Apache HBase ™ Reference Guide

Preface

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

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

About This Guide

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

mvn site

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

Contributing to the Documentation

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

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

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

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

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

Reporting Bugs

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

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

Support and Testing Expectations

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

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

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

Not Supported

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

Tested

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

Not Tested

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

Getting Started

1. Introduction

Quickstart will get you up and running on a single-node, standalone instance of HBase.

2. Quick Start - Standalone HBase

This section describes the setup of a single-node standalone HBase. A standalone instance has all HBase daemons — the Master, RegionServers, and ZooKeeper — running in a single JVM persisting to the local filesystem. It is our most basic deploy profile. We will show you how to create a table in HBase using the hbase shell CLI, insert rows into the table, perform put and scan operations against the table, enable or disable the table, and start and stop HBase.

Apart from downloading HBase, this procedure should take less than 10 minutes.

2.1. JDK Version Requirements

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

2.2. Get Started with HBase

Procedure: Download, Configure, and Start HBase in Standalone Mode
  1. Choose a download site from this list of Apache Download Mirrors . Click on the suggested top link. This will take you to a mirror of HBase Releases. Click on the folder named stable and then download the binary file that ends in .tar.gz to your local filesystem. Do not download the file ending in src.tar.gz for now.

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

    $ tar xzvf hbase-2.1.9-bin.tar.gz
    $ cd hbase-2.1.9/
  3. You are required to set the JAVA_HOME environment variable before starting HBase. 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
  4. Edit conf/hbase-site.xml, which is the main HBase configuration file. At this time, you need to specify the directory on the local filesystem where HBase and ZooKeeper write data and acknowledge some risks. 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. 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>
      <property>
        <name>hbase.unsafe.stream.capability.enforce</name>
        <value>false</value>
        <description>
          Controls whether HBase will check for stream capabilities (hflush/hsync).
    
          Disable this if you intend to run on LocalFileSystem, denoted by a rootdir
          with the 'file://' scheme, but be mindful of the NOTE below.
    
          WARNING: Setting this to false blinds you to potential data loss and
          inconsistent system state in the event of process and/or node failures. If
          HBase is complaining of an inability to use hsync or hflush it's most
          likely not a false positive.
        </description>
      </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.

    The hbase.rootdir in the above example points to a directory in the local filesystem. The 'file://' prefix is how we denote local filesystem. You should take the WARNING present in the configuration example to heart. In standalone mode HBase makes use of the local filesystem abstraction from the Apache Hadoop project. That abstraction doesn’t provide the durability promises that HBase needs to operate safely. This is fine for local development and testing use cases where the cost of cluster failure is well contained. It is not appropriate for production deployments; eventually you will lose data.

To home HBase on an existing instance of HDFS, set the hbase.rootdir to point at a directory up on your instance: e.g. hdfs://namenode.example.org:8020/hbase. For more on this variant, see the section below on Standalone HBase over HDFS.

  1. The bin/start-hbase.sh script is provided as a convenient way to start HBase. Issue the command, and if all goes well, a message is logged to standard output showing that HBase started successfully. You can use the jps command to verify that you have one running process called HMaster. In standalone mode HBase runs all daemons within this single JVM, i.e. the HMaster, a single HRegionServer, and the ZooKeeper daemon. Go to http://localhost:16010 to view the HBase Web UI.

    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 on your system.
Procedure: Use HBase For the First Time
  1. Connect to HBase.

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

    $ ./bin/hbase shell
    hbase(main):001:0>
  2. Display HBase Shell Help Text.

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

  3. Create a table.

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

    hbase(main):001:0> create 'test', 'cf'
    0 row(s) in 0.4170 seconds
    
    => Hbase::Table - test
  4. List Information About your Table

    Use the list command to confirm your table exists

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

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

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

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

    hbase(main):003:0> put 'test', 'row1', 'cf:a', 'value1'
    0 row(s) in 0.0850 seconds
    
    hbase(main):004:0> put 'test', 'row2', 'cf:b', 'value2'
    0 row(s) in 0.0110 seconds
    
    hbase(main):005:0> put 'test', 'row3', 'cf:c', 'value3'
    0 row(s) in 0.0100 seconds

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

  6. Scan the table for all data at once.

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

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

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

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

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

    hbase(main):008:0> disable 'test'
    0 row(s) in 1.1820 seconds
    
    hbase(main):009:0> enable 'test'
    0 row(s) in 0.1770 seconds

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

    hbase(main):010:0> disable 'test'
    0 row(s) in 1.1820 seconds
  9. Drop the table.

