The Apache HBase™ Reference Guide

Revision History
Revision 2.0.0-SNAPSHOT 2014-10-28T08:12

Abstract

This is the official reference guide of Apache HBase™, a distributed, versioned, big data store built on top of Apache Hadoop™ and Apache ZooKeeper™.


Table of Contents

Preface
1. Getting Started
1.1. Introduction
1.2. Quick Start - Standalone HBase
2. Apache HBase Configuration
2.1. Basic Prerequisites
2.2. HBase run modes: Standalone and Distributed
2.3. Running and Confirming Your Installation
2.4. Configuration Files
2.5. Example Configurations
2.6. The Important Configurations
2.7. Dynamic Configuration
3. Upgrading
3.1. HBase version numbers
3.2. Upgrading from 0.98.x to 1.0.x
3.3. Upgrading from 0.96.x to 0.98.x
3.4. Upgrading from 0.94.x to 0.98.x
3.5. Upgrading from 0.94.x to 0.96.x
3.6. Upgrading from 0.92.x to 0.94.x
3.7. Upgrading from 0.90.x to 0.92.x
3.8. Upgrading to HBase 0.90.x from 0.20.x or 0.89.x
4. The Apache HBase Shell
4.1. Scripting with Ruby
4.2. Running the Shell in Non-Interactive Mode
4.3. HBase Shell in OS Scripts
4.4. Read HBase Shell Commands from a Command File
4.5. Passing VM Options to the Shell
4.6. Shell Tricks
5. Data Model
5.1. Conceptual View
5.2. Physical View
5.3. Namespace
5.4. Table
5.5. Row
5.6. Column Family
5.7. Cells
5.8. Data Model Operations
5.9. Versions
5.10. Sort Order
5.11. Column Metadata
5.12. Joins
5.13. ACID
6. HBase and Schema Design
6.1. Schema Creation
6.2. On the number of column families
6.3. Rowkey Design
6.4. Number of Versions
6.5. Supported Datatypes
6.6. Joins
6.7. Time To Live (TTL)
6.8. Keeping Deleted Cells
6.9. Secondary Indexes and Alternate Query Paths
6.10. Constraints
6.11. Schema Design Case Studies
6.12. Operational and Performance Configuration Options
7. HBase and MapReduce
7.1. HBase, MapReduce, and the CLASSPATH
7.2. MapReduce Scan Caching
7.3. Bundled HBase MapReduce Jobs
7.4. HBase as a MapReduce Job Data Source and Data Sink
7.5. Writing HFiles Directly During Bulk Import
7.6. RowCounter Example
7.7. Map-Task Splitting
7.8. HBase MapReduce Examples
7.9. Accessing Other HBase Tables in a MapReduce Job
7.10. Speculative Execution
8. Secure Apache HBase
8.1. Secure Client Access to Apache HBase
8.2. Simple User Access to Apache HBase
8.3. Securing Access To Your Data
8.4. Security Configuration Example
9. Architecture
9.1. Overview
9.2. Catalog Tables
9.3. Client
9.4. Client Request Filters
9.5. Master
9.6. RegionServer
9.7. Regions
9.8. Bulk Loading
9.9. HDFS
9.10. Timeline-consistent High Available Reads
10. Apache HBase APIs
11. Apache HBase External APIs
11.1. Non-Java Languages Talking to the JVM
11.2. REST
11.3. Thrift
11.4. C/C++ Apache HBase Client
12. Thrift API and Filter Language
12.1. Filter Language
13. Apache HBase Coprocessors
13.1. Coprocessor Framework
13.2. Examples
13.3. Building A Coprocessor
13.4. Check the Status of a Coprocessor
13.5. Monitor Time Spent in Coprocessors
13.6. Status of Coprocessors in HBase
14. Apache HBase Performance Tuning
14.1. Operating System
14.2. Network
14.3. Java
14.4. HBase Configurations
14.5. ZooKeeper
14.6. Schema Design
14.7. HBase General Patterns
14.8. Writing to HBase
14.9. Reading from HBase
14.10. Deleting from HBase
14.11. HDFS
14.12. Amazon EC2
14.13. Collocating HBase and MapReduce
14.14. Case Studies
15. Troubleshooting and Debugging Apache HBase
15.1. General Guidelines
15.2. Logs
15.3. Resources
15.4. Tools
15.5. Client
15.6. MapReduce
15.7. NameNode
15.8. Network
15.9. RegionServer
15.10. Master
15.11. ZooKeeper
15.12. Amazon EC2
15.13. HBase and Hadoop version issues
15.14. IPC Configuration Conflicts with Hadoop
15.15. HBase and HDFS
15.16. Running unit or integration tests
15.17. Case Studies
15.18. Cryptographic Features
15.19. Operating System Specific Issues
15.20. JDK Issues
16. Apache HBase Case Studies
16.1. Overview
16.2. Schema Design
16.3. Performance/Troubleshooting
17. Apache HBase Operational Management
17.1. HBase Tools and Utilities
17.2. Region Management
17.3. Node Management
17.4. HBase Metrics
17.5. HBase Monitoring
17.6. Cluster Replication
17.7. HBase Backup
17.8. HBase Snapshots
17.9. Capacity Planning and Region Sizing
17.10. Table Rename
18. Building and Developing Apache HBase
18.1. Getting Involved
18.2. Apache HBase Repositories
18.3. IDEs
18.4. Building Apache HBase
18.5. Releasing Apache HBase
18.6. Voting on Release Candidates
18.7. Generating the HBase Reference Guide
18.8. Updating hbase.apache.org
18.9. Tests
18.10. Developer Guidelines
19. Unit Testing HBase Applications
19.1. JUnit
19.2. Mockito
19.3. MRUnit
19.4. Integration Testing with a HBase Mini-Cluster
20. ZooKeeper
20.1. Using existing ZooKeeper ensemble
20.2. SASL Authentication with ZooKeeper
21. Community
21.1. Decisions
21.2. Community Roles
21.3. Commit Message format
A. Contributing to Documentation
A.1. Getting Access to the Wiki
A.2. Contributing to Documentation or Other Strings
A.3. Editing the HBase Website
A.4. Editing the HBase Reference Guide
A.5. Auto-Generated Content
A.6. Multi-Page and Single-Page Output
A.7. Images in the HBase Reference Guide
A.8. Adding a New Chapter to the HBase Reference Guide
A.9. Docbook Common Issues
B. FAQ
C. hbck In Depth
C.1. Running hbck to identify inconsistencies
C.2. Inconsistencies
C.3. Localized repairs
C.4. Region Overlap Repairs
D. Access Control Matrix
E. Compression and Data Block Encoding In HBase
E.1. Which Compressor or Data Block Encoder To Use
E.2. Making use of Hadoop Native Libraries in HBase
E.3. Compressor Configuration, Installation, and Use
E.4. Enable Data Block Encoding
F. SQL over HBase
F.1. Apache Phoenix
F.2. Trafodion
G. YCSB: The Yahoo! Cloud Serving Benchmark and HBase
H. HFile format
H.1. HBase File Format (version 1)
H.2. HBase file format with inline blocks (version 2)
H.3. HBase File Format with Security Enhancements (version 3)
I. Other Information About HBase
I.1. HBase Videos
I.2. HBase Presentations (Slides)
I.3. HBase Papers
I.4. HBase Sites
I.5. HBase Books
I.6. Hadoop Books
J. HBase History
K. HBase and the Apache Software Foundation
K.1. ASF Development Process
K.2. ASF Board Reporting
L. Apache HBase Orca
M. Enabling Dapper-like Tracing in HBase
M.1. SpanReceivers
M.2. Client Modifications
M.3. Tracing from HBase Shell
N. 0.95 RPC Specification
N.1. Goals
N.2. TODO
N.3. RPC
N.4. Notes
Index

List of Figures

9.1. Region State Transitions
9.2. HFile Version 1
13.1. Coprocessor Metrics UI
17.1. Basic Info
17.2. Config
17.3. Stats
17.4. L1 and L2
17.5. Replication Architecture Overview
E.1. ColumnFamily with No Encoding
E.2. ColumnFamily with Prefix Encoding
E.3. ColumnFamily with Diff Encoding
H.1. HFile V1 Format

List of Tables

1.1. Distributed Cluster Demo Architecture
2.1. Java
2.2. Hadoop version support matrix
5.1. Table webtable
5.2. ColumnFamily anchor
5.3. ColumnFamily contents
8.1. Operation To Permission Mapping
8.2. Examples of Visibility Expressions
9.1. Stripe Sizing Settings
18.1. Release Managers
D.1. ACL Matrix

List of Examples

1.1. Example /etc/hosts File for Ubuntu
1.2. Example hbase-site.xml for Standalone HBase
1.3. node-a jps Output
1.4. node-b jps Output
1.5. node-c jps Output
2.1. Calculate the Potential Number of Open Files
2.2. Example Distributed HBase Cluster
4.1. Passing Commands to the HBase Shell
4.2. Checking the Result of a Scripted Command
4.3. Example Command File
4.4. Directing HBase Shell to Execute the Commands
5.1. Examples
5.2. Examples
5.3. Modify the Maximum Number of Versions for a Column
5.4. Modify the Minimum Number of Versions for a Column
6.1. Salting Example
6.2. Hashing Example
6.3. Change the Value of KEEP_DELETED_CELLS Using HBase Shell
6.4. Change the Value of KEEP_DELETED_CELLS Using the API
8.1. HBase Shell
8.2. API
8.3. Revoking Access To a Table
8.4. HBase Shell
8.5. API
8.6. HBase Shell
8.7. Java API
8.8. HBase Shell
8.9. Java API
8.10. HBase Shell
8.11. Java API
8.12. HBase Shell
8.13. Java API
8.14. HBase Shell
8.15. Java API
8.16. Example Security Settings in hbase-site.xml
8.17. Example Group Mapper in Hadoop core-site.xml
9.1. Pre-Creating a HConnection
10.1. Create a Table Using Java
10.2. Add, Modify, and Delete a Table
12.1. Compound Operators
12.2. Precedence Example
12.3. Example 1
12.4. Example 2
12.5. Example 3
12.6. Example 4
13.1. Example RegionObserver Configuration
13.2. Load a Coprocessor On a Table Using HBase Shell
13.3. Unload a Coprocessor From a Table Using HBase Shell
14.1. Enable Prefetch Using HBase Shell
14.2. Enable Prefetch Using the API
14.3. Hedged Reads Configuration Example
17.1. rolling-restart.sh General Usage
18.1. Code Blocks in Jira Comments
18.2. Example ~/.m2/settings.xml File
18.3. Example of Committing a Patch
B.1. Maven Dependency for HBase 0.98
B.2. Maven Dependency for HBase 0.96
B.3. Maven Dependency for HBase 0.94
E.1. Enabling Compression on a ColumnFamily of an Existing Table using HBase Shell
E.2. Creating a New Table with Compression On a ColumnFamily
E.3. Verifying a ColumnFamily's Compression Settings
E.4. LoadTestTool Usage
E.5. Example Usage of LoadTestTool
E.6. Enable Data Block Encoding On a Table
E.7. Verifying a ColumnFamily's Data Block Encoding

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, JIRA or wiki 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/docbkx directory of the HBase source. This reference guide is marked up using DocBook from which the 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 Docbook and suggestions to get started contributing to the documentation, see Appendix A, Contributing to Documentation.

Providing Feedback. This guide allows you to leave comments or questions on any page, using Disqus. Look for the Comments area at the bottom of the page. Answering these questions is a volunteer effort, and may be delayed.

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' 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. Its a fun place to be. Yours, the HBase Community.

Chapter 1. Getting Started

1.1. Introduction

Section 1.2, “Quick Start - Standalone HBase” will get you up and running on a single-node, standalone instance of HBase, followed by a pseudo-distributed single-machine instance, and finally a fully-distributed cluster.

1.2. Quick Start - Standalone HBase

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

Local Filesystem and Durability

The below advice is for HBase 0.98.2 and earlier releases only. This is fixed in HBase 0.98.3 and beyond. See HBASE-11272 and HBASE-11218.

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

Loopback IP - HBase 0.94.x and earlier

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

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

Example 1.1. Example /etc/hosts File for Ubuntu

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

127.0.0.1 localhost
127.0.0.1 ubuntu.ubuntu-domain ubuntu
        

1.2.1. JDK Version Requirements

HBase requires that a JDK be installed. See Table 2.1, “Java” for information about supported JDK versions.

1.2.2. Get Started with HBase

Procedure 1.1. Download, Configure, and Start HBase

  1. Choose a download site from this list of Apache Download Mirrors. Click on the suggested top link. This will take you to a mirror of HBase Releases. Click on the folder named stable and then download the binary file that ends in .tar.gz to your local filesystem. Be sure to choose the version that corresponds with the version of Hadoop you are likely to use later. In most cases, you should choose the file for Hadoop 2, which will be called something like hbase-0.98.3-hadoop2-bin.tar.gz. Do not download the file ending in src.tar.gz for now.

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

    $ tar xzvf hbase-<?eval ${project.version}?>-hadoop2-bin.tar.gz  
    $ cd hbase-<?eval ${project.version}?>-hadoop2/
              
  3. For HBase 0.98.5 and later, you are required to set the JAVA_HOME environment variable before starting HBase. Prior to 0.98.5, HBase attempted to detect the location of Java if the variables was not set. You can set the variable via your operating system's usual mechanism, but HBase provides a central mechanism, conf/hbase-env.sh. Edit this file, uncomment the line starting with JAVA_HOME, and set it to the appropriate location for your operating system. The JAVA_HOME variable should be set to a directory which contains the executable file bin/java. Most modern Linux operating systems provide a mechanism, such as /usr/bin/alternatives on RHEL or CentOS, for transparently switching between versions of executables such as Java. In this case, you can set JAVA_HOME to the directory containing the symbolic link to bin/java, which is usually /usr.

    JAVA_HOME=/usr

    Note

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

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

    Example 1.2. Example hbase-site.xml for Standalone HBase

    <configuration>
      <property>
        <name>hbase.rootdir</name>
        <value>file:///home/testuser/hbase</value>
      </property>
      <property>
        <name>hbase.zookeeper.property.dataDir</name>
        <value>/home/testuser/zookeeper</value>
      </property>
    </configuration>              
                  
                

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

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

    Note

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

Procedure 1.2. 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> create 'test', 'cf'    
    0 row(s) in 1.2200 seconds
              
  4. List Information About your Table

    Use the list command to

    hbase> list 'test'
    TABLE
    test
    1 row(s) in 0.0350 seconds
    
    => ["test"]
              
  5. Put data into your table.

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

    hbase> put 'test', 'row1', 'cf:a', 'value1'
    0 row(s) in 0.1770 seconds
    
    hbase> put 'test', 'row2', 'cf:b', 'value2'
    0 row(s) in 0.0160 seconds
    
    hbase> put 'test', 'row3', 'cf:c', 'value3'
    0 row(s) in 0.0260 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> scan 'test'
    ROW                   COLUMN+CELL
     row1                 column=cf:a, timestamp=1403759475114, value=value1
     row2                 column=cf:b, timestamp=1403759492807, value=value2
     row3                 column=cf:c, timestamp=1403759503155, value=value3
    3 row(s) in 0.0440 seconds
              
  7. Get a single row of data.

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

    hbase> get 'test', 'row1'
    COLUMN                CELL
     cf:a                 timestamp=1403759475114, value=value1
    1 row(s) in 0.0230 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> disable 'test'
    0 row(s) in 1.6270 seconds
    
    hbase> enable 'test'
    0 row(s) in 0.4500 seconds
              

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

    hbase> disable 'test'
    0 row(s) in 1.6270 seconds            
              
  9. Drop the table.

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

    hbase> drop 'test'
    0 row(s) in 0.2900 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 1.3. 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.

1.2.3. Intermediate - Pseudo-Distributed Local Install

After working your way through Section 1.2, “Quick Start - Standalone HBase”, you can re-configure HBase to run in pseudo-distributed mode. Pseudo-distributed mode means that HBase still runs completely on a single host, but each HBase daemon (HMaster, HRegionServer, and Zookeeper) runs as a separate process. By default, unless you configure the hbase.rootdir property as described in Section 1.2, “Quick Start - Standalone HBase”, 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. Currently, the documentation on the Hadoop website does not include a quick start for Hadoop 2, but the guide at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide is a good starting point.

  1. Stop HBase if it is running.

    If you have just finished Section 1.2, “Quick Start - Standalone HBase” and HBase is still running, stop it. This procedure will create a totally new directory where HBase will store its data, so any databases you created before will be lost.

  2. Configure HBase.

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

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

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

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

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

  3. Start HBase.

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

  4. Check the HBase directory in HDFS.

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

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

    You can use the HBase Shell to create a table, populate it with data, scan and get values from it, using the same procedure as in Procedure 1.2, “Use HBase For the First Time”.

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

    Note

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

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

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

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

    $ cat /tmp/hbase-testuser-1-master.pid |xargs kill -9            
              
  7. Start and stop additional RegionServers

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

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

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

    $ .bin/local-regionservers.sh stop 3
  8. Stop HBase.

    You can stop HBase the same way as in the Section 1.2, “Quick Start - Standalone HBase” procedure, using the bin/stop-hbase.sh command.

1.2.4. Advanced - Fully Distributed

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

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

Table 1.1. Distributed Cluster Demo Architecture

Node NameMasterZooKeeperRegionServer
node-a.example.comyesyesno
node-b.example.combackupyesyes
node-c.example.comnoyesyes

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, Section 1.2.3, “Intermediate - Pseudo-Distributed Local Install”, assuming that the system you configured in that procedure is now node-a. Stop HBase on node-a before continuing.

Note

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 1.4. Configure Password-Less SSH Access

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

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

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

    $ ssh-keygen -t rsa

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

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

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

  3. Copy the public key to the other nodes.

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

    $ cat id_rsa.pub >> ~/.ssh/authorized_keys
  4. Test password-less login.

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

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

Procedure 1.5. Prepare node-a

node-a will run your primary master and ZooKeeper processes, but no RegionServers.

  1. Stop the RegionServer from starting on node-a.

    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 Chapter 20, ZooKeeper. This configuration will direct HBase to start and manage a ZooKeeper instance on each node of the cluster.

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

    <property>
      <name>hbase.zookeeper.quorum</name>
      <value>node-a.example.com,node-b.example.com,node-c.example.com</value>
    </property>
    <property>
      <name>hbase.zookeeper.property.dataDir</name>
      <value>/usr/local/zookeeper</value>
    </property>            
                
  4. Everywhere in your configuration that you have referred to node-a as localhost, change the reference to point to the hostname that the other nodes will use to refer to node-a. In these examples, the hostname is node-a.example.com.

Procedure 1.6. 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 1.7. Start and Test Your Cluster

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

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

  2. Start the cluster.

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

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

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

  3. Verify that the processes are running.

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

    Example 1.3. node-a jps Output

    $ jps
    20355 Jps
    20071 HQuorumPeer
    20137 HMaster    
                

    Example 1.4. node-b jps Output

    $ jps
    15930 HRegionServer
    16194 Jps
    15838 HQuorumPeer
    16010 HMaster            
                

    Example 1.5. 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 Chapter 20, ZooKeeper.

  4. Browse to the Web UI.

    Web UI Port Changes

    In HBase newer than 0.98.x, the HTTP ports used by the HBase Web UI changed from 60010 for the Master and 60030 for each RegionServer to 16610 for the Master and 16030 for the RegionServer.

    If everything is set up correctly, you should be able to connect to the UI for the Master http://node-a.example.com:60110/ or the secondary master at http://node-b.example.com:60110/ for the secondary master, using a web browser. If you can connect via localhost but not from another host, check your firewall rules. You can see the web UI for each of the RegionServers at port 60130 of their IP addresses, or by clicking their links in the web UI for the Master.

  5. Test what happens when nodes or services disappear.

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

1.2.5. Where to go next

The next chapter, Chapter 2, Apache HBase 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.

Chapter 2. Apache HBase Configuration

This chapter expands upon the Chapter 1, Getting Started chapter to further explain configuration of Apache HBase. Please read this chapter carefully, especially Section 2.1, “Basic Prerequisites” to ensure that your HBase testing and deployment goes smoothly, and prevent data loss.

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 Files

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.

Note

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

hbase-policy.xml

The default policy configuration file used by RPC servers to make authorization decisions on client requests. Only used if HBase security (Chapter 8, Secure Apache HBase) is enabled.

hbase-site.xml

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

log4j.properties

Configuration file for HBase logging via log4j.

regionservers

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

Checking XML Validity

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

Keep Configuration In Sync Across the Cluster

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

2.1. Basic Prerequisites

This section lists required services and some required system configuration.

Table 2.1. Java

HBase VersionJDK 6JDK 7JDK 8
1.0Not Supportedyes

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

0.98yesyes

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

0.96yesyes 
0.94yesyes 

Note

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

Operating System Utilities

ssh

HBase uses the Secure Shell (ssh) command and utilities extensively to communicate between cluster nodes. Each server in the cluster must be running ssh so that the Hadoop and HBase daemons can be managed. You must be able to connect to all nodes via SSH, including the local node, from the Master as well as any backup Master, using a shared key rather than a password. You can see the basic methodology for such a set-up in Linux or Unix systems at Procedure 1.4, “Configure Password-Less SSH Access”. If your cluster nodes use OS X, see the section, SSH: Setting up Remote Desktop and Enabling Self-Login on the Hadoop wiki.

DNS

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

If your server has multiple network interfaces, HBase defaults to using the interface that the primary hostname resolves to. To override this behavior, set the hbase.regionserver.dns.interface property to a different interface. This will only work if each server in your cluster uses the same network interface configuration.

To choose a different DNS nameserver than the system default, set the hbase.regionserver.dns.nameserver property to the IP address of that nameserver.

Loopback IP

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

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 Section 15.9.2.2, “java.io.IOException...(Too many open files)” 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 6 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.

Example 2.1. 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 nproc that is too low can cause OutOfMemoryError exceptions. See Jack Levin's major hdfs issues thread on the hbase-users mailing list, from 2011.

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

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
Windows

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

To run versions of HBase prior to 0.96 on Microsoft Windows, you must install Cygwin and run HBase within the Cygwin environment. This provides support for Linux/Unix commands and scripts. The full details are explained in the Windows Installation guide. Also search our user mailing list to pick up latest fixes figured by Windows users.

Post-hbase-0.96.0, hbase runs natively on windows with supporting *.cmd scripts bundled.

2.1.1. Hadoop

The following table summarizes the versions of Hadoop supported with each version of HBase. Based on the version of HBase, you should select the most appropriate version of Hadoop. You can use Apache Hadoop, or a vendor's distribution of Hadoop. No distinction is made here. See http://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support for information about vendors of Hadoop.

Hadoop 2.x is recommended.

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

Use the following legend to interpret this table:

S = supported and tested,
X = not supported,
NT = it should run, but not tested enough.

Table 2.2. Hadoop version support matrix

HBase-0.92.xHBase-0.94.xHBase-0.96.x

HBase-0.98.x (Support for Hadoop 1.x is deprecated.)

HBase-1.0.x (Hadoop 1.x is NOT supported)

Hadoop-0.20.205SXXXX
Hadoop-0.22.x SXXXX

Hadoop-1.0.0-1.0.2 (HBase requires hadoop 1.0.3 at a minimum; there is an issue where we cannot find KerberosUtil compiling against earlier versions of Hadoop.)

XXXXX
Hadoop-1.0.3+SSSXX
Hadoop-1.1.x NTSSXX
Hadoop-0.23.x XSNTXX
Hadoop-2.0.x-alpha XNTXXX
Hadoop-2.1.0-beta XNTSXX
Hadoop-2.2.0 XNTSSNT
Hadoop-2.3.xXNTSSNT
Hadoop-2.4.xXNTSSS
Hadoop-2.5.xXNTSSS

Replace the Hadoop Bundled With HBase!

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

2.1.1.1. Apache HBase 0.94 with Hadoop 2

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

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

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

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

  • Type the following commands:

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

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

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

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

2.1.1.2. Apache HBase 0.92 and 0.94

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

2.1.1.3. Apache HBase 0.96

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

2.1.1.4. Hadoop versions 0.20.x - 1.x

HBase will lose data unless it is running on an HDFS that has a durable sync implementation. DO NOT use Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0 which DO NOT have this attribute. Currently only Hadoop versions 0.20.205.x or any release in excess of this version -- this includes hadoop-1.0.0 -- have a working, durable sync. The Cloudera blog post An update on Apache Hadoop 1.0 by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate. Its worth checking out if you are having trouble making sense of the Hadoop version morass.

Sync has to be explicitly enabled by setting dfs.support.append equal to true on both the client side -- in hbase-site.xml -- and on the serverside in hdfs-site.xml (The sync facility HBase needs is a subset of the append code path).

  
<property>
  <name>dfs.support.append</name>
  <value>true</value>
</property>

You will have to restart your cluster after making this edit. Ignore the chicken-little comment you'll find in the hdfs-default.xml in the description for the dfs.support.append configuration.

2.1.1.5. Apache HBase on Secure Hadoop

Apache HBase will run on any Hadoop 0.20.x that incorporates Hadoop security features as long as you do as suggested above and replace the Hadoop jar that ships with HBase with the secure version. If you want to read more about how to setup Secure HBase, see Section 8.1, “Secure Client Access to Apache HBase”.

2.1.1.6. dfs.datanode.max.transfer.threads

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

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

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

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

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

See also Section 16.3.4, “Case Study #4 (max.transfer.threads Config)” and note that this property was previously known as dfs.datanode.max.xcievers (e.g. Hadoop HDFS: Deceived by Xciever).

2.1.2. ZooKeeper Requirements

ZooKeeper 3.4.x is required as of HBase 1.0.0. HBase makes use of the multi functionality that is only available since 3.4.0 (The useMulti is defaulted true in HBase 1.0.0). See HBASE-12241 The crash of regionServer when taking deadserver's replication queue breaks replication and Use ZK.multi when available for HBASE-6710 0.92/0.94 compatibility fix for background.

2.2. HBase run modes: Standalone and Distributed

HBase has two run modes: Section 2.2.1, “Standalone HBase” and Section 2.2.2, “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.

2.2.1. Standalone HBase

This is the default mode. Standalone mode is what is described in the Section 1.2, “Quick Start - Standalone HBase” 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.

2.2.2. 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 requirements and instructions for how to set up HDFS for Hadoop 1.x. A good walk-through for setting up HDFS on Hadoop 2 is at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide.

Below we describe the different distributed setups. Starting, verification and exploration of your install, whether a pseudo-distributed or fully-distributed configuration is described in a section that follows, Section 2.3, “Running and Confirming Your Installation”. The same verification script applies to both deploy types.

2.2.2.1. Pseudo-distributed

Pseudo-Distributed Quickstart

A quickstart has been added to the Section 1.2, “Quick Start - Standalone HBase” chapter. See Section 1.2.3, “Intermediate - Pseudo-Distributed Local Install”. Some of the information that was originally in this section has been moved there.

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

2.2.3. Fully-distributed

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

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

In addition, the cluster is configured so that multiple cluster nodes enlist as RegionServers, ZooKeeper QuorumPeers, and backup HMaster servers. These configuration basics are all demonstrated in Section 1.2.4, “Advanced - 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 section Chapter 20, ZooKeeper for ZooKeeper setup for HBase.

Example 2.2. 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 Section 2.4, “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 each node 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 Section 1.2.4, “Advanced - Fully Distributed” for a walk-through of a simple three-node cluster configuration with multiple ZooKeeper, backup HMaster, and RegionServer instances.

Procedure 2.1. HDFS Client Configuration

  • Of note, if you have made HDFS client configuration 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:

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

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

    • 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.

2.3. 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 daemons. These do not need to be started.

If you are managing your own ZooKeeper, start it and confirm its 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 its deployed on the Master host at port 16010 (HBase RegionServers listen on port 16020 by default and put up an informational http server at 16030). If the Master were running on a host named master.example.org on the default port, to see the Master's homepage you'd point your browser at http://master.example.org:16010.

Prior to HBase 0.98, the default ports the master ui was deployed on port 16010, and the HBase RegionServers would listen on port 16020 by default and put up an informational http server at 16030.

Once HBase has started, see the Procedure 1.2, “Use HBase For the First Time” 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.

2.4. Configuration Files

2.4.1. 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 Configuration below or view the raw hbase-default.xml source file in the HBase source code at src/main/resources.

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

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

HBase Default Configuration

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

hbase.tmp.dir

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

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

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

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

Default. localhost

hbase.local.dir

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

Default. ${hbase.tmp.dir}/local/

hbase.master.info.port

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

The bind address for the HBase Master web UI

Default. 0.0.0.0

hbase.master.logcleaner.plugins

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

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

hbase.master.logcleaner.ttl

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

Default. 600000

hbase.master.hfilecleaner.plugins

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

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

hbase.master.catalog.timeout

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

Default. 600000

hbase.master.infoserver.redirect

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

Default. true

hbase.regionserver.port

The port the HBase RegionServer binds to.

Default. 16020

hbase.regionserver.info.port

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

The address for the HBase RegionServer web UI

Default. 0.0.0.0

hbase.regionserver.info.port.auto

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

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

Default. 30

hbase.ipc.server.callqueue.handler.factor

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

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

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

Interval between messages from the RegionServer to Master in milliseconds.

Default. 3000

hbase.regionserver.regionSplitLimit

Limit for the number of regions after which no more region splitting should take place. This is not a hard limit for the number of regions but acts as a guideline for the regionserver to stop splitting after a certain limit. Default is MAX_INT; i.e. do not block splitting.

Default. 2147483647

hbase.regionserver.logroll.period

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

Default. 3600000

hbase.regionserver.logroll.errors.tolerated

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

Default. 2

hbase.regionserver.hlog.reader.impl

The HLog file reader implementation.

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

hbase.regionserver.hlog.writer.impl

The HLog file writer implementation.

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

hbase.master.distributed.log.replay

Enable 'distributed log replay' as default engine splitting WAL files on server crash. This default is new in hbase 1.0. To fall back to the old mode 'distributed log splitter', set the value to 'false'. 'Disributed log replay' improves MTTR because it does not write intermediate files. 'DLR' required that 'hfile.format.version' be set to version 3 or higher.

Default. true

hbase.regionserver.global.memstore.size

Maximum size of all memstores in a region server before new updates are blocked and flushes are forced. Defaults to 40% of heap. Updates are blocked and flushes are forced until size of all memstores in a region server hits hbase.regionserver.global.memstore.size.lower.limit.

Default. 0.4

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

Maximum size of all memstores in a region server before flushes are forced. Defaults to 95% of hbase.regionserver.global.memstore.size. A 100% value for this value causes the minimum possible flushing to occur when updates are blocked due to memstore limiting.

Default. 0.95

hbase.regionserver.optionalcacheflushinterval

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

Default. 3600000

hbase.regionserver.catalog.timeout

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

Default. 600000

hbase.regionserver.dns.interface

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

Default. default

hbase.regionserver.dns.nameserver

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

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

Default. org.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicy

zookeeper.session.timeout

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

Default. 90000

zookeeper.znode.parent

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

Default. /hbase

zookeeper.znode.rootserver

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

Default. root-region-server

zookeeper.znode.acl.parent

Root ZNode for access control lists.

Default. acl

hbase.zookeeper.dns.interface

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

Default. default

hbase.zookeeper.dns.nameserver

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

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

Default. 2888

hbase.zookeeper.leaderport

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

Default. 3888

hbase.zookeeper.useMulti

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

Default. true

hbase.config.read.zookeeper.config

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

Default. false

hbase.zookeeper.property.initLimit

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

Default. 10

hbase.zookeeper.property.syncLimit

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

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

Default. ${hbase.tmp.dir}/zookeeper

hbase.zookeeper.property.clientPort

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

Default. 2181

hbase.zookeeper.property.maxClientCnxns

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

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

Default. 2097152

hbase.client.pause

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

Default. 100

hbase.client.retries.number

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

Default. 35

hbase.client.max.total.tasks

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

Default. 100

hbase.client.max.perserver.tasks

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

Default. 5

hbase.client.max.perregion.tasks

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

Default. 1

hbase.client.scanner.caching

Number of rows that will be fetched when calling next on a scanner if it is not served from (local, client) memory. 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. 100

hbase.client.keyvalue.maxsize

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

Default. 10485760

hbase.client.scanner.timeout.period

Client scanner lease period in milliseconds.

