will be in an mmaped file. Use mmap:PATH_TO_FILE. 'pmem' is bucket cache over a file on the persistent memory device. Use pmem:PATH_TO_FILE. See http://hbase.apache.org/book.html#offheap.blockcache for more information.

Default

none

hbase.hstore.compaction.throughput.lower.bound
Description

The target lower bound on aggregate compaction throughput, in bytes/sec. Allows you to tune the minimum available compaction throughput when the PressureAwareCompactionThroughputController throughput controller is active. (It is active by default.)

Default

52428800

hbase.hstore.compaction.throughput.higher.bound
Description

The target upper bound on aggregate compaction throughput, in bytes/sec. Allows you to control aggregate compaction throughput demand when the PressureAwareCompactionThroughputController throughput controller is active. (It is active by default.) The maximum throughput will be tuned between the lower and upper bounds when compaction pressure is within the range [0.0, 1.0]. If compaction pressure is 1.0 or greater the higher bound will be ignored until pressure returns to the normal range.

Default

104857600

hbase.bucketcache.size
Description

It is the total capacity in megabytes of BucketCache. Default: 0.0

Default

none

hbase.bucketcache.bucket.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 256 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). 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.unsafe.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.unsafe.regionserver.hostname.disable.master.reversedns
Description

This config is for experts: don’t set its value unless you really know what you are doing. When set to true, regionserver will use the current node hostname for the servername and HMaster will skip reverse DNS lookup and use the hostname sent by regionserver instead. Note that this config and hbase.unsafe.regionserver.hostname are mutually exclusive. See https://issues.apache.org/jira/browse/HBASE-18226 for more details.

Default

false

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. Group names should be prefixed with "@".

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.ipc.server.fallback-to-simple-auth-allowed
Description

When a server is configured to require secure connections, it will reject connection attempts from clients using SASL SIMPLE (unsecure) authentication. This setting allows secure servers to accept SASL SIMPLE connections from clients when the client requests. When false (the default), the server will not allow the fallback to SIMPLE authentication, and will reject the connection. WARNING: This setting should ONLY be used as a temporary measure while converting clients over to secure authentication. It MUST BE DISABLED for secure operation.

Default

false

hbase.unsafe.client.kerberos.hostname.disable.reversedns
Description

This config is for experts: don’t set its value unless you really know what you are doing. When set to true, HBase client using SASL Kerberos will skip reverse DNS lookup and use provided hostname of the destination for the principal instead. See https://issues.apache.org/jira/browse/HBASE-25665 for more details.

Default

false

hbase.display.keys
Description

When this is set to true the webUI and such will display all start/end keys as part of the table details, region names, etc. When this is set to false, the keys are hidden.

Default

true

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 region observer or endpoint 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, add it to HBase’s classpath and add the fully qualified class name here. A coprocessor can also be loaded on demand by setting HTableDescriptor or the HBase shell.

Default

none

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.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 RuntimeException 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.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.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, in MB

Default

2

hbase.regionserver.thrift.compact
Description

Use Thrift TCompactProtocol binary serialization protocol.

Default

false

hbase.rootdir.perms
Description

FS Permissions for the root data subdirectory 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.wal.dir.perms
Description

FS Permissions for the root WAL directory in a secure(kerberos) setup. When master starts, it creates the WAL dir 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.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.snapshot.working.dir
Description

Location where the snapshotting process will occur. The location of the completed snapshots will not change, but the temporary directory where the snapshot process occurs will be set to this location. This can be a separate filesystem than the root directory, for performance increase purposes. See HBASE-21098 for more information

Default

none

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

dfs.client.read.shortcircuit
Description

If set to true, this configuration parameter enables short-circuit local reads.

Default

none

dfs.domain.socket.path
Description

This is a path to a UNIX domain socket that will be used for communication between the DataNode and local HDFS clients, if dfs.client.read.shortcircuit is set to true. If the string "_PORT" is present in this path, it will be replaced by the TCP port of the DataNode. Be careful about permissions for the directory that hosts the shared domain socket; dfsclient will complain if open to other users than the HBase user.

Default

none

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

CRC32C

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 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. Does not apply to coprocessors.

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.master.loadbalance.bytable
Description

Factor Table name when the balancer runs. Default: false.

Default

false

hbase.master.normalizer.class
Description

Class used to execute the region normalization when the period occurs. See the class comment for more on how it works http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/normalizer/SimpleRegionNormalizer.html

Default

org.apache.hadoop.hbase.master.normalizer.SimpleRegionNormalizer

hbase.rest.csrf.enabled
Description

Set to true to enable protection against cross-site request forgery (CSRF)

Default

false

hbase.rest-csrf.browser-useragents-regex
Description

A comma-separated list of regular expressions used to match against an HTTP request’s User-Agent header when protection against cross-site request forgery (CSRF) is enabled for REST server by setting hbase.rest.csrf.enabled to true. If the incoming User-Agent matches any of these regular expressions, then the request is considered to be sent by a browser, and therefore CSRF prevention is enforced. If the request’s User-Agent does not match any of these regular expressions, then the request is considered to be sent by something other than a browser, such as scripted automation. In this case, CSRF is not a potential attack vector, so the prevention is not enforced. This helps achieve backwards-compatibility with existing automation that has not been updated to send the CSRF prevention header.

Default

Mozilla.,Opera.

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. We have a separated implementation for replicating the WAL without using the general inter-cluster replication framework, so now we will not add any replication peers.

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

16

hbase.http.metrics.servlets
Description

Comma separated list of servlet names to enable for metrics collection. Supported servlets are jmx, metrics, prometheus

Default

jmx,metrics,prometheus

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.replication.source.maxthreads
Description

The maximum number of threads any replication source will use for shipping edits to the sinks in parallel. This also limits the number of chunks each replication batch is broken into. Larger values can improve the replication throughput between the master and slave clusters. The default of 10 will rarely need to be changed.

Default

10

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.mob.file.cache.size
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 handlers" The default value is 1000.

Default

1000

hbase.mob.cache.evict.period
Description

The amount of time in seconds before the mob cache evicts cached mob files. The default value is 3600 seconds.

Default

3600

hbase.mob.cache.evict.remain.ratio
Description

The ratio (between 0.0 and 1.0) of files that remains cached after an eviction is triggered when the number of cached mob files exceeds the hbase.mob.file.cache.size. The default value is 0.5f.

Default

0.5f

hbase.master.mob.cleaner.period
Description

The period that MobFileCleanerChore runs. The unit is second. The default value is one day. The MOB file name uses only the date part of the file creation time in it. We use this time for deciding TTL expiry of the files. So the removal of TTL expired files might be delayed. The max delay might be 24 hrs.

Default

86400

hbase.mob.major.compaction.region.batch.size
Description

The max number of a MOB table regions that is allowed in a batch of the mob compaction. By setting this number to a custom value, users can control the overall effect of a major compaction of a large MOB-enabled table. Default is 0 - means no limit - all regions of a MOB table will be compacted at once

Default

0

hbase.mob.compaction.chore.period
Description

The period that MobCompactionChore runs. The unit is second. The default value is one week.

Default

604800

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

hbase.rpc.rows.warning.threshold
Description

Number of rows in a batch operation above which a warning will be logged.

Default

5000

hbase.master.wait.on.service.seconds
Description

Default is 5 minutes. Make it 30 seconds for tests. See HBASE-19794 for some context.

Default

30

hbase.master.cleaner.snapshot.interval
Description

Snapshot Cleanup chore interval in milliseconds. The cleanup thread keeps running at this interval to find all snapshots that are expired based on TTL and delete them.

Default

1800000

hbase.master.snapshot.ttl
Description

Default Snapshot TTL to be considered when the user does not specify TTL while creating snapshot. Default value 0 indicates FOREVERE - snapshot should not be automatically deleted until it is manually deleted

Default

0

hbase.master.regions.recovery.check.interval
Description

Regions Recovery Chore interval in milliseconds. This chore keeps running at this interval to find all regions with configurable max store file ref count and reopens them.

Default

1200000

hbase.regions.recovery.store.file.ref.count
Description

Very large number of ref count on a compacted store file indicates that it is a ref leak on that object(compacted store file). Such files can not be removed after it is invalidated via compaction. Only way to recover in such scenario is to reopen the region which can release all resources, like the refcount, leases, etc. This config represents Store files Ref Count threshold value considered for reopening regions. Any region with compacted store files ref count > this value would be eligible for reopening by master. Here, we get the max refCount among all refCounts on all compacted away store files that belong to a particular region. Default value -1 indicates this feature is turned off. Only positive integer value should be provided to enable this feature.

Default

-1

hbase.regionserver.slowlog.ringbuffer.size
Description

Default size of ringbuffer to be maintained by each RegionServer in order to store online slowlog responses. This is an in-memory ring buffer of requests that were judged to be too slow in addition to the responseTooSlow logging. The in-memory representation would be complete. For more details, please look into Doc Section: Get Slow Response Log from shell

Default

256

hbase.regionserver.slowlog.buffer.enabled
Description

Indicates whether RegionServers have ring buffer running for storing Online Slow logs in FIFO manner with limited entries. The size of the ring buffer is indicated by config: hbase.regionserver.slowlog.ringbuffer.size The default value is false, turn this on and get latest slowlog responses with complete data.

Default

false

hbase.regionserver.slowlog.systable.enabled
Description

Should be enabled only if hbase.regionserver.slowlog.buffer.enabled is enabled. If enabled (true), all slow/large RPC logs would be persisted to system table hbase:slowlog (in addition to in-memory ring buffer at each RegionServer). The records are stored in increasing order of time. Operators can scan the table with various combination of ColumnValueFilter. More details are provided in the doc section: "Get Slow/Large Response Logs from System table hbase:slowlog"

Default

false

hbase.master.metafixer.max.merge.count
Description

Maximum regions to merge at a time when we fix overlaps noted in CJ consistency report, but avoid merging 100 regions in one go!

Default

64

hbase.rpc.rows.size.threshold.reject
Description

If value is true, RegionServer will abort batch requests of Put/Delete with number of rows in a batch operation exceeding threshold defined by value of config: hbase.rpc.rows.warning.threshold. The default value is false and hence, by default, only warning will be logged. This config should be turned on to prevent RegionServer from serving very large batch size of rows and this way we can improve CPU usages by discarding too large batch request.

Default

false

hbase.namedqueue.provider.classes
Description

Default values for NamedQueueService implementors. This comma separated full class names represent all implementors of NamedQueueService that we would like to be invoked by LogEvent handler service. One example of NamedQueue service is SlowLogQueueService which is used to store slow/large RPC logs in ringbuffer at each RegionServer. All implementors of NamedQueueService should be found under package: "org.apache.hadoop.hbase.namequeues.impl"

Default

org.apache.hadoop.hbase.namequeues.impl.SlowLogQueueService,org.apache.hadoop.hbase.namequeues.impl.BalancerDecisionQueueService,org.apache.hadoop.hbase.namequeues.impl.BalancerRejectionQueueService,org.apache.hadoop.hbase.namequeues.WALEventTrackerQueueService

hbase.master.balancer.decision.buffer.enabled
Description

Indicates whether active HMaster has ring buffer running for storing balancer decisions in FIFO manner with limited entries. The size of the ring buffer is indicated by config: hbase.master.balancer.decision.queue.size

Default

false

hbase.master.balancer.rejection.buffer.enabled
Description

Indicates whether active HMaster has ring buffer running for storing balancer rejection in FIFO manner with limited entries. The size of the ring buffer is indicated by config: hbase.master.balancer.rejection.queue.size

Default

false

hbase.locality.inputstream.derive.enabled
Description

If true, derive StoreFile locality metrics from the underlying DFSInputStream backing reads for that StoreFile. This value will update as the DFSInputStream’s block locations are updated over time. Otherwise, locality is computed on StoreFile open, and cached until the StoreFile is closed.

Default

false

hbase.locality.inputstream.derive.cache.period
Description

If deriving StoreFile locality metrics from the underlying DFSInputStream, how long should the derived values be cached for. The derivation process may involve hitting the namenode, if the DFSInputStream’s block list is incomplete.

Default

60000

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. log4j2.xml

Since version 3.0.0, HBase has upgraded to Log4j2, so the configuration file name and format has changed. Read more in Apache Log4j2.

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.

Starting release 3.0.0, the default connection registry has been switched to a rpc based implementation. Refer to Rpc Connection Registry (new as of 2.5.0) for more details about what a connection registry is and implications of this change. Depending on your HBase version, following is the expected minimal client configuration.

7.5.1. Up until 2.x.y releases

In 2.x.y releases, the default connection registry was based on ZooKeeper as the source of truth. This means that the clients always looked up ZooKeeper znodes to fetch the required metadata. For example, if an active master crashed and the a new master is elected, clients looked up the master znode to fetch the active master address (similarly for meta locations). This meant that the clients needed to have access to ZooKeeper and need to know the ZooKeeper ensemble information before they can do anything. This can be configured in the client configuration xml 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> Zookeeper ensemble information</description>
  </property>
</configuration>

7.5.2. Starting from 3.0.0 release

The default implementation was switched to a rpc based connection registry. With this implementation, by default clients contact the active or stand-by master RPC end points to fetch the connection registry information. This means that the clients should have access to the list of active and master end points before they can do anything. This can be configured in the client configuration xml as follows:

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
  <property>
    <name>hbase.masters</name>
    <value>example1,example2,example3</value>
    <description>List of master rpc end points for the hbase cluster.</description>
  </property>
</configuration>

The configuration value for hbase.masters is a comma separated list of host:port values. If no port value is specified, the default of 16000 is assumed.

Of course you are free to specify bootstrap nodes other than masters, like:

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<property>
    <name>hbase.client.bootstrap.servers</name>
    <value>server1:16020,server2:16020,server3:16020</value>
</property>

The configuration value for hbase.client.bootstrap.servers is a comma separated list of host:port values. Notice that port must be specified here.

Usually these configurations are 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).

For Java applications using Maven, including the hbase-shaded-client module is the recommended dependency when connecting to a cluster:

<dependency>
  <groupId>org.apache.hbase</groupId>
  <artifactId>hbase-shaded-client</artifactId>
  <version>2.0.0</version>
</dependency>

7.5.3. 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 or bootstrap nodes for the cluster programmatically do as follows:

Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost");  // Until 2.x.y versions
// ---- or ----
config.set("hbase.client.bootstrap.servers", "localhost:1234"); // Starting 3.0.0 version

7.6. Timeout settings

HBase provides a wide variety of timeout settings to limit the execution time of various remote operations.

  • hbase.rpc.timeout

  • hbase.rpc.read.timeout

  • hbase.rpc.write.timeout

  • hbase.client.operation.timeout

  • hbase.client.meta.operation.timeout

  • hbase.client.scanner.timeout.period

The hbase.rpc.timeout property limits how long a single RPC call can run before timing out. To fine tune read or write related RPC timeouts set hbase.rpc.read.timeout and hbase.rpc.write.timeout configuration properties. In the absence of these properties hbase.rpc.timeout will be used.

A higher-level timeout is hbase.client.operation.timeout which is valid for each client call. When an RPC call fails for instance for a timeout due to hbase.rpc.timeout it will be retried until hbase.client.operation.timeout is reached. Client operation timeout for system tables can be fine tuned by setting hbase.client.meta.operation.timeout configuration value. When this is not set its value will use hbase.client.operation.timeout.

Timeout for scan operations is controlled differently. Use hbase.client.scanner.timeout.period property to set this timeout.

8. Example Configurations

8.1. Basic Distributed HBase Install

Here is a basic configuration example 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) 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/jdk1.8.0/

# 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 90 seconds (specified in milliseconds). This means that if a server crashes, it will be 90 seconds before the Master notices the crash and starts recovery. You might need to tune the timeout down to a minute or even less so the Master notices failures 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 across 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.

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).

9.2.3. 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 find the following configuration options helpful. TODO.

9.2.4. 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.

9.2.5. 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.

9.2.6. Managed Splitting

HBase generally handles splitting of 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 usage patterns, 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. 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, you can set region split policy in either cluster configuration or table configuration to be org.apache.hadoop.hbase.regionserver.DisabledRegionSplitPolicy

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 with 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. The org.apache.hadoop.hbase.util.RegionSplitter utility also provides a network-IO-safe rolling split of all regions.

9.2.7. Managed Compactions

By default, major compactions are scheduled to run once in a 7-day period.

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

9.2.8. 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 hfile.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 cause 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 to 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, 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

It is possible to change a subset of the configuration without requiring a server restart. In the HBase shell, the operations update_config, update_all_config and update_rsgroup_config will prompt a server, all servers or all servers in the RSGroup to reload configuration.

