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 * to you under the Apache License, Version 2.0 (the
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[[configuration]]
= Apache HBase Configuration
:doctype: book
:numbered:
:toc: left
:icons: font
:experimental:

This chapter expands upon the <<getting_started>> chapter to further explain configuration of
Apache HBase. Please read this chapter carefully, especially the
<<basic.prerequisites,Basic Prerequisites>> to ensure that your HBase testing and deployment goes
smoothly. Familiarize yourself with <<hbase_supported_tested_definitions>> as well.

== 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 link:https://cwiki.apache.org/confluence/display/HADOOP2/HADOOP-6728-MetricsV2[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,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 [label]#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
[TIP]
====
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
[WARNING]
====
When running in distributed mode, after you make an edit to an HBase configuration, make sure you
copy the contents of the _conf/_ directory to all nodes of the cluster. HBase will not do this for
you. Use a configuration management tool for managing and copying the configuration files to your
nodes. For most configurations, a restart is needed for servers to pick up changes. Dynamic
configuration is an exception to this, to be described later below.
====

[[basic.prerequisites]]
== Basic Prerequisites

This section lists required services and some required system configuration.

[[java]]
.Java

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

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

.Long-Term Support JDKs are Recommended
[WARNING]
====
HBase recommends downstream users rely only on JDK releases that are marked as Long-Term Supported
(LTS), either from the OpenJDK project or vendors. At the time of this writing, the following JDK
releases are NOT LTS releases and are NOT tested or advocated for use by the Apache HBase
community: JDK9, JDK10, JDK12, JDK13, and JDK14. Community discussion around this decision is
recorded on link:https://issues.apache.org/jira/browse/HBASE-20264[HBASE-20264].
====

.HotSpot vs. OpenJ9
[TIP]
====
At this time, all testing performed by the Apache HBase project runs on the HotSpot variant of the
JVM. When selecting your JDK distribution, please take this into consideration.
====

.Java support by release line
[cols="4*^.^", options="header"]
|===
|Java Version
|HBase 1.3+
|HBase 2.1+
|HBase 2.3+

|JDK7
|icon:check-circle[role="green"]
|icon:times-circle[role="red"]
|icon:times-circle[role="red"]

|JDK8
|icon:check-circle[role="green"]
|icon:check-circle[role="green"]
|icon:check-circle[role="green"]

|JDK11
|icon:times-circle[role="red"]
|icon:times-circle[role="red"]
|icon:exclamation-circle[role="yellow"]*

|===

.A Note on JDK11 icon:exclamation-circle[role="yellow"]*
[WARNING]
====
Preliminary support for JDK11 is introduced with HBase 2.3.0. This support is limited to
compilation and running the full test suite. There are open questions regarding the runtime
compatibility of JDK11 with Apache ZooKeeper and Apache Hadoop
(link:https://issues.apache.org/jira/browse/HADOOP-15338[HADOOP-15338]). Significantly, neither
project has yet released a version with explicit runtime support for JDK11. The remaining known
issues in HBase are catalogued in
link:https://issues.apache.org/jira/browse/HBASE-22972[HBASE-22972].
====

NOTE: You must set `JAVA_HOME` on each node of your cluster. _hbase-env.sh_ provides a handy
mechanism to do this.

[[os]]
.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
"<<passwordless.ssh.quickstart>>". If your cluster nodes use OS X, see the section,
link:https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=120730246#RunningHadoopOnOSX10.564-bit(Single-NodeCluster)-SSH:SettingupRemoteDesktopandEnablingSelf-Login[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
link:http://www.tldp.org/LDP/sag/html/basic-ntp-config.html[Basic NTP Configuration] at
[citetitle]_The Linux Documentation Project (TLDP)_ to set up NTP.

[[ulimit]]
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
<<trouble.rs.runtime.filehandles,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.
+
.`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
link:http://www.gnu.org/software/bash[GNU Bash] shell.

Windows::
  Running production systems on Windows machines is not recommended.

[[hadoop]]
=== link:https://hadoop.apache.org[Hadoop](((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
link:https://cwiki.apache.org/confluence/display/HADOOP2/Distributions+and+Commercial+Support[the Hadoop wiki]
for information about vendors of Hadoop.