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

    hbase(main):011:0> drop 'test'
    0 row(s) in 0.1370 seconds
  10. Exit the HBase Shell.

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

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

    $ ./bin/stop-hbase.sh
    stopping hbase....................
    $
  2. After issuing the command, it can take several minutes for the processes to shut down. Use the jps to be sure that the HMaster and HRegionServer processes are shut down.

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

2.3. Pseudo-Distributed Local Install

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

Hadoop Configuration

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

  1. Stop HBase if it is running.

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

  2. Configure HBase.

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

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

    Next, 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. Be sure to either remove the entry for hbase.unsafe.stream.capability.enforce or set it to true.

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

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

  3. Start HBase.

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

  4. Check the HBase directory in HDFS.

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

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

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

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

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

    The HMaster server controls the HBase cluster. You can start up to 9 backup HMaster servers, which makes 10 total HMasters, counting the primary. To start a backup HMaster, use the local-master-backup.sh. For each backup master you want to start, add a parameter representing the port offset for that master. Each HMaster uses two ports (16000 and 16010 by default). The port offset is added to these ports, so using an offset of 2, the backup HMaster would use ports 16002 and 16012. The following command starts 3 backup servers using ports 16002/16012, 16003/16013, and 16005/16015.

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

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

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

    The HRegionServer manages the data in its StoreFiles as directed by the HMaster. Generally, one HRegionServer runs per node in the cluster. Running multiple HRegionServers on the same system can be useful for testing in pseudo-distributed mode. The local-regionservers.sh command allows you to run multiple RegionServers. It works in a similar way to the local-master-backup.sh command, in that each parameter you provide represents the port offset for an instance. Each RegionServer requires two ports, and the default ports are 16020 and 16030. Since HBase version 1.1.0, HMaster doesn’t use region server ports, this leaves 10 ports (16020 to 16029 and 16030 to 16039) to be used for RegionServers. For supporting additional RegionServers, set environment variables HBASE_RS_BASE_PORT and HBASE_RS_INFO_BASE_PORT to appropriate values before running script local-regionservers.sh. e.g. With values 16200 and 16300 for base ports, 99 additional RegionServers can be supported, on a server. The following command starts four additional RegionServers, running on sequential ports starting at 16022/16032 (base ports 16020/16030 plus 2).

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

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

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

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

2.4. Advanced - Fully Distributed

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

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

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

node-a.example.com

yes

yes

no

node-b.example.com

backup

yes

yes

node-c.example.com

no

yes

yes

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

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

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

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

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

    $ ssh-keygen -t rsa

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

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

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

  3. Copy the public key to the other nodes.

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

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

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

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

Procedure: Prepare node-a

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

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

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

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

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

  3. Configure ZooKeeper

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

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

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

Procedure: Prepare node-b and node-c

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

  1. Download and unpack HBase.

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

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

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

Procedure: Start and Test Your Cluster
  1. Be sure HBase is not running on any node.

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

  2. Start the cluster.

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

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

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

  3. Verify that the processes are running.

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

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

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

  4. Browse to the Web UI.

    Web UI Port Changes
    Web UI Port Changes

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

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

  5. Test what happens when nodes or services disappear.

    With a three-node cluster you have configured, things will not be very resilient. You can still test the behavior of the primary Master or a RegionServer by killing the associated processes and watching the logs.

2.5. Where to go next

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

Apache HBase Configuration

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

3. Configuration Files

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

HBase Configuration File Descriptions
backup-masters

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

hadoop-metrics2-hbase.properties

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

hbase-env.cmd and hbase-env.sh

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

hbase-policy.xml

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

hbase-site.xml

The main HBase configuration file. This file specifies configuration options which override HBase’s default configuration. You can view (but do not edit) the default configuration file at 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 contents 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 configurations, a restart is needed for servers to pick up changes. Dynamic configuration is an exception to this, to be described later below.

4. Basic Prerequisites

This section lists required services and some required system configuration.

Java

The following table summarizes the recommendation of the HBase community wrt deploying on various Java versions. An entry of "yes" is meant to indicate a base level of testing and willingness to help diagnose and address issues you might run into. Similarly, an entry of "no" or "Not Supported" generally means that should you run into an issue the community is likely to ask you to change the Java environment before proceeding to help. In some cases, specific guidance on limitations (e.g. wether compiling / unit tests work, specific operational issues, etc) will also be noted.