Default. 60000

hbase.client.localityCheck.threadPoolSize

Default. 2

hbase.bulkload.retries.number

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. 0

hbase.balancer.period

Period at which the region balancer runs in the Master.

Default. 300000

hbase.regions.slop

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

Default. 0.2

hbase.server.thread.wakefrequency

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

Default. 10000

hbase.server.versionfile.writeattempts

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

Default. 3

hbase.hregion.memstore.flush.size

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.preclose.flush.size

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

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

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

Default. true

hbase.hregion.max.filesize

Maximum HFile size. If the sum of the sizes of a region's HFiles has grown to exceed this value, the region is split in two.

Default. 10737418240

hbase.hregion.majorcompaction

Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based automatic major compactions. User-requested and size-based major compactions will still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at a somewhat-random time during a given window of time. 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

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

If more than 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.hstore.flusher.count

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

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. 10

hbase.hstore.blockingWaitTime

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

The minimum number of StoreFiles which must be eligible for compaction before compaction can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with too many tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction each time you have two StoreFiles in a Store, and this is probably not appropriate. If you set this value too high, all the other values will need to be adjusted accordingly. For most cases, the default value is appropriate. In previous versions of HBase, the parameter hbase.hstore.compaction.min was named hbase.hstore.compactionThreshold.

Default. 3

hbase.hstore.compaction.max

The maximum number of StoreFiles which will be selected for a single minor compaction, regardless of the number of eligible StoreFiles. Effectively, the value of hbase.hstore.compaction.max controls the length of time it takes a single compaction to complete. Setting it larger means that more StoreFiles are included in a compaction. For most cases, the default value is appropriate.

Default. 10

hbase.hstore.compaction.min.size

A StoreFile smaller than this size will always be eligible for minor compaction. 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

A StoreFile larger than this size will be excluded from compaction. The effect of raising hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get compacted often. If you feel that compaction is happening too often without much benefit, you can try raising this value. Default: the value of LONG.MAX_VALUE, expressed in bytes.

Default. 9223372036854775807

hbase.hstore.compaction.ratio

For minor compaction, this ratio is used to determine whether a given StoreFile which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction of large 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

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

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

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

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

There are two different thread pools for compactions, one for large compactions and the other for small compactions. This helps to keep compaction of lean tables (such as hbase:meta) fast. If a compaction is larger than this threshold, it goes into the large compaction pool. In most cases, the default value is appropriate. Default: 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults to 128). The value field assumes that the value of hbase.hregion.memstore.flush.size is unchanged from the default.

Default. 2560

hbase.hstore.compaction.kv.max

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

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

Default. false

hbase.storescanner.parallel.seek.threads

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

Default. 10

hfile.block.cache.size

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

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

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

Where to store the contents of the bucketcache. One of: onheap, offheap, or file. If a file, set it to file:PATH_TO_FILE. See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html for more information.

Default. 

hbase.bucketcache.combinedcache.enabled

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

Default. true

hbase.bucketcache.size

The size of the buckets for the bucketcache if you only use a single size. Defaults to the default blocksize, which is 64 * 1024.

Default. 65536

hbase.bucketcache.sizes

A comma-separated list of sizes for buckets for the bucketcache if you use multiple sizes. Should be a list of block sizes in order from smallest to largest. The sizes you use will depend on your data access patterns.

Default. 

hfile.format.version

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

Default. 3

hfile.block.bloom.cacheonwrite

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

Default. false

io.storefile.bloom.block.size

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

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

Default. false

hbase.rpc.timeout

This is for the RPC layer to define how long 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.rpc.shortoperation.timeout

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

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.master.keytab.file

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

Default. 

hbase.master.kerberos.principal

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. 

hbase.regionserver.keytab.file

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

Default. 

hbase.regionserver.kerberos.principal

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. 

hadoop.policy.file

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

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

Default. 

hbase.auth.key.update.interval

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

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

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.display.keys

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.region.classes

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

Default. 

hbase.rest.port

The port for the HBase REST server.

Default. 8080

hbase.rest.readonly

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

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

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

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

Default. false

hbase.defaults.for.version.skip

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

Default. false

hbase.coprocessor.master.classes

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. 

hbase.coprocessor.abortonerror

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

Default. true

hbase.online.schema.update.enable

Set true to enable online schema changes.

Default. true

hbase.table.lock.enable

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

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

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

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

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

Default. 1000

hbase.thrift.htablepool.size.max

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

Default. 1000

hbase.regionserver.thrift.framed

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

Default frame size when using framed transport

Default. 2

hbase.regionserver.thrift.compact

Use Thrift TCompactProtocol binary serialization protocol.

Default. false

hbase.data.umask.enable

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

Default. false

hbase.data.umask

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

Default. 000

hbase.metrics.showTableName

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

Default. true

hbase.metrics.exposeOperationTimes

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

Default. true

hbase.snapshot.enabled

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

Default. true

hbase.snapshot.restore.take.failsafe.snapshot

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

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

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

hbase.server.compactchecker.interval.multiplier

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

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

Default. 900000

hbase.lease.recovery.dfs.timeout

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

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

Default. 1

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

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

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

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

Default. 16384

hbase.hstore.checksum.algorithm

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

Default. CRC32

hbase.status.published

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

Implementation of the status publication with a multicast message.

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

hbase.status.listener.class

Implementation of the status listener with a multicast message.

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

hbase.status.multicast.address.ip

Multicast address to use for the status publication by multicast.

Default. 226.1.1.3

hbase.status.multicast.address.port

Multicast port to use for the status publication by multicast.

Default. 16100

hbase.dynamic.jars.dir

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

Default. ${hbase.rootdir}/lib

hbase.security.authentication

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

Default. simple

hbase.rest.filter.classes

Servlet filters for REST service.

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

hbase.master.loadbalancer.class

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

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

hbase.security.exec.permission.checks

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

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. 

hbase.procedure.master.classes

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. 

hbase.coordinated.state.manager.class

Fully qualified name of class implementing coordinated state manager.

Default. org.apache.hadoop.hbase.coordination.ZkCoordinatedStateManager

hbase.regionserver.storefile.refresh.period

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

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

Default. false

hbase.http.filter.initializers

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

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

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

Default. 10

hbase.replication.rpc.codec

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

Default. org.apache.hadoop.hbase.codec.KeyValueCodecWithTags

hbase.http.staticuser.user

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

2.4.2. 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.

2.4.3. log4j.properties

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

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

2.4.4. Client configuration and dependencies connecting to an HBase cluster

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

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

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

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

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

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

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

2.4.4.1. Java client configuration

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

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

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

2.5. Example Configurations

2.5.1. Basic Distributed HBase Install

Here is an example basic configuration for a distributed ten node cluster. The nodes are named example0, example1, etc., through node example9 in this example. The HBase Master and the HDFS namenode are running on the node example0. RegionServers run on nodes example1-example9. A 3-node ZooKeeper ensemble runs on example1, example2, and example3 on the default ports. ZooKeeper data is persisted to the directory /export/zookeeper. Below we show what the main configuration files -- hbase-site.xml, regionservers, and hbase-env.sh -- found in the HBase conf directory might look like.

2.5.1.1. 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>

        

2.5.1.2. 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
        

2.5.1.3. hbase-env.sh

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

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

# The maximum amount of heap to use, in MB. Default is 1000.
export HBASE_HEAPSIZE=4096
        

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

2.6. The Important Configurations

Below we list what the important Configurations. We've divided this section into required configuration and worth-a-look recommended configs.

2.6.1. Required Configurations

Review the Operating System Utilities and Section 2.1.1, “Hadoop” sections.

2.6.1.1. Big Cluster Configurations

If a cluster with a lot of regions, it is possible if an eager beaver regionserver checks in soon after master start while all the rest in the cluster are laggardly, this first server to checkin will be assigned all regions. If lots of regions, this first server could buckle under the load. To prevent the above scenario happening up the hbase.master.wait.on.regionservers.mintostart 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.

2.6.1.2. If a backup Master, making primary Master fail fast

If the primary Master loses its connection with ZooKeeper, it will fall into a loop where it keeps trying to reconnect. Disable this functionality if you are running more than one Master: i.e. a backup Master. Failing to do so, the dying Master may continue to receive RPCs though another Master has assumed the role of primary. See the configuration ???.

2.6.2. Recommended Configurations

2.6.2.1. ZooKeeper Configuration

2.6.2.1.1. zookeeper.session.timeout

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

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

We set this value high to save our having to field noob 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.

2.6.2.1.2. Number of ZooKeeper Instances

See Chapter 20, ZooKeeper.

2.6.2.2. HDFS Configurations

2.6.2.2.1. 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. If you have > three or four disks, you might want to set this to 1 or if you have many disks, two or more.

2.6.2.3. hbase.regionserver.handler.count

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

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

The reason why it is dangerous to keep this setting high is that the aggregate size of all the puts that are currently happening in a region server may impose too much pressure on its memory, or even trigger an OutOfMemoryError. A region server 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 region server 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 Section 15.2.2.1, “Enabling RPC-level logging” on an individual RegionServer then tailing its logs (Queued requests consume memory).

2.6.2.4. Configuration for large memory machines

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

2.6.2.5. Compression

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

See Appendix E, Compression and Data Block Encoding In HBase for more information.

2.6.2.6. Configuring the size and number of WAL files

HBase uses Section 9.6.5, “Write Ahead Log (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, HDFS block is 64Mb and WAL file is ~60Mb).

HBase also has a limit on 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 allocated 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.

2.6.2.7. Managed Splitting

HBase generally handles splitting your regions, based upon the settings in your hbase-default.xml and hbase-site.xml configuration files. Important settings include hbase.regionserver.region.split.policy, hbase.hregion.max.filesize, hbase.regionserver.regionSplitLimit. A simplistic view of splitting is that when a region grows to hbase.hregion.max.filesize, it is split. For most use patterns, most of the time, you should use automatic splitting. See Section 9.7.5, “Manual Region Splitting” for more information about manual region splitting.

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

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

Automatic Splitting Is Recommended

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

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

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

2.6.2.8. Managed Compactions

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

If you need to control exactly when and how often major compaction runs, you can disable managed major compactions. See the entry for hbase.hregion.majorcompaction in the Section 9.7.7.7.1.4, “Parameters Used by Compaction Algorithm” 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 HBaseAdmin API.

For more information about compactions and the compaction file selection process, see Section 9.7.7.7, “Compaction”

2.6.2.9. Speculative Execution

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

2.6.3. Other Configurations

2.6.3.1. 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 Section 9.5.4.1, “LoadBalancer” for more information on the LoadBalancer.

2.6.3.2. Disabling Blockcache

Do not turn off block cache (You'd do it by setting hbase.block.cache.size to zero). Currently we do not do well if you do this because the regionserver will spend all its time loading hfile indices over and over again. If your working set it 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).

2.6.3.3. 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.

2.6.3.4. 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 an Nicolas Liochon blog post Introduction to HBase Mean Time to Recover (MTTR) for a brief introduction.

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

<property>
<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>

2.6.3.5. 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 official document for more information. Historically, besides above port mentioned, JMX opens 2 additional random TCP listening ports, which could lead to port conflict problem.(See HBASE-10289 for details)

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

<property>
    <name>hbase.coprocessor.regionserver.classes</name>
    <value>org.apache.hadoop.hbase.JMXListener</value>
</property>

NOTE: DO NOT set com.sun.management.jmxremote.port for Java VM at the same time.

Currently it supports Master and RegionServer Java VM. The reason why you only configure coprocessor for 'regionserver' is that, starting from HBase 0.99, a Master IS also a RegionServer. (See HBASE-10569 for more information.) 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 client using the key store:

jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore
      

NOTE: for HBase 0.98, 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)

2.7. Dynamic Configuration

Changing Configuration Without Restarting Servers

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

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

Chapter 3. Upgrading

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

Note

It may be possible to skip across versions -- for example go from 0.92.2 straight to 0.98.0 just following the 0.96.x upgrade instructions -- but we have not tried it so cannot say whether it works or not.

Review Chapter 2, Apache HBase Configuration, in particular the section on Hadoop version.

3.1. HBase version numbers

HBase has not walked a straight line where version numbers are concerned. Since we came up out of hadoop itself, we originally tracked hadoop versioning. Later we left hadoop versioning behind because we were moving at a different rate to that of our parent. If you are into the arcane, checkout our old wiki page on HBase Versioning which tries to connect the HBase version dots.

3.1.1. Odd/Even Versioning or "Development"" Series Releases

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

Our first "Development" Series was the 0.89 set that came out ahead of HBase 0.90.0. HBase 0.95 is another "Development" Series that portends HBase 0.96.0.

3.1.2. 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 binary compatible. You can safely do rolling upgrades between binary compatible versions; i.e. across point versions: e.g. from 0.94.5 to 0.94.6. See Does compatibility between versions also mean binary compatibility? discussion on the hbaes dev mailing list.

3.1.3. Rolling Upgrades

A rolling upgrade is the process by which you update the servers in your cluster a server at a time. You can rolling upgrade across HBase versions if they are binary or wire compatible. See <xlnk></xlnk> 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 <xlink></xlink> 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 cluser, 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.

3.1.3.1. Rolling Upgrade between versions that are Binary/Wire compatibile

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

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

3.2. Upgrading from 0.98.x to 1.0.x

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

3.2.1. Changes of Note!

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

3.2.1.1. ZooKeeper 3.4 is required in HBase 1.0.0

See Section 2.1.2, “ZooKeeper Requirements”.

3.2.1.2. HBase Default Ports Changed

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

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

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

3.2.2. Rolling upgrade from 0.98.x to HBase 1.0.0

From 0.96.x to 1.0.0

You cannot do a <xlink></xlink> from 0.96.x to 1.0.0 without first doing a rolling upgrade to 0.98.x. See comment in HBASE-11164 Document and test rolling updates from 0.98 -> 1.0 for the why.

There are no known issues running a <xlink></xlink> from hbase-0.98.x to hbase-1.0.0.

3.2.3. Upgrading to 1.0 from 0.94

You cannot rolling upgrade from 0.94.x to 1.x.x. You must stop your cluster, install the 1.x.x software, run the migration described at Section 3.5.1, “Executing the 0.96 Upgrade” (substituting 1.x.x. wherever we make mention of 0.96.x in the section below), and then restart. Be sure to upgrade your zookeeper if it is a version less than the required 3.4.x.

3.3. Upgrading from 0.96.x to 0.98.x

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

Additional steps are required to take advantage of some of the new features of 0.98.x, including cell visibility labels, cell ACLs, and transparent server side encryption. See the Chapter 8, Secure Apache HBase chapter of this guide for more information. Significant performance improvements include a change to the write ahead log threading model that provides higher transaction throughput under high load, reverse scanners, MapReduce over snapshot files, and striped compaction.

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

3.4. Upgrading from 0.94.x to 0.98.x

A rolling upgrade from 0.94.x directly to 0.98.x does not work. The upgrade path follows the same procedures as Section 3.5, “Upgrading from 0.94.x to 0.96.x”. Additional steps are required to use some of the new features of 0.98.x. See Section 3.3, “Upgrading from 0.96.x to 0.98.x” for an abbreviated list of these features.

3.5. Upgrading from 0.94.x to 0.96.x

The "Singularity"

HBase 0.96.x was EOL'd, September 1st, 2014

Do not deploy 0.96.x Deploy a 0.98.x at least. See EOL 0.96.

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

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

3.5.1. Executing the 0.96 Upgrade

HDFS and ZooKeeper must be up!

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

hbase-0.96.0 comes with an upgrade script. Run

$ bin/hbase upgrade

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

3.5.1.1. check

The check step is run against a running 0.94 cluster. Run it from a downloaded 0.96.x binary. The check step is looking for the presence of HFileV1 files. These are unsupported in hbase-0.96.0. To purge them -- have them rewritten as HFileV2 -- you must run a compaction.

The check step prints stats at the end of its run (grep for “Result:” in the log) printing absolute path of the tables it scanned, any HFileV1 files found, the regions containing said files (the regions we need to major compact to purge the HFileV1s), and any corrupted files if any found. A corrupt file is unreadable, and so is undefined (neither HFileV1 nor HFileV2).

To run the check step, run $ bin/hbase upgrade -check. Here is sample output:

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

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

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

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

In the above sample output, there are two HFileV1 in two regions, and one corrupt file. Corrupt files should probably be removed. The regions that have HFileV1s need to be major compacted. To major compact, start up the hbase shell and review how to compact an individual region. After the major compaction is done, rerun the check step and the HFileV1s shoudl be gone, replaced by HFileV2 instances.

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

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

The above command would detect HFileV1s in the /myHBase/testTable directory.

Once the check step reports all the HFileV1 files have been rewritten, it is safe to proceed with the upgrade.

3.5.1.2. execute

After the check step shows the cluster is free of HFileV1, it is safe to proceed with the upgrade. Next is the execute step. You must SHUTDOWN YOUR 0.94.x CLUSTER before you can run the execute step. The execute step will not run if it detects running HBase masters or regionservers.

Note

HDFS and ZooKeeper should be up and running during the upgrade process. If zookeeper is managed by HBase, then you can start zookeeper so it is available to the upgrade by running $ ./hbase/bin/hbase-daemon.sh start zookeeper

The execute upgrade step is made of three substeps.

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

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

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

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

$ bin/hbase upgrade -execute

Here is some sample output.

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

Starting Log splitting
…
Successfully completed Log splitting
         

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

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

Now start up hbase-0.96.0.

3.5.1.3. Troubleshooting

3.5.1.3.1. Old Client connecting to 0.96 cluster

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

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

3.5.1.4. Upgrading META to use Protocol Buffers (Protobuf)

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

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

3.6. Upgrading from 0.92.x to 0.94.x

We used to think that 0.92 and 0.94 were interface compatible and that you can do a rolling upgrade between these versions but then we figured that HBASE-5357 Use builder pattern in HColumnDescriptor changed method signatures so rather than return void they instead return HColumnDescriptor. This will throw

java.lang.NoSuchMethodError: org.apache.hadoop.hbase.HColumnDescriptor.setMaxVersions(I)V

.... so 0.92 and 0.94 are NOT compatible. You cannot do a rolling upgrade between them.

3.7. Upgrading from 0.90.x to 0.92.x

Upgrade Guide

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

tl;dr

If you've not patience, here are the important things to know upgrading.

  1. Once you upgrade, you can’t go back.

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

  3. Distributed Log Splitting is on by default. It should make region server failover faster.

  4. There’s a separate tarball for security.

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

3.7.1. You can’t go back!

To move to 0.92.0, all you need to do is shutdown your cluster, replace your hbase 0.90.x with hbase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart). On startup, the .META. table content is rewritten removing the table schema from the info:regioninfo column. Also, any flushes done post first startup will write out data in the new 0.92.0 file format, HFile V2. This means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase data directory.

3.7.2. MSLAB is ON by default

In 0.92.0, the hbase.hregion.memstore.mslab.enabled flag is set to true (See Section 14.3.1.1, “Long GC pauses”). In 0.90.x it was false. When it is enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the memstore has zero or just a few small elements. This is fine usually but if you had lots of regions per regionserver in a 0.90.x cluster (and MSLAB was off), you may find yourself OOME'ing on upgrade because the thousands of regions * number of column families * 2MB MSLAB (at a minimum) puts your heap over the top. Set hbase.hregion.memstore.mslab.enabled to false or set the MSLAB size down from 2MB by setting hbase.hregion.memstore.mslab.chunksize to something less.

3.7.3. Distributed Log Splitting is on by default

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

3.7.4. Memory accounting is different now

In 0.92.0, Section H.2, “ HBase file format with inline blocks (version 2) ” indices and bloom filters take up residence in the same LRU used caching blocks that come from the filesystem. In 0.90.x, the HFile v1 indices lived outside of the LRU so they took up space even if the index was on a ‘cold’ file, one that wasn’t being actively used. With the indices now in the LRU, you may find you have less space for block caching. Adjust your block cache accordingly. See the Section 9.6.4, “Block Cache” for more detail. The block size default size has been changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25.

3.7.5. On the Hadoop version to use

Run 0.92.0 on Hadoop 1.0.x (or CDH3u3 when it ships). The performance benefits are worth making the move. Otherwise, our Hadoop prescription is as it has been; you need an Hadoop that supports a working sync. See Section 2.1.1, “Hadoop”.

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

3.7.6. HBase 0.92.0 ships with ZooKeeper 3.4.2

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

3.7.7. Online alter is off by default

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

3.7.8. WebUI

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

3.7.9. Security tarball

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

3.7.10. Changes in HBase replication

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

3.7.11. RegionServer now aborts if OOME

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

3.7.12. HFile V2 and the “Bigger, Fewer” Tendency

0.92.0 stores data in a new format, Section H.2, “ HBase file format with inline blocks (version 2) ”. As HBase runs, it will move all your data from HFile v1 to HFile v2 format. This auto-migration will run in the background as flushes and compactions run. HFile V2 allows HBase run with larger regions/files. In fact, we encourage that all HBasers going forward tend toward Facebook axiom #1, run with larger, fewer regions. If you have lots of regions now -- more than 100s per host -- you should look into setting your region size up after you move to 0.92.0 (In 0.92.0, default size is now 1G, up from 256M), and then running online merge tool (See “HBASE-1621 merge tool should work on online cluster, but disabled table”).

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

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

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

Finally, if upgrading from 0.20.x, check your .META. schema in the shell. In the past we would recommend that users run with a 16kb MEMSTORE_FLUSHSIZE. Run hbase> scan '-ROOT-' in the shell. This will output the current .META. schema. Check MEMSTORE_FLUSHSIZE size. Is it 16kb (16384)? If so, you will need to change this (The 'normal'/default value is 64MB (67108864)). Run the script bin/set_meta_memstore_size.rb. This will make the necessary edit to your .META. schema. Failure to run this change will make for a slow cluster. See HBASE-3499 Users upgrading to 0.90.0 need to have their .META. table updated with the right MEMSTORE_SIZE

Chapter 4. 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 emission 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 Procedure 1.2, “Use HBase For the First Time” for example basic shell operation.

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

4.1. 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

4.2. 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.

4.3. 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.

Note

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 4.1. Passing Commands to the HBase Shell

You can pass commands to the HBase Shell in non-interactive mode (see ???) 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 4.2. 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
            

4.3.1. 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.

4.4. 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 4.3. 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 4.4. 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                
            

4.5. 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            
        

4.6. Shell Tricks

4.6.1. Table variables

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

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

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

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

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

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

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

hbase(main):006:0> 
	  

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

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

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

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

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

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

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

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

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

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

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

4.6.2. irbrc

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

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

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

4.6.3. 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.

4.6.4. Debug

4.6.4.1. 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>

4.6.4.2. DEBUG log level

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

$ ./bin/hbase shell -d

4.6.5. Commands

4.6.5.1. 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.

Chapter 5. 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 families are specified when you create your table, and influence the way your data is stored in the underlying filesystem. Therefore, the column families should be considered carefully during schema design.

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.

A cell's value is an uninterpreted array of bytes.

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.

Caution

Direct manipulation of timestamps is an advanced feature which is only exposed for special cases that are deeply integrated with HBase, and is discouraged in general. Encoding a timestamp at the application level is the preferred pattern.

You can specify the maximum number of versions of a value that HBase retains, per column family. When the maximum number of versions is reached, the oldest versions are eventually deleted. By default, only the newest version is kept.

5.1. 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), 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 5.1. Table webtable

Row KeyTime StampColumnFamily contentsColumnFamily anchorColumnFamily people
"com.cnn.www"t9 anchor:cnnsi.com = "CNN" 
"com.cnn.www"t8 anchor:my.look.ca = "CNN.com" 
"com.cnn.www"t6contents:html = "<html>..."  
"com.cnn.www"t5contents:html = "<html>..."  
"com.cnn.www"t3contents:html = "<html>..."  
"com.example.www"t5contents: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"
		}
	}
}        
        

5.2. Physical View

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

Table 5.2. ColumnFamily anchor

Row KeyTime StampColumn Family anchor
"com.cnn.www"t9anchor:cnnsi.com = "CNN"
"com.cnn.www"t8anchor:my.look.ca = "CNN.com"

Table 5.3. ColumnFamily contents

Row KeyTime StampColumnFamily "contents:"
"com.cnn.www"t6contents:html = "<html>..."
"com.cnn.www"t5contents:html = "<html>..."
"com.cnn.www"t3contents: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 Section 9.7, “Regions”.

5.3. 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 (ie regions, tables) a namespace can consume.

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

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

5.3.1. 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 5.1. 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'}
        

5.3.2. 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 5.2. Examples

#namespace=foo and table qualifier=bar
create 'foo:bar', 'fam'

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

5.4. Table

Tables are declared up front at schema definition time.

5.5. Row

Row keys are uninterrpreted 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.

5.6. 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 . 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 an 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.

5.7. Cells

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

5.8. Data Model Operations

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

5.8.1. Get

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

5.8.2. 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 HTable.put (writeBuffer) or HTable.batch (non-writeBuffer).

5.8.3. Scans

Scan allow iteration over multiple rows for specified attributes.

The following is an example of a on an HTable 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();
...

HTable htable = ...      // instantiate HTable

Scan scan = new Scan();
scan.addColumn(CF, ATTR);
scan.setRowPrefixFilter(Bytes.toBytes("row"));
ResultScanner rs = htable.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.

5.8.4. Delete

Delete removes a row from a table. Deletes are executed via HTable.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 Section 5.9.2.5, “Delete” for more information on deleting versions of columns, and see Section 9.7.7.7, “Compaction” for more information on compactions.

5.9. 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 limiitation 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.

5.9.1. 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 5.3. Modify the Maximum Number of Versions for a Column

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

hbase> alter ‘t1′, NAME => ‘f1′, VERSIONS => 5

Example 5.4. Modify the Minimum Number of Versions for a Column

You can also specify the minimum number of versions to store. By default, this is set to 0, which means the feature is disabled. The following example sets the minimum number of versions on field 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.

5.9.2. Versions and HBase Operations

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

5.9.2.1. 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.

5.9.2.2. 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 = htable.get(get);
byte[] b = r.getValue(CF, ATTR);  // returns current version of value

5.9.2.3. 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 = htable.get(get);
byte[] b = r.getValue(CF, ATTR);  // returns current version of value
List<KeyValue> kv = r.getColumn(CF, ATTR);  // returns all versions of this column

5.9.2.4. 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 would overshadow.

5.9.2.4.1. 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));
htable.put(put);
5.9.2.4.2. 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));
htable.put(put);

Caution: the version timestamp is 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 a part of the rowkey, or both.

5.9.2.5. 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 Section 9.7.7.6, “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. 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.

Note

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.

5.9.3. Current Limitations

5.9.3.1. Deletes mask Puts

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

5.9.3.2. Major compactions change query results

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

5.10. 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).

5.11. 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 heterogenous 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 Section 9.7.7.6, “KeyValue”.

5.12. 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.

5.13. ACID

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

Chapter 6. HBase and Schema Design

A good general introduction on the strength and weaknesses modelling on the various non-rdbms datastores is Ian Varley's Master thesis, No Relation: The Mixed Blessings of Non-Relational Databases. Recommended. Also, read Section 9.7.7.6, “KeyValue” for how HBase stores data internally, and the section on Section 6.11, “Schema Design Case Studies”.

6.1.  Schema Creation

HBase schemas can be created or updated with Chapter 4, The Apache HBase Shell or by using HBaseAdmin in the Java API.

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

Configuration config = HBaseConfiguration.create();
HBaseAdmin admin = new HBaseAdmin(conf);
String table = "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 Section 2.4.4, “Client configuration and dependencies connecting to an HBase cluster” for more information about configuring client connections.

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

6.1.1. 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 Section 9.7.7, “Store” for more information on StoreFiles.

6.2.  On the number of column families

HBase currently does not do well with anything above two or three column families so keep the number of column families in your schema low. Currently, flushing and compactions are done on a per Region basis so if one column family is carrying the bulk of the data bringing on flushes, the adjacent families will also be flushed though the amount of data they carry is small. When many column families the flushing and compaction interaction can make for a bunch of needless i/o loading (To be addressed by changing flushing and compaction to work on a per column family basis). For more information on compactions, see Section 9.7.7.7, “Compaction”.

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

6.2.1. 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.

6.3. Rowkey Design

6.3.1. 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 6.1. 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 6.2. 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.

6.3.2.  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 Section 6.11, “Schema Design Case Studies” for some rowkey design examples.

6.3.3. Try to minimize row and column sizes

Or why are my StoreFile indices large?

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 (Section 9.7.7.4, “StoreFile (HFile)”) to facilitate random access may end up occupyng 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 Section 9.7.7.6, “KeyValue” for more information on HBase stores data internally to see why this is important.

6.3.3.1. Column Families

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

See Section 9.7.7.6, “KeyValue” for more information on HBase stores data internally to see why this is important.

6.3.3.2. Attributes

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

See Section 9.7.7.6, “KeyValue” for more information on HBase stores data internally to see why this is important.

6.3.3.3. 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.

6.3.3.4. 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 = "" + 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.

6.3.4. Reverse Timestamps

Reverse Scan API

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

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

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

This technique would be used instead of using Section 6.4, “ 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.

6.3.5. Rowkeys and ColumnFamilies

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

6.3.6. 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).

6.3.7. 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 HBaseAdmin.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 regions will never be used. To make pre-spliting 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(HBaseAdmin 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;
}

6.4.  Number of Versions

6.4.1. 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 Chapter 5, 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.

6.4.2.  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.

6.5.  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 mailling list for conversations on this topic. All rows in HBase conform to the Chapter 5, Data Model, and that includes versioning. Take that into consideration when making your design, as well as block size for the ColumnFamily.

6.5.1. Counters

One supported datatype that deserves special mention are "counters" (i.e., the ability to do atomic increments of numbers). See Increment in HTable.

Synchronization on counters are done on the RegionServer, not in the client.

6.6. Joins

If you have multiple tables, don't forget to factor in the potential for Section 5.12, “Joins” into the schema design.

6.7. 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.

6.8.  Keeping Deleted Cells

By default, delete markers extend back to the beginning of time. Therefore, Get or Scan operations will not see a deleted cell (row or column), even when the Get or Scan operation indicates a time range before the delete marker was placed.

ColumnFamilies can optionally keep deleted cells. In this case, deleted cells can still be retrieved, as long as these operations specify a time range that ends before the timestamp of any delete that would affect the cells. This allows for point-in-time queries even in the presence of deletes.

Deleted cells are still subject to TTL and there will never be more than "maximum number of versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete markers.

Example 6.3. Change the Value of KEEP_DELETED_CELLS Using HBase Shell

hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true

Example 6.4. Change the Value of KEEP_DELETED_CELLS Using the API

...
HColumnDescriptor.setKeepDeletedCells(true);
...
      

See the API documentation for KEEP_DELETED_CELLS for more information.

6.9.  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 Chapter 14, 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

6.9.1.  Filter Query

Depending on the case, it may be appropriate to use Section 9.4, “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).

6.9.2.  Periodic-Update Secondary Index

A secondary index could be created in an other 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 Section 7.8.2, “HBase MapReduce Read/Write Example” for more information.

6.9.3.  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 Section 6.9.2, “ Periodic-Update Secondary Index ”).

6.9.4.  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 Section 7.8.4, “HBase MapReduce Summary to HBase Example” for more information.

6.9.5.  Coprocessor Secondary Index

Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, see Section 9.6.3, “Coprocessors”

6.10. 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 (eg. 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.

6.11. 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 Chapter 6, 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

6.11.1. 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?

6.11.1.1. Timestamp In The Rowkey Lead Position

The rowkey [timestamp][hostname][log-event] suffers from the monotonically increasing rowkey problem described in Section 6.3.2, “ 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.