Only a subset of all configurations can currently be changed in the running server. Here are those configurations:

Table 9. Configurations support dynamically change
Key

hbase.ipc.server.fallback-to-simple-auth-allowed

hbase.cleaner.scan.dir.concurrent.size

hbase.coprocessor.master.classes

hbase.coprocessor.region.classes

hbase.coprocessor.regionserver.classes

hbase.coprocessor.user.region.classes

hbase.regionserver.thread.compaction.large

hbase.regionserver.thread.compaction.small

hbase.regionserver.thread.split

hbase.regionserver.throughput.controller

hbase.regionserver.thread.hfilecleaner.throttle

hbase.regionserver.hfilecleaner.large.queue.size

hbase.regionserver.hfilecleaner.small.queue.size

hbase.regionserver.hfilecleaner.large.thread.count

hbase.regionserver.hfilecleaner.small.thread.count

hbase.regionserver.hfilecleaner.thread.timeout.msec

hbase.regionserver.hfilecleaner.thread.check.interval.msec

hbase.regionserver.flush.throughput.controller

hbase.hstore.compaction.max.size

hbase.hstore.compaction.max.size.offpeak

hbase.hstore.compaction.min.size

hbase.hstore.compaction.min

hbase.hstore.compaction.max

hbase.hstore.compaction.ratio

hbase.hstore.compaction.ratio.offpeak

hbase.regionserver.thread.compaction.throttle

hbase.hregion.majorcompaction

hbase.hregion.majorcompaction.jitter

hbase.hstore.min.locality.to.skip.major.compact

hbase.hstore.compaction.date.tiered.max.storefile.age.millis

hbase.hstore.compaction.date.tiered.incoming.window.min

hbase.hstore.compaction.date.tiered.window.policy.class

hbase.hstore.compaction.date.tiered.single.output.for.minor.compaction

hbase.hstore.compaction.date.tiered.window.factory.class

hbase.offpeak.start.hour

hbase.offpeak.end.hour

hbase.oldwals.cleaner.thread.size

hbase.oldwals.cleaner.thread.timeout.msec

hbase.oldwals.cleaner.thread.check.interval.msec

hbase.procedure.worker.keep.alive.time.msec

hbase.procedure.worker.add.stuck.percentage

hbase.procedure.worker.monitor.interval.msec

hbase.procedure.worker.stuck.threshold.msec

hbase.regions.slop

hbase.regions.overallSlop

hbase.balancer.tablesOnMaster

hbase.balancer.tablesOnMaster.systemTablesOnly

hbase.util.ip.to.rack.determiner

hbase.ipc.server.max.callqueue.length

hbase.ipc.server.priority.max.callqueue.length

hbase.ipc.server.callqueue.type

hbase.ipc.server.callqueue.codel.target.delay

hbase.ipc.server.callqueue.codel.interval

hbase.ipc.server.callqueue.codel.lifo.threshold

hbase.master.balancer.stochastic.maxSteps

hbase.master.balancer.stochastic.stepsPerRegion

hbase.master.balancer.stochastic.maxRunningTime

hbase.master.balancer.stochastic.runMaxSteps

hbase.master.balancer.stochastic.numRegionLoadsToRemember

hbase.master.loadbalance.bytable

hbase.master.balancer.stochastic.minCostNeedBalance

hbase.master.balancer.stochastic.localityCost

hbase.master.balancer.stochastic.rackLocalityCost

hbase.master.balancer.stochastic.readRequestCost

hbase.master.balancer.stochastic.writeRequestCost

hbase.master.balancer.stochastic.memstoreSizeCost

hbase.master.balancer.stochastic.storefileSizeCost

hbase.master.balancer.stochastic.regionReplicaHostCostKey

hbase.master.balancer.stochastic.regionReplicaRackCostKey

hbase.master.balancer.stochastic.regionCountCost

hbase.master.balancer.stochastic.primaryRegionCountCost

hbase.master.balancer.stochastic.moveCost

hbase.master.balancer.stochastic.moveCost.offpeak

hbase.master.balancer.stochastic.maxMovePercent

hbase.master.balancer.stochastic.tableSkewCost

hbase.master.regions.recovery.check.interval

hbase.regions.recovery.store.file.ref.count

hbase.rsgroup.fallback.enable

Upgrading

You cannot skip major versions when upgrading. If you are upgrading from version 0.98.x to 2.x, you must first go from 0.98.x to 1.2.x and then go from 1.2.x to 2.x.

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

11. HBase version number and compatibility

11.1. Aspirational Semantic Versioning

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 downgrade 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 whole major version before we will change/remove it.

    • An example: An API was deprecated in 2.0.1 and will be marked for deletion in 4.0.0. On the other hand, an API deprecated in 2.0.0 can be removed in 3.0.0.

    • Occasionally mistakes are made and internal classes are marked with a higher access level than they should. In these rare circumstances, we will accelerate the deprecation schedule to the next major version (i.e., deprecated in 2.2.x, marked IA.Private 3.0.0). Such changes are communicated and explained via release note in Jira.

  • 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.

  • If a Client implements an HBase Interface, a recompile MAY be required upgrading to a newer minor version (See release notes for warning about incompatible changes). All effort will be made to provide a default implementation so this case should not arise.

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, except for Apache Hadoop.

  • An upgrade of HBase will not require an incompatible upgrade of the Java runtime.

  • Example: Upgrading HBase to a version that supports Dependency Compatibility won’t require that you upgrade your Apache ZooKeeper service.

  • Example: If your current version of HBase supported running on JDK 8, then an upgrade to a version that supports Dependency Compatibility will also run on JDK 8.

Hadoop Versions

Previously, we tried to maintain dependency compatibility for the underly Hadoop service but over the last few years this has proven untenable. While the HBase project attempts to maintain support for older versions of Hadoop, we drop the "supported" designator for minor versions that fail to continue to see releases. Additionally, the Hadoop project has its own set of compatibility guidelines, which means in some cases having to update to a newer supported minor release might break some of our compatibility promises.
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 10. 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

HBase 1.7.0 release violated client-server wire compatibility guarantees and was subsequently withdrawn after the incompatibilities were reported and fixed in 1.7.1. If you are considering an upgrade to 1.7.x line, see Upgrading to 1.7.1+.

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 Apache Yetus Audience Annotations to guide downstream expectations for stability.

  • InterfaceAudience (javadocs): captures the intended audience, possible values include:

    • Public: safe for end users and external projects

    • LimitedPrivate: used for internals we expect to be pluggable, such as coprocessors

    • Private: strictly for use within HBase itself Classes which are defined as IA.Private may be used as parameters or return values for interfaces which are declared IA.LimitedPrivate. Treat the IA.Private object as opaque; do not try to access its methods or fields directly.

  • InterfaceStability (javadocs): describes what types of interface changes are permitted. Possible values include:

    • Stable: the interface is fixed and is not expected to change

    • Evolving: the interface may change in future minor versions

    • Unstable: the interface may change at any time

Please keep in mind the following interactions between the InterfaceAudience and InterfaceStability annotations within the HBase project:

  • IA.Public classes are inherently stable and adhere to our stability guarantees relating to the type of upgrade (major, minor, or patch).

  • IA.LimitedPrivate classes should always be annotated with one of the given InterfaceStability values. If they are not, you should presume they are IS.Unstable.

  • IA.Private classes should be considered implicitly unstable, with no guarantee of stability between releases.

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.

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 maintenance releases: e.g. from 1.4.4 to 1.4.6. See Does compatibility between versions also mean binary compatibility? discussion on the HBase dev mailing list.

11.2. 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 minor versions are binary compatible. You can do a Rolling Upgrades between HBase point versions. For example, you can go to 1.4.4 from 1.4.6 by doing a rolling upgrade across the cluster replacing the 1.4.4 binary with a 1.4.6 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.

12. Rollback

Sometimes things don’t go as planned when attempting an upgrade. This section explains how to perform a rollback to an earlier HBase release. Note that this should only be needed between Major and some Minor releases. You should always be able to downgrade between HBase Patch releases within the same Minor version. These instructions may require you to take steps before you start the upgrade process, so be sure to read through this section beforehand.

12.1. Caveats

Rollback vs Downgrade

This section describes how to perform a rollback on an upgrade between HBase minor and major versions. In this document, rollback refers to the process of taking an upgraded cluster and restoring it to the old version while losing all changes that have occurred since upgrade. By contrast, a cluster downgrade would restore an upgraded cluster to the old version while maintaining any data written since the upgrade. We currently only offer instructions to rollback HBase clusters. Further, rollback only works when these instructions are followed prior to performing the upgrade.

When these instructions talk about rollback vs downgrade of prerequisite cluster services (i.e. HDFS), you should treat leaving the service version the same as a degenerate case of downgrade.

Replication

Unless you are doing an all-service rollback, the HBase cluster will lose any configured peers for HBase replication. If your cluster is configured for HBase replication, then prior to following these instructions you should document all replication peers. After performing the rollback you should then add each documented peer back to the cluster. For more information on enabling HBase replication, listing peers, and adding a peer see Managing and Configuring Cluster Replication. Note also that data written to the cluster since the upgrade may or may not have already been replicated to any peers. Determining which, if any, peers have seen replication data as well as rolling back the data in those peers is out of the scope of this guide.

Data Locality

Unless you are doing an all-service rollback, going through a rollback procedure will likely destroy all locality for Region Servers. You should expect degraded performance until after the cluster has had time to go through compactions to restore data locality. Optionally, you can force a compaction to speed this process up at the cost of generating cluster load.

Configurable Locations

The instructions below assume default locations for the HBase data directory and the HBase znode. Both of these locations are configurable and you should verify the value used in your cluster before proceeding. In the event that you have a different value, just replace the default with the one found in your configuration * HBase data directory is configured via the key 'hbase.rootdir' and has a default value of '/hbase'. * HBase znode is configured via the key 'zookeeper.znode.parent' and has a default value of '/hbase'.

12.2. All service rollback

If you will be performing a rollback of both the HDFS and ZooKeeper services, then HBase’s data will be rolled back in the process.

Requirements

  • Ability to rollback HDFS and ZooKeeper

Before upgrade

No additional steps are needed pre-upgrade. As an extra precautionary measure, you may wish to use distcp to back up the HBase data off of the cluster to be upgraded. To do so, follow the steps in the 'Before upgrade' section of 'Rollback after HDFS downgrade' but copy to another HDFS instance instead of within the same instance.

Performing a rollback

  1. Stop HBase

  2. Perform a rollback for HDFS and ZooKeeper (HBase should remain stopped)

  3. Change the installed version of HBase to the previous version

  4. Start HBase

  5. Verify HBase contents—use the HBase shell to list tables and scan some known values.

12.3. Rollback after HDFS rollback and ZooKeeper downgrade

If you will be rolling back HDFS but going through a ZooKeeper downgrade, then HBase will be in an inconsistent state. You must ensure the cluster is not started until you complete this process.

Requirements

  • Ability to rollback HDFS

  • Ability to downgrade ZooKeeper

Before upgrade

No additional steps are needed pre-upgrade. As an extra precautionary measure, you may wish to use distcp to back up the HBase data off of the cluster to be upgraded. To do so, follow the steps in the 'Before upgrade' section of 'Rollback after HDFS downgrade' but copy to another HDFS instance instead of within the same instance.

Performing a rollback

  1. Stop HBase

  2. Perform a rollback for HDFS and a downgrade for ZooKeeper (HBase should remain stopped)

  3. Change the installed version of HBase to the previous version

  4. Clean out ZooKeeper information related to HBase. WARNING: This step will permanently destroy all replication peers. Please see the section on HBase Replication under Caveats for more information.

    Clean HBase information out of ZooKeeper
    [hpnewton@gateway_node.example.com ~]$ zookeeper-client -server zookeeper1.example.com:2181,zookeeper2.example.com:2181,zookeeper3.example.com:2181
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
quit
Quitting...

  5. Start HBase

  6. Verify HBase contents—use the HBase shell to list tables and scan some known values.

12.4. Rollback after HDFS downgrade

If you will be performing an HDFS downgrade, then you’ll need to follow these instructions regardless of whether ZooKeeper goes through rollback, downgrade, or reinstallation.

Requirements

  • Ability to downgrade HDFS

  • Pre-upgrade cluster must be able to run MapReduce jobs

  • HDFS super user access

  • Sufficient space in HDFS for at least two copies of the HBase data directory

Before upgrade

Before beginning the upgrade process, you must take a complete backup of HBase’s backing data. The following instructions cover backing up the data within the current HDFS instance. Alternatively, you can use the distcp command to copy the data to another HDFS cluster.

  1. Stop the HBase cluster

  2. Copy the HBase data directory to a backup location using the distcp command as the HDFS super user (shown below on a security enabled cluster)

    Using distcp to backup the HBase data directory
    [hpnewton@gateway_node.example.com ~]$ kinit -k -t hdfs.keytab hdfs@EXAMPLE.COM
[hpnewton@gateway_node.example.com ~]$ hadoop distcp /hbase /hbase-pre-upgrade-backup

  3. Distcp will launch a mapreduce job to handle copying the files in a distributed fashion. Check the output of the distcp command to ensure this job completed successfully.

Performing a rollback

  1. Stop HBase

  2. Perform a downgrade for HDFS and a downgrade/rollback for ZooKeeper (HBase should remain stopped)

  3. Change the installed version of HBase to the previous version

  4. Restore the HBase data directory from prior to the upgrade as the HDFS super user (shown below on a security enabled cluster). If you backed up your data on another HDFS cluster instead of locally, you will need to use the distcp command to copy it back to the current HDFS cluster.

    Restore the HBase data directory
    [hpnewton@gateway_node.example.com ~]$ kinit -k -t hdfs.keytab hdfs@EXAMPLE.COM
[hpnewton@gateway_node.example.com ~]$ hdfs dfs -mv /hbase /hbase-upgrade-rollback
[hpnewton@gateway_node.example.com ~]$ hdfs dfs -mv /hbase-pre-upgrade-backup /hbase

  5. Clean out ZooKeeper information related to HBase. WARNING: This step will permanently destroy all replication peers. Please see the section on HBase Replication under Caveats for more information.

    Clean HBase information out of ZooKeeper
    [hpnewton@gateway_node.example.com ~]$ zookeeper-client -server zookeeper1.example.com:2181,zookeeper2.example.com:2181,zookeeper3.example.com:2181
Welcome to ZooKeeper!
JLine support is disabled
rmr /hbase
quit
Quitting...

  6. Start HBase

  7. Verify HBase contents–use the HBase shell to list tables and scan some known values.

13. Upgrade Paths

13.1. Upgrade from 2.x to 3.x

The RegionServer Grouping feature has been reimplemented. See section Migrating From Old Implementation in Apache HBase Operational Management for more details.

The hbase:namespace table has been removed and fold into hbase:meta. See section About hbase:namespace table in Data Model for more details.

There is no special consideration upgrading to hbase-2.4.x from 2.3.x. And for earlier versions, just follow the Upgrade from 2.0.x-2.2.x to 2.3+ guide. In general, 2.2.x should be rolling upgradeable, for 2.1.x or 2.0.x, you will need to clear the Upgrade from 2.0 or 2.1 to 2.2+ hurdle first.

13.2. Upgrade from 2.0.x-2.2.x to 2.3+

There is no special consideration upgrading to hbase-2.3.x from earlier versions. From 2.2.x, it should be rolling upgradeable. From 2.1.x or 2.0.x, you will need to clear the Upgrade from 2.0 or 2.1 to 2.2+ hurdle first.

13.2.1. Upgraded ZooKeeper Dependency Version

Our dependency on Apache ZooKeeper has been upgraded to 3.5.7 (HBASE-24132), as 3.4.x is EOL. The newer 3.5.x client is compatible with the older 3.4.x server. However, if you’re using HBase in stand-alone mode and perform an in-place upgrade, there are some upgrade steps documented by the ZooKeeper community. This doesn’t impact a production deployment, but would impact a developer’s local environment.

13.2.2. New In-Master Procedure Store

Of note, HBase 2.3.0 changes the in-Master Procedure Store implementation. It was a dedicated custom store (see MasterProcWAL) to instead use a standard HBase Region (HBASE-23326). The migration from the old to new format is automatic run by the new 2.3.0 Master on startup. The old MasterProcWALs dir which hosted the old custom implementation files in ${hbase.rootdir} is deleted on successful migration. A new MasterProc sub-directory replaces it to host the Store files and WALs for the new Procedure Store in-Master Region. The in-Master Region is unusual in that it writes to an alternate location at ${hbase.rootdir}/MasterProc rather than under ${hbase.rootdir}/data in the filesystem and the special Procedure Store in-Master Region is hidden from all clients other than the active Master itself. Otherwise, it is like any other with the Master process running flushes and compactions, archiving WALs when over-flushed, and so on. Its files are readable by standard Region and Store file tooling for triage and analysis as long as they are pointed to the appropriate location in the filesystem.

Notice that, after the migration, you should make sure to not start an active master with old code, as it can not recognize the new procedure store. So it is suggested to upgrade backup master(s) to new 2.3 first, and then upgrade the active master. And unless explicitly mentioned, this is the suggested way for all upgrading, i.e, upgrading backup master(s) first, then active master, and then region servers.

13.3. Upgrade from 2.0 or 2.1 to 2.2+

HBase 2.2+ uses a new Procedure form assigning/unassigning/moving Regions. It does not process HBase 2.1 and 2.0’s Unassign/Assign Procedure types. Upgrade requires that we first drain the Master Procedure Store of old style Procedures before starting the new 2.2 Master. So you need to make sure that before you kill the old version (2.0 or 2.1) Master, there is no region in transition. And once the new version (2.2+) Master is up, you can rolling upgrade RegionServers one by one.

And there is a more safer way if you are running 2.1.1+ or 2.0.3+ cluster. It need four steps to upgrade Master.

  1. Shutdown both active and standby Masters (Your cluster will continue to server reads and writes without interruption).

  2. Set the property hbase.procedure.upgrade-to-2-2 to true in hbase-site.xml for the Master, and start only one Master, still using the 2.1.1+ (or 2.0.3+) version.