.Hadoop 2.x is recommended.
[TIP]
====
Hadoop 2.x is faster and includes features, such as short-circuit reads (see
<<perf.hdfs.configs.localread>>), 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

* icon:check-circle[role="green"] = Tested to be fully-functional
* icon:times-circle[role="red"] = Known to not be fully-functional, or there are
link:https://hadoop.apache.org/cve_list.html[CVEs] so we drop the support in newer minor releases
* icon:exclamation-circle[role="yellow"] = Not tested, may/may-not function

[cols="1,5*^.^", options="header"]
|===
| | HBase-1.4.x | HBase-1.6.x | HBase-1.7.x | HBase-2.2.x | HBase-2.3.x
|Hadoop-2.7.0 | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-2.7.1+ | icon:check-circle[role="green"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-2.8.[0-2] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-2.8.[3-4] | icon:exclamation-circle[role="yellow"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-2.8.5+ | icon:exclamation-circle[role="yellow"] | icon:check-circle[role="green"] | icon:times-circle[role="red"] | icon:check-circle[role="green"] | icon:times-circle[role="red"]
|Hadoop-2.9.[0-1] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-2.9.2+ | icon:exclamation-circle[role="yellow"] | icon:check-circle[role="green"] | icon:times-circle[role="red"] | icon:check-circle[role="green"] | icon:times-circle[role="red"]
|Hadoop-2.10.x | icon:exclamation-circle[role="yellow"] | icon:check-circle[role="green"] | icon:check-circle[role="green"] | icon:exclamation-circle[role="yellow"] | icon:check-circle[role="green"]
|Hadoop-3.1.0 | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"]
|Hadoop-3.1.1+ | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:check-circle[role="green"] | icon:check-circle[role="green"]
|Hadoop-3.2.x | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:times-circle[role="red"] | icon:check-circle[role="green"] | icon:check-circle[role="green"]
|===

.Hadoop Pre-2.6.1 and JDK 1.8 Kerberos
[TIP]
====
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 link:https://issues.apache.org/jira/browse/HADOOP-10786[HADOOP-10786] for additional
details. Consider upgrading to Hadoop 2.6.1+ in this case.
====

.Hadoop 2.6.x
[TIP]
====
Hadoop distributions based on the 2.6.x line *must* have
link:https://issues.apache.org/jira/browse/HADOOP-11710[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
[TIP]
====
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 link:https://s.apache.org/hadoop-2.7.0-announcement[Apache Hadoop 2.7.0],
link:https://s.apache.org/hadoop-2.8.0-announcement[Apache Hadoop 2.8.0],
link:https://s.apache.org/hadoop-2.8.1-announcement[Apache Hadoop 2.8.1], and
link:https://s.apache.org/hadoop-2.9.0-announcement[Apache Hadoop 2.9.0].
====

.Hadoop 3.0.x Releases
[TIP]
====
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
link:https://issues.apache.org/jira/browse/YARN-7190[YARN-7190] is present in their YARN service
(currently fixed in 2.9.1+ and 3.1.0+).
====

.Hadoop 3.1.0 Release
[TIP]
====
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 link:https://s.apache.org/hadoop-3.1.0-announcement[release announcement for Hadoop 3.1.0].
====

.Replace the Hadoop Bundled With HBase!
[NOTE]
====
Because HBase depends on Hadoop, it bundles Hadoop jars under its _lib_ directory. The bundled jars
are ONLY for use in stand-alone mode. In distributed mode, it is _critical_ that the version of
Hadoop that is out on your cluster match what is under HBase. Replace the hadoop jars found in the
HBase lib directory with the equivalent hadoop jars from the version you are running on your
cluster to avoid version mismatch issues. Make sure you replace the jars under HBase across your
whole cluster. Hadoop version mismatch issues have various manifestations. Check for mismatch if
HBase appears hung.
====

[[dfs.datanode.max.transfer.threads]]
==== `dfs.datanode.max.transfer.threads` (((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:

[source,xml]
----

<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,casestudies.max.transfer.threads>> and note that this
property was previously known as `dfs.datanode.max.xcievers` (e.g.
link:http://ccgtech.blogspot.com/2010/02/hadoop-hdfs-deceived-by-xciever.html[Hadoop HDFS: Deceived by Xciever]).

[[zookeeper.requirements]]
=== ZooKeeper Requirements

An Apache ZooKeeper quorum is required. The exact version depends on your version of HBase, though
the minimum ZooKeeper version is 3.4.x due to the `useMulti` feature made default in 1.0.0
(see https://issues.apache.org/jira/browse/HBASE-16598[HBASE-16598]).

[[standalone_dist]]
== HBase run modes: Standalone and Distributed

HBase has two run modes: <<standalone,standalone>> and <<distributed,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 [code]+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 [var]+JAVA_HOME+ to point at the root of
your +java+ install.

[[standalone]]
=== Standalone HBase

This is the default mode.
Standalone mode is what is described in the <<quickstart,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.