6.11.1.2. 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.

6.11.1.3. Timestamp, or Reverse Timestamp?

If the most important access path is to pull most recent events, then storing the timestamps as reverse-timestamps (e.g., timestamp = Long.MAX_VALUE – timestamp) will create the property of being able to do a Scan on [hostname][log-event] to obtain the quickly obtain the most recently captured events.

Neither approach is wrong, it just depends on what is most appropriate for the situation.

Reverse Scan API

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

6.11.1.4. Variangle 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.

6.11.2. 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.

6.11.3. 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:

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 a 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

6.11.3.1. 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.

6.11.3.2. 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.

6.11.3.2.1. 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: Section 6.11.3, “Case Study - Customer/Order”

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.

6.11.3.2.2. Single Table With Record Types

With this approach, there would exist a single table ORDER that would contain

The Order rowkey was described above: Section 6.11.3, “Case Study - Customer/Order”

  • [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.)

6.11.3.2.3. 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 heirarchy, but one of the cons is that updating gets more complicated in case any of this information changes.

6.11.3.2.4. 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: Section 6.11.3, “Case Study - Customer/Order”, 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 compatibilty 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 compatiblity 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.

6.11.4. 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.

6.11.4.1. 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 overwite with each successive update.

Preference: Rows (generally speaking).

6.11.4.2. 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."

6.11.4.3. 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 Section 6.11.2, “Case Study - Log Data and Timeseries Data on Steroids”.

6.11.5. Case Study - List Data

The following is an exchange from the user dist-list regarding a fairly common question: how to handle per-user list data in Apache HBase.

*** QUESTION ***

We're looking at how to store a large amount of (per-user) list data in HBase, and we were trying to figure out what kind of access pattern made the most sense. One option is store the majority of the data in a key, so we could have something like:

<FixedWidthUserName><FixedWidthValueId1>:"" (no value)
<FixedWidthUserName><FixedWidthValueId2>:"" (no value)
<FixedWidthUserName><FixedWidthValueId3>:"" (no value)

The other option we had was to do this entirely using:

<FixedWidthUserName><FixedWidthPageNum0>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
<FixedWidthUserName><FixedWidthPageNum1>:<FixedWidthLength><FixedIdNextPageNum><ValueId1><ValueId2><ValueId3>...
    		

where each row would contain multiple values. So in one case reading the first thirty values would be:

scan { STARTROW => 'FixedWidthUsername' LIMIT => 30}
    		

And in the second case it would be

get 'FixedWidthUserName\x00\x00\x00\x00'
    		

The general usage pattern would be to read only the first 30 values of these lists, with infrequent access reading deeper into the lists. Some users would have <= 30 total values in these lists, and some users would have millions (i.e. power-law distribution)

The single-value format seems like it would take up more space on HBase, but would offer some improved retrieval / pagination flexibility. Would there be any significant performance advantages to be able to paginate via gets vs paginating with scans?

My initial understanding was that doing a scan should be faster if our paging size is unknown (and caching is set appropriately), but that gets should be faster if we'll always need the same page size. I've ended up hearing different people tell me opposite things about performance. I assume the page sizes would be relatively consistent, so for most use cases we could guarantee that we only wanted one page of data in the fixed-page-length case. I would also assume that we would have infrequent updates, but may have inserts into the middle of these lists (meaning we'd need to update all subsequent rows).

Thanks for help / suggestions / follow-up questions.

*** ANSWER ***

If I understand you correctly, you're ultimately trying to store triples in the form "user, valueid, value", right? E.g., something like:

"user123, firstname, Paul",
"user234, lastname, Smith"
			

(But the usernames are fixed width, and the valueids are fixed width).

And, your access pattern is along the lines of: "for user X, list the next 30 values, starting with valueid Y". Is that right? And these values should be returned sorted by valueid?

The tl;dr version is that you should probably go with one row per user+value, and not build a complicated intra-row pagination scheme on your own unless you're really sure it is needed.

Your two options mirror a common question people have when designing HBase schemas: should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for one user, and so there are many rows in the table for each user; the row key is user + valueid, and there would be (presumably) a single column qualifier that means "the value". This is great if you want to scan over rows in sorted order by row key (thus my question above, about whether these ids are sorted correctly). You can start a scan at any user+valueid, read the next 30, and be done. What you're giving up is the ability to have transactional guarantees around all the rows for one user, but it doesn't sound like you need that. Doing it this way is generally recommended (see here http://hbase.apache.org/book.html#schema.smackdown).

Your second option is "wide": you store a bunch of values in one row, using different qualifiers (where the qualifier is the valueid). The simple way to do that would be to just store ALL values for one user in a single row. I'm guessing you jumped to the "paginated" version because you're assuming that storing millions of columns in a single row would be bad for performance, which may or may not be true; as long as you're not trying to do too much in a single request, or do things like scanning over and returning all of the cells in the row, it shouldn't be fundamentally worse. The client has methods that allow you to get specific slices of columns.

Note that neither case fundamentally uses more disk space than the other; you're just "shifting" part of the identifying information for a value either to the left (into the row key, in option one) or to the right (into the column qualifiers in option 2). Under the covers, every key/value still stores the whole row key, and column family name. (If this is a bit confusing, take an hour and watch Lars George's excellent video about understanding HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk).

A manually paginated version has lots more complexities, as you note, like having to keep track of how many things are in each page, re-shuffling if new values are inserted, etc. That seems significantly more complex. It might have some slight speed advantages (or disadvantages!) at extremely high throughput, and the only way to really know that would be to try it out. If you don't have time to build it both ways and compare, my advice would be to start with the simplest option (one row per user+value). Start simple and iterate! :)

6.12. Operational and Performance Configuration Options

See the Performance section Section 14.6, “Schema Design” for more information operational and performance schema design options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes.

Chapter 7. HBase and MapReduce

Apache MapReduce is a software framework used to analyze large amounts of data, and is the framework used most often with Apache Hadoop. MapReduce itself is out of the scope of this document. A good place to get started with MapReduce is http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.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.

mapred and mapreduce

There are two mapreduce packages in HBase as in MapReduce itself: org.apache.hadoop.hbase.mapred and org.apache.hadoop.hbase.mapreduce. The former does old-style API and the latter the new style. The latter has more facility though you can usually find an equivalent in the older package. Pick the package that goes with your mapreduce deploy. When in doubt or starting over, pick the org.apache.hadoop.hbase.mapreduce. In the notes below, we refer to o.a.h.h.mapreduce but replace with the o.a.h.h.mapred if that is what you are using.

7.1. HBase, MapReduce, and the CLASSPATH

Ny 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 the $HADOOP_HOME/conf/ directory and add the HBase JARs to the HADOOP_HOME/conf/ directory, then copy these changes across your cluster. You could add hbase-site.xml to $HADOOP_HOME/conf and add HBase jars to the $HADOOP_HOME/lib. You would then need to copy these changes across your cluster or edit $HADOOP_HOMEconf/hadoop-env.sh and add them to the HADOOP_CLASSPATH variable. However, this approach is not recommended because it will pollute your Hadoop install with HBase references. It also requires you to restart the Hadoop cluster before Hadoop can use the HBase data.

Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself. The dependencies only need to be available on the local CLASSPATH. The following example runs the bundled HBase RowCounter MapReduce job against a table named usertable If you have not set the environment variables expected in the command (the parts prefixed by a $ sign and curly braces), you can use the actual system paths instead. Be sure to use the correct version of the HBase JAR for your system. The backticks (` symbols) cause ths shell to execute the sub-commands, setting the CLASSPATH as part of the command. This example assumes you use a BASH-compatible shell.

$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter usertable

When the command runs, internally, the HBase JAR finds the dependencies it needs for zookeeper, guava, and its other dependencies on the passed HADOOP_CLASSPATH and adds the JARs to the MapReduce job configuration. See the source at TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done.

Note

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=${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar:`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar rowcounter usertable

Notice to Mapreduce users of HBase 0.96.1 and above

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 Section 7.1, “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.

7.2. 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.

7.3. Bundled HBase MapReduce Jobs

The HBase JAR also serves as a Driver for some bundled mapreduce jobs. To learn about the bundled MapReduce jobs, run the following command.

$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar
An example program must be given as the first argument.
Valid program names are:
  copytable: Export a table from local cluster to peer cluster
  completebulkload: Complete a bulk data load.
  export: Write table data to HDFS.
  import: Import data written by Export.
  importtsv: Import data in TSV format.
  rowcounter: Count rows in HBase table
    

Each of the valid program names are bundled MapReduce jobs. To run one of the jobs, model your command after the following example.

$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter myTable

7.4. 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 if you are running a TaskTracer 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, you myour map might emit counts of records processed for reporting at the end of the jobj, 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.

7.5. 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 Section 9.8, “Bulk Loading”.

7.6. 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 rowcouner usage advice to standard output. Specify the tablename, column to count, and output directory. If you have classpath errors, see Section 7.1, “HBase, MapReduce, and the CLASSPATH”.

7.7. Map-Task Splitting

7.7.1. 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.

7.7.2. 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.

7.8. HBase MapReduce Examples

7.8.1. 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. There 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.
   }
}
    

7.8.2. HBase MapReduce Read/Write Example

The following is an example of using HBase both as a source and as a sink with MapReduce. This example will simply copy data from one table to another.

Configuration config = HBaseConfiguration.create();
Job job = new Job(config,"ExampleReadWrite");
job.setJarByClass(MyReadWriteJob.class);    // class that contains mapper

Scan scan = new Scan();
scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
scan.setCacheBlocks(false);  // don't set to true for MR jobs
// set other scan attrs

TableMapReduceUtil.initTableMapperJob(
	sourceTable,      // input table
	scan,	          // Scan instance to control CF and attribute selection
	MyMapper.class,   // mapper class
	null,	          // mapper output key
	null,	          // mapper output value
	job);
TableMapReduceUtil.initTableReducerJob(
	targetTable,      // output table
	null,             // reducer class
	job);
job.setNumReduceTasks(0);

boolean b = job.waitForCompletion(true);
if (!b) {
    throw new IOException("error with job!");
}
    

An explanation is required of what TableMapReduceUtil is doing, especially with the reducer. TableOutputFormat is being used as the outputFormat class, and several parameters are being set on the config (e.g., TableOutputFormat.OUTPUT_TABLE), as well as setting the reducer output key to ImmutableBytesWritable and reducer value to Writable. These could be set by the programmer on the job and conf, but TableMapReduceUtil tries to make things easier.

The following is the example mapper, which will create a Put and matching the input Result and emit it. Note: this is what the CopyTable utility does.

public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put>  {

	public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
		// this example is just copying the data from the source table...
   		context.write(row, resultToPut(row,value));
   	}

  	private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException {
  		Put put = new Put(key.get());
 		for (KeyValue kv : result.raw()) {
			put.add(kv);
		}
		return put;
   	}
}
    

There isn't actually a reducer step, so TableOutputFormat takes care of sending the Put to the target table.

This is just an example, developers could choose not to use TableOutputFormat and connect to the target table themselves.

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

TODO: example for MultiTableOutputFormat.

7.8.4. 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);
   	}
}
    

7.8.5. 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));
	}
}
    

7.8.6. 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 HTable 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 incremeneted for each map-task, and make one update per key at during the cleanup method of the mapper. However, your milage may vary depending on the number of rows to be processed and unique keys.

In the end, the summary results are in HBase.

7.8.7. 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.

7.9. 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 HTable instance in the setup method of the Mapper.

public class MyMapper extends TableMapper<Text, LongWritable> {
  private HTable myOtherTable;

  public void setup(Context context) {
    myOtherTable = new HTable("myOtherTable");
  }

  public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
	// process Result...
	// use 'myOtherTable' for lookups
  }

  

7.10. 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, on 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 Section 2.6.2.9, “Speculative Execution” for more information.

Chapter 8. Secure Apache HBase

8.1. 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.

8.1.1. Prerequisites

Hadoop Authentication Configuration

To run HBase RPC with strong authentication, you must set hbase.security.authentication to true. In this case, you must also set hadoop.security.authentication to true. 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.

A HBase configured for secure client access is expected to be running on top of a secured HDFS cluster. HBase must be able to authenticate to HDFS services. HBase needs Kerberos credentials to interact with the Kerberos-enabled HDFS daemons. Authenticating a service should be done using a keytab file. The procedure for creating keytabs for HBase service is the same as for creating keytabs for Hadoop. Those steps are omitted here. Copy the resulting keytab files to wherever HBase Master and RegionServer processes are deployed and make them readable only to the user account under which the HBase daemons will run.

A Kerberos principal has three parts, with the form username/fully.qualified.domain.name@YOUR-REALM.COM. We recommend using hbase as the username portion.

The following is an example of the configuration properties for Kerberos operation that must be added to the hbase-site.xml file on every server machine in the cluster. Required for even the most basic interactions with a secure Hadoop configuration, independent of HBase security.

<property>
  <name>hbase.regionserver.kerberos.principal</name>
  <value>hbase/_HOST@YOUR-REALM.COM</value>
</property>
<property>
  <name>hbase.regionserver.keytab.file</name>
  <value>/etc/hbase/conf/keytab.krb5</value>
</property>
<property>
  <name>hbase.master.kerberos.principal</name>
  <value>hbase/_HOST@YOUR-REALM.COM</value>
</property>
<property>
  <name>hbase.master.keytab.file</name>
  <value>/etc/hbase/conf/keytab.krb5</value>
</property>
    

Each HBase client user should also be given a Kerberos principal. This principal should have a password assigned to it (as opposed to a keytab file). The client principal's maxrenewlife should be set so that it can be renewed enough times for the HBase client process to complete. For example, if a user runs a long-running HBase client process that takes at most 3 days, we might create this user's principal within kadmin with: addprinc -maxrenewlife 3days

Long running daemons with indefinite lifetimes that require client access to HBase can instead be configured to log in from a keytab. For each host running such daemons, create a keytab with kadmin or kadmin.local. The procedure for creating keytabs for HBase service is the same as for creating keytabs for Hadoop. Those steps are omitted here. Copy the resulting keytab files to where the client daemon will execute and make them readable only to the user account under which the daemon will run.

8.1.2. Server-side Configuration for Secure Operation

First, refer to Section 8.1.1, “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.

8.1.3. Client-side Configuration for Secure Operation

First, refer to Section 8.1.1, “Prerequisites” and ensure that your underlying HDFS configuration is secure.

Add the following to the hbase-site.xml file on every client:

<property>
  <name>hbase.security.authentication</name>
  <value>kerberos</value>
</property>
    

The client environment must be logged in to Kerberos from KDC or keytab via the kinit command before communication with the HBase cluster will be possible.

Be advised that if the hbase.security.authentication in the client- and server-side site files do not match, the client will not be able to communicate with the cluster.

Once HBase is configured for secure RPC it is possible to optionally configure encrypted communication. To do so, add the following to the hbase-site.xml file on every client:

<property>
  <name>hbase.rpc.protection</name>
  <value>privacy</value>
</property>
    

This configuration property can also be set on a per connection basis. Set it in the Configuration supplied to HTable:

Configuration conf = HBaseConfiguration.create();
conf.set("hbase.rpc.protection", "privacy");
HTable table = new HTable(conf, tablename);
    

Expect a ~10% performance penalty for encrypted communication.

8.1.4. 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>
    

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 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.

8.1.5. Configure the Thrift Gateway to Authenticate on Behalf of the Client

Section 8.1.4, “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 Section 8.1.4, “Client-side Configuration for Secure Operation - Thrift Gateway”.

  2. Be sure that HBase is configured to allow proxy users, as described in Section 8.1.7, “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:

    • auth-conf - authentication, integrity, and confidentiality checking

    • auth-int - authentication and integrity checking

    • auth - 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.

8.1.6. 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. 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.

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 section

It should be possible for clients to authenticate with the HBase cluster through the REST gateway in a pass-through manner via SPEGNO HTTP authentication. This is future work.

8.1.7. 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 acutal/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.

8.2. 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.

8.2.1. 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 HBase and complete all of the steps described there.

8.2.1.1. Prerequisites

None

8.2.1.1.1. 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.

8.2.1.1.2. 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.

8.2.1.1.3. 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 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.

8.2.1.1.4. 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 section

It should be possible for clients to authenticate with the HBase cluster through the REST gateway in a pass-through manner via SPEGNO HTTP authentication. This is future work.

8.3. 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 Section 8.3.1, “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.

Caution

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 8.1. 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 Section 8.1.1, “Prerequisites” and Section 20.2, “SASL Authentication with ZooKeeper”.

8.3.1. 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.

8.3.1.1. Implementation Details

Every cell can have zero or more tags. Every tag has a type and the actual tag byte array.

Just as row keys, column families, qualifiers and values can be encoded (see Data Block Encoding Types), tags can also be encoded as well. You can enable or disable tag encoding at the level of the column family, and it is enabled by default. Use the HColumnDescriptor#setCompressionTags(boolean compressTags) method to manage encoding settings on a column family. You also need to enable the DataBlockEncoder for the column family, for encoding of tags to take effect.

You can enable compression of each tag in the WAL, if WAL compression is also enabled, by setting the value of hbase.regionserver.wal.tags.enablecompression to true in hbase-site.xml. Tag compression uses dictionary encoding.

Tag compression is not supported when using WAL encryption.

8.3.2. Access Control Labels (ACLs)

8.3.2.1. 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).

Note

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. Accordingly, the important operations condense to four permissions: READ, WRITE, CREATE, and ADMIN.

Table 8.1. Operation To Permission Mapping

PermissionOperation
ReadGet
 Exists
 Scan
WritePut
 Delete
 IncrementColumnValue
 CheckAndDelete/Put
CreateCreate
 Alter
 Drop
 Bulk Load
AdminEnable/Disable
 Snapshot/Restore/Clone
 Split
 Flush
 Compact
 Major Compact
 Roll HLog
 Grant
 Revoke
 Shutdown
ExecuteExecute coprocessor endpoints

Permissions can be granted in any of the following scopes, though CREATE and ADMIN permissions are effective only at table, namespace, and global scopes.

Namespace
  • Read: User can read any table in the namespace.

  • Write: User can write to any table in the namespace.

  • Create: User can create tables in the namespace.

  • Admin: User can alter table attributes; add, alter, or drop column families; and enable, disable, or drop the table. User can also trigger region (re)assignments or relocation.

Table
  • Read: User can read from any column family in table

  • Write: User can write to any column family in table

  • Create: User can alter table attributes; add, alter, or drop column families; and drop the table.

  • Admin: User can alter table attributes; add, alter, or drop column families; and enable, disable, or drop the table. User can also trigger region (re)assignments or relocation.

Column Family / Column Qualifier / Cell
  • Read: User can read at the specified scope.

  • Write: User can write at the specified scope.

Coprocessor Endpoint

Execute: the user can execute the coprocessor endpoint.

Global

Superusers are specified as a comma-separated list of users and groups, in the hbase.superuser option in hbase-site.xml. The superuser is equivalent to the root user in a UNIX environment. As a minimum, the superuser should include the principal used to run the HMaster process. Global admin privileges, which are implicitly granted to the superuser, are required to create namespaces, switch the balancer on and off, or take other actions with global consequences. The superuser can also grant all permissions to all resources.

ACL Matrix. For more details on how ACLs map to specific HBase operations and tasks, see Appendix D, Access Control Matrix.

Cell-level ACLs are implemented using tags (see Section 8.3.1, “Tags”). In order to use cell-level ACLs, you must be using HFile v3 and HBase 0.98 or newer.

ACL Implementation Caveats

  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.

8.3.2.2. Server-Side Configuration

  1. As a prerequisite, perform the steps in Procedure 8.1, “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.

    Note

    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 Section 8.4, “Security Configuration Example”.

    <property>
      <name>hbase.coprocessor.region.classes</name>
      <value>org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.token.TokenProvider</value>
    </property>
    <property>
      <name>hbase.coprocessor.master.classes</name>
      <value>org.apache.hadoop.hbase.security.access.AccessController</value>
    </property>
    <property>
      <name>hbase.coprocessor.regionserver.classes</name>
      <value>org.apache.hadoop.hbase.security.access.AccessController</value>
    </property>
    <property>
      <name>hbase.security.exec.permission.checks</name>
      <value>true</value>
    </property>
              

    Optionally, you can enable transport security, by setting hbase.rpc.protection to auth-conf. 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
                

8.3.2.3. 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.

    Note

    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 8.1. HBase Shell

    • Global:

      hbase> grant '@admins', 'RWXCA'
    • Namespace:

      hbase> grant 'service', 'RWXCA', '@test-NS'
    • Table:

      hbase> grant 'service', 'RWXCA', 'user'
    • Column Family:

      hbase> grant '@developers', 'RW', 'user', 'i'
    • Column Qualifier:

      hbase> grant 'service, 'RW', 'user', 'i', 'foo'
    • Cell:

      The syntax for granting cell ACLs uses the following syntax:

      grant <table>, \
        { '<user-or-group>' => \
          '<permissions>', ... }, \
        { <scanner-specification> }
      • <user-or-group> is the user or group name, prefixed with @ in the case of a group.

      • <permissions> is a string containing any or all of "RWXCA", though only R and W are meaningful at cell scope.

      • <scanner-specification> is the scanner specification syntax and conventions used by the 'scan' shell command. For some examples of scanner specifications, issue the following HBase Shell command.

        hbase> help "scan"

      This example grants read access to the 'testuser' user and read/write access to the 'developers' group, on cells in the 'pii' column which match the filter.

      hbase> grant 'user', \
        { '@developers' => 'RW', 'testuser' => 'R' }, \
        { COLUMNS => 'pii', FILTER => "(PrefixFilter ('test'))" }

      The shell will run a scanner with the given criteria, rewrite the found cells with new ACLs, and store them back to their exact coordinates.


    Example 8.2. API

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

    public static void grantOnTable(final HBaseTestingUtility util, final String user,
        final TableName table, final byte[] family, final byte[] qualifier,
        final Permission.Action... actions) throws Exception {
      SecureTestUtil.updateACLs(util, new Callable<Void>() {
        @Override
        public Void call() throws Exception {
          HTable acl = new HTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME);
          try {
            BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
            AccessControlService.BlockingInterface protocol =
                AccessControlService.newBlockingStub(service);
            ProtobufUtil.grant(protocol, user, table, family, qualifier, actions);
          } finally {
            acl.close();
          }
          return null;
        }
      });
    }               
                  

    To grant permissions at the cell level, you can use the Mutation.setACL method:

    Mutation.setACL(String user, Permission perms)
    Mutation.setACL(Map<String, Permission> perms)
        
                  

    Specifically, this example provides read permission to a user called user1 on any cells contained in a particular Put operation:

    put.setACL(“user1”, new Permission(Permission.Action.READ))
        

  3. Revoking Access Control From a Namespace, Table, Column Family, or Cell

    The revoke command and API are twins of the grant command and API, and the syntax is exactly the same. The only exception is that you cannot revoke permissions at the cell level. You can only revoke access that has previously been granted, and a revoke statement is not the same thing as explicit denial to a resource.

    Note

    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 8.3. Revoking Access To a Table

    public static void revokeFromTable(final HBaseTestingUtility util, final String user,
        final TableName table, final byte[] family, final byte[] qualifier,
        final Permission.Action... actions) throws Exception {
      SecureTestUtil.updateACLs(util, new Callable<Void>() {
        @Override
        public Void call() throws Exception {
          HTable acl = new HTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME);
          try {
            BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW);
            AccessControlService.BlockingInterface protocol =
                AccessControlService.newBlockingStub(service);
            ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions);
          } finally {
            acl.close();
          }
          return null;
        }
      });
    } 
                  

  4. Showing a User's Effective Permissions

    Example 8.4. HBase Shell

    hbase> user_permission 'user'
    hbase> user_permission '.*'
    hbase> user_permission JAVA_REGEX

    Example 8.5. 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<?>) {
          List<?> results = (List<?>) 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");
      }
    }
                  

8.3.3. 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 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.

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.

Table 8.2. Examples of Visibility Expressions

ExpressionInterpretation
fulltime

Allow accesss 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.


8.3.3.1. Server-Side Configuration

  1. As a prerequisite, perform the steps in Procedure 8.1, “Basic Server-Side Configuration”.

  2. Install and configure the VisibilityController coprocessor by setting the following properties in hbase-site.xml. These properties take a list of class names.

    <property>
      <name>hbase.coprocessor.region.classes</name>
      <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
    </property>
    <property>
      <name>hbase.coprocessor.master.classes</name>
      <value>org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
    </property>
              

    Note

    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.

8.3.3.2. Administration

Administration tasks can be performed using the HBase Shell or the Java API. For defining the list of visibility labels and associating labels with users, the HBase Shell is probably simpler.

API Examples

Many of the Java API examples in this section are taken from the source file hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabels.java. Refer to that file or the API documentation for more context.

Neither these examples, nor the source file they were taken from, are part of the public HBase API, and are provided for illustration only. Refer to the official API for usage instructions.

  1. Define the List of Visibility Labels

    Example 8.6. HBase Shell

    hbase< add_labels [ 'admin', 'service', 'developer', 'test' ]

    Example 8.7. Java API

    public static void addLabels() throws Exception {
      PrivilegedExceptionAction<VisibilityLabelsResponse> action =
          new PrivilegedExceptionAction<VisibilityLabelsResponse>() {
        public VisibilityLabelsResponse run() throws Exception {
          String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE, COPYRIGHT, ACCENT,
              UNICODE_VIS_TAG, UC1, UC2 };
          try {
            VisibilityClient.addLabels(conf, labels);
          } catch (Throwable t) {
            throw new IOException(t);
          }
          return null;
        }
      };
      SUPERUSER.runAs(action);
    }
                    

  2. Associate Labels with Users

    Example 8.8. HBase Shell

    hbase< set_auths 'service', [ 'service' ]
    hbase< set_auths 'testuser', [ 'test' ]
    hbase< set_auths 'qa', [ 'test', 'developer' ]

    Example 8.9. Java API

    public void testSetAndGetUserAuths() throws Throwable {
      final String user = "user1";
      PrivilegedExceptionAction<Void> action = new PrivilegedExceptionAction<Void>() {
        public Void run() throws Exception {
          String[] auths = { SECRET, CONFIDENTIAL };
          try {
            VisibilityClient.setAuths(conf, auths, user);
          } catch (Throwable e) {
          }
          return null;
        }
        ...
                    

  3. Clear Labels From Users

    Example 8.10. HBase Shell

    hbase< clear_auths 'service', [ 'service' ]
    hbase< clear_auths 'testuser', [ 'test' ]
    hbase< clear_auths 'qa', [ 'test', 'developer' ]

    Example 8.11. Java API

    ...
    auths = new String[] { SECRET, PUBLIC, CONFIDENTIAL };
    VisibilityLabelsResponse response = null;
    try {
      response = VisibilityClient.clearAuths(conf, auths, user);
    } catch (Throwable e) {
      fail("Should not have failed");
    ...
                    

  4. Apply a Label or Expression to a Cell

    The label is only applied when data is written. The label is associated with a given version of the cell.

    Example 8.12. HBase Shell

    hbase< set_visibility 'user', 'admin|service|developer', \
      { COLUMNS => 'i' }
    hbase< set_visibility 'user', 'admin|service', \
      { COLUMNS => ' pii' }
    hbase< COLUMNS => [ 'i', 'pii' ], \
        FILTER => "(PrefixFilter ('test'))" }

    Note

    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 8.13. Java API

    static HTable createTableAndWriteDataWithLabels(TableName tableName, String... labelExps)
        throws Exception {
      HTable 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();
        }
      }
                    

8.3.3.3. 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 by using the property hbase.regionserver.scan.visibility.label.generator.class. The default implementation class is org.apache.hadoop.hbase.security.visibility.DefaultScanLabelGenerator. You can also configure a set of ScanLabelGenerators to be used by the system, as a comma-separated list.

8.3.4. 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.

8.3.4.1. 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 explictly 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.

8.3.4.2. Server-Side Configuration

This procedure assumes you are using the default Java keystore implementation. If you are using a custom implementation, check its documentation and adjust accordingly.

  1. Create a secret key of appropriate length for AES encryption, using the keytool utility.

    $ keytool -keystore /path/to/hbase/conf/hbase.jks \
      -storetype jceks -storepass **** \
      -genseckey -keyalg AES -keysize 128 \
      -alias <alias>

    Replace **** with the password for the keystore file and <alias> with the username of the HBase service account, or an arbitrary string. If you use an arbitrary string, you will need to configure HBase to use it, and that is covered below. Specify a keysize that is appropriate. Do not specify a separate password for the key, but press Return when prompted.

  2. Set appropriate permissions on the keyfile and distribute it to all the HBase servers.

    The previous command created a file called hbase.jks in the HBase conf/ directory. Set the permissions and ownership on this file such that only the HBase service account user can read the file, and securely distribute the key to all HBase servers.

  3. Configure the HBase daemons.

    Set the following properties in hbase-site.xml on the region servers, to configure HBase daemons to use a key provider backed by the KeyStore file or retrieving the cluster master key. In the example below, replace **** with the password.

    <property>
        <name>hbase.crypto.keyprovider</name>
        <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
    </property>
    <property>
        <name>hbase.crypto.keyprovider.parameters</name>
        <value>jceks:///path/to/hbase/conf/hbase.jks?password=****</value>
    </property>
                

    By default, the HBase service account name will be used to resolve the cluster master key. However, you can store it with an arbitrary alias (in the keytool command). In that case, set the following property to the alias you used.

    <property>
        <name>hbase.crypto.master.key.name</name>
        <value>my-alias</value>
    </property>
                

    You also need to be sure your HFiles use HFile v3, in order to use transparent encryption. This is the default configuration for HBase 1.0 onward. For previous versions, set the following property in your hbase-site.xml file.

    <property>
        <name>hfile.format.version</name>
        <value>3</value>
    </property>
                

    Optionally, you can use a different cipher provider, either a Java Cryptography Encryption (JCE) algorithm provider or a custom HBase cipher implementation.

    1. 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.

    2. Custom HBase Cipher:

      • Implement org.apache.hadoop.hbase.io.crypto.CipherProvider.

      • Add the implementation to the server classpath.

      • Update hbase.crypto.cipherprovider in hbase-site.xml.

  4. Configure WAL encryption.

    Configure WAL encryption in every RegionServer's hbase-site.xml, by setting the following properties. You can include these in the HMaster's hbase-site.xml as well, but the HMaster does not have a WAL and will not use them.

    <property>
        <name>hbase.regionserver.hlog.reader.impl</name>
        <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
    </property>
    <property>
        <name>hbase.regionserver.hlog.writer.impl</name>
        <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
    </property>
    <property>
        <name>hbase.regionserver.wal.encryption</name>
        <value>true</value>
    </property>
                  
  5. Configure permissions on the hbase-site.xml file.

    Because the keystore password is stored in the hbase-site.xml, you need to ensure that only the HBase user can read the hbase-site.xml file, using file ownership and permissions.

  6. Restart your cluster.

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

8.3.4.3. Administration

Administrative tasks can be performed in HBase Shell or the Java API.

Java API

Java API examples in this section are taken from the source file hbase-server/src/test/java/org/apache/hadoop/hbase/util/TestHBaseFsckEncryption.java. .

Neither these examples, nor the source files they are taken from, are part of the public HBase API, and are provided for illustration only. Refer to the official API for usage instructions.

Enable Encryption on a Column Family

To enable encryption on a column family, you can either use HBase Shell or the Java API. After enabling encryption, trigger a major compaction. When the major compaction completes, the HFiles will be encrypted.

Example 8.14. HBase Shell

hbase> disable 'mytable'
hbase> alter 'mytable', 'mycf', {ENCRYPTION => AES}
hbase> enable 'mytable'
                

Example 8.15. Java API

You can use the HBaseAdmin#modifyColumn API to modify the ENCRYPTION attribute on a Column Family. Additionally, you can specify the specific key to use as the wrapper, by setting the ENCRYPTION_KEY attribute. This is only possible via the Java API, and not the HBase Shell. The default behavior if you do not specify an ENCRYPTION_KEY for a column family is for a random key to be generated for each encrypted column family (per HFile). This provides additional defense in the (unlikely, but theoretically possible) occurrence of storing the same data in multiple HFiles with exactly the same block layout, the same data key, and the same randomly-generated initialization vector.