  3. Wait until the Master quits. Confirm that there is a 'UPGRADE OK: All existed procedures have been finished, quit…​' message in the Master log as the cause of the shutdown. The Procedure Store is now empty.

  4. Start new Masters with the new 2.2+ version.

Then you can rolling upgrade RegionServers one by one. See HBASE-21075 for more details.

In case these steps are not done, on starting 2.2+ master, you would see the following exception in the master logs:

org.apache.hadoop.hbase.HBaseIOException: Unsupported procedure type class org.apache.hadoop.hbase.master.assignment.UnassignProcedure found

13.4. Upgrading from 1.x to 2.x

In this section we will first call out significant changes compared to the prior stable HBase release and then go over the upgrade process. Be sure to read the former with care so you avoid surprises.

13.4.1. Changes of Note!

First we’ll cover deployment / operational changes that you might hit when upgrading to HBase 2.0+. After that we’ll call out changes for downstream applications. Please note that Coprocessors are covered in the operational section. Also note that this section is not meant to convey information about new features that may be of interest to you. For a complete summary of changes, please see the CHANGES.txt file in the source release artifact for the version you are planning to upgrade to.

Update to basic prerequisite minimums in HBase 2.0+

As noted in the section Basic Prerequisites, HBase 2.0+ requires a minimum of Java 8 and Hadoop 2.6. The HBase community recommends ensuring you have already completed any needed upgrades in prerequisites prior to upgrading your HBase version.

HBCK must match HBase server version

You must not use an HBase 1.x version of HBCK against an HBase 2.0+ cluster. HBCK is strongly tied to the HBase server version. Using the HBCK tool from an earlier release against an HBase 2.0+ cluster will destructively alter said cluster in unrecoverable ways.

As of HBase 2.0, HBCK (A.K.A HBCK1 or hbck1) is a read-only tool that can report the status of some non-public system internals but will often misread state because it does not understand the workings of hbase2.

To read about HBCK’s replacement, see HBase HBCK2 in Apache HBase Operational Management.

Related, before you upgrade, ensure that hbck1 reports no INCONSISTENCIES. Fixing hbase1-type inconsistencies post-upgrade is an involved process.
Configuration settings no longer in HBase 2.0+

The following configuration settings are no longer applicable or available. For details, please see the detailed release notes.

Configuration properties that were renamed in HBase 2.0+

The following properties have been renamed. Attempts to set the old property will be ignored at run time.

Table 11. Renamed properties
Old name New name

hbase.rpc.server.nativetransport

hbase.netty.nativetransport

hbase.netty.rpc.server.worker.count

hbase.netty.worker.count

hbase.hfile.compactions.discharger.interval

hbase.hfile.compaction.discharger.interval

hbase.hregion.percolumnfamilyflush.size.lower.bound

hbase.hregion.percolumnfamilyflush.size.lower.bound.min
Configuration settings with different defaults in HBase 2.0+

The following configuration settings changed their default value. Where applicable, the value to set to restore the behavior of HBase 1.2 is given.

  • hbase.security.authorization now defaults to false. set to true to restore same behavior as previous default.

  • hbase.client.retries.number is now set to 10. Previously it was 35. Downstream users are advised to use client timeouts as described in section Timeout settings instead.

  • hbase.client.serverside.retries.multiplier is now set to 3. Previously it was 10. Downstream users are advised to use client timesout as describe in section Timeout settings instead.

  • hbase.master.fileSplitTimeout is now set to 10 minutes. Previously it was 30 seconds.

  • hbase.regionserver.logroll.multiplier is now set to 0.5. Previously it was 0.95. This change is tied with the following doubling of block size. Combined, these two configuration changes should make for WALs of about the same size as those in hbase-1.x but there should be less incidence of small blocks because we fail to roll the WAL before we hit the blocksize threshold. See HBASE-19148 for discussion.

  • hbase.regionserver.hlog.blocksize defaults to 2x the HDFS default block size for the WAL dir. Previously it was equal to the HDFS default block size for the WAL dir.

  • hbase.client.start.log.errors.counter changed to 5. Previously it was 9.

  • hbase.ipc.server.callqueue.type changed to 'fifo'. In HBase versions 1.0 - 1.2 it was 'deadline'. In prior and later 1.x versions it already defaults to 'fifo'.

  • hbase.hregion.memstore.chunkpool.maxsize is 1.0 by default. Previously it was 0.0. Effectively, this means previously we would not use a chunk pool when our memstore is onheap and now we will. See the section Long GC pauses for more information about the MSLAB chunk pool.

  • hbase.master.cleaner.interval is now set to 10 minutes. Previously it was 1 minute.

  • hbase.master.procedure.threads will now default to 1/4 of the number of available CPUs, but not less than 16 threads. Previously it would be number of threads equal to number of CPUs.

  • hbase.hstore.blockingStoreFiles is now 16. Previously it was 10.

  • hbase.http.max.threads is now 16. Previously it was 10.

  • hbase.client.max.perserver.tasks is now 2. Previously it was 5.

  • hbase.normalizer.period is now 5 minutes. Previously it was 30 minutes.

  • hbase.regionserver.region.split.policy is now SteppingSplitPolicy. Previously it was IncreasingToUpperBoundRegionSplitPolicy.

  • replication.source.ratio is now 0.5. Previously it was 0.1.

"Master hosting regions" feature broken and unsupported

The feature "Master acts as region server" and associated follow-on work available in HBase 1.y is non-functional in HBase 2.y and should not be used in a production setting due to deadlock on Master initialization. Downstream users are advised to treat related configuration settings as experimental and the feature as inappropriate for production settings.

A brief summary of related changes:

  • Master no longer carries regions by default

  • hbase.balancer.tablesOnMaster is a boolean, default false (if it holds an HBase 1.x list of tables, will default to false)

  • hbase.balancer.tablesOnMaster.systemTablesOnly is boolean to keep user tables off master. default false

  • those wishing to replicate old list-of-servers config should deploy a stand-alone RegionServer process and then rely on Region Server Groups

"Distributed Log Replay" feature broken and removed

The Distributed Log Replay feature was broken and has been removed from HBase 2.y+. As a consequence all related configs, metrics, RPC fields, and logging have also been removed. Note that this feature was found to be unreliable in the run up to HBase 1.0, defaulted to being unused, and was effectively removed in HBase 1.2.0 when we started ignoring the config that turns it on (HBASE-14465). If you are currently using the feature, be sure to perform a clean shutdown, ensure all DLR work is complete, and disable the feature prior to upgrading.

prefix-tree encoding removed

The prefix-tree encoding was removed from HBase 2.0.0 (HBASE-19179). It was (late!) deprecated in hbase-1.2.7, hbase-1.4.0, and hbase-1.3.2.

This feature was removed because it as not being actively maintained. If interested in reviving this sweet facility which improved random read latencies at the expensive of slowed writes, write the HBase developers list at dev at hbase dot apache dot org.

The prefix-tree encoding needs to be removed from all tables before upgrading to HBase 2.0+. To do that first you need to change the encoding from PREFIX_TREE to something else that is supported in HBase 2.0. After that you have to major compact the tables that were using PREFIX_TREE encoding before. To check which column families are using incompatible data block encoding you can use Pre-Upgrade Validator.

Changed metrics

The following metrics have changed names:

  • Metrics previously published under the name "AssignmentManger" [sic] are now published under the name "AssignmentManager"

The following metrics have changed their meaning:

  • The metric 'blockCacheEvictionCount' published on a per-region server basis no longer includes blocks removed from the cache due to the invalidation of the hfiles they are from (e.g. via compaction).

  • The metric 'totalRequestCount' increments once per request; previously it incremented by the number of Actions carried in the request; e.g. if a request was a multi made of four Gets and two Puts, we’d increment 'totalRequestCount' by six; now we increment by one regardless. Expect to see lower values for this metric in hbase-2.0.0.

  • The 'readRequestCount' now counts reads that return a non-empty row where in older hbases, we’d increment 'readRequestCount' whether a Result or not. This change will flatten the profile of the read-requests graphs if requests for non-existent rows. A YCSB read-heavy workload can do this dependent on how the database was loaded.

The following metrics have been removed:

The following metrics have been added:

  • 'totalRowActionRequestCount' is a count of region row actions summing reads and writes.

Changed logging

HBase-2.0.0 now uses slf4j as its logging frontend. Previously, we used log4j (1.2). For most the transition should be seamless; slf4j does a good job interpreting log4j.properties logging configuration files such that you should not notice any difference in your log system emissions.

That said, your log4j.properties may need freshening. See HBASE-20351 for example, where a stale log configuration file manifest as netty configuration being dumped at DEBUG level as preamble on every shell command invocation.

ZooKeeper configs no longer read from zoo.cfg

HBase no longer optionally reads the 'zoo.cfg' file for ZooKeeper related configuration settings. If you previously relied on the 'hbase.config.read.zookeeper.config' config for this functionality, you should migrate any needed settings to the hbase-site.xml file while adding the prefix 'hbase.zookeeper.property.' to each property name.

Changes in permissions

The following permission related changes either altered semantics or defaults:

  • Permissions granted to a user now merge with existing permissions for that user, rather than over-writing them. (see the release note on HBASE-17472 for details)

  • Region Server Group commands (added in 1.4.0) now require admin privileges.

Most Admin APIs don’t work against an HBase 2.0+ cluster from pre-HBase 2.0 clients

A number of admin commands are known to not work when used from a pre-HBase 2.0 client. This includes an HBase Shell that has the library jars from pre-HBase 2.0. You will need to plan for an outage of use of admin APIs and commands until you can also update to the needed client version.

The following client operations do not work against HBase 2.0+ cluster when executed from a pre-HBase 2.0 client:

  • list_procedures

  • split

  • merge_region

  • list_quotas

  • enable_table_replication

  • disable_table_replication

  • Snapshot related commands

Deprecated in 1.0 admin commands have been removed.

The following commands that were deprecated in 1.0 have been removed. Where applicable the replacement command is listed.

  • The 'hlog' command has been removed. Downstream users should rely on the 'wal' command instead.

Region Server memory consumption changes.

Users upgrading from versions prior to HBase 1.4 should read the instructions in section Region Server memory consumption changes..

Additionally, HBase 2.0 has changed how memstore memory is tracked for flushing decisions. Previously, both the data size and overhead for storage were used to calculate utilization against the flush threshold. Now, only data size is used to make these per-region decisions. Globally the addition of the storage overhead is used to make decisions about forced flushes.

Web UI for splitting and merging operate on row prefixes

Previously, the Web UI included functionality on table status pages to merge or split based on an encoded region name. In HBase 2.0, instead this functionality works by taking a row prefix.

Special upgrading for Replication users from pre-HBase 1.4

User running versions of HBase prior to the 1.4.0 release that make use of replication should be sure to read the instructions in the section Replication peer’s TableCFs config.

HBase shell changes

The HBase shell command relies on a bundled JRuby instance. This bundled JRuby been updated from version 1.6.8 to version 9.1.10.0. The represents a change from Ruby 1.8 to Ruby 2.3.3, which introduces non-compatible language changes for user scripts.

The HBase shell command now ignores the '--return-values' flag that was present in early HBase 1.4 releases. Instead the shell always behaves as though that flag were passed. If you wish to avoid having expression results printed in the console you should alter your IRB configuration as noted in the section irbrc.

Coprocessor APIs have changed in HBase 2.0+

All Coprocessor APIs have been refactored to improve supportability around binary API compatibility for future versions of HBase. If you or applications you rely on have custom HBase coprocessors, you should read the release notes for HBASE-18169 for details of changes you will need to make prior to upgrading to HBase 2.0+.

For example, if you had a BaseRegionObserver in HBase 1.2 then at a minimum you will need to update it to implement both RegionObserver and RegionCoprocessor and add the method

...
  @Override
  public Optional<RegionObserver> getRegionObserver() {
    return Optional.of(this);
  }
...
HBase 2.0+ can no longer write HFile v2 files.

HBase has simplified our internal HFile handling. As a result, we can no longer write HFile versions earlier than the default of version 3. Upgrading users should ensure that hfile.format.version is not set to 2 in hbase-site.xml before upgrading. Failing to do so will cause Region Server failure. HBase can still read HFiles written in the older version 2 format.

HBase 2.0+ can no longer read Sequence File based WAL file.

HBase can no longer read the deprecated WAL files written in the Apache Hadoop Sequence File format. The hbase.regionserver.hlog.reader.impl and hbase.regionserver.hlog.writer.impl configuration entries should be set to use the Protobuf based WAL reader / writer classes. This implementation has been the default since HBase 0.96, so legacy WAL files should not be a concern for most downstream users.

Starting from 2.6.0, the hbase.regionserver.hlog.reader.impl and hbase.regionserver.hlog.writer.impl configuration entries are removed since the only valid values are protobuf based reader/writer. Setting them in hbase-site.xml will have no real effect.

A clean cluster shutdown should ensure there are no WAL files. If you are unsure of a given WAL file’s format you can use the hbase wal command to parse files while the HBase cluster is offline. In HBase 2.0+, this command will not be able to read a Sequence File based WAL. For more information on the tool see the section WALPrettyPrinter.

Change in behavior for filters

The Filter ReturnCode NEXT_ROW has been redefined as skipping to next row in current family, not to next row in all family. it’s more reasonable, because ReturnCode is a concept in store level, not in region level.

Downstream HBase 2.0+ users should use the shaded client

Downstream users are strongly urged to rely on the Maven coordinates org.apache.hbase:hbase-shaded-client for their runtime use. This artifact contains all the needed implementation details for talking to an HBase cluster while minimizing the number of third party dependencies exposed.

Note that this artifact exposes some classes in the org.apache.hadoop package space (e.g. o.a.h.configuration.Configuration) so that we can maintain source compatibility with our public API. Those classes are included so that they can be altered to use the same relocated third party dependencies as the rest of the HBase client code. In the event that you need to also use Hadoop in your code, you should ensure all Hadoop related jars precede the HBase client jar in your classpath.

Downstream HBase 2.0+ users of MapReduce must switch to new artifact

Downstream users of HBase’s integration for Apache Hadoop MapReduce must switch to relying on the org.apache.hbase:hbase-shaded-mapreduce module for their runtime use. Historically, downstream users relied on either the org.apache.hbase:hbase-server or org.apache.hbase:hbase-shaded-server artifacts for these classes. Both uses are no longer supported and in the vast majority of cases will fail at runtime.

Note that this artifact exposes some classes in the org.apache.hadoop package space (e.g. o.a.h.configuration.Configuration) so that we can maintain source compatibility with our public API. Those classes are included so that they can be altered to use the same relocated third party dependencies as the rest of the HBase client code. In the event that you need to also use Hadoop in your code, you should ensure all Hadoop related jars precede the HBase client jar in your classpath.

Significant changes to runtime classpath

A number of internal dependencies for HBase were updated or removed from the runtime classpath. Downstream client users who do not follow the guidance in Downstream HBase 2.0+ users should use the shaded client will have to examine the set of dependencies Maven pulls in for impact. Downstream users of LimitedPrivate Coprocessor APIs will need to examine the runtime environment for impact. For details on our new handling of third party libraries that have historically been a problem with respect to harmonizing compatible runtime versions, see the reference guide section The hbase-thirdparty dependency and shading/relocation.

Multiple breaking changes to source and binary compatibility for client API

The Java client API for HBase has a number of changes that break both source and binary compatibility for details see the Compatibility Check Report for the release you’ll be upgrading to.

Tracing implementation changes

The backing implementation of HBase’s tracing features was updated from Apache HTrace 3 to HTrace 4, which includes several breaking changes. While HTrace 3 and 4 can coexist in the same runtime, they will not integrate with each other, leading to disjoint trace information.

The internal changes to HBase during this upgrade were sufficient for compilation, but it has not been confirmed that there are no regressions in tracing functionality. Please consider this feature experimental for the immediate future.

If you previously relied on client side tracing integrated with HBase operations, it is recommended that you upgrade your usage to HTrace 4 as well.

After the Apache HTrace project moved to the Attic/retired, the traces in HBase are left broken and unmaintained since HBase 2.0. A new project HBASE-22120 will replace HTrace with OpenTelemetry. It will be shipped in 3.0.0 release. Please see the reference guide section Tracing for more details.

HFile lose forward compatibility

HFiles generated by 2.0.0, 2.0.1, 2.1.0 are not forward compatible to 1.4.6-, 1.3.2.1-, 1.2.6.1-, and other inactive releases. Why HFile lose compatibility is hbase in new versions (2.0.0, 2.0.1, 2.1.0) use protobuf to serialize/deserialize TimeRangeTracker (TRT) while old versions use DataInput/DataOutput. To solve this, We have to put HBASE-21012 to 2.x and put HBASE-21013 in 1.x. For more information, please check HBASE-21008.

Performance

You will likely see a change in the performance profile on upgrade to hbase-2.0.0 given read and write paths have undergone significant change. On release, writes may be slower with reads about the same or much better, dependent on context. Be prepared to spend time re-tuning (See Apache HBase Performance Tuning). Performance is also an area that is now under active review so look forward to improvement in coming releases (See HBASE-20188 TESTING Performance).