[[standalone.over.hdfs]]
==== 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:

[source,xml]
----
<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>
----

[[distributed]]
=== 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 link:https://hadoop.apache.org/docs/current/[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.

[[pseudo]]
==== Pseudo-distributed

.Pseudo-Distributed Quickstart
[NOTE]
====
A quickstart has been added to the <<quickstart,quickstart>> chapter.
See <<quickstart_pseudo,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.

[[fully_dist]]
=== Fully-distributed

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

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

In addition, the cluster is configured so that multiple cluster nodes enlist as RegionServers,
ZooKeeper QuorumPeers, and backup HMaster servers. These configuration basics are all demonstrated
in <<quickstart_fully_distributed,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,ZooKeeper>> section for ZooKeeper setup instructions for HBase.

.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 "<<config.files,Configuration Files>>" for more information.

[source,xml]
----

<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.

[source]
----

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.

[source]
----

node-b.example.com
node-c.example.com
----
====

.Distributed HBase Quickstart
See <<quickstart_fully_distributed,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
. 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:
+
a. Add a pointer to your `HADOOP_CONF_DIR` to the `HBASE_CLASSPATH` environment variable in
_hbase-env.sh_.
b. Add a copy of _hdfs-site.xml_ (or _hadoop-site.xml_) or, better, symlinks, under
_${HBASE_HOME}/conf_, or
c. 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.

[[confirm]]
== 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
pass:[http://master.example.org:16010] to see the web interface.

Once HBase has started, see the <<shell_exercises,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.

[[config.files]]
== Default Configuration

[[hbase.site]]
=== _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,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.
// hbase/src/main/asciidoc
//
include::{docdir}/../../../target/asciidoc/hbase-default.adoc[]


[[hbase.env.sh]]
=== _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.

[[log4j]]
=== _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.

[[client_dependencies]]
=== Client configuration and dependencies connecting to an HBase cluster

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

Starting release 3.0.0, the default connection registry has been switched to a master based
implementation. Refer to <<client.masterregistry>> for more details about what a connection
registry is and implications of this change. Depending on your HBase version, following is the
expected minimal client configuration.

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

[source,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> Zookeeper ensemble information</description>
  </property>
</configuration>
----

==== Starting 3.0.0 release

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

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

The configuration value for _hbase.masters_ is a comma separated list of _host:port_ values. If no
port value is specified, the default of _16000_ is assumed.

Usually this configuration is kept out in the _hbase-site.xml_ and is picked up by the client from
the `CLASSPATH`.

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

For Java applications using Maven, including the hbase-shaded-client module is the recommended
dependency when connecting to a cluster:
[source,xml]
----
<dependency>
  <groupId>org.apache.hbase</groupId>
  <artifactId>hbase-shaded-client</artifactId>
  <version>2.0.0</version>
</dependency>
----

[[java.client.config]]
==== Java client configuration

The configuration used by a Java client is kept in an
link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/HBaseConfiguration[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:

[source,java]
----
Configuration config = HBaseConfiguration.create();
config.set("hbase.zookeeper.quorum", "localhost");  // Until 2.x.y versions
// ---- or ----
config.set("hbase.masters", "localhost:1234"); // Starting 3.0.0 version
----

[[config_timeouts]]
=== Timeout settings

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

* hbase.rpc.timeout
* hbase.rpc.read.timeout
* hbase.rpc.write.timeout
* hbase.client.operation.timeout
* hbase.client.meta.operation.timeout
* hbase.client.scanner.timeout.period

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

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

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

[[example_config]]
== Example Configurations

=== Basic Distributed HBase Install

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

Below we show what the main configuration files -- _hbase-site.xml_, _regionservers_, and
_hbase-env.sh_ -- found in the HBase _conf_ directory might look like.

[[hbase_site]]
==== _hbase-site.xml_

[source,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>
----

[[regionservers]]
==== _regionservers_

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

[source]
----
example1
example2
example3
example4
example5
example6
example7
example8
example9
----

[[hbase_env]]
==== _hbase-env.sh_

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

----
# The java implementation to use.
export JAVA_HOME=/usr/java/jdk1.8.0/

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

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

[[important_configurations]]
== The Important Configurations

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

[[required_configuration]]
=== Required Configurations

Review the <<os,os>> and <<hadoop,hadoop>> sections.