Long Term Support JDKs are recommended

HBase recommends downstream users rely on JDK releases that are marked as Long Term Supported (LTS) either from the OpenJDK project or vendors. As of March 2018 that means Java 8 is the only applicable version and that the next likely version to see testing will be Java 11 near Q3 2018.

Table 2. Java support by release line
HBase Version JDK 7 JDK 8 JDK 9 JDK 10

2.0

Not Supported

yes

Not Supported

Not Supported

1.3

yes

yes

Not Supported

Not Supported

1.2

yes

yes

Not Supported

Not Supported

HBase will neither build nor run with Java 6.
You must set JAVA_HOME on each node of your cluster. hbase-env.sh provides a handy mechanism to do this.
Operating System Utilities
ssh

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

DNS

HBase uses the local hostname to self-report its IP address.

NTP

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

Limits on Number of Files and Processes (ulimit)

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

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

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

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

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

Another related setting is the number of processes a user is allowed to run at once. In Linux and Unix, the number of processes is set using the ulimit -u command. This should not be confused with the nproc command, which controls the number of CPUs available to a given user. Under load, a ulimit -u that is too low can cause OutOfMemoryError exceptions.

Configuring the maximum number of file descriptors and processes for the user who is running the HBase process is an operating system configuration, rather than an HBase configuration. It is also important to be sure that the settings are changed for the user that actually runs HBase. To see which user started HBase, and that user’s ulimit configuration, look at the first line of the HBase log for that instance.

Example 2. ulimit Settings on Ubuntu

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

hadoop  -       nofile  32768
hadoop  -       nproc   32000

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

session required  pam_limits.so
Linux Shell

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

Windows

Running production systems on Windows machines is not recommended.

4.1. Hadoop

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

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

Hadoop 2.x is recommended.

Hadoop 2.x is faster and includes features, such as short-circuit reads (see Leveraging local data), which will help improve your HBase random read profile. Hadoop 2.x also includes important bug fixes that will improve your overall HBase experience. HBase does not support running with earlier versions of Hadoop. See the table below for requirements specific to different HBase versions.

Hadoop 3.x is still in early access releases and has not yet been sufficiently tested by the HBase community for production use cases.

Use the following legend to interpret this table:

Hadoop version support matrix
  • "S" = supported

  • "X" = not supported

  • "NT" = Not tested

HBase-1.2.x HBase-1.3.x HBase-1.5.x HBase-2.0.x HBase-2.1.x

Hadoop-2.4.x

S

S

X

X

X

Hadoop-2.5.x

S

S

X

X

X

Hadoop-2.6.0

X

X

X

X

X

Hadoop-2.6.1+

S

S

X

S

X

Hadoop-2.7.0

X

X

X

X

X

Hadoop-2.7.1+

S

S

S

S

S

Hadoop-2.8.[0-1]

X

X

X

X

X

Hadoop-2.8.2

NT

NT

NT

NT

NT

Hadoop-2.8.3+

NT

NT

NT

S

S

Hadoop-2.9.0

X

X

X

X

X

Hadoop-2.9.1+

NT

NT

NT

NT

NT

Hadoop-3.0.x

X

X

X

X

X

Hadoop-3.1.0

X

X

X

X

X

Hadoop Pre-2.6.1 and JDK 1.8 Kerberos

When using pre-2.6.1 Hadoop versions and JDK 1.8 in a Kerberos environment, HBase server can fail and abort due to Kerberos keytab relogin error. Late version of JDK 1.7 (1.7.0_80) has the problem too. Refer to HADOOP-10786 for additional details. Consider upgrading to Hadoop 2.6.1+ in this case.

Hadoop 2.6.x

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

Hadoop 2.y.0 Releases

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

Hadoop 3.0.x Releases

Hadoop distributions that include the Application Timeline Service feature may cause unexpected versions of HBase classes to be present in the application classpath. Users planning on running MapReduce applications with HBase should make sure that YARN-7190 is present in their YARN service (currently fixed in 2.9.1+ and 3.1.0+).

Hadoop 3.1.0 Release

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

Replace the Hadoop Bundled With HBase!

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

4.1.1. dfs.datanode.max.transfer.threads

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

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

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

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

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

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

4.2. ZooKeeper Requirements

ZooKeeper 3.4.x is required.