Integration Tests and Kerberos

Integration Tests (IntegrationTests*) used to rely on the Kerberos credential cache for authentication against secured clusters. This used to lead to tests failing due to authentication failures when the tickets in the credential cache expired. As of hbase-2.0.0 (and hbase-1.3.0+), the integration test clients will make use of the configuration properties hbase.client.keytab.file and hbase.client.kerberos.principal. They are required. The clients will perform a login from the configured keytab file and automatically refresh the credentials in the background for the process lifetime (See HBASE-16231).

Default Compaction Throughput

HBase 2.x comes with default limits to the speed at which compactions can execute. This limit is defined per RegionServer. In previous versions of HBase earlier than 1.5, there was no limit to the speed at which a compaction could run by default. Applying a limit to the throughput of a compaction should ensure more stable operations from RegionServers.

Take care to notice that this limit is per RegionServer, not per compaction.

The throughput limit is defined as a range of bytes written per second, and is allowed to vary within the given lower and upper bound. RegionServers observe the current throughput of a compaction and apply a linear formula to adjust the allowed throughput, within the lower and upper bound, with respect to external pressure. For compactions, external pressure is defined as the number of store files with respect to the maximum number of allowed store files. The more store files, the higher the compaction pressure.

Configuration of this throughput is governed by the following properties.

  • The lower bound is defined by hbase.hstore.compaction.throughput.lower.bound and defaults to 50 MB/s (52428800).

  • The upper bound is defined by hbase.hstore.compaction.throughput.higher.bound and defaults to 100 MB/s (104857600).

To revert this behavior to the unlimited compaction throughput of earlier versions of HBase, please set the following property to the implementation that applies no limits to compactions.

hbase.regionserver.throughput.controller=org.apache.hadoop.hbase.regionserver.throttle.NoLimitThroughputController

13.4.2. Upgrading Coprocessors to 2.0

Coprocessors have changed substantially in 2.0 ranging from top level design changes in class hierarchies to changed/removed methods, interfaces, etc. (Parent jira: HBASE-18169 Coprocessor fix and cleanup before 2.0.0 release). Some of the reasons for such widespread changes:

  1. Pass Interfaces instead of Implementations; e.g. TableDescriptor instead of HTableDescriptor and Region instead of HRegion (HBASE-18241 Change client.Table and client.Admin to not use HTableDescriptor).

  2. Design refactor so implementers need to fill out less boilerplate and so we can do more compile-time checking (HBASE-17732)

  3. Purge Protocol Buffers from Coprocessor API (HBASE-18859, HBASE-16769, etc)

  4. Cut back on what we expose to Coprocessors removing hooks on internals that were too private to expose (for eg. HBASE-18453 CompactionRequest should not be exposed to user directly; HBASE-18298 RegionServerServices Interface cleanup for CP expose; etc)

To use coprocessors in 2.0, they should be rebuilt against new API otherwise they will fail to load and HBase processes will die.

Suggested order of changes to upgrade the coprocessors:

  1. Directly implement observer interfaces instead of extending Base*Observer classes. Change Foo extends BaseXXXObserver to Foo implements XXXObserver. (HBASE-17312).

  2. Adapt to design change from Inheritence to Composition (HBASE-17732) by following this example.

  3. getTable() has been removed from the CoprocessorEnvrionment, coprocessors should self-manage Table instances.

Some examples of writing coprocessors with new API can be found in hbase-example module here .

Lastly, if an api has been changed/removed that breaks you in an irreparable way, and if there’s a good justification to add it back, bring it our notice (dev@hbase.apache.org).

13.4.3. Rolling Upgrade from 1.x to 2.x

Rolling upgrades are currently an experimental feature. They have had limited testing. There are likely corner cases as yet uncovered in our limited experience so you should be careful if you go this route. The stop/upgrade/start described in the next section, Upgrade process from 1.x to 2.x, is the safest route.

That said, the below is a prescription for a rolling upgrade of a 1.4 cluster.

Pre-Requirements

  • Upgrade to the latest 1.4.x release. Pre 1.4 releases may also work but are not tested, so please upgrade to 1.4.3+ before upgrading to 2.x, unless you are an expert and familiar with the region assignment and crash processing. See the section Upgrading from pre-1.4 to 1.4+ on how to upgrade to 1.4.x.

  • Make sure that the zk-less assignment is enabled, i.e, set hbase.assignment.usezk to false. This is the most important thing. It allows the 1.x master to assign/unassign regions to/from 2.x region servers. See the release note section of HBASE-11059 on how to migrate from zk based assignment to zk less assignment.

  • Before you upgrade, ensure that hbck1 reports no INCONSISTENCIES. Fixing hbase1-type inconsistencies post-upgrade is an involved process.

  • We have tested rolling upgrading from 1.4.3 to 2.1.0, but it should also work if you want to upgrade to 2.0.x.

Instructions

  1. Unload a region server and upgrade it to 2.1.0. With HBASE-17931 in place, the meta region and regions for other system tables will be moved to this region server immediately. If not, please move them manually to the new region server. This is very important because

    • The schema of meta region is hard coded, if meta is on an old region server, then the new region servers can not access it as it does not have some families, for example, table state.

    • Client with lower version can communicate with server with higher version, but not vice versa. If the meta region is on an old region server, the new region server will use a client with higher version to communicate with a server with lower version, this may introduce strange problems.

  2. Rolling upgrade all other region servers.

  3. Upgrading masters.

It is OK that during the rolling upgrading there are region server crashes. The 1.x master can assign regions to both 1.x and 2.x region servers, and HBASE-19166 fixed a problem so that 1.x region server can also read the WALs written by 2.x region server and split them.

please read the Changes of Note! section carefully before rolling upgrading. Make sure that you do not use the removed features in 2.0, for example, the prefix-tree encoding, the old hfile format, etc. They could both fail the upgrading and leave the cluster in an intermediate state and hard to recover.
If you have success running this prescription, please notify the dev list with a note on your experience and/or update the above with any deviations you may have taken so others going this route can benefit from your efforts.

13.4.4. Upgrade process from 1.x to 2.x

To upgrade an existing HBase 1.x cluster, you should:

  • Ensure that hbck1 reports no INCONSISTENCIES. Fixing hbase1-type inconsistencies post-upgrade is an involved process. Fix all hbck1 complaints before proceeding.

  • Clean shutdown of existing 1.x cluster

  • Update coprocessors

  • Upgrade Master roles first

  • Upgrade RegionServers

  • (Eventually) Upgrade Clients

13.5. Upgrading to 1.7.1+

HBase release 1.7.0 introduced an incompatible table metadata serialization format that broke the minor release compatibility guarantees. The issue was reported in HBASE-26021 and the problematic serialization patch was reverted in HBase 1.7.1. Some important notes about 1.7.x upgrades below.

  • If you are considering an upgrade to 1.7.x version, skip 1.7.0 completely and upgrade to 1.7.1+ version. 1.7.0 was withdrawn and removed from the Apache sites.

  • If you already installed a 1.7.0 cluster from scratch and are looking to migrate to 1.7.1+, you cannot follow the regular rolling upgrade procedures due to broken compatibility contracts. Instead shutdown the cluster and reboot with 1.7.1+ binaries. Newer versions detect any existing tables with incompatible serialization and rewrite them using the correct format at bootstrap.

  • If you are already on 1.7.1+ version, everything is good and no additional steps need to be performed.

13.6. Upgrading from pre-1.4 to 1.4+

13.6.1. Region Server memory consumption changes.

Users upgrading from versions prior to HBase 1.4 should be aware that the estimates of heap usage by the memstore objects (KeyValue, object and array header sizes, etc) have been made more accurate for heap sizes up to 32G (using CompressedOops), resulting in them dropping by 10-50% in practice. This also results in less number of flushes and compactions due to "fatter" flushes. YMMV. As a result, the actual heap usage of the memstore before being flushed may increase by up to 100%. If configured memory limits for the region server had been tuned based on observed usage, this change could result in worse GC behavior or even OutOfMemory errors. Set the environment property (not hbase-site.xml) "hbase.memorylayout.use.unsafe" to false to disable.

13.6.2. Replication peer’s TableCFs config

Before 1.4, the table name can’t include namespace for replication peer’s TableCFs config. It was fixed by add TableCFs to ReplicationPeerConfig which was stored on Zookeeper. So when upgrade to 1.4, you have to update the original ReplicationPeerConfig data on Zookeeper firstly. There are four steps to upgrade when your cluster have a replication peer with TableCFs config.

  • Disable the replication peer.

  • If master has permission to write replication peer znode, then rolling update master directly. If not, use TableCFsUpdater tool to update the replication peer’s config.

$ bin/hbase org.apache.hadoop.hbase.replication.master.TableCFsUpdater update

  • Rolling update regionservers.

  • Enable the replication peer.

Notes:

  • Can’t use the old client(before 1.4) to change the replication peer’s config. Because the client will write config to Zookeeper directly, the old client will miss TableCFs config. And the old client write TableCFs config to the old tablecfs znode, it will not work for new version regionserver.

13.6.3. Raw scan now ignores TTL

Doing a raw scan will now return results that have expired according to TTL settings.

13.7. Upgrading from pre-1.3 to 1.3+

If running Integration Tests under Kerberos, see Integration Tests and Kerberos.

13.8. Upgrading to 1.x

Please consult the documentation published specifically for the version of HBase that you are upgrading to for details on the upgrade process.

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.

14. 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

15. 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.

16. 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 3. 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 4. 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

16.1. 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.

17. 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 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 5. 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

18. 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

19. Overriding configuration starting the HBase Shell

As of hbase-2.0.5/hbase-2.1.3/hbase-2.2.0/hbase-1.4.10/hbase-1.5.0, you can pass or override hbase configuration as specified in hbase-*.xml by passing your key/values prefixed with -D on the command-line as follows:

$ ./bin/hbase shell -Dhbase.zookeeper.quorum=ZK0.remote.cluster.example.org,ZK1.remote.cluster.example.org,ZK2.remote.cluster.example.org -Draining=false
...
hbase(main):001:0> @shell.hbase.configuration.get("hbase.zookeeper.quorum")
=> "ZK0.remote.cluster.example.org,ZK1.remote.cluster.example.org,ZK2.remote.cluster.example.org"
hbase(main):002:0> @shell.hbase.configuration.get("raining")
=> "false"

20. Shell Tricks

20.1. 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

=> ["t"]
hbase(main):017:0> tables.map { |t| disable t ; drop  t}
0 row(s) in 2.2510 seconds

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

20.2. 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"

If you’d like to avoid printing the result of evaluting each expression to stderr, for example the array of tables returned from the "list" command:

$ echo "IRB.conf[:ECHO] = false" >>~/.irbrc

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

20.3. 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.

20.4. Query Shell Configuration

hbase(main):001:0> @shell.hbase.configuration.get("hbase.rpc.timeout")
=> "60000"

To set a config in the shell:

hbase(main):005:0> @shell.hbase.configuration.setInt("hbase.rpc.timeout", 61010)
hbase(main):006:0> @shell.hbase.configuration.get("hbase.rpc.timeout")
=> "61010"

20.5. 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.

20.6. Debug

20.6.1. 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>

20.6.2. DEBUG log level

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

$ ./bin/hbase shell -d

20.7. Commands

20.7.1. 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.

21. 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 12. 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>…​"

"com.example.www"

t5

contents:html = "<html>…​"

people:author = "John Doe"

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"
    }
  }
}

22. 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 13. 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 14. 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.

23. 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 coarse level of isolation.

23.1. 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 6. 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'}

23.2. 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 7. Examples
#namespace=foo and table qualifier=bar
create 'foo:bar', 'fam'

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

23.3. About hbase:namespace table

We used to have a system table called hbase:namespace for storing the namespace information.

It introduced some painful bugs in the past, especially that it may hang the master startup thus hang the whole cluster. This is because meta table also has a namespace, so it depends on namespace table. But namespace table also depends on meta table as meta table stores the location of all regions. This is a cyclic dependency so sometimes namespace and meta table will wait for each other to online and hang the master start up.

It is not easy to fix so in 3.0.0, we decided to completely remove the hbase:namespace table and fold its content into the ns family in hbase:meta table. When upgrading from 2.x to 3.x, the migration will be done automatically and the hbase:namespace table will be disabled after the migration is done. You are free to leave it there for sometime and finally drop it.

For more tails, please see HBASE-21154.

24. Table

Tables are declared up front at schema definition time.

25. 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.

26. 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.

27. Cells

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

28. Data Model Operations

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

28.1. Get

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

28.2. 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 (non-writeBuffer) or Table.batch (non-writeBuffer)

28.3. 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.setStartStopRowForPrefixScan(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.

28.4. 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.

29. 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.

29.1. 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 8. 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 9. 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.

29.2. Versions and HBase Operations

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

29.2.1. 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.

29.2.2. 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

29.2.3. 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<Cell> cells = r.getColumnCells(CF, ATTR);  // returns all versions of this column

29.2.4. 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.

Cell Version Example

The following Put uses a method getCellBuilder() to get a CellBuilder instance that already has relevant Type and Row set.

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

Put put = new Put(Bytes.toBytes(row));
put.add(put.getCellBuilder().setQualifier(ATTR)
   .setFamily(CF)
   .setValue(Bytes.toBytes(data))
   .build());
table.put(put);

29.2.5. 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.

29.3. Optional New Version and Delete behavior in HBase-2.0.0

In hbase-2.0.0, the operator can specify an alternate version and delete treatment by setting the column descriptor property NEW_VERSION_BEHAVIOR to true (To set a property on a column family descriptor, you must first disable the table and then alter the column family descriptor; see Keeping Deleted Cells for an example of editing an attribute on a column family descriptor).

The 'new version behavior', undoes the limitations listed below whereby a Delete ALWAYS overshadows a Put if at the same location — i.e. same row, column family, qualifier and timestamp — regardless of which arrived first. Version accounting is also changed as deleted versions are considered toward total version count. This is done to ensure results are not changed should a major compaction intercede. See HBASE-15968 and linked issues for discussion.

Running with this new configuration currently costs; we factor the Cell MVCC on every compare so we burn more CPU. The slow down will depend. In testing we’ve seen between 0% and 25% degradation.

If replicating, it is advised that you run with the new serial replication feature (See HBASE-9465; the serial replication feature did NOT make it into hbase-2.0.0 but should arrive in a subsequent hbase-2.x release) as now the order in which Mutations arrive is a factor.

29.4. Current Limitations

The below limitations are addressed in hbase-2.0.0. See the section above, Optional New Version and Delete behavior in HBase-2.0.0.

29.4.1. 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.

29.4.2. 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.)

30. 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).

31. 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.

32. 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.

33. 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.

See also Robert Yokota’s HBase Application Archetypes (an update on work done by other HBasers), for a helpful categorization of use cases that do well on top of the HBase model.

34. 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.

34.1. 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.

35. 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

36. 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 is 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 interaction can make for a bunch of needless i/o (To be addressed by changing flushing to work on a per column family basis). In addition, compactions triggered at table/region level will happen per store too.

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.

36.1. 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.

37. Rowkey Design

37.1. 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 10. 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 11. 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.

37.2. 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.

37.3. 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.

37.3.1. 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.

37.3.2. 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.

37.3.3. 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.

37.3.4. 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.

37.4. 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 Scan.setReversed() 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.

37.5. Rowkeys and ColumnFamilies

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

37.6. 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).

37.7. 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;
}

38. Number of Versions

38.1. 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.

38.2. 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.

39. 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.

39.1. 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.

40. Joins

If you have multiple tables, don’t forget to factor in the potential for Joins into the schema design.

41. 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.

42. 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.

Change the Value of KEEP_DELETED_CELLS Using HBase Shell
hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true
Example 12. 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.

43. 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

43.1. 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).

43.2. 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.

43.3. 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).

43.4. 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.

43.5. Coprocessor Secondary Index

Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see coprocessors

44. 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.

45. 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

45.1. 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?

45.1.1. 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.

45.1.2. 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.

45.1.3. 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 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 Scan.setReversed() for more information.

45.1.4. 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.

45.2. 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.

45.3. 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

45.3.1. 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.

45.3.2. 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.

45.4. 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.

45.4.1. 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).

45.4.2. 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."

45.4.3. 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.

45.5. 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 https://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! :)

46. Operational and Performance Configuration Options

46.1. Tune HBase Server RPC Handling

  • Set hbase.regionserver.handler.count (in hbase-site.xml) to cores x spindles for concurrency.

  • Optionally, split the call queues into separate read and write queues for differentiated service. The parameter hbase.ipc.server.callqueue.handler.factor specifies the number of call queues:

    • 0 means a single shared queue

    • 1 means one queue for each handler.

    • A value between 0 and 1 allocates the number of queues proportionally to the number of handlers. For instance, a value of .5 shares one queue between each two handlers.

  • Use hbase.ipc.server.callqueue.read.ratio (hbase.ipc.server.callqueue.read.share in 0.98) to split the call queues into read and write queues:

    • 0.5 means there will be the same number of read and write queues

    • < 0.5 for more write than read

    • > 0.5 for more read than write

  • Set hbase.ipc.server.callqueue.scan.ratio (HBase 1.0+) to split read call queues into small-read and long-read queues:

    • 0.5 means that there will be the same number of short-read and long-read queues

    • < 0.5 for more short-read

    • > 0.5 for more long-read

46.2. Disable Nagle for RPC

Disable Nagle’s algorithm. Delayed ACKs can add up to ~200ms to RPC round trip time. Set the following parameters:

  • In Hadoop’s core-site.xml:

    • ipc.server.tcpnodelay = true

    • ipc.client.tcpnodelay = true

  • In HBase’s hbase-site.xml:

    • hbase.ipc.client.tcpnodelay = true

    • hbase.ipc.server.tcpnodelay = true

46.3. Limit Server Failure Impact

Detect regionserver failure as fast as reasonable. Set the following parameters:

  • In hbase-site.xml, set zookeeper.session.timeout to 30 seconds or less to bound failure detection (20-30 seconds is a good start).