This example shows how to programmatically set the transparent encryption both in the server configuration and at the column family, as part of a test which uses the Minicluster configuration.

@Before
public void setUp() throws Exception {
  conf = TEST_UTIL.getConfiguration();
  conf.setInt("hfile.format.version", 3);
  conf.set(HConstants.CRYPTO_KEYPROVIDER_CONF_KEY, KeyProviderForTesting.class.getName());
  conf.set(HConstants.CRYPTO_MASTERKEY_NAME_CONF_KEY, "hbase");

  // Create the test encryption key
  SecureRandom rng = new SecureRandom();
  byte[] keyBytes = new byte[AES.KEY_LENGTH];
  rng.nextBytes(keyBytes);
  cfKey = new SecretKeySpec(keyBytes, "AES");

  // Start the minicluster
  TEST_UTIL.startMiniCluster(3);

  // Create the table
  htd = new HTableDescriptor(TableName.valueOf("default", "TestHBaseFsckEncryption"));
  HColumnDescriptor hcd = new HColumnDescriptor("cf");
  hcd.setEncryptionType("AES");
  hcd.setEncryptionKey(EncryptionUtil.wrapKey(conf,
    conf.get(HConstants.CRYPTO_MASTERKEY_NAME_CONF_KEY, User.getCurrent().getShortName()),
    cfKey));
  htd.addFamily(hcd);
  TEST_UTIL.getHBaseAdmin().createTable(htd);
  TEST_UTIL.waitTableAvailable(htd.getName(), 5000);
}
                

Rotate the Data Key

To rotate the data key, first change the ColumnFamily key in the column descriptor, then trigger a major compaction. When compaction is complete, all HFiles will be re-encrypted using the new data key. Until the compaction completes, the old HFiles will still be readable using the old key.

If you rely on HBase's default behavior of generating a random key for each HFile, there is no need to rotate data keys. A major compaction will re-encrypt the HFile with a new 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.

<property>
  <name>hbase.crypto.master.alternate.key.name</name>
  <value>hbase.old</value>
</property>
                

Rolling restart your cluster for this change to take effect. Trigger a major compaction on each table. At the end of the major compaction, all HFiles will be re-encrypted with data keys wrapped by the new cluster key. At this point, you can remove the old master key from the KeyStore, remove the configuration for the fallback master key from the hbase-site.xml, and perform a second rolling restart at some point. This second rolling restart is not time-sensitive.

8.3.5. Secure Bulk Load

Bulk loading in secure mode is a bit more involved than normal setup, since the client has to transfer the ownership of the files generated from the mapreduce job to HBase. Secure bulk loading is implemented by a coprocessor, named SecureBulkLoadEndpoint, which uses a staging directory configured by the configuration property hbase.bulkload.staging.dir, which defaults to /tmp/hbase-staging/.

Secure Bulk Load Algorithm

  • One time only, create a staging directory which is world-traversable and owned by the user which runs HBase (mode 711, or rwx--x--x). A listing of this directory will look similar to the following:

    $ ls -ld /tmp/hbase-staging
    drwx--x--x  2 hbase  hbase  68  3 Sep 14:54 /tmp/hbase-staging
              
  • A user writes out data to a secure output directory owned by that user. For example, /user/foo/data.

  • Internally, HBase creates a secret staging directory which is globally readable/writable (-rwxrwxrwx, 777). For example, /tmp/hbase-staging/averylongandrandomdirectoryname. The name and location of this directory is not exposed to the user. HBase manages creation and deletion of this directory.

  • The user makes the data world-readable and world-writable, moves it into the random staging directory, then calls the SecureBulkLoadClient#bulkLoadHFiles method.

The strength of the security lies in the length and randomness of the secret directory.

To enable secure bulk load, add the following properties to hbase-site.xml.

<property>
  <name>hbase.bulkload.staging.dir</name>
  <value>/tmp/hbase-staging</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.token.TokenProvider,
  org.apache.hadoop.hbase.security.access.AccessController</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.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>
    

8.4. 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 8.16. Example Security Settings in hbase-site.xml

<!-- HFile v3 Support -->
<property>
  <name>hfile.format.version</name>
  <value>3</value>
</property>
<!-- HBase Superuser -->
<property>
  <name>hbase.superuser</name>
  <value>hbase, admin</value>
</property>
<!-- Coprocessors for ACLs and Visibility Tags -->
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.visibility.VisibilityController,
  org.apache.hadoop.hbase.security.token.TokenProvider</value>
</property>
<property>
  <name>hbase.coprocessor.master.classes</name>
  <value>org.apache.hadoop.hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.visibility.VisibilityController</value>
</property>
<property>
  <name>hbase.coprocessor.regionserver.classes</name>
  <value>org.apache.hadoop/hbase.security.access.AccessController,
  org.apache.hadoop.hbase.security.access.VisibilityController</value>
</property>
<!-- Executable ACL for Coprocessor Endpoints -->
<property>
  <name>hbase.security.exec.permission.checks</name>
  <value>true</value>
</property>
<!-- Whether a user needs authorization for a visibility tag to set it on a cell -->
<property>
  <name>hbase.security.visibility.mutations.checkauth</name>
  <value>false</value>
</property>
<!-- Secure RPC Transport -->
<property>
  <name>hbase.rpc.protection</name>
  <value>auth-conf</value>
 </property>
 <!-- Transparent Encryption -->
<property>
    <name>hbase.crypto.keyprovider</name>
    <value>org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider</value>
</property>
<property>
    <name>hbase.crypto.keyprovider.parameters</name>
    <value>jceks:///path/to/hbase/conf/hbase.jks?password=***</value>
</property>
<property>
    <name>hbase.crypto.master.key.name</name>
    <value>hbase</value>
</property>
<!-- WAL Encryption -->
<property>
    <name>hbase.regionserver.hlog.reader.impl</name>
    <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader</value>
</property>
<property>
    <name>hbase.regionserver.hlog.writer.impl</name>
    <value>org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter</value>
</property>
<property>
    <name>hbase.regionserver.wal.encryption</name>
    <value>true</value>
</property>
<!-- For key rotation -->
<property>
  <name>hbase.crypto.master.alternate.key.name</name>
  <value>hbase.old</value>
</property>
<!-- Secure Bulk Load -->
<property>
  <name>hbase.bulkload.staging.dir</name>
  <value>/tmp/hbase-staging</value>
</property>
<property>
  <name>hbase.coprocessor.region.classes</name>
  <value>org.apache.hadoop.hbase.security.token.TokenProvider,
  org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint</value>
</property>
        

Example 8.17. 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>
        

Chapter 9. Architecture

9.1. Overview

9.1.1. 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. 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.

9.1.2. 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.

9.1.3. 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 Chapter 5, Data Model and the rest of this chapter for more information on how HBase achieves its goals.

9.2. 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.

9.2.1. -ROOT-

Note

The -ROOT- table was removed in HBase 0.96.0. Information here should be considered historical.

The -ROOT- table kept track of the location of the .META table (the previous name for the table now called hbase:meta) prior to HBase 0.96. The -ROOT- table structure was as follows:

Key

  • .META. region key (.META.,,1)

Values

  • info:regioninfo (serialized HRegionInfo instance of hbase:meta)

  • info:server (server:port of the RegionServer holding hbase:meta)

  • info:serverstartcode (start-time of the RegionServer process holding hbase:meta)

9.2.2. hbase:meta

The hbase:meta table (previously called .META.) keeps a list of all regions in the system. The location of hbase:meta was previously tracked within the -ROOT- table, but is now stored in Zookeeper.

The hbase:meta table structure is as follows:

Key

  • Region key of the format ([table],[region start key],[region id])

Values

  • info:regioninfo (serialized HRegionInfo instance for this region)

  • info:server (server:port of the RegionServer containing this region)

  • info:serverstartcode (start-time of the RegionServer process containing this region)

When a table is in the process of splitting, two other columns will be created, called info:splitA and info:splitB. These columns represent the two daughter regions. The values for these columns are also serialized HRegionInfo instances. After the region has been split, eventually this row will be deleted.

Note on HRegionInfo

The empty key is used to denote table start and table end. A region with an empty start key is the first region in a table. If a region has both an empty start and an empty end key, it is the only region in the table

In the (hopefully unlikely) event that programmatic processing of catalog metadata is required, see the Writables utility.

9.2.3. Startup Sequencing

First, the location of hbase:meta is looked up in Zookeeper. Next, hbase:meta is updated with server and startcode values.

For information on region-RegionServer assignment, see Section 9.7.2, “Region-RegionServer Assignment”.

9.3. Client

The HBase client HTable is responsible for finding RegionServers that are serving the particular row range of interest. It does this by querying the hbase:meta table. See Section 9.2.2, “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 Section 9.5.2, “Runtime Impact” for more information about the impact of the Master on HBase Client communication.

Administrative functions are handled through HBaseAdmin

9.3.1. Connections

For connection configuration information, see Section 2.4.4, “Client configuration and dependencies connecting to an HBase cluster”.

HTable instances are not thread-safe. Only one thread use an instance of HTable at any given time. When creating HTable 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 HConnectionManager.

9.3.1.1. 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 an HConnection, as shown in the following example:

Example 9.1. Pre-Creating a HConnection

// Create a connection to the cluster.
HConnection connection = HConnectionManager.createConnection(Configuration);
HTableInterface table = connection.getTable("myTable");
// use table as needed, the table returned is lightweight
table.close();
// use the connection for other access to the cluster
connection.close();

Constructing HTableInterface implementation is very lightweight and resources are controlled.

HTablePool is Deprecated

Previous versions of this guide discussed HTablePool, which was deprecated in HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by HBASE-6500. Please use HConnection instead.

9.3.2. WriteBuffer and Batch Methods

If Section 14.8.4, “HBase Client: AutoFlush” is turned off on HTable, Puts are sent to RegionServers when the writebuffer is filled. The writebuffer is 2MB by default. Before an HTable instance is discarded, either close() or flushCommits() should be invoked so Puts will not be lost.

Note: htable.delete(Delete); does not go in the writebuffer! This only applies to Puts.

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 HTable.

9.3.3. External Clients

Information on non-Java clients and custom protocols is covered in Chapter 11, Apache HBase External APIs

9.4. 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.

9.4.1. Structural

Structural Filters contain other Filters.

9.4.1.1. FilterList

FilterList represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or' between two Filters (checking for either 'my value' or 'my other value' on the same attribute).

FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE);
SingleColumnValueFilter filter1 = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	Bytes.toBytes("my value")
	);
list.add(filter1);
SingleColumnValueFilter filter2 = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	Bytes.toBytes("my other value")
	);
list.add(filter2);
scan.setFilter(list);

9.4.2. Column Value

9.4.2.1. SingleColumnValueFilter

SingleColumnValueFilter can be used to test column values for equivalence (CompareOp.EQUAL ), inequality (CompareOp.NOT_EQUAL), or ranges (e.g., CompareOp.GREATER). The following is example of testing equivalence a column to a String value "my value"...

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

9.4.3. 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 Section 9.4.2.1, “SingleColumnValueFilter”.

9.4.3.1. RegexStringComparator

RegexStringComparator supports regular expressions for value comparisons.

RegexStringComparator comp = new RegexStringComparator("my.");   // any value that starts with 'my'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	comp
	);
scan.setFilter(filter);

See the Oracle JavaDoc for supported RegEx patterns in Java.

9.4.3.2. SubstringComparator

SubstringComparator can be used to determine if a given substring exists in a value. The comparison is case-insensitive.

SubstringComparator comp = new SubstringComparator("y val");   // looking for 'my value'
SingleColumnValueFilter filter = new SingleColumnValueFilter(
	cf,
	column,
	CompareOp.EQUAL,
	comp
	);
scan.setFilter(filter);

9.4.3.3. BinaryPrefixComparator

See BinaryPrefixComparator.

9.4.3.4. BinaryComparator

See BinaryComparator.

9.4.4. 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.

9.4.4.1. 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.

9.4.4.2. QualifierFilter

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

9.4.4.3. ColumnPrefixFilter

ColumnPrefixFilter can be used to filter based on the lead portion of Column (aka Qualifier) names.

A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row and for each involved column family. It can be used to efficiently get a subset of the columns in very wide rows.

Note: The same column qualifier can be used in different column families. This filter returns all matching columns.

Example: Find all columns in a row and family that start with "abc"

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] prefix = Bytes.toBytes("abc");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnPrefixFilter(prefix);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

9.4.4.4. MultipleColumnPrefixFilter

MultipleColumnPrefixFilter behaves like ColumnPrefixFilter but allows specifying multiple prefixes.

Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the first column matching the lowest prefix and also seeks past ranges of columns between prefixes. It can be used to efficiently get discontinuous sets of columns from very wide rows.

Example: Find all columns in a row and family that start with "abc" or "xyz"

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")};
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new MultipleColumnPrefixFilter(prefixes);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

9.4.4.5. ColumnRangeFilter

A ColumnRangeFilter allows efficient intra row scanning.

A ColumnRangeFilter can seek ahead to the first matching column for each involved column family. It can be used to efficiently get a 'slice' of the columns of a very wide row. i.e. you have a million columns in a row but you only want to look at columns bbbb-bbdd.

Note: The same column qualifier can be used in different column families. This filter returns all matching columns.

Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" (inclusive)

HTableInterface t = ...;
byte[] row = ...;
byte[] family = ...;
byte[] startColumn = Bytes.toBytes("bbbb");
byte[] endColumn = Bytes.toBytes("bbdd");
Scan scan = new Scan(row, row); // (optional) limit to one row
scan.addFamily(family); // (optional) limit to one family
Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true);
scan.setFilter(f);
scan.setBatch(10); // set this if there could be many columns returned
ResultScanner rs = t.getScanner(scan);
for (Result r = rs.next(); r != null; r = rs.next()) {
  for (KeyValue kv : r.raw()) {
    // each kv represents a column
  }
}
rs.close();

Note: Introduced in HBase 0.92

9.4.5. RowKey

9.4.5.1. RowFilter

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

9.4.6. Utility

9.4.6.1. FirstKeyOnlyFilter

This is primarily used for rowcount jobs. See FirstKeyOnlyFilter.

9.5. 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 Section 9.9.1, “NameNode”. J Mohamed Zahoor goes into some more detail on the Master Architecture in this blog posting, HBase HMaster Architecture .

9.5.1. 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 then the remaining Masters jostle to take over the Master role.

9.5.2. Runtime Impact

A common dist-list question involves what happens to an HBase cluster when the Master goes down. Because the HBase client talks directly to the RegionServers, the cluster can still function in a "steady state." Additionally, per Section 9.2, “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.

9.5.3. 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 HBaseAdmin method disableTable is invoked, it is serviced by the Master server.

9.5.4. Processes

The Master runs several background threads:

9.5.4.1. 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 Section 2.6.3.1, “Balancer” for configuring this property.

See Section 9.7.2, “Region-RegionServer Assignment” for more information on region assignment.

9.5.4.2. CatalogJanitor

Periodically checks and cleans up the hbase:meta table. See Section 9.2.2, “hbase:meta” for more information on META.

9.6. RegionServer

HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a Section 9.9.2, “DataNode”.

9.6.1. 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 HBaseAdmin 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.

9.6.2. Processes

The RegionServer runs a variety of background threads:

9.6.2.1. CompactSplitThread

Checks for splits and handle minor compactions.

9.6.2.2. MajorCompactionChecker

Checks for major compactions.

9.6.2.3. MemStoreFlusher

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

9.6.2.4. LogRoller

Periodically checks the RegionServer's HLog.

9.6.3. 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.

9.6.4. Block Cache

HBase provides two different BlockCache implementations: the default onheap LruBlockCache and BucketCache, which is (usually) offheap. This section discusses benefits and drawbacks of each implementation, how to choose the appropriate option, and configuration options for each.

Block Cache Reporting: UI

See the RegionServer UI for detail on caching deploy. Since HBase-0.98.4, the Block Cache detail has been significantly extended showing configurations, sizings, current usage, time-in-the-cache, and even detail on block counts and types.

9.6.4.1. Cache Choices

LruBlockCache is the original implementation, and is entirely within the Java heap. BucketCache is mainly intended for keeping blockcache data offheap, although BucketCache can also keep data onheap and serve from a file-backed cache.

BucketCache is production ready as of hbase-0.98.6

To run with BucketCache, you need HBASE-11678. This was included in hbase-0.98.6.

Fetching will always be slower when fetching from BucketCache, as compared to the native onheap 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 offheap mode, this memory is not managed by the GC at all. This is why you'd use BucketCache, so your latencies are less erratic and to mitigate GCs and heap fragmentation. See Nick Dimiduk's BlockCache 101 for comparisons running onheap vs offheap tests. Also see Comparing BlockCache Deploys which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise if you are experiencing cache churn (or you want your cache to exist beyond the vagaries of java GC), use BucketCache.

When you enable BucketCache, you are enabling a two tier caching system, an L1 cache which is implemented by an instance of LruBlockCache and an offheap L2 cache which is implemented by BucketCache. Management of these two tiers and the policy that dictates how blocks move between them is done by CombinedBlockCache. It keeps all DATA blocks in the L2 BucketCache and meta blocks -- INDEX and BLOOM blocks -- onheap in the L1 LruBlockCache. See Section 9.6.4.5, “Offheap Block Cache” for more detail on going offheap.

9.6.4.2. General Cache Configurations

Apart from the cache implementation itself, you can set some general configuration options to control how the cache performs. See http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html. After setting any of these options, restart or rolling restart your cluster for the configuration to take effect. Check logs for errors or unexpected behavior.

See also Section 14.4.4, “Prefetch Option for Blockcache”, which discusses a new option introduced in HBASE-9857.

9.6.4.3. 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.

  • Mutli 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

9.6.4.4. LruBlockCache Usage

Block caching is enabled by default for all the user tables which means that any read operation will load the LRU cache. This might be good for a large number of use cases, but further tunings are usually required in order to achieve better performance. An important concept is the working set size, or WSS, which is: "the amount of memory needed to compute the answer to a problem". For a website, this would be the data that's needed to answer the queries over a short amount of time.

The way to calculate how much memory is available in HBase for caching is:

            number of region servers * heap size * hfile.block.cache.size * 0.99
        

The default value for the block cache is 0.25 which represents 25% of the available heap. The last value (99%) is the default acceptable loading factor in the LRU cache after which eviction is started. The reason it is included in this equation is that it would be unrealistic to say that it is possible to use 100% of the available memory since this would make the process blocking from the point where it loads new blocks. Here are some examples:

  • One region server with the default heap size (1 GB) and the default block cache size will have 253 MB of block cache available.

  • 20 region servers with the heap size set to 8 GB and a default block cache size will have 39.6 of block cache.

  • 100 region servers with the heap size set to 24 GB and a block cache size of 0.5 will have about 1.16 TB of block cache.

Your data is not the only resident of the block cache. Here are others that you may have to take into account:

Catalog Tables

The -ROOT- (prior to HBase 0.96. See Section 9.2.1, “-ROOT-”) and hbase:meta tables are forced into the block cache and have the in-memory priority which means that they are harder to evict. The former never uses more than a few hundreds of bytes while the latter can occupy a few MBs (depending on the number of regions).

HFiles Indexes

An hfile is the file format that HBase uses to store data in HDFS. It contains a multi-layered index which allows HBase to seek to the data without having to read the whole file. The size of those indexes is a factor of the block size (64KB by default), the size of your keys and the amount of data you are storing. For big data sets it's not unusual to see numbers around 1GB per region server, although not all of it will be in cache because the LRU will evict indexes that aren't used.

Keys

The values that are stored are only half the picture, since each value is stored along with its keys (row key, family qualifier, and timestamp). See Section 6.3.3, “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 detail on BlockCache stats and metrics in a special Block Cache section in the UI.

It's generally bad to use block caching when the WSS doesn't fit in memory. This is the case when you have for example 40GB available across all your region servers' block caches but you need to process 1TB of data. One of the reasons is that the churn generated by the evictions will trigger more garbage collections unnecessarily. Here are two use cases:

  • Fully random reading pattern: This is a case where you almost never access the same row twice within a short amount of time such that the chance of hitting a cached block is close to 0. Setting block caching on such a table is a waste of memory and CPU cycles, more so that it will generate more garbage to pick up by the JVM. For more information on monitoring GC, see Section 15.2.3, “JVM Garbage Collection Logs”.

  • Mapping a table: In a typical MapReduce job that takes a table in input, every row will be read only once so there's no need to put them into the block cache. The Scan object has the option of turning this off via the setCaching method (set it to false). You can still keep block caching turned on on this table if you need fast random read access. An example would be counting the number of rows in a table that serves live traffic, caching every block of that table would create massive churn and would surely evict data that's currently in use.

9.6.4.4.1. 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.

9.6.4.5. Offheap Block Cache

9.6.4.5.1. How to Enable BucketCache

The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an L1 onheap cache implemented by LruBlockCache and a second L2 cache implemented with BucketCache. The managing class is CombinedBlockCache by default. The just-previous link describes the caching 'policy' implemented by CombinedBlockCache. In short, it works by keeping meta blocks -- INDEX and BLOOM in the L1, onheap LruBlockCache tier -- and DATA blocks are kept in the L2, BucketCache tier. It is possible to amend this behavior in HBase since version 1.0 and ask that a column family have both its meta and DATA blocks hosted onheap in the L1 tier by setting cacheDataInL1 via (HColumnDescriptor.setCacheDataInL1(true) or in the shell, creating or amending column families setting CACHE_DATA_IN_L1 to true: e.g.

hbase(main):003:0> create 't', {NAME => 't', CONFIGURATION => {CACHE_DATA_IN_L1 => 'true'}}

The BucketCache Block Cache can be deployed onheap, offheap, or file based. You set which via the hbase.bucketcache.ioengine setting. Setting it to heap will have BucketCache deployed inside the allocated java heap. Setting it to offheap will have BucketCache make its allocations offheap, and an ioengine setting of file:PATH_TO_FILE will direct BucketCache to use a file caching (Useful in particular if you have some fast i/o attached to the box such as SSDs).

It is possible to deploy an L1+L2 setup where we bypass the CombinedBlockCache policy and have BucketCache working as a strict L2 cache to the L1 LruBlockCache. For such a setup, set CacheConfig.BUCKET_CACHE_COMBINED_KEY to false. In this mode, on eviction from L1, blocks go to L2. When a block is cached, it is cached first in L1. When we go to look for a cached block, we look first in L1 and if none found, then search L2. Let us call this deploy format, .

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.

Procedure 9.1. BucketCache Example Configuration

This sample provides a configuration for a 4 GB offheap BucketCache with a 1 GB onheap 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 -XX:MaxDirectMemorySize to a value greater than the offheap size wanted, in this case, 4 GB (expressed as 4G). Lets set it to 5G. That'll be 4G for our offheap cache and 1G for any other uses of offheap memory (there are other users of offheap memory other than BlockCache; e.g. DFSClient in RegionServer can make use of offheap memory). See Direct Memory Usage In HBase.

    -XX:MaxDirectMemorySize=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 bucketcache to be 4G. The onheap lrublockcache we configured to have 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 you would when there is no L2 BucketCache present.

HBASE-10641 introduced the ability to configure multiple sizes for the buckets of the bucketcache, in HBase 0.98 and newer. To configurable multiple bucket sizes, configure the new property hfile.block.cache.sizes (instead of hfile.block.cache.size) to a comma-separated list of block sizes, ordered from smallest to largest, with no spaces. The goal is to optimize the bucket sizes based on your data access patterns. The following example configures buckets of size 4096 and 8192.

<property>
  <name>hfile.block.cache.sizes</name>
  <value>4096,8192</value>
</property>
              

Direct Memory Usage In HBase

The default maximum direct memory varies by JVM. Traditionally it is 64M or some relation to allocated heap size (-Xmx) or no limit at all (JDK7 apparently). HBase servers use direct memory, in particular short-circuit reading, the hosted DFSClient will allocate direct memory buffers. If you do offheap block caching, you'll be making use of direct memory. Starting your JVM, make sure the -XX:MaxDirectMemorySize setting in conf/hbase-env.sh is set to some value that is higher than what you have allocated to your offheap blockcache (hbase.bucketcache.size). It should be larger than your offheap block cache and then some for DFSClient usage (How much the DFSClient uses is not easy to quantify; it is the number of open hfiles * hbase.dfs.client.read.shortcircuit.buffer.size where hbase.dfs.client.read.shortcircuit.buffer.size is set to 128k in HBase -- see hbase-default.xml default configurations). Direct memory, which is part of the Java process heap, is separate from the object heap allocated by -Xmx. The value allocated by MaxDirectMemorySize must not exceed physical RAM, and is likely to be less than the total available RAM due to other memory requirements and system constraints.

You can see how much memory -- onheap and offheap/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 offheap memory in java. It is for their product BigMemory but alot of the issues noted apply in general to any attempt at going offheap. 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 between 0 and 1.0.

In 1.0, it should be more straight-forward. L1 LruBlockCache size is set as a fraction of java heap using hfile.block.cache.size setting (not the best name) and L2 is set as above either in absolute megabytes or as a fraction of allocated maximum direct memory.

9.6.5. Write Ahead Log (WAL)

9.6.5.1. 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 HLog interface for the WAL. Usually, there is only one instance of a WAL per RegionServer. The RegionServer records Puts and Deletes to it, before recording them to the Section 9.7.7.1, “MemStore” for the affected Section 9.7.7, “Store”.

The WAL resides in HDFS in the /hbase/WALs/ directory (prior to HBase 0.94, they were stored in /hbase/.logs/), with subdirectories per region.

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

9.6.5.2. WAL Flushing

TODO (describe).

9.6.5.3. 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 9.2. 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.

9.6.5.3.1. 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 optionset 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 thats it. We need to do more than just fail split if this flag is set.

9.6.5.3.1.1. How EOFExceptions are treated when splitting a crashed RegionServers' 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 is likely to be 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

9.6.5.3.2. 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. Section 9.6.5.3.2.1, “Distributed Log Splitting” and Section 9.6.5.3.2.2, “Distributed Log Replay” were developed to improve performance during log splitting.

9.6.5.3.2.1. Distributed Log Splitting

Distributed Log Splitting was added in HBase version 0.92 (HBASE-1364) by Prakash Khemani from Facebook. It reduces the time to complete log splitting dramatically, improving the availability of regions and tables. For example, recovering a crashed cluster took around 9 hours with single-threaded log splitting, but only about six minutes with distributed log splitting.

The information in this section is sourced from Jimmy Xiang's blog post at http://blog.cloudera.com/blog/2012/07/hbase-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.

Procedure 9.3. Distributed Log Splitting, Step by Step

After configuring distributed log splitting, the HMaster controls the process. The HMaster enrolls each RegionServer in the log splitting process, and the actual work of splitting the logs is done by the RegionServers. The general process for log splitting, as described in Procedure 9.2, “Log Splitting, Step by Step” still applies here.

  1. If distributed log processing is enabled, the HMaster creates a split log manager instance when the cluster is started. The split log manager manages all log files which need to be scanned and split. The split log manager places all the logs into the ZooKeeper splitlog node (/hbase/splitlog) as tasks. You can view the contents of the splitlog by issuing the following zkcli command. Example output is shown.

    ls /hbase/splitlog
    [hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost8.sample.com%2C57020%2C1340474893275-splitting%2Fhost8.sample.com%253A57020.1340474893900, 
    hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost3.sample.com%2C57020%2C1340474893299-splitting%2Fhost3.sample.com%253A57020.1340474893931, 
    hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost4.sample.com%2C57020%2C1340474893287-splitting%2Fhost4.sample.com%253A57020.1340474893946]                  
                      

    The output contains some non-ASCII characters. When decoded, it looks much more simple:

    [hdfs://host2.sample.com:56020/hbase/.logs
    /host8.sample.com,57020,1340474893275-splitting
    /host8.sample.com%3A57020.1340474893900, 
    hdfs://host2.sample.com:56020/hbase/.logs
    /host3.sample.com,57020,1340474893299-splitting
    /host3.sample.com%3A57020.1340474893931, 
    hdfs://host2.sample.com:56020/hbase/.logs
    /host4.sample.com,57020,1340474893287-splitting
    /host4.sample.com%3A57020.1340474893946]                    
                      

    The listing represents WAL file names to be scanned and split, which is a list of log splitting tasks.

  2. The split log manager monitors the log-splitting tasks and workers.

    The split log manager is responsible for the following ongoing tasks:

    • Once the split log manager publishes all the tasks to the splitlog znode, it monitors these task nodes and waits for them to be processed.

    • Checks to see if there are any dead split log workers queued up. If it finds tasks claimed by unresponsive workers, it will resubmit those tasks. If the resubmit fails due to some ZooKeeper exception, the dead worker is queued up again for retry.

    • Checks to see if there are any unassigned tasks. If it finds any, it create an ephemeral rescan node so that each split log worker is notified to re-scan unassigned tasks via the nodeChildrenChanged ZooKeeper event.

    • Checks for tasks which are assigned but expired. If any are found, they are moved back to TASK_UNASSIGNED state again so that they can be retried. It is possible that these tasks are assigned to slow workers, or they may already be finished. This is not a problem, because log splitting tasks have the property of idempotence. In other words, the same log splitting task can be processed many times without causing any problem.

    • The split log manager watches the HBase split log znodes constantly. If any split log task node data is changed, the split log manager retrieves the node data. The node data contains the current state of the task. You can use the zkcli get command to retrieve the current state of a task. In the example output below, the first line of the output shows that the task is currently unassigned.

      get /hbase/splitlog/hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost6.sample.com%2C57020%2C1340474893287-splitting%2Fhost6.sample.com%253A57020.1340474893945
       
      unassigned host2.sample.com:57000
      cZxid = 0×7115
      ctime = Sat Jun 23 11:13:40 PDT 2012
      ...  
                            

      Based on the state of the task whose data is changed, the split log manager does one of the following:

      • Resubmit the task if it is unassigned

      • Heartbeat the task if it is assigned

      • Resubmit or fail the task if it is resigned (see Reasons a Task Will Fail)

      • Resubmit or fail the task if it is completed with errors (see Reasons a Task Will Fail)

      • Resubmit or fail the task if it could not complete due to errors (see Reasons a Task Will Fail)

      • Delete the task if it is successfully completed or failed

      Reasons a Task Will Fail

      • The task has been deleted.

      • The node no longer exists.

      • The log status manager failed to move the state of the task to TASK_UNASSIGNED.

      • The number of resubmits is over the resubmit threshold.

  3. Each RegionServer's split log worker performs the log-splitting tasks.

    Each RegionServer runs a daemon thread called the split log worker, which does the work to split the logs. The daemon thread starts when the RegionServer starts, and registers itself to watch HBase znodes. If any splitlog znode children change, it notifies a sleeping worker thread to wake up and grab more tasks. If if a worker's current task’s node data is changed, the worker checks to see if the task has been taken by another worker. If so, the worker thread stops work on the current task.

    The worker monitors the splitlog znode constantly. When a new task appears, the split log worker retrieves the task paths and checks each one until it finds an unclaimed task, which it attempts to claim. If the claim was successful, it attempts to perform the task and updates the task's state property based on the splitting outcome. At this point, the split log worker scans for another unclaimed task.

    How the Split Log Worker Approaches a Task

    • It queries the task state and only takes action if the task is in TASK_UNASSIGNED state.

    • If the task is is in TASK_UNASSIGNED state, the worker attempts to set the state to TASK_OWNED by itself. If it fails to set the state, another worker will try to grab it. The split log manager will also ask all workers to rescan later if the task remains unassigned.

    • If the worker succeeds in taking ownership of the task, it tries to get the task state again to make sure it really gets it asynchronously. In the meantime, it starts a split task executor to do the actual work:

      • Get the HBase root folder, create a temp folder under the root, and split the log file to the temp folder.