5. HBase run modes: Standalone and Distributed

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

5.1. Standalone HBase

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

5.1.1. Standalone HBase over HDFS

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

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

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

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

5.2. Distributed

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

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

5.2.1. Pseudo-distributed

Pseudo-Distributed Quickstart

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

A pseudo-distributed mode is simply a fully-distributed mode run on a single host. Use this HBase configuration for testing and prototyping purposes only. Do not use this configuration for production or for performance evaluation.

5.3. Fully-distributed

By default, HBase runs in 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 advised. In distributed mode, multiple instances of HBase daemons run on multiple servers in the cluster.

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

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

Distributed RegionServers

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

ZooKeeper and HBase

See the ZooKeeper section for ZooKeeper setup instructions for HBase.

Example 3. Example Distributed HBase Cluster

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

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

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

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

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

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

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

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

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

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

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

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

6. Running and Confirming Your Installation

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

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

Start HBase with the following command:

bin/start-hbase.sh

Run the above from the HBASE_HOME directory.

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

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

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

To stop HBase after exiting the HBase shell enter

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

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

7. Default Configuration

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

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

Not all configuration options make it out to hbase-default.xml. Some configurations would only appear in source code; the only way to identify these changes are through code review.

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

7.2. HBase Default Configuration

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

hbase.tmp.dir
Description

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

Default

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

hbase.rootdir
Description

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

Default

${hbase.tmp.dir}/hbase

hbase.cluster.distributed
Description

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

Default

false

hbase.zookeeper.quorum
Description

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

Default

localhost

zookeeper.recovery.retry.maxsleeptime
Description

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

Default

60000

hbase.local.dir
Description

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

Default

${hbase.tmp.dir}/local/

hbase.master.port
Description

The port the HBase Master should bind to.

Default

16000

hbase.master.info.port
Description

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

Default

16010

hbase.master.info.bindAddress
Description

The bind address for the HBase Master web UI

Default

0.0.0.0

hbase.master.logcleaner.plugins
Description

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

Default

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

hbase.master.logcleaner.ttl
Description

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

Default

600000

hbase.master.procedurewalcleaner.ttl
Description

How long a Procedure WAL will remain in the archive directory, after which it will be cleaned by a Master thread. The value is in milliseconds.

Default

604800000

hbase.master.hfilecleaner.plugins
Description

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

Default

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

hbase.master.infoserver.redirect
Description

Whether or not the Master listens to the Master web UI port (hbase.master.info.port) and redirects requests to the web UI server shared by the Master and RegionServer. Config. makes sense when Master is serving Regions (not the default).

Default

true

hbase.master.fileSplitTimeout
Description

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

Default

600000

hbase.regionserver.port
Description

The port the HBase RegionServer binds to.

Default

16020

hbase.regionserver.info.port
Description

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

Default

16030

hbase.regionserver.info.bindAddress
Description

The address for the HBase RegionServer web UI

Default

0.0.0.0

hbase.regionserver.info.port.auto
Description

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

Default

false

hbase.regionserver.handler.count
Description

Count of RPC Listener instances spun up on RegionServers. Same property is used by the Master for count of master handlers. Too many handlers can be counter-productive. Make it a multiple of CPU count. If mostly read-only, handlers count close to cpu count does well. Start with twice the CPU count and tune from there.

Default

30

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

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

Default

0.1

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

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

Default

0

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

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

Default

0

hbase.regionserver.msginterval
Description

Interval between messages from the RegionServer to Master in milliseconds.

Default

3000

hbase.regionserver.logroll.period
Description

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

Default

3600000

hbase.regionserver.logroll.errors.tolerated
Description

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

Default

2

hbase.regionserver.hlog.reader.impl
Description

The WAL file reader implementation.

Default

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

hbase.regionserver.hlog.writer.impl
Description

The WAL file writer implementation.

Default

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

hbase.regionserver.global.memstore.size
Description

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

Default

none

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

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

Default

none

hbase.systemtables.compacting.memstore.type
Description

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

Default

NONE

hbase.regionserver.optionalcacheflushinterval
Description

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

Default

3600000

hbase.regionserver.dns.interface
Description

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

Default

default

hbase.regionserver.dns.nameserver
Description

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

Default

default

hbase.regionserver.region.split.policy
Description

A split policy determines when a region should be split. The various other split policies that are available currently are BusyRegionSplitPolicy, ConstantSizeRegionSplitPolicy, DisabledRegionSplitPolicy, DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy, and SteppingSplitPolicy. DisabledRegionSplitPolicy blocks manual region splitting.