    • Note: Zookeeper clients negotiate a session timeout with the server during client init. Server enforces this timeout to be in the range [minSessionTimeout, maxSessionTimeout] and both these timeouts (measured in milliseconds) are configurable in Zookeeper service configuration. If not configured, these default to 2 * tickTime and 20 * tickTime respectively (tickTime is the basic time unit used by ZooKeeper, as measured in milliseconds. It is used to regulate heartbeats, timeouts etc.). Refer to Zookeeper documentation for additional details.

  • Detect and avoid unhealthy or failed HDFS DataNodes: in hdfs-site.xml and hbase-site.xml, set the following parameters:

    • dfs.namenode.avoid.read.stale.datanode = true

    • dfs.namenode.avoid.write.stale.datanode = true

46.4. Optimize on the Server Side for Low Latency

Skip the network for local blocks when the RegionServer goes to read from HDFS by exploiting HDFS’s Short-Circuit Local Reads facility. Note how setup must be done both at the datanode and on the dfsclient ends of the conneciton — i.e. at the RegionServer and how both ends need to have loaded the hadoop native .so library. After configuring your hadoop setting dfs.client.read.shortcircuit to true and configuring the dfs.domain.socket.path path for the datanode and dfsclient to share and restarting, next configure the regionserver/dfsclient side.

  • In hbase-site.xml, set the following parameters:

    • dfs.client.read.shortcircuit = true

    • dfs.client.read.shortcircuit.skip.checksum = true so we don’t double checksum (HBase does its own checksumming to save on i/os. See hbase.regionserver.checksum.verify for more on this.

    • dfs.domain.socket.path to match what was set for the datanodes.

    • dfs.client.read.shortcircuit.buffer.size = 131072 Important to avoid OOME — hbase has a default it uses if unset, see hbase.dfs.client.read.shortcircuit.buffer.size; its default is 131072.

  • Ensure data locality. In hbase-site.xml, set hbase.hstore.min.locality.to.skip.major.compact = 0.7 (Meaning that 0.7 <= n <= 1)

  • Make sure DataNodes have enough handlers for block transfers. In hdfs-site.xml, set the following parameters:

    • dfs.datanode.max.xcievers >= 8192

    • dfs.datanode.handler.count = number of spindles

Check the RegionServer logs after restart. You should only see complaint if misconfiguration. Otherwise, shortcircuit read operates quietly in background. It does not provide metrics so no optics on how effective it is but read latencies should show a marked improvement, especially if good data locality, lots of random reads, and dataset is larger than available cache.

Other advanced configurations that you might play with, especially if shortcircuit functionality is complaining in the logs, include dfs.client.read.shortcircuit.streams.cache.size and dfs.client.socketcache.capacity. Documentation is sparse on these options. You’ll have to read source code.

RegionServer metric system exposes HDFS short circuit read metrics shortCircuitBytesRead. Other HDFS read metrics, including totalBytesRead (The total number of bytes read from HDFS), localBytesRead (The number of bytes read from the local HDFS DataNode), zeroCopyBytesRead (The number of bytes read through HDFS zero copy) are available and can be used to troubleshoot short-circuit read issues.

For more on short-circuit reads, see Colin’s old blog on rollout, How Improved Short-Circuit Local Reads Bring Better Performance and Security to Hadoop. The HDFS-347 issue also makes for an interesting read showing the HDFS community at its best (caveat a few comments).

46.5. JVM Tuning

46.5.1. Tune JVM GC for low collection latencies

  • Use the CMS collector: -XX:+UseConcMarkSweepGC

  • Keep eden space as small as possible to minimize average collection time. Example:

    -XX:CMSInitiatingOccupancyFraction=70

  • Optimize for low collection latency rather than throughput: -Xmn512m

  • Collect eden in parallel: -XX:+UseParNewGC

  • Avoid collection under pressure: -XX:+UseCMSInitiatingOccupancyOnly

  • Limit per request scanner result sizing so everything fits into survivor space but doesn’t tenure. In hbase-site.xml, set hbase.client.scanner.max.result.size to 1/8th of eden space (with -Xmn512m this is ~51MB )

  • Set max.result.size x handler.count less than survivor space

46.5.2. OS-Level Tuning

  • Turn transparent huge pages (THP) off:

    echo never > /sys/kernel/mm/transparent_hugepage/enabled
echo never > /sys/kernel/mm/transparent_hugepage/defrag

  • Set vm.swappiness = 0

  • Set vm.min_free_kbytes to at least 1GB (8GB on larger memory systems)

  • Disable NUMA zone reclaim with vm.zone_reclaim_mode = 0

47. Special Cases

47.1. For applications where failing quickly is better than waiting

  • In hbase-site.xml on the client side, set the following parameters:

    • Set hbase.client.pause = 1000

    • Set hbase.client.retries.number = 3

    • If you want to ride over splits and region moves, increase hbase.client.retries.number substantially (>= 20)

    • Set the RecoverableZookeeper retry count: zookeeper.recovery.retry = 1 (no retry)

  • In hbase-site.xml on the server side, set the Zookeeper session timeout for detecting server failures: zookeeper.session.timeout ⇐ 30 seconds (20-30 is good).

47.2. For applications that can tolerate slightly out of date information

HBase timeline consistency (HBASE-10070) With read replicas enabled, read-only copies of regions (replicas) are distributed over the cluster. One RegionServer services the default or primary replica, which is the only replica that can service writes. Other RegionServers serve the secondary replicas, follow the primary RegionServer, and only see committed updates. The secondary replicas are read-only, but can serve reads immediately while the primary is failing over, cutting read availability blips from seconds to milliseconds. Phoenix supports timeline consistency as of 4.4.0 Tips:

  • Deploy HBase 1.0.0 or later.

  • Enable timeline consistent replicas on the server side.

  • Use one of the following methods to set timeline consistency:

    • Use ALTER SESSION SET CONSISTENCY = 'TIMELINE’

    • Set the connection property Consistency to timeline in the JDBC connect string

47.3. More Information

See the Performance section perf.schema for more information about 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. It is provided by Apache Hadoop. MapReduce itself is out of the scope of this document. A good place to get started with MapReduce is https://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 mode. 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 org.apache.hadoop.hbase.mapreduce. In the notes below, we refer to o.a.h.h.mapreduce but replace with o.a.h.h.mapred if that is what you are using.

48. 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 could edit $HADOOP_HOME/conf/hadoop-env.sh and add hbase dependencies to the HADOOP_CLASSPATH variable. Neither of these approaches is recommended because it will pollute your Hadoop install with HBase references. It also requires you restart the Hadoop cluster before Hadoop can use the HBase data.

The recommended approach is to let HBase add its dependency jars 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 and from here they’ll be picked up and bundled into the fat job jar deployed to the MapReduce cluster. A basic trick just passes the full hbase classpath — all hbase and dependent jars as well as configurations — to the mapreduce job runner letting hbase utility pick out from the full-on classpath what it needs adding them to the MapReduce job configuration (See the source at TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done).

The following example runs the bundled HBase RowCounter MapReduce job against a table named usertable. It sets into HADOOP_CLASSPATH the jars hbase needs to run in an MapReduce context (including configuration files such as hbase-site.xml). Be sure to use the correct version of the HBase JAR for your system; replace the VERSION string in the below command line w/ the version of your local hbase install. The backticks (` symbols) cause the shell to execute the sub-commands, setting the output of hbase classpath into 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-mapreduce-VERSION.jar \
  org.apache.hadoop.hbase.mapreduce.RowCounter usertable

The above command will launch a row counting mapreduce job against the hbase cluster that is pointed to by your local configuration on a cluster that the hadoop configs are pointing to.

The main for the hbase-mapreduce.jar is a Driver that lists a few basic mapreduce tasks that ship with hbase. For example, presuming your install is hbase 2.0.0-SNAPSHOT:

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \
  ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-2.0.0-SNAPSHOT.jar
An example program must be given as the first argument.
Valid program names are:
  CellCounter: Count cells in HBase table.
  WALPlayer: Replay WAL files.
  completebulkload: Complete a bulk data load.
  copytable: Export a table from local cluster to peer cluster.
  export: Write table data to HDFS.
  exportsnapshot: Export the specific snapshot to a given FileSystem.
  import: Import data written by Export.
  importtsv: Import data in TSV format.
  rowcounter: Count rows in HBase table.
  verifyrep: Compare the data from tables in two different clusters. WARNING: It doesn't work for incrementColumnValues'd cells since the timestamp is changed after being appended to the log.

You can use the above listed shortnames for mapreduce jobs as in the below re-run of the row counter job (again, presuming your install is hbase 2.0.0-SNAPSHOT):

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` \
  ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/lib/hbase-mapreduce-2.0.0-SNAPSHOT.jar \
  rowcounter usertable

You might find the more selective hbase mapredcp tool output of interest; it lists the minimum set of jars needed to run a basic mapreduce job against an hbase install. It does not include configuration. You’ll probably need to add these if you want your MapReduce job to find the target cluster. You’ll probably have to also add pointers to extra jars once you start to do anything of substance. Just specify the extras by passing the system propery -Dtmpjars when you run hbase mapredcp.

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=${HBASE_BUILD_HOME}/hbase-mapreduce/target/hbase-mapreduce-VERSION-SNAPSHOT.jar:`${HBASE_BUILD_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_BUILD_HOME}/hbase-mapreduce/target/hbase-mapreduce-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.

49. 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.

50. 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-mapreduce-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-mapreduce-VERSION.jar rowcounter myTable

51. 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.

52. 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.

53. 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.

54. Map-Task Splitting

54.1. 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.

54.2. 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.

55. HBase MapReduce Examples

55.1. 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.
   }
}

55.2. 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 (Cell cell : result.listCells()) {
        put.add(cell);
      }
      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.

55.3. HBase MapReduce Read/Write Example With Multi-Table Output

TODO: example for MultiTableOutputFormat.

55.4. 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);
  }
}

55.5. 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));
  }
}

55.6. 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.

55.7. 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.

56. 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
  }

57. 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.

58. 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@hbase.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.

59. Web UI Security

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.

59.1. 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(Please prepare SSL certificate and ssl configuration file in advance). 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.

59.2. Disable cache in HBase UI

Set the following configuration in hbase-site to set max age to zero and disable cache for the web UI:

<property>
  <name>hbase.http.filter.no-store.enable</name>
  <value>true</value>
</property>

59.3. 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>

59.4. Defining administrators of the Web UI

In the previous section, we cover how to enable authentication for the Web UI via SPNEGO. However, some portions of the Web UI could be used to impact the availability and performance of an HBase cluster. As such, it is desirable to ensure that only those with proper authority can interact with these sensitive endpoints.

HBase allows the adminstrators to be defined via a list of usernames or groups in hbase-site.xml

<property>
  <name>hbase.security.authentication.spnego.admin.users</name>
  <value></value>
</property>
<property>
  <name>hbase.security.authentication.spnego.admin.groups</name>
  <value></value>
</property>

The usernames are those which the Kerberos identity maps to, given the Hadoop auth_to_local rules in core-site.xml. The groups here are the Unix groups associated with the mapped usernames.

Consider the following scenario to describe how the configuration properties operate. Consider three users which are defined in the Kerberos KDC:

  • alice@COMPANY.COM

  • bob@COMPANY.COM

  • charlie@COMPANY.COM

The default Hadoop auth_to_local rules map these principals to the "shortname":

  • alice

  • bob

  • charlie

Unix groups membership define that alice is a member of the group admins. bob and charlie are not members of the admins group.

<property>
  <name>hbase.security.authentication.spnego.admin.users</name>
  <value>charlie</value>
</property>
<property>
  <name>hbase.security.authentication.spnego.admin.groups</name>
  <value>admins</value>
</property>

Given the above configuration, alice is allowed to access sensitive endpoints in the Web UI as she is a member of the admins group. charlie is also allowed to access sensitive endpoints because he is explicitly listed as an admin in the configuration. bob is not allowed to access sensitive endpoints because he is not a member of the admins group nor is listed as an explicit admin user via hbase.security.authentication.spnego.admin.users, but can still use any non-sensitive endpoints in the Web UI.

If it doesn’t go without saying: non-authenticated users cannot access any part of the Web UI.

59.5. Using LDAP authentication with Web UIs

LDAP authentication to HBase Web UIs can be enabled via configuring LDAP with the hbase.security.authentication.ui property in hbase-site.xml. The hbase.http.filter.initializers property also needs to have the AuthenticationFilterInitializer class.

IMPORTANT: A LDAP server must be configured and running. When TLS is enabled for communication with LDAP server (either via ldaps scheme or ‘start TLS’ extension), configure the public certificate of the LDAP server in the local truststore. The LDAP authentication mechanism uses HTTP Basic authentication scheme to verify user specified credentials against a configured LDAP (or Active Directory) server. The authentication filter must be configured with the following init parameters:

<property>
  <name>hbase.security.authentication.ui</name>
  <value>ldap</value>
  <description>Controls what kind of authentication should be used for the HBase web UIs.</description>
</property>
<property>
  <name>hbase.http.filter.initializers</name>
  <value>org.apache.hadoop.hbase.http.lib.AuthenticationFilterInitializer</value>
  <description>Comma separated class names corresponding to the Filters that will be initialized.
  Then, the Filters will be applied to all user facing jsp and servlet web pages.</description>
</property>
<property>
  <name>hadoop.http.authentication.type</name>
  <value>ldap</value>
  <description>Defines authentication used for the HTTP web-consoles in Hadoop ecosystem.</description>
</property>

A number of properties exist to configure LDAP authentication for the web server:

<property>
  <name>hadoop.http.authentication.ldap.binddomain</name>
  <value>EXAMPLE.COM</value>
  <description>The LDAP bind domain value to be used with the LDAP server. This property is optional
   and useful only in case of Active Directory server (e.g. example.com).</description>
</property>
<property>
  <name>hadoop.http.authentication.ldap.providerurl</name>
  <value>ldap://ldap-server-host:8920</value>
  <description>The url of the LDAP server.</description>
</property>
<property>
  <name>hadoop.http.authentication.ldap.enablestarttls</name>
  <value>false</value>
  <description>A boolean value used to define if the LDAP server supports ‘StartTLS’ extension.</description>
</property>
<property>
  <name>hadoop.http.authentication.ldap.basedn</name>
  <value>ou=users,dc=example,dc=com</value>
  <description>The base distinguished name (DN) to be used with the LDAP server. This value is
  appended to the provided user id for authentication purpose. This property is not useful in case
  of Active Directory server.</description>
</property>

59.6. Other UI security-related configuration

While it is a clear anti-pattern for HBase developers, the developers acknowledge that the HBase configuration (including Hadoop configuration files) may contain sensitive information. As such, a user may find that they do not want to expose the HBase service-level configuration to all authenticated users. They may configure HBase to require a user must be an admin to access the service-level configuration via the HBase UI. This configuration is false by default (any authenticated user may access the configuration).

Users who wish to change this would set the following in their hbase-site.xml:

<property>
  <name>hbase.security.authentication.ui.config.protected</name>
  <value>true</value>
</property>

To disable showing stack traces in HBase UI for hiding sensitive information, set the following in hbase-site:

<property>
  <name>hbase.ui.show-stack-traces</name>
  <value>false</value>
</property>

60. 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.

60.1. 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.

60.2. 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.

60.3. 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>

Before 2.2.0 version, 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.

Since 2.2.0, client can specify the following configurations in hbase-site.xml:

<property>
  <name>hbase.client.keytab.file</name>
  <value>/local/path/to/client/keytab</value>
</property>

<property>
  <name>hbase.client.keytab.principal</name>
  <value>foo@EXAMPLE.COM</value>
</property>

Then application can automatically do the login and credential renewal jobs without client interference.

It’s optional feature, client, who upgrades to 2.2.0, can still keep their login and credential renewal logic already did in older version, as long as keeping hbase.client.keytab.file and hbase.client.keytab.principal are unset.

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);
     Table table = connection.getTable(TableName.valueOf(tablename))) {
  .... do your stuff
}

Expect a ~10% performance penalty for encrypted communication.

60.4. 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.

60.5. 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.

60.6. 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.

60.7. 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.

60.8. 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.

61. 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.

61.1. 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.

61.2. Prerequisites

None

61.3. 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.

61.4. 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.

61.4.1. 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.

61.4.2. 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.

62. Transport Level Security (TLS) in HBase RPC communication

Since version 2.6.0 HBase supports TLS encryption in server-client and Master-RegionServer communication. Transport Layer Security (TLS) is a standard cryptographic protocol designed to provide communications security over a computer network. HBase TLS implementation works exactly how secure websites are accessed via https prefix in a web browser: once established all communication on the channel will be securely hidden from malicious access.

The encryption works at the transport level which means it’s independent of the configured authentication method. Secure client access mentioned in the previous section requires Kerberos to be configured and used in HBase authentication, while TLS can be configured with any other SASL mechanism or even with simple client access methods, effectively preventing attackers from eavesdropping the communication. No Kerberos KDC or other complicated infrastructure required.