      • If the split was successful, the task executor sets the task to state TASK_DONE.

      • If the worker catches an unexpected IOException, the task is set to state TASK_ERR.

      • If the worker is shutting down, set the the task to state TASK_RESIGNED.

      • If the task is taken by another worker, just log it.

  4. The split log manager monitors for uncompleted tasks.

    The split log manager returns when all tasks are completed successfully. If all tasks are completed with some failures, the split log manager throws an exception so that the log splitting can be retried. Due to an asynchronous implementation, in very rare cases, the split log manager loses track of some completed tasks. For that reason, it periodically checks for remaining uncompleted task in its task map or ZooKeeper. If none are found, it throws an exception so that the log splitting can be retried right away instead of hanging there waiting for something that won’t happen.

9.6.5.3.2.2. Distributed Log Replay

After a RegionServer fails, its failed region is assigned to another RegionServer, which is marked as "recovering" in ZooKeeper. A split log worker directly replays edits from the WAL of the failed region server to the region at its new location. When a region is in "recovering" state, it can accept writes but no reads (including Append and Increment), region splits or merges.

Distributed Log Replay extends the Section 9.6.5.3.2.1, “Distributed Log Splitting” framework. It works by directly replaying WAL edits to another RegionServer instead of creating recovered.edits files. It provides the following advantages over distributed log splitting alone:

  • It eliminates the overhead of writing and reading a large number of recovered.edits files. It is not unusual for thousands of recovered.edits files to be created and written concurrently during a RegionServer recovery. Many small random writes can degrade overall system performance.

  • It allows writes even when a region is in recovering state. It only takes seconds for a recovering region to accept writes again.

Enabling Distributed Log Replay. To enable distributed log replay, set hbase.master.distributed.log.replay to true. This will be the default for HBase 0.99 (HBASE-10888).

You must also enable HFile version 3 (which is the default HFile format starting in HBase 0.99. See HBASE-10855). Distributed log replay is unsafe for rolling upgrades.

9.6.5.4. Disabling the WAL

It is possible to disable the WAL, to improve performace 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.

Warning

If you disable the WAL for anything other than bulk loads, your data is at risk.

9.7. Regions

Regions are the basic element of availability and distribution for tables, and are comprised of a Store per Column Family. The heirarchy 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 Section 15.7.2, “Browsing HDFS for HBase Objects”.

9.7.1. 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:

9.7.1.1. Why cannot I have too many regions?

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 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 Section 17.9.2.2, “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 imporoved a bunch in 0.96 hbase).

  4. In older versions of HBase (pre-v2 hfile, 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 Section 17.9.2, “Determining region count and size” for configuration guidelines.

9.7.2. Region-RegionServer Assignment

This section describes how Regions are assigned to RegionServers.

9.7.2.1. 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 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 DefaultLoadBalancer will randomly assign the region to a RegionServer.

  5. 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.

9.7.2.2. 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

9.7.2.3. Region Load Balancing

Regions can be periodically moved by the Section 9.5.4.1, “LoadBalancer”.

9.7.2.4. Region State Transition

HBase maintains a state for each region and persists the state in META. The state of the 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 region server has notified the master

  • FAILED_OPEN: the region server failed to open the region

  • CLOSING: the region is in the process of being closed

  • CLOSED: the region server has closed the region and notified the master

  • FAILED_CLOSE: the region server failed to close the region

  • SPLITTING: the region server notified the master that the region is splitting

  • SPLIT: the region server 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 region server notified the master that this region is being merged with another region

  • MERGED: the region server notified the master that this region has been merged

  • MERGING_NEW: this region is being created by a merge of two regions

Figure 9.1. Region State Transitions

Region State Transitions

This graph shows all allowed transitions a region can undergo. In the graph, each node is a state. A node has a color based on the state type, for readability. A directed line in the graph is a possible state transition.


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

Region State Transitions Explained

  1. The master moves a region from OFFLINE to OPENING state and tries to assign the region to a region server. The region server may or may not have received the open region request. The master retries sending the open region request to the region server until the RPC goes through or the master runs out of retries. After the region server receives the open region request, the region server begins opening the region.

  2. If the master is running out of retries, the master prevents the region server from opening the region by moving the region to CLOSING state and trying to close it, even if the region server is starting to open the region.

  3. After the region server opens the region, it continues to try to notify the master until the master moves the region to OPEN state and notifies the region server. The region is now open.

  4. If the region server 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 region server.

  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 region server 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 region server is not online, or throws NotServingRegionException, the master moves the region to OFFLINE state and re-assigns it to a different region server.

  8. If the region server 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 region server 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 region server.

  10. Before assigning a region, the master moves the region to OFFLINE state automatically if it is in CLOSED state.

  11. When a region server 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 region server. These two regions are in SPLITING_NEW state initially.

  12. After notifying the master, the region server starts to split the region. Once past the point of no return, the region server 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 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 region server is about to merge two regions, it notifies the master first. The master moves the two regions to be merged from OPEN to MERGINGstate, and adds the new region which will hold the contents of the merged regions region to the region server. The new region is in MERGING_NEW state initially.

  15. After notifying the master, the region server starts to merge the two regions. Once past the point of no return, the region server 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 region server 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.

9.7.3. 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.

9.7.4. 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 META, opens daughters on the parent's hosting RegionServer and then reports the split to the Master. See Section 2.6.2.7, “Managed Splitting” for how to manually manage splits (and for why you might do this)

9.7.4.1. Custom Split Policies

The default split policy can be overwritten using a custom RegionSplitPolicy (HBase 0.94+). Typically a custom split policy should extend HBase's default split policy: ConstantSizeRegionSplitPolicy.

The policy can set globally through the HBaseConfiguration used or on a per table basis:

HTableDescriptor myHtd = ...;
myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName());

9.7.5. 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 Section 6.3.2, “ 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 Section 14.8.8, “Anti-Pattern: One Hot Region” for more discussion about this particular scenario.

  • After a big increase to the number of Region Servers 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 Section 2.6.2.7, “Managed Splitting” for a discussion about the dangers and possible benefits of managing splitting completely manually.

9.7.5.1. 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.

hbase> create 'test_table', 'f1', SPLITS=> ['a', 'e', 'i', 'o', 'u']

The following command splits an existing table at split point '2'.

hbase> split 'test_table', '2'

You can also split a specific region by referring to its ID. You can find the region ID by looking at either the table or region in the Web UI. It will be a long number such as t2,1,1410227759524.829850c6eaba1acc689480acd8f081bd.. The format is table_name,start_key,region_idTo split that region into two, as close to equally as possible (at the nearest row boundary), issue the following command.

hbase> split 't2,1,1410227759524.829850c6eaba1acc689480acd8f081bd.'

The split key is optional. If it is omitted, the table or region is split in half.

The following example shows how to use the RegionSplitter to create 10 regions, split at hexadecimal values.

hbase org.apache.hadoop.hbase.util.RegionSplitter test_table HexStringSplit -c 10 -f f1
Using a Custom Algorithm

The RegionSplitter tool is provided with HBase, and uses a SplitAlgorithm to determine split points for you. As parameters, you give it the algorithm, desired number of regions, and column families. It includes two split algorithms. The first is the HexStringSplit algorithm, which assumes the row keys are hexadecimal strings. The second, UniformSplit, assumes the row keys are random byte arrays. You will probably need to develop your own SplitAlgorithm, using the provided ones as models.

9.7.6. Online Region Merges

Both Master and Regionserver participate in the event of online region merges. Client sends merge RPC to master, then master moves the regions together to the same regionserver where the more heavily loaded region resided, finally master send merge request to this regionserver and regionserver run the region merges. Similar with process of region splits, region merges run as a local transaction on the regionserver, offlines the regions and then merges two regions on the file system, atomically delete merging regions from META and add merged region to the META, opens merged region on the regionserver and reports the merge to Master at last.

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 ('force' merges regardless else merge will fail unless passed adjacent regions. 'force' is for expert use only)

9.7.7. 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.

9.7.7.1. 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.

9.7.7.2. MemStoreFlush

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 value specified by hbase.hregion.memstore.flush.size, all MemStores that belong to its region will be flushed out to disk.

  2. When 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 Region Server. 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 HLog per region server reaches the value specified in hbase.regionserver.max.logs, MemStores from various regions will be flushed out to disk to reduce HLog count. The flush order is based on time. Regions with the oldest MemStores are flushed first until HLog count drops below hbase.regionserver.max.logs.

9.7.7.3. 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 merge 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 MultiVersionConsistencyControl read point, which is the current memstoreTS, filtering out any new updates beyond the read point.

9.7.7.4. StoreFile (HFile)

StoreFiles are where your data lives.

9.7.7.4.1. 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 Section H.2, “ HBase file format with inline blocks (version 2) ” for information about the HFile v2 format that was included in 0.92.

9.7.7.4.2. HFile Tool

To view a textualized version of hfile content, you can do use the org.apache.hadoop.hbase.io.hfile.HFile tool. Type the following to see usage:

$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile  

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

 $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475  

If you leave off the option -v to see just a summary on the hfile. See usage for other things to do with the HFile tool.

9.7.7.4.3. StoreFile Directory Structure on HDFS

For more information of what StoreFiles look like on HDFS with respect to the directory structure, see Section 15.7.2, “Browsing HDFS for HBase Objects”.

9.7.7.5. 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 Appendix E, Compression and Data Block Encoding In HBase.

For more information on blocks, see the HFileBlock source code.

9.7.7.6. KeyValue

The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into passed array at 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.

9.7.7.6.1. 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.

9.7.7.7. Compaction

Ambiguous Terminology

  • A StoreFile is a facade of HFile. In terms of compaction, use of StoreFile seems to have prevailed in the past.

  • A Store is the same thing as a ColumnFamily. StoreFiles are related to a Store, or ColumnFamily.

  • If you want to read more about StoreFiles versus HFiles and Stores versus ColumnFamilies, see HBASE-11316.

When the MemStore reaches a given size (hbase.hregion.memstore.flush.size), it flushes its contents to a StoreFile. The number of StoreFiles in a Store increases over time. Compaction is an operation which reduces the number of StoreFiles in a Store, by merging them together, in order to increase performance on read operations. Compactions can be resource-intensive to perform, and can either help or hinder performance depending on many factors.

Compactions fall into two categories: minor and major. Minor and major compactions differ in the following ways.

Minor compactions usually select a small number of small, adjacent StoreFiles and rewrite them as a single StoreFile. Minor compactions do not drop (filter out) deletes or expired versions, because of potential side effects. See Compaction and Deletions and Compaction and Versions for information on how deletes and versions are handled in relation to compactions. The end result of a minor compaction is fewer, larger StoreFiles for a given Store.

The end result of a major compaction is a single StoreFile per Store. Major compactions also process delete markers and max versions. See Compaction and Deletions and Compaction and Versions for information on how deletes and versions are handled in relation to compactions.

Compaction and Deletions.  When an explicit deletion occurs in HBase, the data is not actually deleted. Instead, a tombstone marker is written. The tombstone marker prevents the data from being returned with queries. During a major compaction, the data is actually deleted, and the tombstone marker is removed from the StoreFile. If the deletion happens because of an expired TTL, no tombstone is created. Instead, the expired data is filtered out and is not written back to the compacted StoreFile.

Compaction and Versions.  When you create a Column Family, you can specify the maximum number of versions to keep, by specifying HColumnDescriptor.setMaxVersions(int versions). The default value is 3. If more versions than the specified maximum exist, the excess versions are filtered out and not written back to the compacted StoreFile.

Major Compactions Can Impact Query Results

In some situations, older versions can be inadvertently resurrected if a newer version is explicitly deleted. See Section 5.9.3.2, “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 Section 2.6.2.8, “Managed Compactions”.

Compactions do not perform region merges. See Section 17.2.2, “Merge” for more information on region merging.

9.7.7.7.1. Compaction Policy - HBase 0.96.x and newer

Compacting large StoreFiles, or too many StoreFiles at once, can cause more IO load than your cluster is able to handle without causing performance problems. The method by which HBase selects which StoreFiles to include in a compaction (and whether the compaction is a minor or major compaction) is called the compaction policy.

Prior to HBase 0.96.x, there was only one compaction policy. That original compaction policy is still available as RatioBasedCompactionPolicy The new compaction default policy, called ExploringCompactionPolicy, was subsequently backported to HBase 0.94 and HBase 0.95, and is the default in HBase 0.96 and newer. It was implemented in HBASE-7842. In short, ExploringCompactionPolicy attempts to select the best possible set of StoreFiles to compact with the least amount of work, while the RatioBasedCompactionPolicy selects the first set that meets the criteria.

Regardless of the compaction policy used, file selection is controlled by several configurable parameters and happens in a multi-step approach. These parameters will be explained in context, and then will be given in a table which shows their descriptions, defaults, and implications of changing them.

9.7.7.7.1.1. Being Stuck

When the MemStore gets too large, it needs to flush its contents to a StoreFile. However, a Store can only have hbase.hstore.blockingStoreFiles files, so the MemStore needs to wait for the number of StoreFiles to be reduced by one or more compactions. However, if the MemStore grows larger than hbase.hregion.memstore.flush.size, it is not able to flush its contents to a StoreFile. If the MemStore is too large and the number of StpreFo;es is also too high, the algorithm is said to be "stuck". The compaction algorithm checks for this "stuck" situation and provides mechanisms to alleviate it.

9.7.7.7.1.2. The ExploringCompactionPolicy Algorithm

The ExploringCompactionPolicy algorithm considers each possible set of adjacent StoreFiles before choosing the set where compaction will have the most benefit.

One situation where the ExploringCompactionPolicy works especially well is when you are bulk-loading data and the bulk loads create larger StoreFiles than the StoreFiles which are holding data older than the bulk-loaded data. This can "trick" HBase into choosing to perform a major compaction each time a compaction is needed, and cause a lot of extra overhead. With the ExploringCompactionPolicy, major compactions happen much less frequently because minor compactions are more efficient.

In general, ExploringCompactionPolicy is the right choice for most situations, and thus is the default compaction policy. You can also use ExploringCompactionPolicy along with Section 9.7.7.7.3, “Experimental: Stripe Compactions”.

The logic of this policy can be examined in hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/compactions/ExploringCompactionPolicy.java. The following is a walk-through of the logic of the ExploringCompactionPolicy.

  1. Make a list of all existing StoreFiles in the Store. The rest of the algorithm filters this list to come up with the subset of HFiles which will be chosen for compaction.

  2. If this was a user-requested compaction, attempt to perform the requested compaction type, regardless of what would normally be chosen. Note that even if the user requests a major compaction, it may not be possible to perform a major compaction. This may be because not all StoreFiles in the Column Family are available to compact or because there are too many Stores in the Column Family.

  3. Some StoreFiles are automatically excluded from consideration. These include:

    • StoreFiles that are larger than hbase.hstore.compaction.max.size

    • StoreFiles that were created by a bulk-load operation which explicitly excluded compaction. You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this, specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the bulk load operation.

  4. Iterate through the list from step 1, and make a list of all potential sets of StoreFiles to compact together. A potential set is a grouping of hbase.hstore.compaction.min contiguous StoreFiles in the list. For each set, perform some sanity-checking and figure out whether this is the best compaction that could be done:

    • If the number of StoreFiles in this set (not the size of the StoreFiles) is fewer than hbase.hstore.compaction.min or more than hbase.hstore.compaction.max, take it out of consideration.

    • Compare the size of this set of StoreFiles with the size of the smallest possible compaction that has been found in the list so far. If the size of this set of StoreFiles represents the smallest compaction that could be done, store it to be used as a fall-back if the algorithm is "stuck" and no StoreFiles would otherwise be chosen. See Section 9.7.7.7.1.1, “Being Stuck”.

    • Do size-based sanity checks against each StoreFile in this set of StoreFiles.

      • If the size of this StoreFile is larger than hbase.hstore.compaction.max.size, take it out of consideration.

      • If the size is greater than or equal to hbase.hstore.compaction.min.size, sanity-check it against the file-based ratio to see whether it is too large to be considered. The sanity-checking is successful if:

        • There is only one StoreFile in this set, or

        • For each StoreFile, its size multiplied by hbase.hstore.compaction.ratio (or hbase.hstore.compaction.ratio.offpeak if off-peak hours are configured and it is during off-peak hours) is less than the sum of the sizes of the other HFiles in the set.

  5. If this set of StoreFiles is still in consideration, compare it to the previously-selected best compaction. If it is better, replace the previously-selected best compaction with this one.

  6. When the entire list of potential compactions has been processed, perform the best compaction that was found. If no StoreFiles were selected for compaction, but there are multiple StoreFiles, assume the algorithm is stuck (see Section 9.7.7.7.1.1, “Being Stuck”) and if so, perform the smallest compaction that was found in step 3.

9.7.7.7.1.3. RatioBasedCompactionPolicy Algorithm

The RatioBasedCompactionPolicy was the only compaction policy prior to HBase 0.96, though ExploringCompactionPolicy has now been backported to HBase 0.94 and 0.95. To use the RatioBasedCompactionPolicy rather than the ExploringCompactionPolicy, set hbase.hstore.defaultengine.compactionpolicy.class to RatioBasedCompactionPolicy in the hbase-site.xml file. To switch back to the ExploringCompactionPolicy, remove the setting from the hbase-site.xml.

The following section walks you through the algorithm used to select StoreFiles for compaction in the RatioBasedCompactionPolicy.

  1. The first phase is to create a list of all candidates for compaction. A list is created of all StoreFiles not already in the compaction queue, and all StoreFiles newer than the newest file that is currently being compacted. This list of StoreFiles is ordered by the sequence ID. The sequence ID is generated when a Put is appended to the write-ahead log (WAL), and is stored in the metadata of the HFile.

  2. Check to see if the algorithm is stuck (see Section 9.7.7.7.1.1, “Being Stuck”, and if so, a major compaction is forced. This is a key area where Section 9.7.7.7.1.2, “The ExploringCompactionPolicy Algorithm” is often a better choice than the RatioBasedCompactionPolicy.

  3. If the compaction was user-requested, try to perform the type of compaction that was requested. Note that a major compaction may not be possible if all HFiles are not available for compaction or if too may StoreFiles exist (more than hbase.hstore.compaction.max).

  4. Some StoreFiles are automatically excluded from consideration. These include:

    • StoreFiles that are larger than hbase.hstore.compaction.max.size

    • StoreFiles that were created by a bulk-load operation which explicitly excluded compaction. You may decide to exclude StoreFiles resulting from bulk loads, from compaction. To do this, specify the hbase.mapreduce.hfileoutputformat.compaction.exclude parameter during the bulk load operation.

  5. The maximum number of StoreFiles allowed in a major compaction is controlled by the hbase.hstore.compaction.max parameter. If the list contains more than this number of StoreFiles, a minor compaction is performed even if a major compaction would otherwise have been done. However, a user-requested major compaction still occurs even if there are more than hbase.hstore.compaction.max StoreFiles to compact.

  6. If the list contains fewer than hbase.hstore.compaction.min StoreFiles to compact, a minor compaction is aborted. Note that a major compaction can be performed on a single HFile. Its function is to remove deletes and expired versions, and reset locality on the StoreFile.

  7. The value of the hbase.hstore.compaction.ratio parameter is multiplied by the sum of StoreFiles smaller than a given file, to determine whether that StoreFile is selected for compaction during a minor compaction. For instance, if hbase.hstore.compaction.ratio is 1.2, FileX is 5 mb, FileY is 2 mb, and FileZ is 3 mb:

    5 <= 1.2 x (2 + 3)            or           5 <= 6

    In this scenario, FileX is eligible for minor compaction. If FileX were 7 mb, it would not be eligible for minor compaction. This ratio favors smaller StoreFile. You can configure a different ratio for use in off-peak hours, using the parameter hbase.hstore.compaction.ratio.offpeak, if you also configure hbase.offpeak.start.hour and hbase.offpeak.end.hour.

  8. If the last major compaction was too long ago and there is more than one StoreFile to be compacted, a major compaction is run, even if it would otherwise have been minor. By default, the maximum time between major compactions is 7 days, plus or minus a 4.8 hour period, and determined randomly within those parameters. Prior to HBase 0.96, the major compaction period was 24 hours. See hbase.hregion.majorcompaction in the table below to tune or disable time-based major compactions.

9.7.7.7.1.4. Parameters Used by Compaction Algorithm

This table contains the main configuration parameters for compaction. This list is not exhaustive. To tune these parameters from the defaults, edit the hbase-default.xml file. For a full list of all configuration parameters available, see Section 2.4, “Configuration Files”

ParameterDescriptionDefault
hbase.hstore.compaction.min

The minimum number of StoreFiles which must be eligible for compaction before compaction can run.

The goal of tuning hbase.hstore.compaction.min is to avoid ending up with too many tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction each time you have two StoreFiles in a Store, and this is probably not appropriate. If you set this value too high, all the other values will need to be adjusted accordingly. For most cases, the default value is appropriate.

In previous versions of HBase, the parameter hbase.hstore.compaction.min was called hbase.hstore.compactionThreshold.

3
hbase.hstore.compaction.max

The maximum number of StoreFiles which will be selected for a single minor compaction, regardless of the number of eligible StoreFiles.

Effectively, the value of hbase.hstore.compaction.max controls the length of time it takes a single compaction to complete. Setting it larger means that more StoreFiles are included in a compaction. For most cases, the default value is appropriate.

10
hbase.hstore.compaction.min.size

A StoreFile smaller than this size will always be eligible for minor compaction. StoreFiles this size or larger are evaluated by hbase.hstore.compaction.ratio to determine if they are eligible.

Because this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to be reduced in write-heavy environments where many files in the 1-2 MB range are being flushed, because every StoreFile will be targeted for compaction and the resulting StoreFiles may still be under the minimum size and require further compaction.

If this parameter is lowered, the ratio check is triggered more quickly. This addressed some issues seen in earlier versions of HBase but changing this parameter is no longer necessary in most situations.

128 MB
hbase.hstore.compaction.max.size

An StoreFile larger than this size will be excluded from compaction. The effect of raising hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get compacted often. If you feel that compaction is happening too often without much benefit, you can try raising this value.

Long.MAX_VALUE
hbase.hstore.compaction.ratio

For minor compaction, this ratio is used to determine whether a given StoreFile which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction of large StoreFile. The value of hbase.hstore.compaction.ratio is expressed as a floating-point decimal.

A large ratio, such as 10, will produce a single giant StoreFile. Conversely, a value of .25, will produce behavior similar to the BigTable compaction algorithm, producing four StoreFiles.

A moderate value of between 1.0 and 1.4 is recommended. When tuning this value, you are balancing write costs with read costs. Raising the value (to something like 1.4) will have more write costs, because you will compact larger StoreFiles. However, during reads, HBase will need to seek through fewer StpreFo;es to accomplish the read. Consider this approach if you cannot take advantage of Section 14.6.4, “Bloom Filters”.

Alternatively, you can lower this value to something like 1.0 to reduce the background cost of writes, and use Section 14.6.4, “Bloom Filters” to limit the number of StoreFiles touched during reads.

For most cases, the default value is appropriate.

1.2F
hbase.hstore.compaction.ratio.offpeakThe compaction ratio used during off-peak compactions, if off-peak hours are also configured (see below). Expressed as a floating-point decimal. This allows for more aggressive (or less aggressive, if you set it lower than hbase.hstore.compaction.ratio) compaction during a set time period. Ignored if off-peak is disabled (default). This works the same as hbase.hstore.compaction.ratio.5.0F
hbase.offpeak.start.hourThe start of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.-1 (disabled)
hbase.offpeak.end.hourThe end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set to -1 to disable off-peak.-1 (disabled)
hbase.regionserver.thread.compaction.throttle

There are two different thread pools for compactions, one for large compactions and the other for small compactions. This helps to keep compaction of lean tables (such as hbase:meta) fast. If a compaction is larger than this threshold, it goes into the large compaction pool. In most cases, the default value is appropriate.

2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults to 128)
hbase.hregion.majorcompaction

Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based automatic major compactions. User-requested and size-based major compactions will still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at a somewhat-random time during a given window of time.

7 days (604800000 milliseconds)
hbase.hregion.majorcompaction.jitter

A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur a given amount of time either side of hbase.hregion.majorcompaction. The smaller the number, the closer the compactions will happen to the hbase.hregion.majorcompaction interval. Expressed as a floating-point decimal.

.50F
9.7.7.7.2. Compaction File Selection

Legacy Information

This section has been preserved for historical reasons and refers to the way compaction worked prior to HBase 0.96.x. You can still use this behavior if you enable Section 9.7.7.7.1.3, “RatioBasedCompactionPolicy Algorithm” For information on the way that compactions work in HBase 0.96.x and later, see Section 9.7.7.7, “Compaction”.

To understand the core algorithm for StoreFile selection, there is some ASCII-art in the Store source code that will serve as useful reference. It has been copied below:

/* normal skew:
 *
 *         older ----> newer
 *     _
 *    | |   _
 *    | |  | |   _
 *  --|-|- |-|- |-|---_-------_-------  minCompactSize
 *    | |  | |  | |  | |  _  | |
 *    | |  | |  | |  | | | | | |
 *    | |  | |  | |  | | | | | |
 */

Important knobs:

  • hbase.hstore.compaction.ratio Ratio used in compaction file selection algorithm (default 1.2f).

  • hbase.hstore.compaction.min (.90 hbase.hstore.compactionThreshold) (files) Minimum number of StoreFiles per Store to be selected for a compaction to occur (default 2).

  • hbase.hstore.compaction.max (files) Maximum number of StoreFiles to compact per minor compaction (default 10).

  • hbase.hstore.compaction.min.size (bytes) Any StoreFile smaller than this setting with automatically be a candidate for compaction. Defaults to hbase.hregion.memstore.flush.size (128 mb).

  • hbase.hstore.compaction.max.size (.92) (bytes) Any StoreFile larger than this setting with automatically be excluded from compaction (default Long.MAX_VALUE).

The minor compaction StoreFile selection logic is size based, and selects a file for compaction when the file <= sum(smaller_files) * hbase.hstore.compaction.ratio.

9.7.7.7.2.1. Minor Compaction File Selection - Example #1 (Basic Example)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 23, 12, and 12.

Why?

  • 100 --> No, because sum(50, 23, 12, 12) * 1.0 = 97.

  • 50 --> No, because sum(23, 12, 12) * 1.0 = 47.

  • 23 --> Yes, because sum(12, 12) * 1.0 = 24.

  • 12 --> Yes, because the previous file has been included, and because this does not exceed the the max-file limit of 5

  • 12 --> Yes, because the previous file had been included, and because this does not exceed the the max-file limit of 5.

9.7.7.7.2.2. Minor Compaction File Selection - Example #2 (Not Enough Files To Compact)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to newest). With the above parameters, no compaction will be started.

Why?

  • 100 --> No, because sum(25, 12, 12) * 1.0 = 47

  • 25 --> No, because sum(12, 12) * 1.0 = 24

  • 12 --> No. Candidate because sum(12) * 1.0 = 12, there are only 2 files to compact and that is less than the threshold of 3

  • 12 --> No. Candidate because the previous StoreFile was, but there are not enough files to compact

9.7.7.7.2.3. Minor Compaction File Selection - Example #3 (Limiting Files To Compact)

This example mirrors an example from the unit test TestCompactSelection.

  • hbase.hstore.compaction.ratio = 1.0f

  • hbase.hstore.compaction.min = 3 (files)

  • hbase.hstore.compaction.max = 5 (files)

  • hbase.hstore.compaction.min.size = 10 (bytes)

  • hbase.hstore.compaction.max.size = 1000 (bytes)

The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece (oldest to newest). With the above parameters, the files that would be selected for minor compaction are 7, 6, 5, 4, 3.

Why?

  • 7 --> Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than the min-size

  • 6 --> Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than the min-size.

  • 5 --> Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the min-size.

  • 4 --> Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the min-size.

  • 3 --> Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the min-size.

  • 2 --> No. Candidate because previous file was selected and 2 is less than the min-size, but the max-number of files to compact has been reached.

  • 1 --> No. Candidate because previous file was selected and 1 is less than the min-size, but max-number of files to compact has been reached.

9.7.7.7.2.3.1. Impact of Key Configuration Options

Note

This information is now included in the configuration parameter table in ???.

9.7.7.7.3. Experimental: Stripe Compactions

Stripe compactions is an experimental feature added in HBase 0.98 which aims to improve compactions for large regions or non-uniformly distributed row keys. In order to achieve smaller and/or more granular compactions, the StoreFiles within a region are maintained separately for several row-key sub-ranges, or "stripes", of the region. The stripes are transparent to the rest of HBase, so other operations on the HFiles or data work without modification.

Stripe compactions change the HFile layout, creating sub-regions within regions. These sub-regions are easier to compact, and should result in fewer major compactions. This approach alleviates some of the challenges of larger regions.

Stripe compaction is fully compatible with Section 9.7.7.7, “Compaction” and works in conjunction with either the ExploringCompactionPolicy or RatioBasedCompactionPolicy. It can be enabled for existing tables, and the table will continue to operate normally if it is disabled later.

9.7.7.7.4. When To Use Stripe Compactions

Consider using stripe compaction if you have either of the following:

  • Large regions. You can get the positive effects of smaller regions without additional overhead for MemStore and region management overhead.

  • Non-uniform keys, such as time dimension in a key. Only the stripes receiving the new keys will need to compact. Old data will not compact as often, if at all

Performance Improvements. Performance testing has shown that the performance of reads improves somewhat, and variability of performance of reads and writes is greatly reduced. An overall long-term performance improvement is seen on large non-uniform-row key regions, such as a hash-prefixed timestamp key. These performance gains are the most dramatic on a table which is already large. It is possible that the performance improvement might extend to region splits.

9.7.7.7.4.1. Enabling Stripe Compaction

You can enable stripe compaction for a table or a column family, by setting its hbase.hstore.engine.class to org.apache.hadoop.hbase.regionserver.StripeStoreEngine. You also need to set the hbase.hstore.blockingStoreFiles to a high number, such as 100 (rather than the default value of 10).

Procedure 9.4. Enable Stripe Compaction

  1. If the table already exists, disable the table.

  2. Run one of following commands in the HBase shell. Replace the table name orders_table with the name of your table.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}
    alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}}
    create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}                  
                    
  3. Configure other options if needed. See Section 9.7.7.7.4.2, “Configuring Stripe Compaction” for more information.

  4. Enable the table.

Procedure 9.5. Disable Stripe Compaction

  1. Disable the table.

  2. Set the hbase.hstore.engine.class option to either nil or org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. Either option has the same effect.

    alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => ''}
                    
  3. Enable the table.

When you enable a large table after changing the store engine either way, a major compaction will likely be performed on most regions. This is not necessary on new tables.

9.7.7.7.4.2. Configuring Stripe Compaction

Each of the settings for stripe compaction should be configured at the table or column family, after disabling the table. If you use HBase shell, the general command pattern is as follows:

alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}}
              
9.7.7.7.4.2.1. Region and stripe sizing

You can configure your stripe sizing bsaed upon your region sizing. By default, your new regions will start with one stripe. On the next compaction after the stripe has grown too large (16 x MemStore flushes size), it is split into two stripes. Stripe splitting continues as the region grows, until the region is large enough to split.

You can improve this pattern for your own data. A good rule is to aim for a stripe size of at least 1 GB, and about 8-12 stripes for uniform row keys. For example, if your regions are 30 GB, 12 x 2.5 GB stripes might be a good starting point.

Table 9.1. Stripe Sizing Settings

SettingNotes
hbase.store.stripe.initialStripeCount

The number of stripes to create when stripe compaction is enabled. You can use it as follows:

  • For relatively uniform row keys, if you know the approximate target number of stripes from the above, you can avoid some splitting overhead by starting with several stripes (2, 5, 10...). If the early data is not representative of overall row key distribution, this will not be as efficient.

  • For existing tables with a large amount of data, this setting will effectively pre-split your stripes.