Default

org.apache.hadoop.hbase.regionserver.SteppingSplitPolicy

hbase.regionserver.regionSplitLimit
Description

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

Default

1000

zookeeper.session.timeout
Description

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

Default

90000

zookeeper.znode.parent
Description

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

Default

/hbase

zookeeper.znode.acl.parent
Description

Root ZNode for access control lists.

Default

acl

hbase.zookeeper.dns.interface
Description

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

Default

default

hbase.zookeeper.dns.nameserver
Description

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

Default

default

hbase.zookeeper.peerport
Description

Port used by ZooKeeper peers to talk to each other. See https://zookeeper.apache.org/doc/r3.3.3/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

2888

hbase.zookeeper.leaderport
Description

Port used by ZooKeeper for leader election. See https://zookeeper.apache.org/doc/r3.3.3/zookeeperStarted.html#sc_RunningReplicatedZooKeeper for more information.

Default

3888

hbase.zookeeper.property.initLimit
Description

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

Default

10

hbase.zookeeper.property.syncLimit
Description

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

Default

5

hbase.zookeeper.property.dataDir
Description

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

Default

${hbase.tmp.dir}/zookeeper

hbase.zookeeper.property.clientPort
Description

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

Default

2181

hbase.zookeeper.property.maxClientCnxns
Description

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

Default

300

hbase.client.write.buffer
Description

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

Default

2097152

hbase.client.pause
Description

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

Default

100

hbase.client.pause.cqtbe
Description

Whether or not to use a special client pause for CallQueueTooBigException (cqtbe). Set this property to a higher value than hbase.client.pause if you observe frequent CQTBE from the same RegionServer and the call queue there keeps full

Default

none

hbase.client.retries.number
Description

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

Default

15

hbase.client.max.total.tasks
Description

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

Default

100

hbase.client.max.perserver.tasks
Description

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

Default

2

hbase.client.max.perregion.tasks
Description

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

Default

1

hbase.client.perserver.requests.threshold
Description

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

Default

2147483647

hbase.client.scanner.caching
Description

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

Default

2147483647

hbase.client.keyvalue.maxsize
Description

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

Default

10485760

hbase.server.keyvalue.maxsize
Description

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

Default

10485760

hbase.client.scanner.timeout.period
Description

Client scanner lease period in milliseconds.

Default

60000

hbase.client.localityCheck.threadPoolSize
Default

2

hbase.bulkload.retries.number
Description

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

Default

10

hbase.master.balancer.maxRitPercent
Description

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

Default

1.0

hbase.balancer.period
Description

Period at which the region balancer runs in the Master.

Default

300000

hbase.normalizer.period
Description

Period at which the region normalizer runs in the Master.

Default

300000

hbase.regions.slop
Description

Rebalance if any regionserver has average + (average * slop) regions. The default value of this parameter is 0.001 in StochasticLoadBalancer (the default load balancer), while the default is 0.2 in other load balancers (i.e., SimpleLoadBalancer).

Default

0.001

hbase.server.thread.wakefrequency
Description

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

Default

10000

hbase.server.versionfile.writeattempts
Description

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

Default

3

hbase.hregion.memstore.flush.size
Description

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

Default

134217728

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

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

Default

16777216

hbase.hregion.preclose.flush.size
Description

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

Default

5242880

hbase.hregion.memstore.block.multiplier
Description

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

Default

4

hbase.hregion.memstore.mslab.enabled
Description

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

Default

true

hbase.hregion.max.filesize
Description

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
Description

Time between major compactions, expressed in milliseconds. Set to 0 to disable time-based automatic major compactions. User-requested and size-based major compactions will still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause compaction to start at a somewhat-random time during a given window of time. The default value is 7 days, expressed in milliseconds. If major compactions are causing disruption in your environment, you can configure them to run at off-peak times for your deployment, or disable time-based major compactions by setting this parameter to 0, and run major compactions in a cron job or by another external mechanism.

Default

604800000

hbase.hregion.majorcompaction.jitter
Description

A multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur a given amount of time either side of hbase.hregion.majorcompaction. The smaller the number, the closer the compactions will happen to the hbase.hregion.majorcompaction interval.