HBase TLS is based on the Netty library therefore it only works with Netty client and server RPC implementations. Netty’s powerful SSL implementation is a great foundation for highly secure and performant communication providing the latest and greatest cryptographic solution at all times.

Since Region Servers effectively work as clients from Master’s perspective, TLS supports encrypted communication between cluster members too.

From version 2.6.0 HBase supports the Hadoop CredentialProvider API to avoid storing sensitive information in HBase configuration files. The recommended way of storing keystore / truststore passwords is to use one of the supported credential providers e.g. the local jceks file provider. You can find more information about how to setup credential providers in the Hadoop documentation.

The CLI interface for accessing the Hadoop Credential Shell is also available in HBase CLI. Type hbase credential to get help.

62.1. Server side configuration

We need to set up Java key store for the server. Key store is the list of private keys that a server can use to configure TLS encryption. See TLS wikipedia page for further details of the protocol. Add the following configuration to hbase-site.xml on Master, Region Servers and HBase clients:

<property>
  <name>hbase.server.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.rpc.tls.keystore.location</name>
  <value>/path/to/keystore.jks</value>
</property>

Use hbase.rpc.tls.keystore.password alias to retrieve key store password from Hadoop credential provider.

The supported storefile formats are based on the registered security providers and the loader can be autodetected from the file extension. If needed, the file format can be explicitly specified with the hbase.rpc.tls.keystore.type property.

62.2. Client side configuration

We need to configure trust store for the client. Trust store contains the list of certificates that the client should trust when doing the handshake with the server. Add the following to hbase-site.xml.

<property>
  <name>hbase.client.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.rpc.tls.truststore.location</name>
  <value>/path/to/truststore.jks</value>
</property>

Use hbase.rpc.tls.truststore.password alias to retrieve trust store password from Hadoop credential provider.

The supported storefile formats are based on the registered security providers and the loader can be autodetected from the file extension. If needed, the file format can be explicitly specified with the hbase.rpc.tls.truststore.type property.

However, specifying a trust store is not always required. Standard JDK implementations are shipped with a standard list of trusted certificates (the certificates of Certificate Authorities) and if your private key is provided by one of them, you don’t need to configure your clients to trust it. Similarly to an internet browser, you don’t need to set up the certificates of every single website you’re planning to visit. Later in this documentation we’ll walk through the steps of creating self-signed certificates which requires a trust store setup.

You can check the list of public certificate authorities shipped with your JDK implementation:

keytool -keystore $JAVA_HOME/jre/lib/security/cacerts -list

Password is empty by default.

62.3. Creating self-signed certificates

While obtaining globally trusted certificates from Certificate Authorities is convenient, it’s perfectly valid to generate your own private/public keypairs and set them up specifically for the HBase cluster. Especially if you don’t want to enable public access to the cluster, paying money for a certificate doesn’t make sense.

Follow the following steps to generate self-signed certificates.

  1. Create SSL key store JKS to store local credentials

Please note that the alias (-alias) and the distinguished name (-dname) must match the hostname of the machine that is associated with, otherwise hostname verification won’t work.

keytool -genkeypair -alias $(hostname -f) -keyalg RSA -keysize 2048 -dname "cn=$(hostname -f)" -keypass password -keystore keystore.jks -storepass password

At the end of this operation you’ll have as many key store files as many servers you have in your cluster. Each cluster member will have its own key store.

  1. Extract the signed public key (certificate) from each key store

keytool -exportcert -alias $(hostname -f) -keystore keystore.jks -file $(hostname -f).cer -rfc

  1. Create SSL trust store JKS containing certificates for the clients

The same truststore (storing all accepted certs) should be shared on participants of the cluster. You need to use different aliases to store multiple certificates in the same truststore. Name of the aliases doesn’t matter.

keytool -importcert -alias [host1..3] -file [host1..3].cer -keystore truststore.jks -storepass password

62.4. Upgrading existing non-TLS cluster with no downtime

Here are the steps needed to upgrade an already running HBase cluster to TLS without downtime by taking advantage of port unification functionality. There’s a property on server side called hbase.server.netty.tls.supportplaintext which makes possible to accept TLS and plaintext connections on the same socket port.

  1. Create the necessary key stores and trust stores for all server participants as described in the previous section.

  2. Enable secure communication on the Master node in server-only mode with plaintext support.

<property>
  <name>hbase.client.netty.tls.enabled</name>
  <value>false</value>
</property>
<property>
  <name>hbase.server.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.server.netty.tls.supportplaintext</name>
  <value>true</value>
</property>
...keystore / truststore setup ...

Restart the Master. Master now accepts both TLS/non-TLS connections and works with non-TLS in client mode.

  1. Enable secure communication on the Region Servers in both server and client mode with plaintext support. Client mode here will ensure that RegionServer’s communication to Master is encrypted.

Replication

If you have read replicas enabled in your cluster or replication between two different clusters, you have to break this into two steps. Secure communication has to be enabled on the server side first with plaintext support and once all Region Servers are upgraded you can repeat the upgrade by enabling client side as well.

You have to prepare all Region Servers for secure communication before upgrading the client side.

 
<property>
  <name>hbase.client.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.server.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.server.netty.tls.supportplaintext</name>
  <value>true</value>
</property>
...keystore / truststore setup ...

Restart Region Servers in rolling restart fashion. They send requests with TLS and accept both TLS and non-TLS communication.

  1. Enable secure communication on the clients.

<property>
  <name>hbase.client.netty.tls.enabled</name>
  <value>true</value>
</property>
...truststore setup ...

  1. Enable client-mode TLS on master and disable plaintext mode.

<property>
  <name>hbase.client.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.server.netty.tls.enabled</name>
  <value>true</value>
</property>
<property>
  <name>hbase.server.netty.tls.supportplaintext</name>
  <value>false</value>
</property>

Restart Master.

  1. Disable plaintext communication on the Region Servers by removing supportplaintext property. Restart RSs in rolling restart fashion.

Once hbase.client.netty.tls.enabled is enabled on the server side, the cluster will only be able to communicate with other clusters which have TLS enabled. For example, this would impact inter-cluster replication.

62.5. Enable automatic certificate reloading

Certificates usually expire after some time to improve security. In this case we need to replace them by modifying Keystore / Truststore files and HBase processes have to be restarted. In order to avoid that you can enable automatic file change detection and certificate reloading with the following option. Default: false.

<property>
  <name>hbase.rpc.tls.certReload</name>
  <value>true</value>
</property>

62.6. Additional configuration

62.6.1. Enabled protocols

Comma-separated list of TLS protocol versions to enable. Default is empty.

<property>
  <name>hbase.client.netty.tls.enabledProtocols</name>
  <value>TLSv1.2,TLSv1.3</value>
</property>

62.6.2. Default protocol

Set the default TLS protocol version to use. Default is TLSv1.2. Use this protocol if enabled protocols is not defined.

<property>
  <name>hbase.client.netty.tls.protocol</name>
  <value>TLSv1.2</value>
</property>

62.6.3. Enabled cipher suites

List of enabled cipher suites in TLS protocol. Useful when you want to disable certain cipher suites due to recent security concerns. Default value is a mix of CBC and GCM ciphers. Due to performance reasons we prefer CBC ciphers for Java 8 and GCM ciphers for Java 9+.

<property>
  <name>hbase.client.netty.tls.ciphersuites</name>
  <value>TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256</value>
</property>

62.6.4. Certificate Revocation Checking

There’s a built-in mechanism in JDK’s TrustManager which automatically checks certificates for revocation. See Managing Server Certificates. Disabled by default.

<property>
  <name>hbase.client.netty.tls.clr</name>
  <value>false</value>
</property>

62.6.5. Online Certificate Status Protocol

Enables OCSP stapling. Please note that not all SSLProvider implementations support OCSP stapling and an exception will be thrown upon. Disabled by default.

<property>
  <name>hbase.client.netty.tls.ocsp</name>
  <value>false</value>
</property>

62.6.6. Client handshake timeout

Set the TLS client handshake timeout is milliseconds. Default is 5 seconds.

<property>
  <name>hbase.client.netty.tls.handshaketimeout</name>
  <value>5000</value>
</property>

63. 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).

63.1. 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.

63.2. 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.

64. 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.

64.1. 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.

64.1.1. 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.

Coprocessors that run server-side on RegionServers can perform get and set operations on cell Tags. Tags are stripped out at the RPC layer before the read response is sent back, so clients do not see these tags. Tag compression is not supported when using WAL encryption.

64.2. Access Control Labels (ACLs)

64.2.1. 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 15. 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.security.authorization</name>
  <value>true</value>
</property>
<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 13. 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"

      If you need to enable cell acl,the hfile.format.version option in hbase-site.xml should be greater than or equal to 3,and the hbase.security.access.early_out option should be set to false.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 14. API

    The following example shows how to grant access at the table level.

    public static void grantOnTable(final HBaseTestingUtil 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 {
      try (Connection connection = ConnectionFactory.createConnection(util.getConfiguration())) {
        connection.getAdmin().grant(new UserPermission(user, Permission.newBuilder(table)
            .withFamily(family).withQualifier(qualifier).withActions(actions).build()),
          false);
      }
      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 15. Revoking Access To a Table
    public static void revokeFromTable(final HBaseTestingUtil 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 {
      try (Connection connection = ConnectionFactory.createConnection(util.getConfiguration())) {
        connection.getAdmin().revoke(new UserPermission(user, Permission.newBuilder(table)
            .withFamily(family).withQualifier(qualifier).withActions(actions).build()));
      }
      return null;
    }
  });
}

  4. Showing a User’s Effective Permissions

    HBase Shell
    hbase> user_permission 'user'

hbase> user_permission '.*'

hbase> user_permission JAVA_REGEX
Example 16. 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");
  }
}

64.3. 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 16. 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.

64.3.1. 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.security.authorization</name>
  <value>true</value>
</property>
<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.

64.3.2. 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

    HBase Shell
    hbase> add_labels [ 'admin', 'service', 'developer', 'test' ]
    Example 17. 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

    HBase Shell
    hbase> set_auths 'service', [ 'service' ]
    hbase> set_auths 'testuser', [ 'test' ]
    hbase> set_auths 'qa', [ 'test', 'developer' ]
    hbase> set_auths '@qagroup', [ 'test' ]
    Example 18. 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

    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 19. 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.

    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 20. 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();
    }
  }

64.3.3. 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 VisibilityClient.setAuths() method.

You can specify a different authorization during the Scan or Get, by passing the AUTHORIZATIONS option in HBase Shell, or the Scan.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.

HBase Shell
hbase> get_auths 'myUser'
hbase> scan 'table1', AUTHORIZATIONS => ['private']
Example 21. 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;
}
...

64.3.4. 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.

64.3.5. 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.security.authorization</name>
  <value>true</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop.hbase.security.visibility.VisibilityController$VisibilityReplication</value>
</property>

64.4. 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.

64.4.1. 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.

64.4.2. Enable or disable the feature.

The "Transparent Encryption of Data At Rest" feature is enabled by default, meaning the users can define tables with column families where the HFiles and WAL files will be encrypted by HBase, assuming the feature is properly configured (see Server-Side Configuration).

In some cases (e.g. due to custom security policies), the operator of the HBase cluster might wish to only rely on an encryption at rest mechanism outside of HBase (e.g. those offered by HDFS) and wants to ensure that HBase’s encryption at rest system is inactive. Since HBASE-25181 it is possible to explicitly disable HBase’s own encryption by setting hbase.crypto.enabled to false. This configuration is true by default. If it is set to false, the users won’t be able to create any table (column family) with HFile and WAL file encryption and the related create table shell (or API) commands will fail if they try.

64.4.3. 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>
Starting from 2.6.0, the hbase.regionserver.hlog.reader.impl and hbase.regionserver.hlog.writer.impl configurations are removed, you do not need to specify them any more. Just set hbase.regionserver.wal.encryption to true is enough to enable WAL encryption.

  1. (Optional) Configure encryption key hash algorithm.

    Since HBASE-25181 it is possible to use custom encryption key hash algorithm instead of the default MD5 algorithm. This hash is needed to verify the secret key during decryption. The MD5 algorithm is considered weak, and can not be used in some (e.g. FIPS compliant) clusters.

    The hash is set via the configuration option hbase.crypto.key.hash.algorithm. It should be set to a JDK MessageDigest algorithm like "MD5", "SHA-384" or "SHA-512". The default is "MD5" for backward compatibility. An example of this configuration parameter on a FIPS-compliant cluster:

    <property>
  <name>hbase.crypto.key.hash.algorithm</name>
  <value>SHA-384</value>
</property>

  2. 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.

  3. Restart your cluster.

    Distribute the new configuration file to all nodes and restart your cluster.

64.4.4. 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 compacted new HFiles will be encrypted. However, depending on the compaction settings, it is possible that not all the HFiles will be rewritten during a major compaction and there still might remain some old unencrypted HFiles. Also please note, that the snapshots are immutable. So the snapshots taken before you enabled the encryption will still contain the unencrypted HFiles.

Rotate the Data Key

To rotate the data key, first change the ColumnFamily key in the column descriptor, then trigger a major compaction. Until the compaction completes, the old HFiles will still be readable using the old key. During compaction, the compacted HFiles will be re-encrypted using the new data key. However, depending on the compaction settings, it is possible that not all the HFiles will be rewritten during a major compaction and there still might remain some old HFiles encrypted with the old key. Also please note, that the snapshots are immutable. So the snapshots taken before the changing of the encryption key will still contain the HFiles written 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.

64.5. 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.security.authorization</name>
  <value>true</value>
</property>
<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>

64.6. Secure Enable

After hbase-2.x, the default 'hbase.security.authorization' changed. Before hbase-2.x, it defaulted to true, in later HBase versions, the default became false. So to enable hbase authorization, the following propertie must be configured in hbase-site.xml. See HBASE-19483;

<property>
  <name>hbase.security.authorization</name>
  <value>true</value>
</property>

65. 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 22. 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,@superuser-group</value>
</property>
<!-- Coprocessors for ACLs and Visibility Tags -->
<property>
  <name>hbase.security.authorization</name>
  <value>true</value>
</property>
<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</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>
Starting from 2.6.0, the hbase.regionserver.hlog.reader.impl and hbase.regionserver.hlog.writer.impl configurations are removed, you do not need to specify them any more. Just set hbase.regionserver.wal.encryption to true is enough to enable WAL encryption.
Example 23. 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

66. Overview

66.1. 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.

66.2. 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.

66.3. 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.

67. 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.

67.1. hbase:meta

The hbase:meta table (previously called .META.) keeps a list of all regions in the system, and the location of hbase:meta is 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 RegionInfo.parseFrom utility.

67.2. 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.

68. 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

68.1. Cluster Connections

The API changed in HBase 1.0. For connection configuration information, see Client configuration and dependencies connecting to an HBase cluster.

68.1.1. 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.

68.1.2. 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 24. Pre-Creating a Connection
// Create a connection to the cluster.
Configuration conf = HBaseConfiguration.create();
try (Connection connection = ConnectionFactory.createConnection(conf);
     Table table = connection.getTable(TableName.valueOf(tablename))) {
  // use table as needed, the table returned is lightweight
}
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-6580, or HConnection, which is deprecated in HBase 1.0 by Connection. Please use Connection instead.

68.2. 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.

In HBase 2.0 and later, HTable does not use BufferedMutator to execute the Put operation. Refer to HBASE-18500 for more information.

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.

68.3. Asynchronous Client

It is a new API introduced in HBase 2.0 which aims to provide the ability to access HBase asynchronously.

You can obtain an AsyncConnection from ConnectionFactory, and then get a asynchronous table instance from it to access HBase. When done, close the AsyncConnection instance(usually when your program exits).

For the asynchronous table, most methods have the same meaning with the old Table interface, expect that the return value is wrapped with a CompletableFuture usually. We do not have any buffer here so there is no close method for asynchronous table, you do not need to close it. And it is thread safe.

There are several differences for scan:

  • There is still a getScanner method which returns a ResultScanner. You can use it in the old way and it works like the old ClientAsyncPrefetchScanner.

  • There is a scanAll method which will return all the results at once. It aims to provide a simpler way for small scans which you want to get the whole results at once usually.

  • The Observer Pattern. There is a scan method which accepts a ScanResultConsumer as a parameter. It will pass the results to the consumer.

Notice that AsyncTable interface is templatized. The template parameter specifies the type of ScanResultConsumerBase used by scans, which means the observer style scan APIs are different. The two types of scan consumers are - ScanResultConsumer and AdvancedScanResultConsumer.

ScanResultConsumer needs a separate thread pool which is used to execute the callbacks registered to the returned CompletableFuture. Because the use of separate thread pool frees up RPC threads, callbacks are free to do anything. Use this if the callbacks are not quick, or when in doubt.

AdvancedScanResultConsumer executes callbacks inside the framework thread. It is not allowed to do time consuming work in the callbacks else it will likely block the framework threads and cause very bad performance impact. As its name, it is designed for advanced users who want to write high performance code. See org.apache.hadoop.hbase.client.example.HttpProxyExample for how to write fully asynchronous code with it.

68.4. Asynchronous Admin

You can obtain an AsyncConnection from ConnectionFactory, and then get a AsyncAdmin instance from it to access HBase. Notice that there are two getAdmin methods to get a AsyncAdmin instance. One method has one extra thread pool parameter which is used to execute callbacks. It is designed for normal users. Another method doesn’t need a thread pool and all the callbacks are executed inside the framework thread so it is not allowed to do time consuming works in the callbacks. It is designed for advanced users.