  • For keys such as hash-prefixed sequential keys, with more than one hash prefix per region, pre-splitting may make sense.

hbase.store.stripe.sizeToSplit The maximum size a stripe grows before splitting. Use this in conjunction with hbase.store.stripe.splitPartCount to control the target stripe size (sizeToSplit = splitPartsCount * target stripe size), according to the above sizing considerations.
hbase.store.stripe.splitPartCount The number of new stripes to create when splitting a stripe. The default is 2, which is appropriate for most cases. For non-uniform row keys, you can experiment with increasing the number to 3 or 4, to isolate the arriving updates into narrower slice of the region without additional splits being required.

9.7.7.7.4.2.2. MemStore Size Settings

By default, the flush creates several files from one MemStore, according to existing stripe boundaries and row keys to flush. This approach minimizes write amplification, but can be undesirable if the MemStore is small and there are many stripes, because the files will be too small.

In this type of situation, you can set hbase.store.stripe.compaction.flushToL0 to true. This will cause a MemStore flush to create a single file instead. When at least hbase.store.stripe.compaction.minFilesL0 such files (by default, 4) accumulate, they will be compacted into striped files.

9.7.7.7.4.2.3. Normal Compaction Configuration and Stripe Compaction

All the settings that apply to normal compactions (see ???) apply to stripe compactions. The exceptions are the minimum and maximum number of files, which are set to higher values by default because the files in stripes are smaller. To control these for stripe compactions, use hbase.store.stripe.compaction.minFiles and hbase.store.stripe.compaction.maxFiles, rather than hbase.hstore.compaction.min and hbase.hstore.compaction.max.

9.8. Bulk Loading

9.8.1. Overview

HBase includes several methods of loading data into tables. The most straightforward method is to either use the TableOutputFormat class from a MapReduce job, or use the normal client APIs; however, these are not always the most efficient methods.

The bulk load feature uses a MapReduce job to output table data in HBase's internal data format, and then directly loads the generated StoreFiles into a running cluster. Using bulk load will use less CPU and network resources than simply using the HBase API.

9.8.2. Bulk Load Limitations

As bulk loading bypasses the write path, the WAL doesn’t get written to as part of the process. Replication works by reading the WAL files so it won’t see the bulk loaded data – and the same goes for the edits that use Put.setWriteToWAL(true). One way to handle that is to ship the raw files or the HFiles to the other cluster and do the other processing there.

9.8.3. Bulk Load Architecture

The HBase bulk load process consists of two main steps.

9.8.3.1. Preparing data via a MapReduce job

The first step of a bulk load is to generate HBase data files (StoreFiles) from a MapReduce job using HFileOutputFormat. This output format writes out data in HBase's internal storage format so that they can be later loaded very efficiently into the cluster.

In order to function efficiently, HFileOutputFormat must be configured such that each output HFile fits within a single region. In order to do this, jobs whose output will be bulk loaded into HBase use Hadoop's TotalOrderPartitioner class to partition the map output into disjoint ranges of the key space, corresponding to the key ranges of the regions in the table.

HFileOutputFormat includes a convenience function, configureIncrementalLoad(), which automatically sets up a TotalOrderPartitioner based on the current region boundaries of a table.

9.8.3.2. Completing the data load

After the data has been prepared using HFileOutputFormat, it is loaded into the cluster using completebulkload. This command line tool iterates through the prepared data files, and for each one determines the region the file belongs to. It then contacts the appropriate Region Server which adopts the HFile, moving it into its storage directory and making the data available to clients.

If the region boundaries have changed during the course of bulk load preparation, or between the preparation and completion steps, the completebulkloads utility will automatically split the data files into pieces corresponding to the new boundaries. This process is not optimally efficient, so users should take care to minimize the delay between preparing a bulk load and importing it into the cluster, especially if other clients are simultaneously loading data through other means.

9.8.4. Importing the prepared data using the completebulkload tool

After a data import has been prepared, either by using the importtsv tool with the "importtsv.bulk.output" option or by some other MapReduce job using the HFileOutputFormat, the completebulkload tool is used to import the data into the running cluster.

The completebulkload tool simply takes the output path where importtsv or your MapReduce job put its results, and the table name to import into. For example:

$ hadoop jar hbase-server-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable

The -c config-file option can be used to specify a file containing the appropriate hbase parameters (e.g., hbase-site.xml) if not supplied already on the CLASSPATH (In addition, the CLASSPATH must contain the directory that has the zookeeper configuration file if zookeeper is NOT managed by HBase).

Note: If the target table does not already exist in HBase, this tool will create the table automatically.

This tool will run quickly, after which point the new data will be visible in the cluster.

9.8.5. See Also

For more information about the referenced utilities, see Section 17.1.11, “ImportTsv” and Section 17.1.12, “CompleteBulkLoad”.

See How-to: Use HBase Bulk Loading, and Why for a recent blog on current state of bulk loading.

9.8.6. Advanced Usage

Although the importtsv tool is useful in many cases, advanced users may want to generate data programatically, or import data from other formats. To get started doing so, dig into ImportTsv.java and check the JavaDoc for HFileOutputFormat.

The import step of the bulk load can also be done programatically. See the LoadIncrementalHFiles class for more information.

9.9. HDFS

As HBase runs on HDFS (and each StoreFile is written as a file on HDFS), it is important to have an understanding of the HDFS Architecture especially in terms of how it stores files, handles failovers, and replicates blocks.

See the Hadoop documentation on HDFS Architecture for more information.

9.9.1. NameNode

The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link for more information.

9.9.2. DataNode

The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link for more information.

9.10. Timeline-consistent High Available Reads

9.10.1. Introduction

HBase, architecturally, always had the strong consistency guarantee from the start. All reads and writes are routed through a single region server, which guarantees that all writes happen in an order, and all reads are seeing the most recent committed data.

However, because of this single homing of the reads to a single location, if the server becomes unavailable, the regions of the table that were hosted in the region server become unavailable for some time. There are three phases in the region recovery process - detection, assignment, and recovery. Of these, the detection is usually the longest and is presently in the order of 20-30 seconds depending on the zookeeper session timeout. During this time and before the recovery is complete, the clients will not be able to read the region data.

However, for some use cases, either the data may be read-only, or doing reads againsts some stale data is acceptable. With timeline-consistent high available reads, HBase can be used for these kind of latency-sensitive use cases where the application can expect to have a time bound on the read completion.

For achieving high availability for reads, HBase provides a feature called “region replication”. In this model, for each region of a table, there will be multiple replicas that are opened in different region servers. By default, the region replication is set to 1, so only a single region replica is deployed and there will not be any changes from the original model. If region replication is set to 2 or more, than the master will assign replicas of the regions of the table. The Load Balancer ensures that the region replicas are not co-hosted in the same region servers and also in the same rack (if possible).

All of the replicas for a single region will have a unique replica_id, starting from 0. The region replica having replica_id==0 is called the primary region, and the others “secondary regions” or secondaries. Only the primary can accept writes from the client, and the primary will always contain the latest changes. Since all writes still have to go through the primary region, the writes are not highly-available (meaning they might block for some time if the region becomes unavailable).

The writes are asynchronously sent to the secondary region replicas using an “Async WAL replication” feature. This works similarly to HBase’s multi-datacenter replication, but instead the data from a region is replicated to the secondary regions. Each secondary replica always receives and observes the writes in the same order that the primary region committed them. This ensures that the secondaries won’t diverge from the primary regions data, but since the log replication is asnyc, the data might be stale in secondary regions. In some sense, this design can be thought of as “in-cluster replication”, where instead of replicating to a different datacenter, the data goes to a secondary region to keep secondary region’s in-memory state up to date. The data files are shared between the primary region and the other replicas, so that there is no extra storage overhead. However, the secondary regions will have recent non-flushed data in their memstores, which increases the memory overhead.

Async WAL replication feature is being implemented in Phase 2 of issue HBASE-10070. Before this, region replicas will only be updated with flushed data files from the primary (see hbase.regionserver.storefile.refresh.period below). It is also possible to use this without setting storefile.refresh.period for read only tables.

9.10.2. Timeline Consistency

With this feature, HBase introduces a Consistency definition, which can be provided per read operation (get or scan).

public enum Consistency {
    STRONG,
    TIMELINE
}
	

Consistency.STRONG is the default consistency model provided by HBase. In case the table has region replication = 1, or in a table with region replicas but the reads are done with this consistency, the read is always performed by the primary regions, so that there will not be any change from the previous behaviour, and the client always observes the latest data.

In case a read is performed with Consistency.TIMELINE, then the read RPC will be sent to the primary region server first. After a short interval (hbase.client.primaryCallTimeout.get, 10ms by default), parallel RPC for secondary region replicas will also be sent if the primary does not respond back. After this, the result is returned from whichever RPC is finished first. If the response came back from the primary region replica, we can always know that the data is latest. For this Result.isStale() API has been added to inspect the staleness. If the result is from a secondary region, then Result.isStale() will be set to true. The user can then inspect this field to possibly reason about the data.

In terms of semantics, TIMELINE consistency as implemented by HBase differs from pure eventual consistency in these respects:

  • Single homed and ordered updates: Region replication or not, on the write side, there is still only 1 defined replica (primary) which can accept writes. This replica is responsible for ordering the edits and preventing conflicts. This guarantees that two different writes are not committed at the same time by different replicas and the data diverges. With this, there is no need to do read-repair or last-timestamp-wins kind of conflict resolution.

  • The secondaries also apply the edits in the order that the primary committed them. This way the secondaries will contain a snapshot of the primaries data at any point in time. This is similar to RDBMS replications and even HBase’s own multi-datacenter replication, however in a single cluster.

  • On the read side, the client can detect whether the read is coming from up-to-date data or is stale data. Also, the client can issue reads with different consistency requirements on a per-operation basis to ensure its own semantic guarantees.

  • The client can still observe edits out-of-order, and can go back in time, if it observes reads from one secondary replica first, then another secondary replica. There is no stickiness to region replicas or a transaction-id based guarantee. If required, this can be implemented later though.

Figure 9.2. HFile Version 1

HFile Version 1

To better understand the TIMELINE semantics, lets look at the above diagram. Lets say that there are two clients, and the first one writes x=1 at first, then x=2 and x=3 later. As above, all writes are handled by the primary region replica. The writes are saved in the write ahead log (WAL), and replicated to the other replicas asynchronously. In the above diagram, notice that replica_id=1 received 2 updates, and it’s data shows that x=2, while the replica_id=2 only received a single update, and its data shows that x=1.

If client1 reads with STRONG consistency, it will only talk with the replica_id=0, and thus is guaranteed to observe the latest value of x=3. In case of a client issuing TIMELINE consistency reads, the RPC will go to all replicas (after primary timeout) and the result from the first response will be returned back. Thus the client can see either 1, 2 or 3 as the value of x. Let’s say that the primary region has failed and log replication cannot continue for some time. If the client does multiple reads with TIMELINE consistency, she can observe x=2 first, then x=1, and so on.

9.10.3. Tradeoffs

Having secondary regions hosted for read availability comes with some tradeoffs which should be carefully evaluated per use case. Following are advantages and disadvantages.

Advantages

  • High availability for read-only tables.

  • High availability for stale reads

  • Ability to do very low latency reads with very high percentile (99.9%+) latencies for stale reads

Disadvantages

  • Double / Triple memstore usage (depending on region replication count) for tables with region replication > 1

  • Increased block cache usage

  • Extra network traffic for log replication

  • Extra backup RPCs for replicas

To serve the region data from multiple replicas, HBase opens the regions in secondary mode in the region servers. The regions opened in secondary mode will share the same data files with the primary region replica, however each secondary region replica will have its own memstore to keep the unflushed data (only primary region can do flushes). Also to serve reads from secondary regions, the blocks of data files may be also cached in the block caches for the secondary regions.

9.10.4. Configuration properties

To use highly available reads, you should set the following properties in hbase-site.xml file. There is no specific configuration to enable or disable region replicas. Instead you can change the number of region replicas per table to increase or decrease at the table creation or with alter table.

9.10.4.1. Server side properties

<property>
    <name>hbase.regionserver.storefile.refresh.period</name>
    <value>0</value>
    <description>
      The period (in milliseconds) for refreshing the store files for the secondary regions. 0 means this feature is disabled. Secondary regions sees new files (from flushes and compactions) from primary once the secondary region refreshes the list of files in the region. But too frequent refreshes might cause extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger value is also recommended with this setting.
    </description>
</property>

One thing to keep in mind also is that, region replica placement policy is only enforced by the StochasticLoadBalancer which is the default balancer. If you are using a custom load balancer property in hbase-site.xml (hbase.master.loadbalancer.class) replicas of regions might end up being hosted in the same server.

9.10.4.2. Client side properties

Ensure to set the following for all clients (and servers) that will use region replicas.

<property>
    <name>hbase.ipc.client.allowsInterrupt</name>
    <value>true</value>
    <description>
      Whether to enable interruption of RPC threads at the client side. This is required for region replicas with fallback RPC’s to secondary regions.
    </description>
</property>
<property>
    <name>hbase.client.primaryCallTimeout.get</name>
    <value>10000</value>
    <description>
      The timeout (in microseconds), before secondary fallback RPC’s are submitted for get requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. 
    </description>
</property>
<property>
    <name>hbase.client.primaryCallTimeout.multiget</name>
    <value>10000</value>
    <description>
      The timeout (in microseconds), before secondary fallback RPC’s are submitted for multi-get requests (HTable.get(List<Get>)) with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. 
    </description>
</property>
<property>
    <name>hbase.client.replicaCallTimeout.scan</name>
    <value>1000000</value>
    <description>
      The timeout (in microseconds), before secondary fallback RPC’s are submitted for scan requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 1 sec. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. 
    </description>
</property>

9.10.5. Creating a table with region replication

Region replication is a per-table property. All tables have REGION_REPLICATION = 1 by default, which means that there is only one replica per region. You can set and change the number of replicas per region of a table by supplying the REGION_REPLICATION property in the table descriptor.

9.10.5.1. Shell

create 't1', 'f1', {REGION_REPLICATION => 2}

describe 't1'
for i in 1..100
put 't1', "r#{i}", 'f1:c1', i
end
flush 't1'

9.10.5.2. Java

HTableDescriptor htd = new HTableDesctiptor(TableName.valueOf(“test_table”)); 
htd.setRegionReplication(2);
...
admin.createTable(htd); 

You can also use setRegionReplication() and alter table to increase, decrease the region replication for a table.

9.10.6. Region splits and merges

Region splits and merges are not compatible with regions with replicas yet. So you have to pre-split the table, and disable the region splits. Also you should not execute region merges on tables with region replicas. To disable region splits you can use DisabledRegionSplitPolicy as the split policy.

9.10.7. User Interface

In the masters user interface, the region replicas of a table are also shown together with the primary regions. You can notice that the replicas of a region will share the same start and end keys and the same region name prefix. The only difference would be the appended replica_id (which is encoded as hex), and the region encoded name will be different. You can also see the replica ids shown explicitly in the UI.

9.10.8. API and Usage

9.10.8.1. Shell

You can do reads in shell using a the Consistency.TIMELINE semantics as follows

hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}

You can simulate a region server pausing or becoming unavailable and do a read from the secondary replica:

$ kill -STOP <pid or primary region server>

hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"}

Using scans is also similar

hbase> scan 't1', {CONSISTENCY => 'TIMELINE'}

9.10.8.2. Java

You can set set the consistency for Gets and Scans and do requests as follows.

Get get = new Get(row);
get.setConsistency(Consistency.TIMELINE);
...
Result result = table.get(get); 

You can also pass multiple gets:

Get get1 = new Get(row);
get1.setConsistency(Consistency.TIMELINE);
...
ArrayList<Get> gets = new ArrayList<Get>();
gets.add(get1);
...
Result[] results = table.get(gets); 

And Scans:

Scan scan = new Scan();
scan.setConsistency(Consistency.TIMELINE);
...
ResultScanner scanner = table.getScanner(scan);

You can inspect whether the results are coming from primary region or not by calling the Result.isStale() method:

Result result = table.get(get); 
if (result.isStale()) {
  ...
}

9.10.9. Resources

  1. More information about the design and implementation can be found at the jira issue: HBASE-10070

  2. HBaseCon 2014 talk also contains some details and slides.

Chapter 10. Apache HBase APIs

This chapter provides information about performing operations using HBase native APIs. This information is not exhaustive, and provides a quick reference in addition to the User API Reference. The examples here are not comprehensive or complete, and should be used for purposes of illustration only.

Apache HBase also works with multiple external APIs. See Chapter 11, Apache HBase External APIs for more information.

Example 10.1. Create a Table Using Java

This example has been tested on HBase 0.96.1.1.

package com.example.hbase.admin;

import java.io.IOException;

import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.HBaseAdmin;
import org.apache.hadoop.hbase.io.compress.Compression.Algorithm;
import org.apache.hadoop.conf.Configuration;

import static com.example.hbase.Constants.*;

public class CreateSchema {

 public static void createOrOverwrite(HBaseAdmin admin, HTableDescriptor table) throws IOException {
    if (admin.tableExists(table.getName())) {
      admin.disableTable(table.getName());
      admin.deleteTable(table.getName());
    }
    admin.createTable(table);
  }

  public static void createSchemaTables (Configuration config) {
    try {
      final HBaseAdmin admin = new HBaseAdmin(config);
      HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME));
      table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY));

      System.out.print("Creating table. ");
      createOrOverwrite(admin, table);
      System.out.println(" Done.");

      admin.close();
    } catch (Exception e) {
      e.printStackTrace();
      System.exit(-1);
    }
  }


}      
      
    

Example 10.2. Add, Modify, and Delete a Table

This example has been tested on HBase 0.96.1.1.

public static void upgradeFrom0 (Configuration config) {

    try {
      final HBaseAdmin admin = new HBaseAdmin(config);
      TableName tableName = TableName.valueOf(TABLE_ASSETMETA);
      HTableDescriptor table_assetmeta = new HTableDescriptor(tableName);
      table_assetmeta.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY));

      // Create a new table.

      System.out.print("Creating table_assetmeta. ");
      admin.createTable(table_assetmeta);
      System.out.println(" Done.");

      // Update existing table
      HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF");
      newColumn.setCompactionCompressionType(Algorithm.GZ);
      newColumn.setMaxVersions(HConstants.ALL_VERSIONS);
      admin.addColumn(tableName, newColumn);

      // Disable an existing table
      admin.disableTable(tableName);

      // Delete an existing column family
      admin.deleteColumn(tableName, CF_DEFAULT);

      // Delete a table (Need to be disabled first)
      admin.deleteTable(tableName);


      admin.close();
    } catch (Exception e) {
      e.printStackTrace();
      System.exit(-1);
    }
  }      
    

Chapter 11. Apache HBase External APIs

This chapter will cover access to Apache HBase either through non-Java languages, or through custom protocols. For information on using the native HBase APIs, refer to User API Reference and the new Chapter 10, Apache HBase APIs chapter.

11.1. Non-Java Languages Talking to the JVM

Currently the documentation on this topic in the Apache HBase Wiki. See also the Thrift API Javadoc.

11.2. REST

Currently most of the documentation on REST exists in the Apache HBase Wiki on REST (The REST gateway used to be called 'Stargate'). There are also a nice set of blogs on How-to: Use the Apache HBase REST Interface by Jesse Anderson.

To run your REST server under SSL, set hbase.rest.ssl.enabled to true and also set the following configs when you launch the REST server:(See example commands in Section 2.6.3.5, “JMX”)

hbase.rest.ssl.keystore.store
hbase.rest.ssl.keystore.password
hbase.rest.ssl.keystore.keypassword

HBase ships a simple REST client, see REST client package for details. To enable SSL support for it, please also import your certificate into local java cacerts keystore:

keytool -import -trustcacerts -file /home/user/restserver.cert -keystore $JAVA_HOME/jre/lib/security/cacerts

11.3. Thrift

Documentation about Thrift has moved to Chapter 12, Thrift API and Filter Language.

11.4. C/C++ Apache HBase Client

FB's Chip Turner wrote a pure C/C++ client. Check it out.

Chapter 12. Thrift API and Filter Language

Apache Thrift is a cross-platform, cross-language development framework. HBase includes a Thrift API and filter language. The Thrift API relies on client and server processes. Documentation about the HBase Thrift API is located at http://wiki.apache.org/hadoop/Hbase/ThriftApi.

You can configure Thrift for secure authentication at the server and client side, by following the procedures in Section 8.1.4, “Client-side Configuration for Secure Operation - Thrift Gateway” and Section 8.1.5, “Configure the Thrift Gateway to Authenticate on Behalf of the Client”.

The rest of this chapter discusses the filter language provided by the Thrift API.

12.1. Filter Language

Thrift Filter Language was introduced in APache HBase 0.92. It allows you to perform server-side filtering when accessing HBase over Thrift or in the HBase shell. You can find out more about shell integration by using the scan help command in the shell.

You specify a filter as a string, which is parsed on the server to construct the filter.

12.1.1. General Filter String Syntax

A simple filter expression is expressed as a string:

“FilterName (argument, argument,... , argument)”

Keep the following syntax guidelines in mind.

  • Specify the name of the filter followed by the comma-separated argument list in parentheses.

  • If the argument represents a string, it should be enclosed in single quotes (').

  • Arguments which represent a boolean, an integer, or a comparison operator (such as <, >, or !=), should not be enclosed in quotes

  • The filter name must be a single word. All ASCII characters are allowed except for whitespace, single quotes and parentheses.

  • The filter’s arguments can contain any ASCII character. If single quotes are present in the argument, they must be escaped by an additional preceding single quote.

12.1.2. Compound Filters and Operators

Binary Operators

AND

If the AND operator is used, the key-vallue must satisfy both the filters.

OR

If the OR operator is used, the key-value must satisfy at least one of the filters.

Unary Operators

SKIP

For a particular row, if any of the key-values fail the filter condition, the entire row is skipped.

WHILE

For a particular row, key-values will be emitted until a key-value is reached t hat fails the filter condition.

Example 12.1. Compound Operators

You can combine multiple operators to create a hierarchy of filters, such as the following example:

(Filter1 AND Filter2) OR (Filter3 AND Filter4)

12.1.3. Order of Evaluation

  1. Parentheses have the highest precedence.

  2. The unary operators SKIP and WHILE are next, and have the same precedence.

  3. The binary operators follow. AND has highest precedence, followed by OR.

Example 12.2. Precedence Example

Filter1 AND Filter2 OR Filter
is evaluated as
(Filter1 AND Filter2) OR Filter3
Filter1 AND SKIP Filter2 OR Filter3
is evaluated as
(Filter1 AND (SKIP Filter2)) OR Filter3

You can use parentheses to explicitly control the order of evaluation.

12.1.4. Compare Operator

The following compare operators are provided:

  1. LESS (<)

  2. LESS_OR_EQUAL (<=)

  3. EQUAL (=)

  4. NOT_EQUAL (!=)

  5. GREATER_OR_EQUAL (>=)

  6. GREATER (>)

  7. NO_OP (no operation)

The client should use the symbols (<, <=, =, !=, >, >=) to express compare operators.

12.1.5. Comparator

A comparator can be any of the following:

  1. BinaryComparator - This lexicographically compares against the specified byte array using Bytes.compareTo(byte[], byte[])

  2. BinaryPrefixComparator - This lexicographically compares against a specified byte array. It only compares up to the length of this byte array.

  3. RegexStringComparator - This compares against the specified byte array using the given regular expression. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator

  4. SubStringComparator - This tests if the given substring appears in a specified byte array. The comparison is case insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this comparator

The general syntax of a comparator is: ComparatorType:ComparatorValue

The ComparatorType for the various comparators is as follows:

  1. BinaryComparator - binary

  2. BinaryPrefixComparator - binaryprefix

  3. RegexStringComparator - regexstring

  4. SubStringComparator - substring

The ComparatorValue can be any value.

Example 12.3. Example 1

>, 'binary:abc' will match everything that is lexicographically greater than "abc"


Example 12.4. Example 2

=, 'binaryprefix:abc' will match everything whose first 3 characters are lexicographically equal to "abc"


Example 12.5. Example 3

!=, 'regexstring:ab*yz' will match everything that doesn't begin with "ab" and ends with "yz"


Example 12.6. Example 4

=, 'substring:abc123' will match everything that begins with the substring "abc123"


12.1.6. Example PHP Client Program that uses the Filter Language

<? $_SERVER['PHP_ROOT'] = realpath(dirname(__FILE__).'/..');
   require_once $_SERVER['PHP_ROOT'].'/flib/__flib.php';
   flib_init(FLIB_CONTEXT_SCRIPT);
   require_module('storage/hbase');
   $hbase = new HBase('<server_name_running_thrift_server>', <port on which thrift server is running>);
   $hbase->open();
   $client = $hbase->getClient();
   $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))");
   $to_print = $client->scannerGetList($result,1);
   while ($to_print) {
      print_r($to_print);
      $to_print = $client->scannerGetList($result,1);
    }
   $client->scannerClose($result);
?>
        

12.1.7. Example Filter Strings

  • “PrefixFilter (‘Row’) AND PageFilter (1) AND FirstKeyOnlyFilter ()” will return all key-value pairs that match the following conditions:

    1. The row containing the key-value should have prefix “Row”

    2. The key-value must be located in the first row of the table

    3. The key-value pair must be the first key-value in the row

    • “(RowFilter (=, ‘binary:Row 1’) AND TimeStampsFilter (74689, 89734)) OR ColumnRangeFilter (‘abc’, true, ‘xyz’, false))” will return all key-value pairs that match both the following conditions:

      • The key-value is in a row having row key “Row 1”

      • The key-value must have a timestamp of either 74689 or 89734.

      • Or it must match the following condition:

        • The key-value pair must be in a column that is lexicographically >= abc and < xyz 

  • “SKIP ValueFilter (0)” will skip the entire row if any of the values in the row is not 0

12.1.8. Individual Filter Syntax

KeyOnlyFilter

This filter doesn’t take any arguments. It returns only the key component of each key-value.

Syntax

  • KeyOnlyFilter ()

Example

  • KeyOnlyFilter ()"
FirstKeyOnlyFilter

This filter doesn’t take any arguments. It returns only the first key-value from each row.

Syntax

  • FirstKeyOnlyFilter ()

Example

  • FirstKeyOnlyFilter ()
PrefixFilter

This filter takes one argument – a prefix of a row key. It returns only those key-values present in a row that starts with the specified row prefix

Syntax

  • PrefixFilter (‘<row_prefix>’)

Example

  • PrefixFilter (‘Row’)
ColumnPrefixFilter

This filter takes one argument – a column prefix. It returns only those key-values present in a column that starts with the specified column prefix. The column prefix must be of the form: “qualifier”.

Syntax

  • ColumnPrefixFilter(‘<column_prefix>’)

Example

  • ColumnPrefixFilter(‘Col’)
MultipleColumnPrefixFilter

This filter takes a list of column prefixes. It returns key-values that are present in a column that starts with any of the specified column prefixes. Each of the column prefixes must be of the form: “qualifier”.

Syntax

  • MultipleColumnPrefixFilter(‘<column_prefix>’, ‘<column_prefix>’, …, ‘<column_prefix>’)

Example

  • MultipleColumnPrefixFilter(‘Col1’, ‘Col2’)
ColumnCountGetFilter

This filter takes one argument – a limit. It returns the first limit number of columns in the table.

Syntax

  • ColumnCountGetFilter
                            (‘<limit>’)

Example

  • ColumnCountGetFilter (4)
PageFilter

This filter takes one argument – a page size. It returns page size number of rows from the table.

Syntax

  • PageFilter (‘<page_size>’)

Example

  • PageFilter (2)
ColumnPaginationFilter

This filter takes two arguments – a limit and offset. It returns limit number of columns after offset number of columns. It does this for all the rows.

Syntax

  • ColumnPaginationFilter(‘<limit>’, ‘<offset>’)

Example

  • ColumnPaginationFilter (3, 5)
InclusiveStopFilter

This filter takes one argument – a row key on which to stop scanning. It returns all key-values present in rows up to and including the specified row.

Syntax

  • InclusiveStopFilter(‘<stop_row_key>’)

Example

  • InclusiveStopFilter ('Row2')
TimeStampsFilter

This filter takes a list of timestamps. It returns those key-values whose timestamps matches any of the specified timestamps.

Syntax

  • TimeStampsFilter (<timestamp>, <timestamp>, ... ,<timestamp>)

Example

  • TimeStampsFilter (5985489, 48895495, 58489845945)
RowFilter

This filter takes a compare operator and a comparator. It compares each row key with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that row.

Syntax

  • RowFilter (<compareOp>, ‘<row_comparator>’)

Example

  • RowFilter (<=, ‘xyz)
Family Filter

This filter takes a compare operator and a comparator. It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column.

Syntax

  • QualifierFilter (<compareOp>, ‘<qualifier_comparator>’)

Example

  • QualifierFilter (=, ‘Column1’)
QualifierFilter

This filter takes a compare operator and a comparator. It compares each qualifier name with the comparator using the compare operator and if the comparison returns true, it returns all the key-values in that column.

Syntax

  • QualifierFilter (<compareOp>,‘<qualifier_comparator>’)

Example

  • QualifierFilter (=,‘Column1’)
ValueFilter

This filter takes a compare operator and a comparator. It compares each value with the comparator using the compare operator and if the comparison returns true, it returns that key-value.

Syntax

  • ValueFilter (<compareOp>,‘<value_comparator>’) 

Example

  • ValueFilter (!=, ‘Value’)
DependentColumnFilter

This filter takes two arguments – a family and a qualifier. It tries to locate this column in each row and returns all key-values in that row that have the same timestamp. If the row doesn’t contain the specified column – none of the key-values in that row will be returned.

The filter can also take an optional boolean argument – dropDependentColumn. If set to true, the column we were depending on doesn’t get returned.

The filter can also take two more additional optional arguments – a compare operator and a value comparator, which are further checks in addition to the family and qualifier. If the dependent column is found, its value should also pass the value check and then only is its timestamp taken into consideration.

Syntax

  • DependentColumnFilter (‘<family>’,‘<qualifier>’, <boolean>, <compare operator>, ‘<value
                            comparator’)
  • DependentColumnFilter (‘<family>’,‘<qualifier>’, <boolean>)
  • DependentColumnFilter (‘<family>’,‘<qualifier>’)

Example

  • DependentColumnFilter (‘conf’, ‘blacklist’, false, >=, ‘zebra’)
  • DependentColumnFilter (‘conf’, 'blacklist', true)
  • DependentColumnFilter (‘conf’, 'blacklist')
SingleColumnValueFilter

This filter takes a column family, a qualifier, a compare operator and a comparator. If the specified column is not found – all the columns of that row will be emitted. If the column is found and the comparison with the comparator returns true, all the columns of the row will be emitted. If the condition fails, the row will not be emitted.

This filter also takes two additional optional boolean arguments – filterIfColumnMissing and setLatestVersionOnly

If the filterIfColumnMissing flag is set to true the columns of the row will not be emitted if the specified column to check is not found in the row. The default value is false.

If the setLatestVersionOnly flag is set to false, it will test previous versions (timestamps) too. The default value is true.

These flags are optional and if you must set neither or both.

Syntax

  • SingleColumnValueFilter(‘<family>’,‘<qualifier>’, <compare operator>, ‘<comparator>’, <filterIfColumnMissing_boolean>, <latest_version_boolean>)
  • SingleColumnValueFilter(‘<family>’, ‘<qualifier>, <compare operator>, ‘<comparator>’)

Example

  • SingleColumnValueFilter (‘FamilyA’, ‘Column1’, <=, ‘abc’, true, false)
  • SingleColumnValueFilter (‘FamilyA’, ‘Column1’, <=, ‘abc’)
SingleColumnValueExcludeFilter

This filter takes the same arguments and behaves same as SingleColumnValueFilter – however, if the column is found and the condition passes, all the columns of the row will be emitted except for the tested column value.