[[big.cluster.config]]
==== Big Cluster Configurations

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

[[recommended_configurations]]
=== Recommended Configurations

[[recommended_configurations.zk]]
==== ZooKeeper Configuration

[[sect.zookeeper.session.timeout]]
===== `zookeeper.session.timeout`

The default timeout is 90 seconds (specified in milliseconds). This means that if a server crashes,
it will be 90 seconds before the Master notices the crash and starts recovery. You might need to
tune the timeout down to a minute or even less so the Master notices failures sooner. Before
changing this value, be sure you have your JVM garbage collection configuration under control,
otherwise, a long garbage collection that lasts beyond the ZooKeeper session timeout will take out
your RegionServer. (You might be fine with this -- you probably want recovery to start on the
server if a RegionServer has been in GC for a long period of time).

To change this configuration, edit _hbase-site.xml_, copy the changed file across the cluster and
restart.

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

[[zookeeper.instances]]
===== Number of ZooKeeper Instances

See <<zookeeper,zookeeper>>.

[[recommended.configurations.hdfs]]
==== HDFS Configurations

[[dfs.datanode.failed.volumes.tolerated]]
===== `dfs.datanode.failed.volumes.tolerated`

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

[[hbase.regionserver.handler.count]]
===== `hbase.regionserver.handler.count`

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

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

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

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

[[big_memory]]
==== Configuration for large memory machines

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

[[config.compression]]
==== Compression

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

See <<compression,compression>> for more information.

[[config.wals]]
==== Configuring the size and number of WAL files

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

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

[[disable.splitting]]
==== Managed Splitting

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

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

.Disable Automatic Splitting
To disable automatic splitting, you can set region split policy in either cluster configuration
or table configuration to be `org.apache.hadoop.hbase.regionserver.DisabledRegionSplitPolicy`

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

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

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

[[managed.compactions]]
==== Managed Compactions

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

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

.Do Not Disable Major Compactions
[WARNING]
====
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
link:https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/Admin.html#majorCompact-org.apache.hadoop.hbase.TableName-[Admin API].
====

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

[[spec.ex]]
==== 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.

[[other_configuration]]
=== Other Configurations

[[balancer_config]]
==== Balancer

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

See <<master.processes.loadbalancer,master.processes.loadbalancer>> for more information on the
LoadBalancer.

[[disabling.blockcache]]
==== Disabling Blockcache

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

[[nagles]]
==== link:http://en.wikipedia.org/wiki/Nagle's_algorithm[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,
link:http://search-hadoop.com/m/pduLg2fydtE/Inconsistent+scan+performance+with+caching+set+&subj=Re+Inconsistent+scan+performance+with+caching+set+to+1[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
link:https://issues.apache.org/jira/browse/HBASE-7008[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.

[[mttr]]
==== Better Mean Time to Recover (MTTR)

This section is about configurations that will make servers come back faster after a fail. See the
Deveraj Das and Nicolas Liochon blog post
link:http://hortonworks.com/blog/introduction-to-hbase-mean-time-to-recover-mttr/[Introduction to HBase Mean Time to Recover (MTTR)]
for a brief introduction.

The issue
link:https://issues.apache.org/jira/browse/HBASE-8389[HBASE-8354 forces Namenode into loop with lease recovery requests]
is messy but has a bunch of good discussion toward the end on low timeouts and how to cause faster
recovery including citation of fixes added to HDFS. Read the Varun Sharma comments. The below
suggested configurations are Varun's suggestions distilled and tested. Make sure you are running
on a late-version HDFS so you have the fixes he refers to and himself adds to HDFS that help HBase
MTTR (e.g. HDFS-3703, HDFS-3712, and HDFS-4791 -- Hadoop 2 for sure has them and late Hadoop 1 has
some). Set the following in the RegionServer.

[source,xml]
----
<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.

[source,xml]
----
<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>
----

[[jmx_config]]
==== JMX

JMX (Java Management Extensions) provides built-in instrumentation that enables you to monitor and
manage the Java VM. To enable monitoring and management from remote systems, you need to set system
property `com.sun.management.jmxremote.port` (the port number through which you want to enable JMX
RMI connections) when you start the Java VM. See the
link:http://docs.oracle.com/javase/8/docs/technotes/guides/management/agent.html[official documentation]
for more information. Historically, besides above port mentioned, JMX opens two additional random
TCP listening ports, which could lead to port conflict problem. (See
link:https://issues.apache.org/jira/browse/HBASE-10289[HBASE-10289] for details)

As an alternative, you can use the coprocessor-based JMX implementation provided by HBase. To
enable it, add below property in _hbase-site.xml_:

[source,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.
By default, the JMX listens on TCP port 10102, you can further configure the port using below
properties:

[source,xml]
----
<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:
[source,bash]
----
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:

[source,bash]
----
#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:

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

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

Finally start `jconsole` on the client using the key store:

[source,bash]
----
jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore
----

NOTE: To enable the HBase JMX implementation on Master, you also need to add below property in
_hbase-site.xml_:

[source,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)

[[dyn_config]]
== Dynamic Configuration

It is possible to change a subset of the configuration without requiring a server restart. In the
HBase shell, the operations `update_config` and `update_all_config` 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 are those configurations:

.Configurations support dynamically change
[cols="1",options="header"]
|===
| Key
| hbase.ipc.server.fallback-to-simple-auth-allowed
| hbase.cleaner.scan.dir.concurrent.size
| hbase.regionserver.thread.compaction.large
| hbase.regionserver.thread.compaction.small
| hbase.regionserver.thread.split
| hbase.regionserver.throughput.controller
| hbase.regionserver.thread.hfilecleaner.throttle
| hbase.regionserver.hfilecleaner.large.queue.size
| hbase.regionserver.hfilecleaner.small.queue.size
| hbase.regionserver.hfilecleaner.large.thread.count
| hbase.regionserver.hfilecleaner.small.thread.count
| hbase.regionserver.hfilecleaner.thread.timeout.msec
| hbase.regionserver.hfilecleaner.thread.check.interval.msec
| hbase.regionserver.flush.throughput.controller
| hbase.hstore.compaction.max.size
| hbase.hstore.compaction.max.size.offpeak
| hbase.hstore.compaction.min.size
| hbase.hstore.compaction.min
| hbase.hstore.compaction.max
| hbase.hstore.compaction.ratio
| hbase.hstore.compaction.ratio.offpeak
| hbase.regionserver.thread.compaction.throttle
| hbase.hregion.majorcompaction
| hbase.hregion.majorcompaction.jitter
| hbase.hstore.min.locality.to.skip.major.compact
| hbase.hstore.compaction.date.tiered.max.storefile.age.millis
| hbase.hstore.compaction.date.tiered.incoming.window.min
| hbase.hstore.compaction.date.tiered.window.policy.class
| hbase.hstore.compaction.date.tiered.single.output.for.minor.compaction
| hbase.hstore.compaction.date.tiered.window.factory.class
| hbase.offpeak.start.hour
| hbase.offpeak.end.hour
| hbase.oldwals.cleaner.thread.size
| hbase.oldwals.cleaner.thread.timeout.msec
| hbase.oldwals.cleaner.thread.check.interval.msec
| hbase.procedure.worker.keep.alive.time.msec
| hbase.procedure.worker.add.stuck.percentage
| hbase.procedure.worker.monitor.interval.msec
| hbase.procedure.worker.stuck.threshold.msec
| hbase.regions.slop
| hbase.regions.overallSlop
| hbase.balancer.tablesOnMaster
| hbase.balancer.tablesOnMaster.systemTablesOnly
| hbase.util.ip.to.rack.determiner
| hbase.ipc.server.max.callqueue.length
| hbase.ipc.server.priority.max.callqueue.length
| hbase.ipc.server.callqueue.type
| hbase.ipc.server.callqueue.codel.target.delay
| hbase.ipc.server.callqueue.codel.interval
| hbase.ipc.server.callqueue.codel.lifo.threshold
| hbase.master.balancer.stochastic.maxSteps
| hbase.master.balancer.stochastic.stepsPerRegion
| hbase.master.balancer.stochastic.maxRunningTime
| hbase.master.balancer.stochastic.runMaxSteps
| hbase.master.balancer.stochastic.numRegionLoadsToRemember
| hbase.master.loadbalance.bytable
| hbase.master.balancer.stochastic.minCostNeedBalance
| hbase.master.balancer.stochastic.localityCost
| hbase.master.balancer.stochastic.rackLocalityCost
| hbase.master.balancer.stochastic.readRequestCost
| hbase.master.balancer.stochastic.writeRequestCost
| hbase.master.balancer.stochastic.memstoreSizeCost
| hbase.master.balancer.stochastic.storefileSizeCost
| hbase.master.balancer.stochastic.regionReplicaHostCostKey
| hbase.master.balancer.stochastic.regionReplicaRackCostKey
| hbase.master.balancer.stochastic.regionCountCost
| hbase.master.balancer.stochastic.primaryRegionCountCost
| hbase.master.balancer.stochastic.moveCost
| hbase.master.balancer.stochastic.moveCost.offpeak
| hbase.master.balancer.stochastic.maxMovePercent
| hbase.master.balancer.stochastic.tableSkewCost
| hbase.master.regions.recovery.check.interval
| hbase.regions.recovery.store.file.ref.count
| hbase.rsgroup.fallback.enable
|===

ifdef::backend-docbook[]
[index]
== Index
// Generated automatically by the DocBook toolchain.
endif::backend-docbook[]