The default getAdmin methods will return a AsyncAdmin instance which use default configs. If you want to customize some configs, you can use getAdminBuilder methods to get a AsyncAdminBuilder for creating AsyncAdmin instance. Users are free to only set the configs they care about to create a new AsyncAdmin instance.

For the AsyncAdmin interface, most methods have the same meaning with the old Admin interface, expect that the return value is wrapped with a CompletableFuture usually.

For most admin operations, when the returned CompletableFuture is done, it means the admin operation has also been done. But for compact operation, it only means the compact request was sent to HBase and may need some time to finish the compact operation. For rollWALWriter method, it only means the rollWALWriter request was sent to the region server and may need some time to finish the rollWALWriter operation.

For region name, we only accept byte[] as the parameter type and it may be a full region name or a encoded region name. For server name, we only accept ServerName as the parameter type. For table name, we only accept TableName as the parameter type. For list* operations, we only accept Pattern as the parameter type if you want to do regex matching.

68.5. External Clients

Information on non-Java clients and custom protocols is covered in Apache HBase External APIs

68.6. Master Registry (new as of 2.3.0)

Starting from 2.5.0, MasterRegistry is deprecated. It’s functionality is completely superseded by the RpcConnectionRegistry. Please see Rpc Connection Registry (new as of 2.5.0) for more details.

Client internally works with a connection registry to fetch the metadata needed by connections. This connection registry implementation is responsible for fetching the following metadata.

  • Active master address

  • Current meta region(s) locations

  • Cluster ID (unique to this cluster)

This information is needed as a part of various client operations like connection set up, scans, gets, etc. Traditionally, the connection registry implementation has been based on ZooKeeper as the source of truth and clients fetched the metadata directly from the ZooKeeper quorum. HBase 2.3.0 introduces a new connection registry implementation based on direct communication with the Masters. With this implementation, clients now fetch required metadata via master RPC end points instead of maintaining connections to ZooKeeper. This change was done for the following reasons.

  • Reduce load on ZooKeeper since that is critical for cluster operation.

  • Holistic client timeout and retry configurations since the new registry brings all the client operations under HBase rpc framework.

  • Remove the ZooKeeper client dependency on HBase client library.

This means:

  • At least a single active or stand by master is needed for cluster connection setup. Refer to Runtime Impact for more details.

  • Master can be in a critical path of read/write operations, especially if the client metadata cache is empty or stale.

  • There is higher connection load on the masters that before since the clients talk directly to HMasters instead of ZooKeeper ensemble`

To reduce hot-spotting on a single master, all the masters (active & stand-by) expose the needed service to fetch the connection metadata. This lets the client connect to any master (not just active). Both ZooKeeper-based and Master-based connection registry implementations are available in 2.3+. For 2.x and earlier, the ZooKeeper-based implementation remains the default configuration. For 3.0.0, RpcConnectionRegistry becomes the default configuration, as the alternate to MasterRegistry.

Change the connection registry implementation by updating the value configured for hbase.client.registry.impl. To explicitly enable the ZooKeeper-based registry, use

<property>
    <name>hbase.client.registry.impl</name>
    <value>org.apache.hadoop.hbase.client.ZKConnectionRegistry</value>
</property>

To explicitly enable the Master-based registry, use

<property>
    <name>hbase.client.registry.impl</name>
    <value>org.apache.hadoop.hbase.client.MasterRegistry</value>
</property>

68.6.1. MasterRegistry RPC hedging

MasterRegistry implements hedging of connection registry RPCs across active and stand-by masters. This lets the client make the same request to multiple servers and which ever responds first is returned back to the client immediately. This improves performance, especially when a subset of servers are under load. The hedging fan out size is configurable, meaning the number of requests that are hedged in a single attempt, using the configuration key hbase.client.master_registry.hedged.fanout in the client configuration. It defaults to 2. With this default, the RPCs are tried in batches of 2. The hedging policy is still primitive and does not adapt to any sort of live rpc performance metrics.

68.6.2. Additional Notes

  • Clients hedge the requests in a randomized order to avoid hot-spotting a single master.

  • Cluster internal connections (masters <-> regionservers) still use ZooKeeper based connection registry.

  • Cluster internal state is still tracked in Zookeeper, hence ZK availability requirements are same as before.

  • Inter cluster replication still uses ZooKeeper based connection registry to simplify configuration management.

For more implementation details, please refer to the design doc and HBASE-18095.

68.7. Rpc Connection Registry (new as of 2.5.0)

As said in the Master Registry (new as of 2.3.0) section, there are some disadvantages and limitations for MasterRegistry, especially that it puts master in the critical path of read/write operations. In order to address these problems, we introduced a more generic RpcConnectionRegistry.

It is also rpc based, like MasterRegistry, with several differences

  1. Region server also implements the necessary rpc service, so you can config any nodes in the cluster as bootstrap nodes, not only masters

  2. Support refreshing bootstrap nodes, for spreading loads across the nodes in the cluster, and also remove the dead nodes in bootstrap nodes.

To explicitly enable the rpc-based registry, use

<property>
    <name>hbase.client.registry.impl</name>
    <value>org.apache.hadoop.hbase.client.RpcConnectionRegistry</value>
</property>

To configure the bootstrap nodes, use

<property>
    <name>hbase.client.bootstrap.servers</name>
    <value>server1:16020,server2:16020,server3:16020</value>
</property>

If not configured, we will fallback to use master addresses as the bootstrap nodes.

RpcConnectionRegistry is available in 2.5+, and becomes the default client registry implementation in 3.0.0.

68.7.1. RpcConnectionRegistry RPC hedging

Hedged read is still supported, the configuration key is now hbase.client.bootstrap.hedged.fanout, and its default value is still 2.

68.7.2. RpcConnectionRegistry bootstrap nodes refreshing

There are basically two reasons for us to refresh the bootstrap nodes

  • Periodically. This is for spreading loads across the nodes in the cluster. There are two configurations

    1. hbase.client.bootstrap.refresh_interval_secs: the refresh interval in seconds, default 300. A value less than or equal to zero means disable refreshing.

    2. hbase.client.bootstrap.initial_refresh_delay_secs: the initial refresh interval in seconds, the default value is 1/10 of hbase.client.bootstrap.refresh_interval_secs. The reason why we want to introduce a separated configuration for the delay for first refreshing is that, as end users could configure any nodes in a cluster as the initial bootstrap nodes, it is possible that different end users will configure the same machine which makes the machine over load. So we should have a shorter delay for the initial refresh, to let users quickly switch to the bootstrap nodes we want them to connect to.

  • When there is a connection error while requesting the nodes, we will refresh immediately, to remove the dead nodes. To avoid putting too much pressure to the cluster, there is a configuration hbase.client.bootstrap.min_secs_between_refreshes, to control the minimum interval between two refreshings. The default value is 60, but notice that, if you change hbase.client.bootstrap.refresh_interval_secs to a small value, you need to make sure to also change hbase.client.bootstrap.min_secs_between_refreshes to a value smaller than hbase.client.bootstrap.refresh_interval_secs, otherwise an IllegalArgumentException will be thrown.

(Advanced) In case of any issues with the rpc/master based registry, use the following configuration to fallback to the ZooKeeper based connection registry implementation. 
<property>
    <name>hbase.client.registry.impl</name>
    <value>org.apache.hadoop.hbase.client.ZKConnectionRegistry</value>
</property>

68.8. Connection URI

Starting from 2.7.0, we add the support for specifying the connection information for a HBase cluster through an URI, which we call a "connection URI". And we’ve added several methods in ConnectionFactory to let you get a connection to the cluster specified by the URI. It looks like:

  URI uri = new URI("hbase+rpc://server1:16020,server2:16020,server3:16020");
  try (Connection conn = ConnectionFactory.createConnection(uri)) {
    ...
  }

68.8.1. Supported Schemes

Currently there are two schemes supported, hbase+rpc for RpcConnectionRegistry and hbase+zk for ZKConnectionRegistry. MasterRegistry is deprecated so we do not expose it through connection URI.

For hbase+rpc, it looks like

hbase+rpc://server1:16020,server2:16020,server3:16020

The authority part server1:16020,server2:16020,server3:16020 specifies the bootstrap nodes and their rpc ports, i.e, the configuration value for hbase.client.bootstrap.servers in the past.

For hbase+zk, it looks like

hbase+zk://zk1:2181,zk2:2181,zk3:2181/hbase

The authority part zk1:2181,zk2:2181,zk3:2181 is the zk quorum, i.e, the configuration value for hbase.zookeeper.quorum in the past. The path part /hbase is the znode parent, i.e, the configuration value for zookeeper.znode.parent in the past.

68.8.2. Specify Configuration through URI Queries

To let users fully specify the connection information through a connection URI, we support specifying configuration values through URI Queries. It looks like:

hbase+rpc://server1:16020?hbase.client.operation.timeout=10000

In this way you can set the operation timeout to 10 seconds. Notice that, the configuration values specified in the connection URI will override the ones in the configuration file.

68.8.3. Implement Your Own Connection Registry

We use ServiceLoader to load different connection registry implementations, the entry point is org.apache.hadoop.hbase.client.ConnectionRegistryURIFactory. So if you implement your own ConnectionRegistryURIFactory which has a different scheme, and register it in the services file, we can load it at runtime.

Connection URI is still a very new feature which has not been used extensively in production, so we do not want to expose the ability to customize ConnectionRegistryURIFactory yet as the API may be changed frequently in the beginning.

If you really want to implement your own connection registry, you can use the above way but take your own risk.

69. 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.

69.1. Structural

Structural Filters contain other Filters.

69.1.1. 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,
  CompareOperator.EQUAL,
  Bytes.toBytes("my value")
  );
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
  cf,
  column,
  CompareOperator.EQUAL,
  Bytes.toBytes("my other value")
  );
list.add(filter2);
scan.setFilter(list);

69.2. Column Value

69.2.1. SingleColumnValueFilter

A SingleColumnValueFilter (see: https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/SingleColumnValueFilter.html) can be used to test column values for equivalence (CompareOperaor.EQUAL), inequality (CompareOperaor.NOT_EQUAL), or ranges (e.g., CompareOperaor.GREATER). The following is an example of testing equivalence of a column to a String value "my value"…​

SingleColumnValueFilter filter = new SingleColumnValueFilter(
  cf,
  column,
  CompareOperaor.EQUAL,
  Bytes.toBytes("my value")
  );
scan.setFilter(filter);

69.2.2. ColumnValueFilter

Introduced in HBase-2.0.0 version as a complementation of SingleColumnValueFilter, ColumnValueFilter gets matched cell only, while SingleColumnValueFilter gets the entire row (has other columns and values) to which the matched cell belongs. Parameters of constructor of ColumnValueFilter are the same as SingleColumnValueFilter.

ColumnValueFilter filter = new ColumnValueFilter(
  cf,
  column,
  CompareOperaor.EQUAL,
  Bytes.toBytes("my value")
  );
scan.setFilter(filter);

Note. For simple query like "equals to a family:qualifier:value", we highly recommend to use the following way instead of using SingleColumnValueFilter or ColumnValueFilter:

Scan scan = new Scan();
scan.addColumn(Bytes.toBytes("family"), Bytes.toBytes("qualifier"));
ValueFilter vf = new ValueFilter(CompareOperator.EQUAL,
  new BinaryComparator(Bytes.toBytes("value")));
scan.setFilter(vf);
...

This scan will restrict to the specified column 'family:qualifier', avoiding scan of unrelated families and columns, which has better performance, and ValueFilter is the condition used to do the value filtering.

But if query is much more complicated beyond this book, then please make your good choice case by case.

69.3. 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.

69.3.1. 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,
  CompareOperaor.EQUAL,
  comp
  );
scan.setFilter(filter);

See the Oracle JavaDoc for supported RegEx patterns in Java.

69.3.2. 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,
  CompareOperaor.EQUAL,
  comp
  );
scan.setFilter(filter);

69.3.3. BinaryPrefixComparator

See BinaryPrefixComparator.

69.3.4. BinaryComparator

See BinaryComparator.

69.3.5. BinaryComponentComparator

BinaryComponentComparator can be used to compare specific value at specific location with in the cell value. The comparison can be done for both ascii and binary data.

byte[] partialValue = Bytes.toBytes("partial_value");
    int partialValueOffset =
    Filter partialValueFilter = new ValueFilter(CompareFilter.CompareOp.GREATER,
            new BinaryComponentComparator(partialValue,partialValueOffset));

See HBASE-22969 for other use cases and details.

69.4. 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.

69.4.1. 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.

69.4.2. QualifierFilter

QualifierFilter can be used to filter based on Column (aka Qualifier) name.

69.4.3. 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"

Table 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 (Cell cell : result.listCells()) {
    // each cell represents a column
  }
}
rs.close();

69.4.4. 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"

Table 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 (Cell cell : result.listCells()) {
    // each cell represents a column
  }
}
rs.close();

69.4.5. 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)

Table 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 (Cell cell : result.listCells()) {
    // each cell represents a column
  }
}
rs.close();

Note: Introduced in HBase 0.92

69.5. RowKey

69.5.1. RowFilter

It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however RowFilter can also be used.

You can supplement a scan (both bounded and unbounded) with RowFilter constructed from BinaryComponentComparator for further filtering out or filtering in rows. See HBASE-22969 for use cases and other details.

69.6. Utility

69.6.1. FirstKeyOnlyFilter

This is primarily used for rowcount jobs. See FirstKeyOnlyFilter.

70. 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 .

70.1. 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.

70.2. Runtime Impact

A common dist-list question involves what happens to an HBase cluster when the Master goes down. This information has changed starting 3.0.0.

70.2.1. Up until releases 2.x.y

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.

70.2.2. Staring release 3.0.0

As mentioned in section Master Registry (new as of 2.3.0), the default connection registry for clients is now based on master rpc end points. Hence the requirements for masters' uptime are even tighter starting this release.

  • At least one active or stand by master is needed for a connection set up, unlike before when all the clients needed was a ZooKeeper ensemble.

  • Master is now in critical path for read/write operations. For example, if the meta region bounces off to a different region server, clients need master to fetch the new locations. Earlier this was done by fetching this information directly from ZooKeeper.

  • Masters will now have higher connection load than before. So, the server side configuration might need adjustment depending on the load.

Overall, the master uptime requirements, when this feature is enabled, are even higher for the client operations to go through.

70.3. 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.

70.4. Processes

The Master runs several background threads:

70.4.1. 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.

70.4.2. CatalogJanitor

Periodically checks and cleans up the hbase:meta table. See hbase:meta for more information on the meta table.

70.5. MasterProcWAL

MasterProcWAL is replaced in hbase-2.3.0 by an alternate Procedure Store implementation; see [in-master-procedure-store-region]. This section pertains to hbase-2.0.0 through hbase-2.2.x

HMaster records administrative operations and their running states, such as the handling of a crashed server, table creation, and other DDLs, into a Procedure Store. The Procedure Store WALs are stored under the MasterProcWALs directory. The Master WALs are not like RegionServer WALs. Keeping up the Master WAL allows us to run a state machine that is resilient across Master failures. For example, if a HMaster was in the middle of creating a table encounters an issue and fails, the next active HMaster can take up where the previous left off and carry the operation to completion. Since hbase-2.0.0, a new AssignmentManager (A.K.A AMv2) was introduced and the HMaster handles region assignment operations, server crash processing, balancing, etc., all via AMv2 persisting all state and transitions into MasterProcWALs rather than up into ZooKeeper, as we do in hbase-1.x.

See AMv2 Description for Devs (and Procedure Framework (Pv2): HBASE-12439 for its basis) if you would like to learn more about the new AssignmentManager.

70.5.1. Configurations for MasterProcWAL

Here are the list of configurations that effect MasterProcWAL operation. You should not have to change your defaults.

hbase.procedure.store.wal.periodic.roll.msec
Description

Frequency of generating a new WAL

Default

1h (3600000 in msec)

hbase.procedure.store.wal.roll.threshold
Description

Threshold in size before the WAL rolls. Every time the WAL reaches this size or the above period, 1 hour, passes since last log roll, the HMaster will generate a new WAL.

Default

32MB (33554432 in byte)

hbase.procedure.store.wal.warn.threshold
Description

If the number of WALs goes beyond this threshold, the following message should appear in the HMaster log with WARN level when rolling.

procedure WALs count=xx above the warning threshold 64. check running procedures to see if something is stuck.
Default

64

hbase.procedure.store.wal.max.retries.before.roll
Description

Max number of retry when syncing slots (records) to its underlying storage, such as HDFS. Every attempt, the following message should appear in the HMaster log.

unable to sync slots, retry=xx
Default

3

hbase.procedure.store.wal.sync.failure.roll.max
Description

After the above 3 retrials, the log is rolled and the retry count is reset to 0, thereon a new set of retrial starts. This configuration controls the max number of attempts of log rolling upon sync failure. That is, HMaster is allowed to fail to sync 9 times in total. Once it exceeds, the following log should appear in the HMaster log.

Sync slots after log roll failed, abort.
Default

3

71. RegionServer

HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a DataNode.

71.1. 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.

71.2. Processes

The RegionServer runs a variety of background threads:

71.2.1. CompactSplitThread

Checks for splits and handle minor compactions.