Syntax

  • SingleColumnValueExcludeFilter('<family>', '<qualifier>', <compare operator>, '<comparator>', <latest_version_boolean>, <filterIfColumnMissing_boolean>)
  • SingleColumnValueExcludeFilter('<family>', '<qualifier>', <compare operator>, '<comparator>')

Example

  • SingleColumnValueExcludeFilter (‘FamilyA’, ‘Column1’, ‘<=’, ‘abc’, ‘false’, ‘true’)
  • SingleColumnValueExcludeFilter (‘FamilyA’, ‘Column1’, ‘<=’, ‘abc’)
ColumnRangeFilter

This filter is used for selecting only those keys with columns that are between minColumn and maxColumn. It also takes two boolean variables to indicate whether to include the minColumn and maxColumn or not.

If you don’t want to set the minColumn or the maxColumn – you can pass in an empty argument.

Syntax

  • ColumnRangeFilter (‘<minColumn>’, <minColumnInclusive_bool>, ‘<maxColumn>’, <maxColumnInclusive_bool>)

Example

  • ColumnRangeFilter (‘abc’, true, ‘xyz’, false)

Chapter 13. Apache HBase Coprocessors

HBase coprocessors are modeled after the coprocessors which are part of Google's BigTable (http://www.scribd.com/doc/21631448/Dean-Keynote-Ladis2009, pages 66-67.). Coprocessors function in a similar way to Linux kernel modules. They provide a way to run server-level code against locally-stored data. The functionality they provide is very powerful, but also carries great risk and can have adverse effects on the system, at the level of the operating system. The information in this chapter is primarily sourced and heavily reused from Mingjie Lai's blog post at https://blogs.apache.org/hbase/entry/coprocessor_introduction.

Coprocessors are not designed to be used by end users of HBase, but by HBase developers who need to add specialized functionality to HBase. One example of the use of coprocessors is pluggable compaction and scan policies, which are provided as coprocessors in HBASE-6427.

13.1. Coprocessor Framework

The implementation of HBase coprocessors diverges from the BigTable implementation. The HBase framework provides a library and runtime environment for executing user code within the HBase region server and master processes.

The framework API is provided in the coprocessor package.

Two different types of coprocessors are provided by the framework, based on their scope.

Types of Coprocessors

System Coprocessors

System coprocessors are loaded globally on all tables and regions hosted by a region server.

Table Coprocessors

You can specify which coprocessors should be loaded on all regions for a table on a per-table basis.

The framework provides two different aspects of extensions as well: observers and endpoints.

Observers

Observers are analogous to triggers in conventional databases. They allow you to insert user code by overriding upcall methods provided by the coprocessor framework. Callback functions are executed from core HBase code when events occur. Callbacks are handled by the framework, and the coprocessor itself only needs to insert the extended or alternate functionality.

Provided Observer Interfaces

RegionObserver

A RegionObserver provides hooks for data manipulation events, such as Get, Put, Delete, Scan. An instance of a RegionObserver coprocessor exists for each table region. The scope of the observations a RegionObserver can make is constrained to that region.

RegionServerObserver

A RegionServerObserver provides for operations related to the RegionServer, such as stopping the RegionServer and performing operations before or after merges, commits, or rollbacks.

WALObserver

A WALObserver provides hooks for operations related to the write-ahead log (WAL). You can observe or intercept WAL writing and reconstruction events. A WALObserver runs in the context of WAL processing. A single WALObserver exists on a single region server.

MasterObserver

A MasterObserver provides hooks for DDL-type operation, such as create, delete, modify table. The MasterObserver runs within the context of the HBase master.

More than one observer of a given type can be loaded at once. Multiple observers are chained to execute sequentially by order of assigned priority. Nothing prevents a coprocessor implementor from communicating internally among its installed observers.

An observer of a higher priority can preempt lower-priority observers by throwing an IOException or a subclass of IOException.

Endpoints (HBase 0.96.x and later)

The implementation for endpoints changed significantly in HBase 0.96.x due to the introduction of protocol buffers (protobufs) (HBASE-5488). If you created endpoints before 0.96.x, you will need to rewrite them. Endpoints are now defined and callable as protobuf services, rather than endpoint invocations passed through as Writable blobs

Dynamic RPC endpoints resemble stored procedures. An endpoint can be invoked at any time from the client. When it is invoked, it is executed remotely at the target region or regions, and results of the executions are returned to the client.

The endpoint implementation is installed on the server and is invoked using HBase RPC. The client library provides convenience methods for invoking these dynamic interfaces.

An endpoint, like an observer, can communicate with any installed observers. This allows you to plug new features into HBase without modifying or recompiling HBase itself.

Steps to Implement an Endpoint

  • Define the coprocessor service and related messages in a .proto file

  • Run the protoc command to generate the code.

  • Write code to implement the following:

  • The client calls the new HTable.coprocessorService() methods to perform the endpoint RPCs.

For more information and examples, refer to the API documentation for the coprocessor package, as well as the included RowCount example in the /hbase-examples/src/test/java/org/apache/hadoop/hbase/coprocessor/example/ of the HBase source.

Endpoints (HBase 0.94.x and earlier)

Dynamic RPC endpoints resemble stored procedures. An endpoint can be invoked at any time from the client. When it is invoked, it is executed remotely at the target region or regions, and results of the executions are returned to the client.

The endpoint implementation is installed on the server and is invoked using HBase RPC. The client library provides convenience methods for invoking these dynamic interfaces.

An endpoint, like an observer, can communicate with any installed observers. This allows you to plug new features into HBase without modifying or recompiling HBase itself.

Steps to Implement an Endpoint

  • Server-Side

    • Create new protocol interface which extends CoprocessorProtocol.

    • Implement the Endpoint interface. The implementation will be loaded into and executed from the region context.

    • Extend the abstract class BaseEndpointCoprocessor. This convenience class hides some internal details that the implementer does not need to be concerned about, ˆ such as coprocessor framework class loading.

  • Client-Side

    Endpoint can be invoked by two new HBase client APIs:

    • HTableInterface.coprocessorProxy(Class<T> protocol, byte[] row) for executing against a single region

    • HTableInterface.coprocessorExec(Class<T> protocol, byte[] startKey, byte[] endKey, Batch.Call<T,R> callable) for executing over a range of regions

13.2. Examples

An example of an observer is included in hbase-examples/src/test/java/org/apache/hadoop/hbase/coprocessor/example/TestZooKeeperScanPolicyObserver.java. Several endpoint examples are included in the same directory.

13.3. Building A Coprocessor

Before you can build a processor, it must be developed, compiled, and packaged in a JAR file. The next step is to configure the coprocessor framework to use your coprocessor. You can load the coprocessor from your HBase configuration, so that the coprocessor starts with HBase, or you can configure the coprocessor from the HBase shell, as a table attribute, so that it is loaded dynamically when the table is opened or reopened.

13.3.1. Load from Configuration

To configure a coprocessor to be loaded when HBase starts, modify the RegionServer's hbase-site.xml and configure one of the following properties, based on the type of observer you are configuring:

  • hbase.coprocessor.region.classesfor RegionObservers and Endpoints

  • hbase.coprocessor.wal.classesfor WALObservers

  • hbase.coprocessor.master.classesfor MasterObservers

Example 13.1. Example RegionObserver Configuration

In this example, one RegionObserver is configured for all the HBase tables.

<property>
    <name>hbase.coprocessor.region.classes</name>
    <value>org.apache.hadoop.hbase.coprocessor.AggregateImplementation</value>
 </property>          
        

If multiple classes are specified for loading, the class names must be comma-separated. The framework attempts to load all the configured classes using the default class loader. Therefore, the jar file must reside on the server-side HBase classpath.

Coprocessors which are loaded in this way will be active on all regions of all tables. These are the system coprocessor introduced earlier. The first listed coprocessors will be assigned the priority Coprocessor.Priority.SYSTEM. Each subsequent coprocessor in the list will have its priority value incremented by one (which reduces its priority, because priorities have the natural sort order of Integers).

When calling out to registered observers, the framework executes their callbacks methods in the sorted order of their priority. Ties are broken arbitrarily.

13.3.2. Load from the HBase Shell

You can load a coprocessor on a specific table via a table attribute. The following example will load the FooRegionObserver observer when table t1 is read or re-read.

Example 13.2. Load a Coprocessor On a Table Using HBase Shell

hbase(main):005:0>  alter 't1', METHOD => 'table_att', 
  'coprocessor'=>'hdfs:///foo.jar|com.foo.FooRegionObserver|1001|arg1=1,arg2=2'
Updating all regions with the new schema...
1/1 regions updated.
Done.
0 row(s) in 1.0730 seconds

hbase(main):006:0> describe 't1'
DESCRIPTION                                                        ENABLED                             
 {NAME => 't1', coprocessor$1 => 'hdfs:///foo.jar|com.foo.FooRegio false                               
 nObserver|1001|arg1=1,arg2=2', FAMILIES => [{NAME => 'c1', DATA_B                                     
 LOCK_ENCODING => 'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE                                     
  => '0', VERSIONS => '3', COMPRESSION => 'NONE', MIN_VERSIONS =>                                      
 '0', TTL => '2147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZ                                     
 E => '65536', IN_MEMORY => 'false', ENCODE_ON_DISK => 'true', BLO                                     
 CKCACHE => 'true'}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE',                                     
  BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3'                                     
 , COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2147483647'                                     
 , KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY                                      
 => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true'}]}                                         
1 row(s) in 0.0190 seconds
        

The coprocessor framework will try to read the class information from the coprocessor table attribute value. The value contains four pieces of information which are separated by the | character.

  • File path: The jar file containing the coprocessor implementation must be in a location where all region servers can read it. You could copy the file onto the local disk on each region server, but it is recommended to store it in HDFS.

  • Class name: The full class name of the coprocessor.

  • Priority: An integer. The framework will determine the execution sequence of all configured observers registered at the same hook using priorities. This field can be left blank. In that case the framework will assign a default priority value.

  • Arguments: This field is passed to the coprocessor implementation.

Example 13.3. Unload a Coprocessor From a Table Using HBase Shell

hbase(main):007:0> alter 't1', METHOD => 'table_att_unset', 
hbase(main):008:0*   NAME => 'coprocessor$1'
Updating all regions with the new schema...
1/1 regions updated.
Done.
0 row(s) in 1.1130 seconds

hbase(main):009:0> describe 't1'
DESCRIPTION                                                        ENABLED                             
 {NAME => 't1', FAMILIES => [{NAME => 'c1', DATA_BLOCK_ENCODING => false                               
  'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSION                                     
 S => '3', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '214                                     
 7483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN                                     
 _MEMORY => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true                                     
 '}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER =>                                      
 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3', COMPRESSION =>                                     
  'NONE', MIN_VERSIONS => '0', TTL => '2147483647', KEEP_DELETED_C                                     
 ELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false', ENCO                                     
 DE_ON_DISK => 'true', BLOCKCACHE => 'true'}]}                                                         
1 row(s) in 0.0180 seconds          
        

Warning

There is no guarantee that the framework will load a given coprocessor successfully. For example, the shell command neither guarantees a jar file exists at a particular location nor verifies whether the given class is actually contained in the jar file.

13.4. Check the Status of a Coprocessor

To check the status of a coprocessor after it has been configured, use the status HBase Shell command.

hbase(main):020:0> status 'detailed'
version 0.92-tm-6
0 regionsInTransition
master coprocessors: []
1 live servers
    localhost:52761 1328082515520
        requestsPerSecond=3, numberOfOnlineRegions=3, usedHeapMB=32, maxHeapMB=995
        -ROOT-,,0
            numberOfStores=1, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, 
storefileIndexSizeMB=0, readRequestsCount=54, writeRequestsCount=1, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, 
totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[]
        .META.,,1
            numberOfStores=1, numberOfStorefiles=0, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, 
storefileIndexSizeMB=0, readRequestsCount=97, writeRequestsCount=4, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, 
totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[]
        t1,,1328082575190.c0491168a27620ffe653ec6c04c9b4d1.
            numberOfStores=2, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, 
storefileIndexSizeMB=0, readRequestsCount=0, writeRequestsCount=0, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, 
totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, 
coprocessors=[AggregateImplementation]
0 dead servers      
    

13.5. Monitor Time Spent in Coprocessors

HBase 0.98.5 introduced the ability to monitor some statistics relating to the amount of time spent executing a given coprocessor. You can see these statistics via the HBase Metrics framework (see Section 17.4, “HBase Metrics” or the Web UI for a given Region Server, via the Coprocessor Metrics tab. These statistics are valuable for debugging and benchmarking the performance impact of a given coprocessor on your cluster. Tracked statistics include min, max, average, and 90th, 95th, and 99th percentile. All times are shown in milliseconds. The statistics are calculated over coprocessor execution samples recorded during the reporting interval, which is 10 seconds by default. The metrics sampling rate as described in Section 17.4, “HBase Metrics”.

Figure 13.1. Coprocessor Metrics UI

Coprocessor Metrics UI

The Coprocessor Metrics UI shows statistics about time spent executing a given coprocessor, including min, max, average, and 90th, 95th, and 99th percentile.


13.6. Status of Coprocessors in HBase

Coprocessors and the coprocessor framework are evolving rapidly and work is ongoing on several different JIRAs.

Chapter 14. Apache HBase Performance Tuning

Table of Contents

14.1. Operating System
14.1.1. Memory
14.1.2. 64-bit
14.1.3. Swapping
14.2. Network
14.2.1. Single Switch
14.2.2. Multiple Switches
14.2.3. Multiple Racks
14.2.4. Network Interfaces
14.3. Java
14.3.1. The Garbage Collector and Apache HBase
14.4. HBase Configurations
14.4.1. Managing Compactions
14.4.2. hbase.regionserver.handler.count
14.4.3. hfile.block.cache.size
14.4.4. Prefetch Option for Blockcache
14.4.5. hbase.regionserver.global.memstore.size
14.4.6. hbase.regionserver.global.memstore.size.lower.limit
14.4.7. hbase.hstore.blockingStoreFiles
14.4.8. hbase.hregion.memstore.block.multiplier
14.4.9. hbase.regionserver.checksum.verify
14.4.10. Tuning callQueue Options
14.5. ZooKeeper
14.6. Schema Design
14.6.1. Number of Column Families
14.6.2. Key and Attribute Lengths
14.6.3. Table RegionSize
14.6.4. Bloom Filters
14.6.5. ColumnFamily BlockSize
14.6.6. In-Memory ColumnFamilies
14.6.7. Compression
14.7. HBase General Patterns
14.7.1. Constants
14.8. Writing to HBase
14.8.1. Batch Loading
14.8.2. Table Creation: Pre-Creating Regions
14.8.3. Table Creation: Deferred Log Flush
14.8.4. HBase Client: AutoFlush
14.8.5. HBase Client: Turn off WAL on Puts
14.8.6. HBase Client: Group Puts by RegionServer
14.8.7. MapReduce: Skip The Reducer
14.8.8. Anti-Pattern: One Hot Region
14.9. Reading from HBase
14.9.1. Scan Caching
14.9.2. Scan Attribute Selection
14.9.3. Avoid scan seeks
14.9.4. MapReduce - Input Splits
14.9.5. Close ResultScanners
14.9.6. Block Cache
14.9.7. Optimal Loading of Row Keys
14.9.8. Concurrency: Monitor Data Spread
14.9.9. Bloom Filters
14.9.10. Hedged Reads
14.10. Deleting from HBase
14.10.1. Using HBase Tables as Queues
14.10.2. Delete RPC Behavior
14.11. HDFS
14.11.1. Current Issues With Low-Latency Reads
14.11.2. Leveraging local data
14.11.3. Performance Comparisons of HBase vs. HDFS
14.12. Amazon EC2
14.13. Collocating HBase and MapReduce
14.14. Case Studies

14.1. Operating System

14.1.1. Memory

RAM, RAM, RAM. Don't starve HBase.

14.1.2. 64-bit

Use a 64-bit platform (and 64-bit JVM).

14.1.3. Swapping

Watch out for swapping. Set swappiness to 0.

14.2. Network

Perhaps the most important factor in avoiding network issues degrading Hadoop and HBase performance is the switching hardware that is used, decisions made early in the scope of the project can cause major problems when you double or triple the size of your cluster (or more).

Important items to consider:

  • Switching capacity of the device

  • Number of systems connected

  • Uplink capacity

14.2.1. Single Switch

The single most important factor in this configuration is that the switching capacity of the hardware is capable of handling the traffic which can be generated by all systems connected to the switch. Some lower priced commodity hardware can have a slower switching capacity than could be utilized by a full switch.

14.2.2. Multiple Switches

Multiple switches are a potential pitfall in the architecture. The most common configuration of lower priced hardware is a simple 1Gbps uplink from one switch to another. This often overlooked pinch point can easily become a bottleneck for cluster communication. Especially with MapReduce jobs that are both reading and writing a lot of data the communication across this uplink could be saturated.

Mitigation of this issue is fairly simple and can be accomplished in multiple ways:

  • Use appropriate hardware for the scale of the cluster which you're attempting to build.

  • Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 port

  • Configure port trunking for uplinks to utilize multiple interfaces to increase cross switch bandwidth.

14.2.3. Multiple Racks

Multiple rack configurations carry the same potential issues as multiple switches, and can suffer performance degradation from two main areas:

  • Poor switch capacity performance

  • Insufficient uplink to another rack

If the the switches in your rack have appropriate switching capacity to handle all the hosts at full speed, the next most likely issue will be caused by homing more of your cluster across racks. The easiest way to avoid issues when spanning multiple racks is to use port trunking to create a bonded uplink to other racks. The downside of this method however, is in the overhead of ports that could potentially be used. An example of this is, creating an 8Gbps port channel from rack A to rack B, using 8 of your 24 ports to communicate between racks gives you a poor ROI, using too few however can mean you're not getting the most out of your cluster.

Using 10Gbe links between racks will greatly increase performance, and assuming your switches support a 10Gbe uplink or allow for an expansion card will allow you to save your ports for machines as opposed to uplinks.

14.2.4. Network Interfaces

Are all the network interfaces functioning correctly? Are you sure? See the Troubleshooting Case Study in Section 16.3.1, “Case Study #1 (Performance Issue On A Single Node)”.

14.3. Java

14.3.1. The Garbage Collector and Apache HBase

14.3.1.1. Long GC pauses

In his presentation, Avoiding Full GCs with MemStore-Local Allocation Buffers, Todd Lipcon describes two cases of stop-the-world garbage collections common in HBase, especially during loading; CMS failure modes and old generation heap fragmentation brought. To address the first, start the CMS earlier than default by adding -XX:CMSInitiatingOccupancyFraction and setting it down from defaults. Start at 60 or 70 percent (The lower you bring down the threshold, the more GCing is done, the more CPU used). To address the second fragmentation issue, Todd added an experimental facility, , that must be explicitly enabled in Apache HBase 0.90.x (Its defaulted to be on in Apache 0.92.x HBase). See hbase.hregion.memstore.mslab.enabled to true in your Configuration. See the cited slides for background and detail. The latest jvms do better regards fragmentation so make sure you are running a recent release. Read down in the message, Identifying concurrent mode failures caused by fragmentation. Be aware that when enabled, each MemStore instance will occupy at least an MSLAB instance of memory. If you have thousands of regions or lots of regions each with many column families, this allocation of MSLAB may be responsible for a good portion of your heap allocation and in an extreme case cause you to OOME. Disable MSLAB in this case, or lower the amount of memory it uses or float less regions per server.

If you have a write-heavy workload, check out HBASE-8163 MemStoreChunkPool: An improvement for JAVA GC when using MSLAB. It describes configurations to lower the amount of young GC during write-heavy loadings. If you do not have HBASE-8163 installed, and you are trying to improve your young GC times, one trick to consider -- courtesy of our Liang Xie -- is to set the GC config -XX:PretenureSizeThreshold in hbase-env.sh to be just smaller than the size of hbase.hregion.memstore.mslab.chunksize so MSLAB allocations happen in the tenured space directly rather than first in the young gen. You'd do this because these MSLAB allocations are going to likely make it to the old gen anyways and rather than pay the price of a copies between s0 and s1 in eden space followed by the copy up from young to old gen after the MSLABs have achieved sufficient tenure, save a bit of YGC churn and allocate in the old gen directly.

For more information about GC logs, see Section 15.2.3, “JVM Garbage Collection Logs”.

Consider also enabling the offheap Block Cache. This has been shown to mitigate GC pause times. See Section 9.6.4, “Block Cache”

14.4. HBase Configurations

See Section 2.6.2, “Recommended Configurations”.

14.4.1. Managing Compactions

For larger systems, managing compactions and splits may be something you want to consider.

14.4.2. hbase.regionserver.handler.count

See hbase.regionserver.handler.count.

14.4.3. hfile.block.cache.size

See hfile.block.cache.size. A memory setting for the RegionServer process.

14.4.4. Prefetch Option for Blockcache

HBASE-9857 adds a new option to prefetch HFile contents when opening the blockcache, if a columnfamily or regionserver property is set. This option is available for HBase 0.98.3 and later. The purpose is to warm the blockcache as rapidly as possible after the cache is opened, using in-memory table data, and not counting the prefetching as cache misses. This is great for fast reads, but is not a good idea if the data to be preloaded will not fit into the blockcache. It is useful for tuning the IO impact of prefetching versus the time before all data blocks are in cache.

To enable prefetching on a given column family, you can use HBase Shell or use the API.

Example 14.1. Enable Prefetch Using HBase Shell

hbase> create 'MyTable', { NAME => 'myCF', PREFETCH_BLOCKS_ON_OPEN => 'true' }

Example 14.2. Enable Prefetch Using the API

// ...
HTableDescriptor tableDesc = new HTableDescriptor("myTable");
HColumnDescriptor cfDesc = new HColumnDescriptor("myCF");
cfDesc.setPrefetchBlocksOnOpen(true);
tableDesc.addFamily(cfDesc);
// ...        
        

See the API documentation for CacheConfig.

14.4.5. hbase.regionserver.global.memstore.size

See hbase.regionserver.global.memstore.size. This memory setting is often adjusted for the RegionServer process depending on needs.

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

See hbase.regionserver.global.memstore.size.lower.limit. This memory setting is often adjusted for the RegionServer process depending on needs.

14.4.7. hbase.hstore.blockingStoreFiles

See hbase.hstore.blockingStoreFiles. If there is blocking in the RegionServer logs, increasing this can help.

14.4.8. hbase.hregion.memstore.block.multiplier

See hbase.hregion.memstore.block.multiplier. If there is enough RAM, increasing this can help.

14.4.9. hbase.regionserver.checksum.verify

Have HBase write the checksum into the datablock and save having to do the checksum seek whenever you read.

See hbase.regionserver.checksum.verify, hbase.hstore.bytes.per.checksum and hbase.hstore.checksum.algorithm For more information see the release note on HBASE-5074 support checksums in HBase block cache.

14.4.10. Tuning callQueue Options

HBASE-11355 introduces several callQueue tuning mechanisms which can increase performance. See the JIRA for some benchmarking information.

  • To increase the number of callqueues, set hbase.ipc.server.num.callqueue to a value greater than 1.

  • To split the callqueue into separate read and write queues, set hbase.ipc.server.callqueue.read.ratio to a value between 0 and 1. This factor weights the queues toward writes (if below .5) or reads (if above .5). Another way to say this is that the factor determines what percentage of the split queues are used for reads. The following examples illustrate some of the possibilities. Note that you always have at least one write queue, no matter what setting you use.

    • The default value of 0 does not split the queue.

    • A value of .3 uses 30% of the queues for reading and 60% for writing. Given a value of 10 for hbase.ipc.server.num.callqueue, 3 queues would be used for reads and 7 for writes.

    • A value of .5 uses the same number of read queues and write queues. Given a value of 10 for hbase.ipc.server.num.callqueue, 5 queues would be used for reads and 5 for writes.

    • A value of .6 uses 60% of the queues for reading and 30% for reading. Given a value of 10 for hbase.ipc.server.num.callqueue, 7 queues would be used for reads and 3 for writes.

    • A value of 1.0 uses one queue to process write requests, and all other queues process read requests. A value higher than 1.0 has the same effect as a value of 1.0. Given a value of 10 for hbase.ipc.server.num.callqueue, 9 queues would be used for reads and 1 for writes.

  • You can also split the read queues so that separate queues are used for short reads (from Get operations) and long reads (from Scan operations), by setting the hbase.ipc.server.callqueue.scan.ratio option. This option is a factor between 0 and 1, which determine the ratio of read queues used for Gets and Scans. More queues are used for Gets if the value is below .5 and more are used for scans if the value is above .5. No matter what setting you use, at least one read queue is used for Get operations.

    • A value of 0 does not split the read queue.

    • A value of .3 uses 60% of the read queues for Gets and 30% for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 7 would be used for Gets and 3 for Scans.

    • A value of .5 uses half the read queues for Gets and half for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 5 would be used for Gets and 5 for Scans.

    • A value of .6 uses 30% of the read queues for Gets and 60% for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 3 would be used for Gets and 7 for Scans.

    • A value of 1.0 uses all but one of the read queues for Scans. Given a value of 20 for hbase.ipc.server.num.callqueue and a value of .5 for hbase.ipc.server.callqueue.read.ratio, 10 queues would be used for reads, out of those 10, 1 would be used for Gets and 9 for Scans.

  • You can use the new option hbase.ipc.server.callqueue.handler.factor to programmatically tune the number of queues:

    • A value of 0 uses a single shared queue between all the handlers.

    • A value of 1 uses a separate queue for each handler.

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

    Having more queues, such as in a situation where you have one queue per handler, reduces contention when adding a task to a queue or selecting it from a queue. The trade-off is that if you have some queues with long-running tasks, a handler may end up waiting to execute from that queue rather than processing another queue which has waiting tasks.

For these values to take effect on a given Region Server, the Region Server must be restarted. These parameters are intended for testing purposes and should be used carefully.

14.5. ZooKeeper

See Chapter 20, ZooKeeper for information on configuring ZooKeeper, and see the part about having a dedicated disk.

14.6. Schema Design

14.6.1. Number of Column Families

See Section 6.2, “ On the number of column families ”.

14.6.2. Key and Attribute Lengths

See Section 6.3.3, “Try to minimize row and column sizes”. See also Section 14.6.7.1, “However...” for compression caveats.

14.6.3. Table RegionSize

The regionsize can be set on a per-table basis via setFileSize on HTableDescriptor in the event where certain tables require different regionsizes than the configured default regionsize.

See Section 17.9.2, “Determining region count and size” for more information.

14.6.4. Bloom Filters

A Bloom filter, named for its creator, Burton Howard Bloom, is a data structure which is designed to predict whether a given element is a member of a set of data. A positive result from a Bloom filter is not always accurate, but a negative result is guaranteed to be accurate. Bloom filters are designed to be "accurate enough" for sets of data which are so large that conventional hashing mechanisms would be impractical. For more information about Bloom filters in general, refer to http://en.wikipedia.org/wiki/Bloom_filter.

In terms of HBase, Bloom filters provide a lightweight in-memory structure to reduce the number of disk reads for a given Get operation (Bloom filters do not work with Scans) to only the StoreFiles likely to contain the desired Row. The potential performance gain increases with the number of parallel reads.

The Bloom filters themselves are stored in the metadata of each HFile and never need to be updated. When an HFile is opened because a region is deployed to a RegionServer, the Bloom filter is loaded into memory.

HBase includes some tuning mechanisms for folding the Bloom filter to reduce the size and keep the false positive rate within a desired range.

Bloom filters were introduced in HBASE-1200. Since HBase 0.96, row-based Bloom filters are enabled by default. (HBASE-)

For more information on Bloom filters in relation to HBase, see Section 14.9.9, “Bloom Filters” for more information, or the following Quora discussion: How are bloom filters used in HBase?.

14.6.4.1. When To Use Bloom Filters

Since HBase 0.96, row-based Bloom filters are enabled by default. You may choose to disable them or to change some tables to use row+column Bloom filters, depending on the characteristics of your data and how it is loaded into HBase.

To determine whether Bloom filters could have a positive impact, check the value of blockCacheHitRatio in the RegionServer metrics. If Bloom filters are enabled, the value of blockCacheHitRatio should increase, because the Bloom filter is filtering out blocks that are definitely not needed.

You can choose to enable Bloom filters for a row or for a row+column combination. If you generally scan entire rows, the row+column combination will not provide any benefit. A row-based Bloom filter can operate on a row+column Get, but not the other way around. However, if you have a large number of column-level Puts, such that a row may be present in every StoreFile, a row-based filter will always return a positive result and provide no benefit. Unless you have one column per row, row+column Bloom filters require more space, in order to store more keys. Bloom filters work best when the size of each data entry is at least a few kilobytes in size.

Overhead will be reduced when your data is stored in a few larger StoreFiles, to avoid extra disk IO during low-level scans to find a specific row.

Bloom filters need to be rebuilt upon deletion, so may not be appropriate in environments with a large number of deletions.

14.6.4.2. Enabling Bloom Filters

Bloom filters are enabled on a Column Family. You can do this by using the setBloomFilterType method of HColumnDescriptor or using the HBase API. Valid values are NONE (the default), ROW, or ROWCOL. See Section 14.6.4.1, “When To Use Bloom Filters” for more information on ROW versus ROWCOL. See also the API documentation for HColumnDescriptor.

The following example creates a table and enables a ROWCOL Bloom filter on the colfam1 column family.

hbase> create 'mytable',{NAME => 'colfam1', BLOOMFILTER => 'ROWCOL'}          
        

14.6.4.3. Configuring Server-Wide Behavior of Bloom Filters

You can configure the following settings in the hbase-site.xml.

ParameterDefaultDescription

io.hfile.bloom.enabled

yes

Set to no to kill bloom filters server-wide if something goes wrong

io.hfile.bloom.error.rate

.01

The average false positive rate for bloom filters. Folding is used to maintain the false positive rate. Expressed as a decimal representation of a percentage.

io.hfile.bloom.max.fold

7

The guaranteed maximum fold rate. Changing this setting should not be necessary and is not recommended.

io.storefile.bloom.max.keys

128000000

For default (single-block) Bloom filters, this specifies the maximum number of keys.

io.storefile.delete.family.bloom.enabled

true

Master switch to enable Delete Family Bloom filters and store them in the StoreFile.

io.storefile.bloom.block.size

65536

Target Bloom block size. Bloom filter blocks of approximately this size are interleaved with data blocks.

hfile.block.bloom.cacheonwrite

false

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

14.6.5. ColumnFamily BlockSize

The blocksize can be configured for each ColumnFamily in a table, and this defaults to 64k. Larger cell values require larger blocksizes. There is an inverse relationship between blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the resulting indexes should be roughly halved).

See HColumnDescriptor and Section 9.7.7, “Store”for more information.

14.6.6. In-Memory ColumnFamilies

ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, just like any other ColumnFamily. In-memory blocks have the highest priority in the Section 9.6.4, “Block Cache”, but it is not a guarantee that the entire table will be in memory.

See HColumnDescriptor for more information.

14.6.7. Compression

Production systems should use compression with their ColumnFamily definitions. See Appendix E, Compression and Data Block Encoding In HBase for more information.

14.6.7.1. However...

Compression deflates data on disk. When it's in-memory (e.g., in the MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's inflated. So while using ColumnFamily compression is a best practice, but it's not going to completely eliminate the impact of over-sized Keys, over-sized ColumnFamily names, or over-sized Column names.

See Section 6.3.3, “Try to minimize row and column sizes” on for schema design tips, and Section 9.7.7.6, “KeyValue” for more information on HBase stores data internally.

14.7. HBase General Patterns

14.7.1. Constants

When people get started with HBase they have a tendency to write code that looks like this:

Get get = new Get(rowkey);
Result r = htable.get(get);
byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr"));  // returns current version of value
      

But especially when inside loops (and MapReduce jobs), converting the columnFamily and column-names to byte-arrays repeatedly is surprisingly expensive. It's better to use constants for the byte-arrays, like this:

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

14.8. Writing to HBase

14.8.1. Batch Loading

Use the bulk load tool if you can. See Section 9.8, “Bulk Loading”. Otherwise, pay attention to the below.