Default

0.50

hbase.hstore.compactionThreshold
Description

If more than 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
Description

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

Default

2

hbase.hstore.blockingStoreFiles
Description

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

Default

16

hbase.hstore.blockingWaitTime
Description

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

Default

90000

hbase.hstore.compaction.min
Description

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

Default

3

hbase.hstore.compaction.max
Description

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

Default

10

hbase.hstore.compaction.min.size
Description

A StoreFile (or a selection of StoreFiles, when using ExploringCompactionPolicy) smaller than this size will always be eligible for minor compaction. HFiles this size or larger are evaluated by hbase.hstore.compaction.ratio to determine if they are eligible. Because this limit represents the "automatic include" limit for all StoreFiles smaller than this value, this value may need to be reduced in write-heavy environments where many StoreFiles in the 1-2 MB range are being flushed, because every StoreFile will be targeted for compaction and the resulting StoreFiles may still be under the minimum size and require further compaction. If this parameter is lowered, the ratio check is triggered more quickly. This addressed some issues seen in earlier versions of HBase but changing this parameter is no longer necessary in most situations. Default: 128 MB expressed in bytes.

Default

134217728

hbase.hstore.compaction.max.size
Description

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

Default

9223372036854775807

hbase.hstore.compaction.ratio
Description

For minor compaction, this ratio is used to determine whether a given StoreFile which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its effect is to limit compaction of large StoreFiles. The value of hbase.hstore.compaction.ratio is expressed as a floating-point decimal. A large ratio, such as 10, will produce a single giant StoreFile. Conversely, a low value, such as .25, will produce behavior similar to the BigTable compaction algorithm, producing four StoreFiles. A moderate value of between 1.0 and 1.4 is recommended. When tuning this value, you are balancing write costs with read costs. Raising the value (to something like 1.4) will have more write costs, because you will compact larger StoreFiles. However, during reads, HBase will need to seek through fewer StoreFiles to accomplish the read. Consider this approach if you cannot take advantage of Bloom filters. Otherwise, you can lower this value to something like 1.0 to reduce the background cost of writes, and use Bloom filters to control the number of StoreFiles touched during reads. For most cases, the default value is appropriate.

Default

1.2F

hbase.hstore.compaction.ratio.offpeak
Description

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

Default

5.0F

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

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

Default

0

hbase.offpeak.start.hour
Description

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

Default

-1

hbase.offpeak.end.hour
Description

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

Default

-1

hbase.regionserver.thread.compaction.throttle
Description

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

Default

2684354560

hbase.regionserver.majorcompaction.pagecache.drop
Description

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

Default

true

hbase.regionserver.minorcompaction.pagecache.drop
Description

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

Default

true

hbase.hstore.compaction.kv.max
Description

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

Default

10

hbase.storescanner.parallel.seek.enable
Description

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

Default

false

hbase.storescanner.parallel.seek.threads
Description

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

Default

10

hfile.block.cache.size
Description

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

Default

0.4

hfile.block.index.cacheonwrite
Description

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

Default

false

hfile.index.block.max.size
Description

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

Default

131072

hbase.bucketcache.ioengine
Description

Where to store the contents of the bucketcache. One of: offheap, file, files or mmap. If a file or files, set it to file(s):PATH_TO_FILE. mmap means the content will be in an mmaped file. Use mmap:PATH_TO_FILE. See http://hbase.apache.org/book.html#offheap.blockcache for more information.

Default

none

hbase.bucketcache.size
Description

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

Default

none

hbase.bucketcache.bucket.sizes
Description

A comma-separated list of sizes for buckets for the bucketcache. Can be multiple sizes. List block sizes in order from smallest to largest. The sizes you use will depend on your data access patterns. Must be a multiple of 256 else you will run into 'java.io.IOException: Invalid HFile block magic' when you go to read from cache. If you specify no values here, then you pick up the default bucketsizes set in code (See BucketAllocator#DEFAULT_BUCKET_SIZES).

Default

none

hfile.format.version
Description

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

Default

3

hfile.block.bloom.cacheonwrite
Description

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

Default

false

io.storefile.bloom.block.size
Description

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

Default

131072

hbase.rs.cacheblocksonwrite
Description

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

Default

false

hbase.rpc.timeout
Description

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

Default

60000

hbase.client.operation.timeout
Description

Operation timeout is a top-level restriction (millisecond) that makes sure a blocking operation in Table will not be blocked more than this. In each operation, if rpc request fails because of timeout or other reason, it will retry until success or th