71.2.2. MajorCompactionChecker

Checks for major compactions.

71.2.3. MemStoreFlusher

Periodically flushes in-memory writes in the MemStore to StoreFiles.

71.2.4. LogRoller

Periodically checks the RegionServer’s WAL.

71.3. 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.

71.4. Block Cache

HBase provides two different BlockCache implementations to cache data read from HDFS: 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. See configurations, sizings, current usage, time-in-the-cache, and even detail on block counts and types.

71.4.1. Cache Choices

LruBlockCache is the original implementation, and is entirely within the Java heap. BucketCache is optional and mainly intended for keeping block cache data off-heap, although BucketCache can also be a file-backed cache. In file-backed we can either use it in the file mode or the mmaped mode. We also have pmem mode where the bucket cache resides on the persistent memory device.

When you enable BucketCache, you are enabling a two tier caching system. We used to describe the tiers as "L1" and "L2" but have deprecated this terminology as of hbase-2.0.0. The "L1" cache referred to an instance of LruBlockCache and "L2" to an off-heap BucketCache. Instead, when BucketCache is enabled, all DATA blocks are kept in the BucketCache tier and meta blocks — INDEX and BLOOM blocks — are on-heap in the LruBlockCache. Management of these two tiers and the policy that dictates how blocks move between them is done by CombinedBlockCache.

71.4.2. General Cache Configurations

Apart from the cache implementation itself, you can set some general configuration options to control how the cache performs. See CacheConfig. 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.

71.4.3. 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

71.4.4. 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 0.4 which represents 40% 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 405 MB of block cache available.

  • 20 region servers with the heap size set to 8 GB and a default block cache size will have 63.3 GB 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 hbase:meta table is forced into the block cache and have the in-memory priority which means that they are harder to evict.

The hbase:meta tables 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 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. As of HBase 2.4.14, you can estimate HFile indexes and bloom filters vs other DATA blocks using blockCacheCount and blockCacheDataBlockCount in JMX. The formula (blockCacheCount - blockCacheDataBlockCount) * blockSize will give you an estimate which can be useful when trying to enable the BucketCache. You should make sure the post-BucketCache config gives enough memory to the on-heap LRU cache to hold at least the same number of non-DATA blocks from pre-BucketCache. Once BucketCache is enabled, the L1 metrics like l1CacheSize, l1CacheCount, and l1CacheEvictionCount can help you further tune the size.

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 setCacheBlocks 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.

71.4.5. Off-heap Block Cache

How to Enable BucketCache

The usual deployment of BucketCache is via a managing class that sets up two caching tiers: an on-heap cache implemented by LruBlockCache and a second 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 on-heap LruBlockCache tier — and DATA blocks are kept in the BucketCache tier.

Pre-hbase-2.0.0 versions

Fetching will always be slower when fetching from BucketCache in pre-hbase-2.0.0, 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 in pre-2.0.0, so your latencies are less erratic, to mitigate GCs and heap fragmentation, and so you can safely use more memory. 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.

In pre-2.0.0, one can configure the BucketCache so it receives the victim of an LruBlockCache eviction. All Data and index blocks are cached in L1 first. When eviction happens from L1, the blocks (or victims) will get moved to L2. Set 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'}}
hbase-2.0.0+ versions

HBASE-11425 changed the HBase read path so it could hold the read-data off-heap avoiding copying of cached data on to the java heap. See Offheap read-path. In hbase-2.0.0, off-heap latencies approach those of on-heap cache latencies with the added benefit of NOT provoking GC.

From HBase 2.0.0 onwards, the notions of L1 and L2 have been deprecated. When BucketCache is turned on, the DATA blocks will always go to BucketCache and INDEX/BLOOM blocks go to on heap LRUBlockCache. cacheDataInL1 support has been removed.

BucketCache Deploy Modes

The BucketCache Block Cache can be deployed offheap, file or mmaped file mode.

You set which via the hbase.bucketcache.ioengine setting. 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 file caching (Useful in particular if you have some fast I/O attached to the box such as SSDs). From 2.0.0, it is possible to have more than one file backing the BucketCache. This is very useful especially when the Cache size requirement is high. For multiple backing files, configure ioengine as files:PATH_TO_FILE1,PATH_TO_FILE2,PATH_TO_FILE3. BucketCache can be configured to use an mmapped file also. Configure ioengine as mmap:PATH_TO_FILE for this.

It is possible to deploy a tiered 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 hbase.bucketcache.combinedcache.enabled 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. NOTE: This L1+L2 mode is removed from 2.0.0. When BucketCache is used, it will be strictly the DATA cache and the LruBlockCache will cache INDEX/META blocks.

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.

To check it enabled, look for the log line describing cache setup; it will detail how BucketCache has been deployed. Also see the UI. It will detail the cache tiering and their configuration.

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 hbase.bucketcache.bucket.sizes 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>hbase.bucketcache.bucket.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 (See Leveraging local data), the hosted DFSClient will allocate direct memory buffers. 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. If you do off-heap block caching, you’ll be making use of direct memory. The RPCServer uses a ByteBuffer pool. From 2.0.0, these buffers are off-heap ByteBuffers. Starting your JVM, make sure the -XX:MaxDirectMemorySize setting in conf/hbase-env.sh considers off-heap BlockCache (hbase.bucketcache.size), DFSClient usage, RPC side ByteBufferPool max size. This has to be bit higher than sum of off heap BlockCache size and max ByteBufferPool size. Allocating an extra of 1-2 GB for the max direct memory size has worked in tests. 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. Onheap LruBlockCache size is set as a fraction of java heap using hfile.block.cache.size setting (not the best name) and BucketCache is set as above in absolute Megabytes.

71.4.6. 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.

71.4.7. Cache Aware Load Balancer

Depending on the data size and the configured cache size, the cache warm up can take anywhere from a few minutes to a few hours. This becomes even more critical for HBase deployments over cloud storage, where compute is separated from storage. Doing this everytime the region server starts can be a very expensive process. To eliminate this, HBASE-27313 implemented the cache persistence feature where the region servers periodically persist the blocks cached in the bucket cache. This persisted information is then used to resurrect the cache in the event of a region server restart because of normal restart or crash.

HBASE-27999 implements the cache aware load balancer, which adds to the load balancer the ability to consider the cache allocation of each region on region servers when calculating a new assignment plan, using the region/region server cache allocation information reported by region servers to calculate the percentage of HFiles cached for each region on the hosting server. This information is then used by the balancer as a factor when deciding on an optimal, new assignment plan.

The master node captures the caching information from all the region servers and uses this information to decide on new region assignments while ensuring a minimal impact on the current cache allocation. A region is assigned to the region server where it has a better cache ratio as compared to the region server where it is currently hosted.

The CacheAwareLoadBalancer uses two cost elements for deciding the region allocation. These are described below:

  1. Cache Cost

    The cache cost is calculated as the percentage of data for a region cached on the region server where it is either currently hosted or was previously hosted. A region may have multiple HFiles, each of different sizes. A HFile is considered to be fully prefetched when all the data blocks in this file are in the cache. The region server hosting this region calculates the ratio of number of HFiles fully cached in the cache to the total number of HFiles in the region. This ratio will vary from 0 (region hosted on this server, but none of its HFiles are cached into the cache) to 1 (region hosted on this server and all the HFiles for this region are cached into the cache).

    Every region server maintains this information for all the regions currently hosted there. In addition to that, this cache ratio is also maintained for the regions which were previously hosted on this region server giving historical information about the regions.

  2. Skewness Cost

The cache aware balancer will consider cache cost with the skewness cost to decide on the region assignment plan under following conditions:

  1. There is an idle server in the cluster. This can happen when an existing server is restarted or a new server is added to the cluster.

  2. When the cost of maintaining the balance in the cluster is greater than the minimum threshold defined by the configuration hbase.master.balancer.stochastic.minCostNeedBalance.

The CacheAwareLoadBalancer can be enabled in the cluster by setting the following configuration properties in the master master configuration:

<property>
  <name>hbase.master.loadbalancer.class</name>
  <value>org.apache.hadoop.hbase.master.balancer.CacheAwareLoadBalancer</value>
</property>
<property>
  <name>hbase.bucketcache.persistent.path</name>
  <value>/path/to/bucketcache_persistent_file</value>
</property>

71.5. 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 shown 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.

71.6. Write Ahead Log (WAL)

71.6.1. 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. An exception is the RegionServer that is carrying hbase:meta; the meta table gets its own dedicated WAL. The RegionServer records Puts and Deletes to its WAL, before recording them these Mutations 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, with subdirectories per RegionServer.

For more general information about the concept of write ahead logs, see the Wikipedia Write-Ahead Log article.

71.6.2. WAL Providers

In HBase, there are a number of WAL implementations (or 'Providers'). Each is known by a short name label (that unfortunately is not always descriptive). You set the provider in hbase-site.xml passing the WAL provider short-name as the value on the hbase.wal.provider property (Set the provider for hbase:meta using the hbase.wal.meta_provider property, otherwise it uses the same provider configured by hbase.wal.provider).

  • asyncfs: The default. New since hbase-2.0.0 (HBASE-15536, HBASE-14790). This AsyncFSWAL provider, as it identifies itself in RegionServer logs, is built on a new non-blocking dfsclient implementation. It is currently resident in the hbase codebase but intent is to move it back up into HDFS itself. WALs edits are written concurrently ("fan-out") style to each of the WAL-block replicas on each DataNode rather than in a chained pipeline as the default client does. Latencies should be better. See Apache HBase Improvements and Practices at Xiaomi at slide 14 onward for more detail on implementation.

  • filesystem: This was the default in hbase-1.x releases. It is built on the blocking DFSClient and writes to replicas in classic DFSCLient pipeline mode. In logs it identifies as FSHLog or FSHLogProvider.

  • multiwal: This provider is made of multiple instances of asyncfs or filesystem. See the next section for more on multiwal.

Look for the lines like the below in the RegionServer log to see which provider is in place (The below shows the default AsyncFSWALProvider):

2018-04-02 13:22:37,983 INFO  [regionserver/ve0528:16020] wal.WALFactory: Instantiating WALProvider of type class org.apache.hadoop.hbase.wal.AsyncFSWALProvider
As the AsyncFSWAL hacks into the internal of DFSClient implementation, it will be easily broken by upgrading the hadoop dependencies, even for a simple patch release. So if you do not specify the wal provider explicitly, we will first try to use the asyncfs, if failed, we will fall back to use filesystem. And notice that this may not always work, so if you still have problem starting HBase due to the problem of starting AsyncFSWAL, please specify filesystem explicitly in the config file.
EC support has been added to hadoop-3.x, and it is incompatible with WAL as the EC output stream does not support hflush/hsync. In order to create a non-EC file in an EC directory, we need to use the new builder-based create API for FileSystem, but it is only introduced in hadoop-2.9+ and for HBase we still need to support hadoop-2.7.x. So please do not enable EC for the WAL directory until we find a way to deal with it.

71.6.3. 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.

71.6.4. WAL Flushing

TODO (describe).

71.6.5. 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.

71.6.6. WAL splitting based on procedureV2

After HBASE-20610, we introduce a new way to do WAL splitting coordination by procedureV2 framework. This can simplify the process of WAL splitting and no need to connect zookeeper any more.

Background

Currently, splitting WAL processes are coordinated by zookeeper. Each region server are trying to grab tasks from zookeeper. And the burden becomes heavier when the number of region server increase.

Implementation on Master side

During ServerCrashProcedure, SplitWALManager will create one SplitWALProcedure for each WAL file which should be split. Then each SplitWALProcedure will spawn a SplitWalRemoteProcedure to send the request to region server. SplitWALProcedure is a StateMachineProcedure and here is the state transfer diagram.

WAL splitting
Figure 2. WAL_splitting_coordination
Implementation on Region Server side

Region Server will receive a SplitWALCallable and execute it, which is much more straightforward than before. It will return null if success and return exception if there is any error.

Performance

According to tests on a cluster which has 5 regionserver and 1 master. procedureV2 coordinated WAL splitting has a better performance than ZK coordinated WAL splitting no master when restarting the whole cluster or one region server crashing.

Enable this feature

To enable this feature, first we should ensure our package of HBase already contains these code. If not, please upgrade the package of HBase cluster without any configuration change first. Then change configuration 'hbase.split.wal.zk.coordinated' to false. Rolling upgrade the master with new configuration. Now WAL splitting are handled by our new implementation. But region server are still trying to grab tasks from zookeeper, we can rolling upgrade the region servers with the new configuration to stop that.

  • steps as follows:

    • Upgrade whole cluster to get the new Implementation.

    • Upgrade Master with new configuration 'hbase.split.wal.zk.coordinated'=false.

    • Upgrade region server to stop grab tasks from zookeeper.

71.6.7. WAL Compression

The content of the WAL can be compressed using LRU Dictionary compression. This can be used to speed up WAL replication to different datanodes. The dictionary can store up to 215 elements; eviction starts after this number is exceeded.

To enable WAL compression, set the hbase.regionserver.wal.enablecompression property to true. The default value for this property is false. By default, WAL tag compression is turned on when WAL compression is enabled. You can turn off WAL tag compression by setting the hbase.regionserver.wal.tags.enablecompression property to 'false'.

A possible downside to WAL compression is that we lose more data from the last block in the WAL if it is ill-terminated mid-write. If entries in this last block were added with new dictionary entries but we failed persist the amended dictionary because of an abrupt termination, a read of this last block may not be able to resolve last-written entries.

71.6.8. Durability

It is possible to set durability on each Mutation or on a Table basis. Options include:

  • SKIP_WAL: Do not write Mutations to the WAL (See the next section, Disabling the WAL).

  • ASYNC_WAL: Write the WAL asynchronously; do not hold-up clients waiting on the sync of their write to the filesystem but return immediately. The edit becomes visible. Meanwhile, in the background, the Mutation will be flushed to the WAL at some time later. This option currently may lose data. See HBASE-16689.

  • SYNC_WAL: The default. Each edit is sync’d to HDFS before we return success to the client.

  • FSYNC_WAL: Each edit is fsync’d to HDFS and the filesystem before we return success to the client.

Do not confuse the ASYNC_WAL option on a Mutation or Table with the AsyncFSWAL writer; they are distinct options unfortunately closely named

71.6.9. Custom WAL Directory

HBASE-17437 added support for specifying a WAL directory outside the HBase root directory or even in a different FileSystem since 1.3.3/2.0+. Some FileSystems (such as Amazon S3) don’t support append or consistent writes, in such scenario WAL directory needs to be configured in a different FileSystem to avoid loss of writes.

Following configurations are added to accomplish this:

  1. hbase.wal.dir

    This defines where the root WAL directory is located, could be on a different FileSystem than the root directory. WAL directory can not be set to a subdirectory of the root directory. The default value of this is the root directory if unset.

  2. hbase.rootdir.perms

    Configures FileSystem permissions to set on the root directory. This is '700' by default.

  3. hbase.wal.dir.perms

    Configures FileSystem permissions to set on the WAL directory FileSystem. This is '700' by default.

While migrating to custom WAL dir (outside the HBase root directory or a different FileSystem) existing WAL files must be copied manually to new WAL dir, otherwise it may lead to data loss/inconsistency as HMaster has no information about previous WAL directory.

71.6.10. 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.

72. 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.

72.1. 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:

72.1.1. 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.

72.2. Region-RegionServer Assignment

This section describes how Regions are assigned to RegionServers.

72.2.1. 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.

72.2.2. 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

72.2.3. Region Load Balancing

Regions can be periodically moved by the LoadBalancer.

72.2.4. 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 3. 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.

72.3. 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.

72.4. 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).

72.4.1. 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 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.

72.5. 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.

72.5.1. 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 three split algorithms. The first is the HexStringSplit algorithm, which assumes the row keys are hexadecimal strings. The second is the DecimalStringSplit algorithm, which assumes the row keys are decimal strings in the range 00000000 to 99999999. The third, UniformSplit, assumes the row keys are random byte arrays. You will probably need to develop your own SplitAlgorithm, using the provided ones as models.

72.6. 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.

72.7. 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.

72.7.1. 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.

72.7.2. 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.

72.7.3. 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.

72.7.4. 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 hbase hfile tool. Type the following to see usage:

$ ${HBASE_HOME}/bin/hbase hfile

For example, to view the content of the file hdfs://10.81.47.41:8020/hbase/default/TEST/1418428042/DSMP/4759508618286845475, type the following:

 $ ${HBASE_HOME}/bin/hbase hfile -v -f hdfs://10.81.47.41:8020/hbase/default/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.

In the output of this tool, you might see 'seqid=0' for certain keys in places such as 'Mid-key'/'firstKey'/'lastKey'. These are 'KeyOnlyKeyValue' type instances - meaning their seqid is irrelevant & we just need the keys of these Key-Value instances.
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.

72.7.5. 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.

72.7.6. 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.

72.7.7. 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 ColumnFamilyDescriptorBuilder.setMaxVersions(int versions). The default value is 1. 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 Switch

We can switch on and off the compactions at region servers. Switching off compactions will also interrupt any currently ongoing compactions. It can be done dynamically using the "compaction_switch" command from hbase shell. If done from the command line, this setting will be lost on restart of the server. To persist the changes across region servers modify the configuration hbase.regionserver .compaction.enabled in hbase-site.xml and restart HBase.

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, ExploringCom