14.8.2.  Table Creation: Pre-Creating Regions

Tables in HBase are initially created with one region by default. For bulk imports, this means that all clients will write to the same region until it is large enough to split and become distributed across the cluster. A useful pattern to speed up the bulk import process is to pre-create empty regions. Be somewhat conservative in this, because too-many regions can actually degrade performance.

There are two different approaches to pre-creating splits. The first approach is to rely on the default HBaseAdmin strategy (which is implemented in Bytes.split)...

byte[] startKey = ...;   	// your lowest key
byte[] endKey = ...;   		// your highest key
int numberOfRegions = ...;	// # of regions to create
admin.createTable(table, startKey, endKey, numberOfRegions);
      

And the other approach is to define the splits yourself...

byte[][] splits = ...;   // create your own splits
admin.createTable(table, splits);

See Section 6.3.7, “Relationship Between RowKeys and Region Splits” for issues related to understanding your keyspace and pre-creating regions. See Section 9.7.5, “Manual Region Splitting” for discussion on manually pre-splitting regions.

14.8.3.  Table Creation: Deferred Log Flush

The default behavior for Puts using the Write Ahead Log (WAL) is that HLog edits will be written immediately. If deferred log flush is used, WAL edits are kept in memory until the flush period. The benefit is aggregated and asynchronous HLog- writes, but the potential downside is that if the RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than not using WAL at all with Puts.

Deferred log flush can be configured on tables via HTableDescriptor. The default value of hbase.regionserver.optionallogflushinterval is 1000ms.

14.8.4. HBase Client: AutoFlush

When performing a lot of Puts, make sure that setAutoFlush is set to false on your HTable instance. Otherwise, the Puts will be sent one at a time to the RegionServer. Puts added via htable.add(Put) and htable.add( <List> Put) wind up in the same write buffer. If autoFlush = false, these messages are not sent until the write-buffer is filled. To explicitly flush the messages, call flushCommits. Calling close on the HTable instance will invoke flushCommits.

14.8.5. HBase Client: Turn off WAL on Puts

A frequent request is to disable the WAL to increase performance of Puts. This is only appropriate for bulk loads, as it puts your data at risk by removing the protection of the WAL in the event of a region server crash. Bulk loads can be re-run in the event of a crash, with little risk of data loss.

Warning

If you disable the WAL for anything other than bulk loads, your data is at risk.

In general, it is best to use WAL for Puts, and where loading throughput is a concern to use bulk loading techniques instead. For normal Puts, you are not likely to see a performance improvement which would outweigh the risk. To disable the WAL, see Section 9.6.5.4, “Disabling the WAL”.

14.8.6. HBase Client: Group Puts by RegionServer

In addition to using the writeBuffer, grouping Puts by RegionServer can reduce the number of client RPC calls per writeBuffer flush. There is a utility HTableUtil currently on TRUNK that does this, but you can either copy that or implement your own version for those still on 0.90.x or earlier.

14.8.7. MapReduce: Skip The Reducer

When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), and specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, then sorted/shuffled to other Reducers that will most likely be off-node. It's far more efficient to just write directly to HBase.

For summary jobs where HBase is used as a source and a sink, then writes will be coming from the Reducer step (e.g., summarize values then write out result). This is a different processing problem than from the the above case.

14.8.8. Anti-Pattern: One Hot Region

If all your data is being written to one region at a time, then re-read the section on processing timeseries data.

Also, if you are pre-splitting regions and all your data is still winding up in a single region even though your keys aren't monotonically increasing, confirm that your keyspace actually works with the split strategy. There are a variety of reasons that regions may appear "well split" but won't work with your data. As the HBase client communicates directly with the RegionServers, this can be obtained via HTable.getRegionLocation.

See Section 14.8.2, “ Table Creation: Pre-Creating Regions ”, as well as Section 14.4, “HBase Configurations”

14.9. Reading from HBase

The mailing list can help if you are having performance issues. For example, here is a good general thread on what to look at addressing read-time issues: HBase Random Read latency > 100ms

14.9.1. Scan Caching

If HBase is used as an input source for a MapReduce job, for example, make sure that the input Scan instance to the MapReduce job has setCaching set to something greater than the default (which is 1). Using the default value means that the map-task will make call back to the region-server for every record processed. Setting this value to 500, for example, will transfer 500 rows at a time to the client to be processed. There is a cost/benefit to have the cache value be large because it costs more in memory for both client and RegionServer, so bigger isn't always better.

14.9.1.1. Scan Caching in MapReduce Jobs

Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., UnknownScannerException) in Map tasks if it takes longer to process a batch of records before the client goes back to the RegionServer for the next set of data. This problem can occur because there is non-trivial processing occuring per row. If you process rows quickly, set caching higher. If you process rows more slowly (e.g., lots of transformations per row, writes), then set caching lower.

Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase client doing a Scan), but the processing that is often performed in MapReduce jobs tends to exacerbate this issue.

14.9.2. Scan Attribute Selection

Whenever a Scan is used to process large numbers of rows (and especially when used as a MapReduce source), be aware of which attributes are selected. If scan.addFamily is called then all of the attributes in the specified ColumnFamily will be returned to the client. If only a small number of the available attributes are to be processed, then only those attributes should be specified in the input scan because attribute over-selection is a non-trivial performance penalty over large datasets.

14.9.3. Avoid scan seeks

When columns are selected explicitly with scan.addColumn, HBase will schedule seek operations to seek between the selected columns. When rows have few columns and each column has only a few versions this can be inefficient. A seek operation is generally slower if does not seek at least past 5-10 columns/versions or 512-1024 bytes.

In order to opportunistically look ahead a few columns/versions to see if the next column/version can be found that way before a seek operation is scheduled, a new attribute Scan.HINT_LOOKAHEAD can be set the on Scan object. The following code instructs the RegionServer to attempt two iterations of next before a seek is scheduled:

Scan scan = new Scan();
scan.addColumn(...);
scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2));
table.getScanner(scan);
      

14.9.4. MapReduce - Input Splits

For MapReduce jobs that use HBase tables as a source, if there a pattern where the "slow" map tasks seem to have the same Input Split (i.e., the RegionServer serving the data), see the Troubleshooting Case Study in Section 16.3.1, “Case Study #1 (Performance Issue On A Single Node)”.

14.9.5. Close ResultScanners

This isn't so much about improving performance but rather avoiding performance problems. If you forget to close ResultScanners you can cause problems on the RegionServers. Always have ResultScanner processing enclosed in try/catch blocks...

Scan scan = new Scan();
// set attrs...
ResultScanner rs = htable.getScanner(scan);
try {
  for (Result r = rs.next(); r != null; r = rs.next()) {
  // process result...
} finally {
  rs.close();  // always close the ResultScanner!
}
htable.close();
      

14.9.6. Block Cache

Scan instances can be set to use the block cache in the RegionServer via the setCacheBlocks method. For input Scans to MapReduce jobs, this should be false. For frequently accessed rows, it is advisable to use the block cache.

Cache more data by moving your Block Cache offheap. See Section 9.6.4.5, “Offheap Block Cache”

14.9.7. Optimal Loading of Row Keys

When performing a table scan where only the row keys are needed (no families, qualifiers, values or timestamps), add a FilterList with a MUST_PASS_ALL operator to the scanner using setFilter. The filter list should include both a FirstKeyOnlyFilter and a KeyOnlyFilter. Using this filter combination will result in a worst case scenario of a RegionServer reading a single value from disk and minimal network traffic to the client for a single row.

14.9.8. Concurrency: Monitor Data Spread

When performing a high number of concurrent reads, monitor the data spread of the target tables. If the target table(s) have too few regions then the reads could likely be served from too few nodes.

See Section 14.8.2, “ Table Creation: Pre-Creating Regions ”, as well as Section 14.4, “HBase Configurations”

14.9.9. Bloom Filters

Enabling Bloom Filters can save your having to go to disk and can help improve read latencies.

Bloom filters were developed over in HBase-1200 Add bloomfilters. For description of the development process -- why static blooms rather than dynamic -- and for an overview of the unique properties that pertain to blooms in HBase, as well as possible future directions, see the Development Process section of the document BloomFilters in HBase attached to HBase-1200. The bloom filters described here are actually version two of blooms in HBase. In versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. The core of the HBase bloom work was later pulled up into Hadoop to implement org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. Version 2 is a rewrite from scratch though again it starts with the one-lab work.

See also Section 14.6.4, “Bloom Filters”.

14.9.9.1. Bloom StoreFile footprint

Bloom filters add an entry to the StoreFile general FileInfo data structure and then two extra entries to the StoreFile metadata section.

14.9.9.1.1. BloomFilter in the StoreFile FileInfo data structure

FileInfo has a BLOOM_FILTER_TYPE entry which is set to NONE, ROW or ROWCOL.

14.9.9.1.2. BloomFilter entries in StoreFile metadata

BLOOM_FILTER_META holds Bloom Size, Hash Function used, etc. Its small in size and is cached on StoreFile.Reader load

BLOOM_FILTER_DATA is the actual bloomfilter data. Obtained on-demand. Stored in the LRU cache, if it is enabled (Its enabled by default).

14.9.9.2. Bloom Filter Configuration

14.9.9.2.1. io.hfile.bloom.enabled global kill switch

io.hfile.bloom.enabled in Configuration serves as the kill switch in case something goes wrong. Default = true.

14.9.9.2.2. io.hfile.bloom.error.rate

io.hfile.bloom.error.rate = average false positive rate. Default = 1%. Decrease rate by ½ (e.g. to .5%) == +1 bit per bloom entry.

14.9.9.2.3. io.hfile.bloom.max.fold

io.hfile.bloom.max.fold = guaranteed minimum fold rate. Most people should leave this alone. Default = 7, or can collapse to at least 1/128th of original size. See the Development Process section of the document BloomFilters in HBase for more on what this option means.

14.9.10. Hedged Reads

Hedged reads are a feature of HDFS, introduced in HDFS-5776. Normally, a single thread is spawned for each read request. However, if hedged reads are enabled, the client waits some configurable amount of time, and if the read does not return, the client spawns a second read request, against a different block replica of the same data. Whichever read returns first is used, and the other read request is discarded. Hedged reads can be helpful for times where a rare slow read is caused by a transient error such as a failing disk or flaky network connection.

Because a HBase RegionServer is a HDFS client, you can enable hedged reads in HBase, by adding the following properties to the RegionServer's hbase-site.xml and tuning the values to suit your environment.

Configuration for Hedged Reads

  • dfs.client.hedged.read.threadpool.size - the number of threads dedicated to servicing hedged reads. If this is set to 0 (the default), hedged reads are disabled.

  • dfs.client.hedged.read.threshold.millis - the number of milliseconds to wait before spawning a second read thread.

Example 14.3. Hedged Reads Configuration Example

<property>
  <name>dfs.client.hedged.read.threadpool.size</name>
  <value>20</value>  <!-- 20 threads -->
</property>
<property>
  <name>dfs.client.hedged.read.threshold.millis</name>
  <value>10</value>  <!-- 10 milliseconds -->
</property>

Use the following metrics to tune the settings for hedged reads on your cluster. See Section 17.4, “HBase Metrics” for more information.

Metrics for Hedged Reads

  • hedgedReadOps - the number of times hedged read threads have been triggered. This could indicate that read requests are often slow, or that hedged reads are triggered too quickly.

  • hedgeReadOpsWin - the number of times the hedged read thread was faster than the original thread. This could indicate that a given RegionServer is having trouble servicing requests.

14.10. Deleting from HBase

14.10.1. Using HBase Tables as Queues

HBase tables are sometimes used as queues. In this case, special care must be taken to regularly perform major compactions on tables used in this manner. As is documented in Chapter 5, Data Model, marking rows as deleted creates additional StoreFiles which then need to be processed on reads. Tombstones only get cleaned up with major compactions.

See also Section 9.7.7.7, “Compaction” and HBaseAdmin.majorCompact.

14.10.2. Delete RPC Behavior

Be aware that htable.delete(Delete) doesn't use the writeBuffer. It will execute an RegionServer RPC with each invocation. For a large number of deletes, consider htable.delete(List).

See http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTable.html#delete%28org.apache.hadoop.hbase.client.Delete%29

14.11. HDFS

Because HBase runs on Section 9.9, “HDFS” it is important to understand how it works and how it affects HBase.

14.11.1. Current Issues With Low-Latency Reads

The original use-case for HDFS was batch processing. As such, there low-latency reads were historically not a priority. With the increased adoption of Apache HBase this is changing, and several improvements are already in development. See the Umbrella Jira Ticket for HDFS Improvements for HBase.

14.11.2. Leveraging local data

Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via HDFS-2246, it is possible for the DFSClient to take a "short circuit" and read directly from the disk instead of going through the DataNode when the data is local. What this means for HBase is that the RegionServers can read directly off their machine's disks instead of having to open a socket to talk to the DataNode, the former being generally much faster. See JD's Performance Talk. Also see HBase, mail # dev - read short circuit thread for more discussion around short circuit reads.

To enable "short circuit" reads, it will depend on your version of Hadoop. The original shortcircuit read patch was much improved upon in Hadoop 2 in HDFS-347. See http://blog.cloudera.com/blog/2013/08/how-improved-short-circuit-local-reads-bring-better-performance-and-security-to-hadoop/ for details on the difference between the old and new implementations. See Hadoop shortcircuit reads configuration page for how to enable the latter, better version of shortcircuit. For example, here is a minimal config. enabling short-circuit reads added to hbase-site.xml:

<property>
  <name>dfs.client.read.shortcircuit</name>
  <value>true</value>
  <description>
    This configuration parameter turns on short-circuit local reads.
  </description>
</property>
<property>
  <name>dfs.domain.socket.path</name>
  <value>/home/stack/sockets/short_circuit_read_socket_PORT</value>
  <description>
    Optional.  This is a path to a UNIX domain socket that will be used for
    communication between the DataNode and local HDFS clients.
    If the string "_PORT" is present in this path, it will be replaced by the
    TCP port of the DataNode.
  </description>
</property>

Be careful about permissions for the directory that hosts the shared domain socket; dfsclient will complain if open to other than the hbase user.

If you are running on an old Hadoop, one that is without HDFS-347 but that has HDFS-2246, you must set two configurations. First, the hdfs-site.xml needs to be amended. Set the property dfs.block.local-path-access.user to be the only user that can use the shortcut. This has to be the user that started HBase. Then in hbase-site.xml, set dfs.client.read.shortcircuit to be true

Services -- at least the HBase RegionServers -- will need to be restarted in order to pick up the new configurations.

dfs.client.read.shortcircuit.buffer.size

The default for this value is too high when running on a highly trafficed HBase. In HBase, if this value has not been set, we set it down from the default of 1M to 128k (Since HBase 0.98.0 and 0.96.1). See HBASE-8143 HBase on Hadoop 2 with local short circuit reads (ssr) causes OOM). The Hadoop DFSClient in HBase will allocate a direct byte buffer of this size for each block it has open; given HBase keeps its HDFS files open all the time, this can add up quickly.

14.11.3. Performance Comparisons of HBase vs. HDFS

A fairly common question on the dist-list is why HBase isn't as performant as HDFS files in a batch context (e.g., as a MapReduce source or sink). The short answer is that HBase is doing a lot more than HDFS (e.g., reading the KeyValues, returning the most current row or specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this processing context. There is room for improvement and this gap will, over time, be reduced, but HDFS will always be faster in this use-case.

14.12. Amazon EC2

Performance questions are common on Amazon EC2 environments because it is a shared environment. You will not see the same throughput as a dedicated server. In terms of running tests on EC2, run them several times for the same reason (i.e., it's a shared environment and you don't know what else is happening on the server).

If you are running on EC2 and post performance questions on the dist-list, please state this fact up-front that because EC2 issues are practically a separate class of performance issues.

14.13. Collocating HBase and MapReduce

It is often recommended to have different clusters for HBase and MapReduce. A better qualification of this is: don't collocate a HBase that serves live requests with a heavy MR workload. OLTP and OLAP-optimized systems have conflicting requirements and one will lose to the other, usually the former. For example, short latency-sensitive disk reads will have to wait in line behind longer reads that are trying to squeeze out as much throughput as possible. MR jobs that write to HBase will also generate flushes and compactions, which will in turn invalidate blocks in the Section 9.6.4, “Block Cache”.

If you need to process the data from your live HBase cluster in MR, you can ship the deltas with ??? or use replication to get the new data in real time on the OLAP cluster. In the worst case, if you really need to collocate both, set MR to use less Map and Reduce slots than you'd normally configure, possibly just one.

When HBase is used for OLAP operations, it's preferable to set it up in a hardened way like configuring the ZooKeeper session timeout higher and giving more memory to the MemStores (the argument being that the Block Cache won't be used much since the workloads are usually long scans).

14.14. Case Studies

For Performance and Troubleshooting Case Studies, see Chapter 16, Apache HBase Case Studies.

Chapter 15. Troubleshooting and Debugging Apache HBase

Table of Contents

15.1. General Guidelines
15.2. Logs
15.2.1. Log Locations
15.2.2. Log Levels
15.2.3. JVM Garbage Collection Logs
15.3. Resources
15.3.1. search-hadoop.com
15.3.2. Mailing Lists
15.3.3. IRC
15.3.4. JIRA
15.4. Tools
15.4.1. Builtin Tools
15.4.2. External Tools
15.5. Client
15.5.1. ScannerTimeoutException or UnknownScannerException
15.5.2. Performance Differences in Thrift and Java APIs
15.5.3. LeaseException when calling Scanner.next
15.5.4. Shell or client application throws lots of scary exceptions during normal operation
15.5.5. Long Client Pauses With Compression
15.5.6. Secure Client Connect ([Caused by GSSException: No valid credentials provided...])
15.5.7. ZooKeeper Client Connection Errors
15.5.8. Client running out of memory though heap size seems to be stable (but the off-heap/direct heap keeps growing)
15.5.9. Client Slowdown When Calling Admin Methods (flush, compact, etc.)
15.5.10. Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided (Mechanism level: Failed to find any Kerberos tgt)])
15.6. MapReduce
15.6.1. You Think You're On The Cluster, But You're Actually Local
15.6.2. Launching a job, you get java.lang.IllegalAccessError: com/google/protobuf/HBaseZeroCopyByteString or class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString
15.7. NameNode
15.7.1. HDFS Utilization of Tables and Regions
15.7.2. Browsing HDFS for HBase Objects
15.8. Network
15.8.1. Network Spikes
15.8.2. Loopback IP
15.8.3. Network Interfaces
15.9. RegionServer
15.9.1. Startup Errors
15.9.2. Runtime Errors
15.9.3. Snapshot Errors Due to Reverse DNS
15.9.4. Shutdown Errors
15.10. Master
15.10.1. Startup Errors
15.10.2. Shutdown Errors
15.11. ZooKeeper
15.11.1. Startup Errors
15.11.2. ZooKeeper, The Cluster Canary
15.12. Amazon EC2
15.12.1. ZooKeeper does not seem to work on Amazon EC2
15.12.2. Instability on Amazon EC2
15.12.3. Remote Java Connection into EC2 Cluster Not Working
15.13. HBase and Hadoop version issues
15.13.1. NoClassDefFoundError when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x)
15.13.2. ...cannot communicate with client version...
15.14. IPC Configuration Conflicts with Hadoop
15.15. HBase and HDFS
15.16. Running unit or integration tests
15.16.1. Runtime exceptions from MiniDFSCluster when running tests
15.17. Case Studies
15.18. Cryptographic Features
15.18.1. sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD
15.19. Operating System Specific Issues
15.19.1. Page Allocation Failure
15.20. JDK Issues
15.20.1. NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet

15.1. General Guidelines

Always start with the master log (TODO: Which lines?). Normally it’s just printing the same lines over and over again. If not, then there’s an issue. Google or search-hadoop.com should return some hits for those exceptions you’re seeing.

An error rarely comes alone in Apache HBase, usually when something gets screwed up what will follow may be hundreds of exceptions and stack traces coming from all over the place. The best way to approach this type of problem is to walk the log up to where it all began, for example one trick with RegionServers is that they will print some metrics when aborting so grepping for Dump should get you around the start of the problem.

RegionServer suicides are “normal”, as this is what they do when something goes wrong. For example, if ulimit and max transfer threads (the two most important initial settings, see Limits on Number of Files and Processes (ulimit) and Section 2.1.1.6, “dfs.datanode.max.transfer.threads) aren’t changed, it will make it impossible at some point for DataNodes to create new threads that from the HBase point of view is seen as if HDFS was gone. Think about what would happen if your MySQL database was suddenly unable to access files on your local file system, well it’s the same with HBase and HDFS. Another very common reason to see RegionServers committing seppuku is when they enter prolonged garbage collection pauses that last longer than the default ZooKeeper session timeout. For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Section 14.3.1.1, “Long GC pauses” above.

15.2. Logs

The key process logs are as follows... (replace <user> with the user that started the service, and <hostname> for the machine name)

NameNode: $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log

DataNode: $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log

JobTracker: $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log

TaskTracker: $HADOOP_HOME/logs/hadoop-<user>-tasktracker-<hostname>.log

HMaster: $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log

RegionServer: $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log

ZooKeeper: TODO

15.2.1. Log Locations

For stand-alone deployments the logs are obviously going to be on a single machine, however this is a development configuration only. Production deployments need to run on a cluster.

15.2.1.1. NameNode

The NameNode log is on the NameNode server. The HBase Master is typically run on the NameNode server, and well as ZooKeeper.

For smaller clusters the JobTracker is typically run on the NameNode server as well.

15.2.1.2. DataNode

Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log for HBase.

Additionally, each DataNode server will also have a TaskTracker log for MapReduce task execution.

15.2.2. Log Levels

15.2.2.1. Enabling RPC-level logging

Enabling the RPC-level logging on a RegionServer can often given insight on timings at the server. Once enabled, the amount of log spewed is voluminous. It is not recommended that you leave this logging on for more than short bursts of time. To enable RPC-level logging, browse to the RegionServer UI and click on Log Level. Set the log level to DEBUG for the package org.apache.hadoop.ipc (Thats right, for hadoop.ipc, NOT, hbase.ipc). Then tail the RegionServers log. Analyze.

To disable, set the logging level back to INFO level.

15.2.3. JVM Garbage Collection Logs

HBase is memory intensive, and using the default GC you can see long pauses in all threads including the Juliet Pause aka "GC of Death". To help debug this or confirm this is happening GC logging can be turned on in the Java virtual machine.

To enable, in hbase-env.sh, uncomment one of the below lines :

# This enables basic gc logging to the .out file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"

# This enables basic gc logging to its own file.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"

# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"

# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR.
          

At this point you should see logs like so:

64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs]
64898.953: [CMS-concurrent-mark-start]
64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs]
          

In this section, the first line indicates a 0.0007360 second pause for the CMS to initially mark. This pauses the entire VM, all threads for that period of time.

The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k. Later on in this cycle we see:

64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs]
64901.445: [CMS-concurrent-preclean-start]
64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs]
64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs]
64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs]
64901.578: [CMS-concurrent-abortable-preclean-start]
64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs]
64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs]
64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
64901.621: [CMS-concurrent-sweep-start]
            

The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 seconds. But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in time.

There are a few more minor GCs, then there is a pause at the 2nd last line:

64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs]
            

The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap.

At this point the sweep starts, and you can watch the heap size go down:

64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
...  lines removed ...
64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs]
64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs]
            

At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to 1.3 GB (approximate).

The key points here is to keep all these pauses low. CMS pauses are always low, but if your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit as high at 400ms.

This can be due to the size of the ParNew, which should be relatively small. If your ParNew is very large after running HBase for a while, in one example a ParNew was about 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the collections take but if its too small, objects are promoted to old gen too quickly). In the below we constrain new gen size to 64m.

Add the below line in hbase-env.sh:

export SERVER_GC_OPTS="$SERVER_GC_OPTS -XX:NewSize=64m -XX:MaxNewSize=64m"
            

Similarly, to enable GC logging for client processes, uncomment one of the below lines in hbase-env.sh:

# This enables basic gc logging to the .out file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps"

# This enables basic gc logging to its own file.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>"

# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+.
# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M"

# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR .
            

For more information on GC pauses, see the 3 part blog post by Todd Lipcon and Section 14.3.1.1, “Long GC pauses” above.

15.3. Resources

15.3.1. search-hadoop.com

search-hadoop.com indexes all the mailing lists and is great for historical searches. Search here first when you have an issue as its more than likely someone has already had your problem.

15.3.2. Mailing Lists

Ask a question on the Apache HBase mailing lists. The 'dev' mailing list is aimed at the community of developers actually building Apache HBase and for features currently under development, and 'user' is generally used for questions on released versions of Apache HBase. Before going to the mailing list, make sure your question has not already been answered by searching the mailing list archives first. Use Section 15.3.1, “search-hadoop.com”. Take some time crafting your question. See Getting Answers for ideas on crafting good questions. A quality question that includes all context and exhibits evidence the author has tried to find answers in the manual and out on lists is more likely to get a prompt response.

15.3.3. IRC

#hbase on irc.freenode.net

15.3.4. JIRA

JIRA is also really helpful when looking for Hadoop/HBase-specific issues.

15.4. Tools

15.4.1. Builtin Tools

15.4.1.1. Master Web Interface

The Master starts a web-interface on port 16010 by default. (Up to and including 0.98 this was port 60010)

The Master web UI lists created tables and their definition (e.g., ColumnFamilies, blocksize, etc.). Additionally, the available RegionServers in the cluster are listed along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap). The Master web UI allows navigation to each RegionServer's web UI.

15.4.1.2. RegionServer Web Interface

RegionServers starts a web-interface on port 16030 by default. (Up to an including 0.98 this was port 60030)

The RegionServer web UI lists online regions and their start/end keys, as well as point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, compactionQueueSize, etc.).

See Section 17.4, “HBase Metrics” for more information in metric definitions.

15.4.1.3. zkcli

zkcli is a very useful tool for investigating ZooKeeper-related issues. To invoke:

./hbase zkcli -server host:port <cmd> <args>

The commands (and arguments) are:

	connect host:port
	get path [watch]
	ls path [watch]
	set path data [version]
	delquota [-n|-b] path
	quit
	printwatches on|off
	create [-s] [-e] path data acl
	stat path [watch]
	close
	ls2 path [watch]
	history
	listquota path
	setAcl path acl
	getAcl path
	sync path
	redo cmdno
	addauth scheme auth
	delete path [version]
	setquota -n|-b val path

15.4.2. External Tools

15.4.2.1. tail

tail is the command line tool that lets you look at the end of a file. Add the “-f” option and it will refresh when new data is available. It’s useful when you are wondering what’s happening, for example, when a cluster is taking a long time to shutdown or startup as you can just fire a new terminal and tail the master log (and maybe a few RegionServers).

15.4.2.2. top

top is probably one of the most important tool when first trying to see what’s running on a machine and how the resources are consumed. Here’s an example from production system:

top - 14:46:59 up 39 days, 11:55,  1 user,  load average: 3.75, 3.57, 3.84
Tasks: 309 total,   1 running, 308 sleeping,   0 stopped,   0 zombie
Cpu(s):  4.5%us,  1.6%sy,  0.0%ni, 91.7%id,  1.4%wa,  0.1%hi,  0.6%si,  0.0%st
Mem:  24414432k total, 24296956k used,   117476k free,     7196k buffers
Swap: 16008732k total,	14348k used, 15994384k free, 11106908k cached

  PID USER  	PR  NI  VIRT  RES  SHR S %CPU %MEM	TIME+  COMMAND
15558 hadoop	18  -2 3292m 2.4g 3556 S   79 10.4   6523:52 java
13268 hadoop	18  -2 8967m 8.2g 4104 S   21 35.1   5170:30 java
 8895 hadoop	18  -2 1581m 497m 3420 S   11  2.1   4002:32 java
…
        

Here we can see that the system load average during the last five minutes is 3.75, which very roughly means that on average 3.75 threads were waiting for CPU time during these 5 minutes. In general, the “perfect” utilization equals to the number of cores, under that number the machine is under utilized and over that the machine is over utilized. This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001.

Apart from load, we can see that the system is using almost all its available RAM but most of it is used for the OS cache (which is good). The swap only has a few KBs in it and this is wanted, high numbers would indicate swapping activity which is the nemesis of performance of Java systems. Another way to detect swapping is when the load average goes through the roof (although this could also be caused by things like a dying disk, among others).

The list of processes isn’t super useful by default, all we know is that 3 java processes are using about 111% of the CPUs. To know which is which, simply type “c” and each line will be expanded. Typing “1” will give you the detail of how each CPU is used instead of the average for all of them like shown here.

15.4.2.3. jps

jps is shipped with every JDK and gives the java process ids for the current user (if root, then it gives the ids for all users). Example:

hadoop@sv4borg12:~$ jps
1322 TaskTracker
17789 HRegionServer
27862 Child
1158 DataNode
25115 HQuorumPeer
2950 Jps
19750 ThriftServer
18776 jmx
        

In order, we see a:

  • Hadoop TaskTracker, manages the local Childs

  • HBase RegionServer, serves regions

  • Child, its MapReduce task, cannot tell which type exactly

  • Hadoop TaskTracker, manages the local Childs

  • Hadoop DataNode, serves blocks

  • HQuorumPeer, a ZooKeeper ensemble member

  • Jps, well… it’s the current process

  • ThriftServer, it’s a special one will be running only if thrift was started

  • jmx, this is a local process that’s part of our monitoring platform ( poorly named maybe). You probably don’t have that.

You can then do stuff like checking out the full command line that started the process:

hadoop@sv4borg12:~$ ps aux | grep HRegionServer
hadoop   17789  155 35.2 9067824 8604364 ?     S<l  Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start
        

15.4.2.4. jstack

jstack is one of the most important tools when trying to figure out what a java process is doing apart from looking at the logs. It has to be used in conjunction with jps in order to give it a process id. It shows a list of threads, each one has a name, and they appear in the order that they were created (so the top ones are the most recent threads). Here’s a few example:

The main thread of a RegionServer that’s waiting for something to do from the master:

"regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70]
java.lang.Thread.State: TIMED_WAITING (parking)
    at sun.misc.Unsafe.park(Native Method)
    - parking to wait for  <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
    at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198)
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963)
    at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395)
    at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647)
    at java.lang.Thread.run(Thread.java:619)

    The MemStore flusher thread that is currently flushing to a file:
"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0]
java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:485)
    at org.apache.hadoop.ipc.Client.call(Client.java:803)
    - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call)
    at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221)
    at $Proxy1.complete(Unknown Source)
    at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
    at java.lang.reflect.Method.invoke(Method.java:597)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
    at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
    at $Proxy1.complete(Unknown Source)
    at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390)
    - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream)
    at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304)
    at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61)
    at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86)
    at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650)
    at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853)
    at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467)
    - locked <0x00007f16d00e6f08> (a java.lang.Object)
    at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427)
    at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80)
    at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359)
    at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907)
    at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834)
    at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224)
    at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146)
        

A handler thread that’s waiting for stuff to do (like put, delete, scan, etc):

"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0]
   java.lang.Thread.State: WAITING (parking)
        	at sun.misc.Unsafe.park(Native Method)
        	- parking to wait for  <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
        	at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158)
        	at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925)
        	at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358)
        	at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013)
        

And one that’s busy doing an increment of a counter (it’s in the phase where it’s trying to create a scanner in order to read the last value):

"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0]
   java.lang.Thread.State: RUNNABLE
        	at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56)
        	at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79)
        	at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202)
        	at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209)
        	at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039)
        	at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875)
        	at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978)
        	at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433)
        	at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source)
        	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
        	at java.lang.reflect.Method.invoke(Method.java:597)
        	at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560)
        	at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027)
        

A thread that receives data from HDFS:

"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0]
   java.lang.Thread.State: RUNNABLE
        	at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
        	at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215)
        	at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65)
        	at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69)
        	- locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1)
        	- locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet)
        	- locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl)
        	at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80)
        	at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332)
        	at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157)