Chapter 1. Apache HBase Configuration

Table of Contents

1.1. Basic Prerequisites
1.1.1. Java
1.1.2. Operating System
1.1.3. Hadoop
1.2. HBase run modes: Standalone and Distributed
1.2.1. Standalone HBase
1.2.2. Distributed
1.2.3. Running and Confirming Your Installation
1.3. Configuration Files
1.3.1. hbase-site.xml and hbase-default.xml
1.3.2. hbase-env.sh
1.3.3. log4j.properties
1.3.4. Client configuration and dependencies connecting to an HBase cluster
1.4. Example Configurations
1.4.1. Basic Distributed HBase Install
1.5. The Important Configurations
1.5.1. Required Configurations
1.5.2. Recommended Configurations
1.5.3. Other Configurations

This chapter is the Not-So-Quick start guide to Apache HBase configuration. It goes over system requirements, Hadoop setup, the different Apache HBase run modes, and the various configurations in HBase. Please read this chapter carefully. At a minimum ensure that all Section 1.1, “Basic Prerequisites” have been satisfied. Failure to do so will cause you (and us) grief debugging strange errors and/or data loss.

Apache HBase uses the same configuration system as Apache Hadoop. To configure a deploy, edit a file of environment variables in conf/hbase-env.sh -- this configuration is used mostly by the launcher shell scripts getting the cluster off the ground -- and then add configuration to an XML file to do things like override HBase defaults, tell HBase what Filesystem to use, and the location of the ZooKeeper ensemble. [1]

When running in distributed mode, after you make an edit to an HBase configuration, make sure you copy the content of the conf directory to all nodes of the cluster. HBase will not do this for you. Use rsync. For most configuration, a restart is needed for servers to pick up changes (caveat dynamic config. to be described later below).

1.1. Basic Prerequisites

This section lists required services and some required system configuration.

1.1.1. Java

Just like Hadoop, HBase requires at least Java 6 from Oracle.

1.1.2. Operating System

1.1.2.1. ssh

ssh must be installed and sshd must be running to use Hadoop's scripts to manage remote Hadoop and HBase daemons. You must be able to ssh to all nodes, including your local node, using passwordless login (Google "ssh passwordless login"). If on mac osx, see the section, SSH: Setting up Remote Desktop and Enabling Self-Login on the hadoop wiki.

1.1.2.2. DNS

HBase uses the local hostname to self-report its IP address. Both forward and reverse DNS resolving must work in versions of HBase previous to 0.92.0 [2].

If your machine has multiple interfaces, HBase will use the interface that the primary hostname resolves to.

If this is insufficient, you can set hbase.regionserver.dns.interface to indicate the primary interface. This only works if your cluster configuration is consistent and every host has the same network interface configuration.

Another alternative is setting hbase.regionserver.dns.nameserver to choose a different nameserver than the system wide default.

1.1.2.3. Loopback IP

Previous to hbase-0.96.0, HBase expects the loopback IP address to be 127.0.0.1. See Section 1.1.2.3, “Loopback IP”

1.1.2.4. NTP

The clocks on cluster members should be in basic alignments. Some skew is tolerable but wild skew could generate odd behaviors. Run NTP on your cluster, or an equivalent.

If you are having problems querying data, or "weird" cluster operations, check system time!

1.1.2.5.  ulimit and nproc

Apache HBase is a database. It uses a lot of files all at the same time. The default ulimit -n -- i.e. user file limit -- of 1024 on most *nix systems is insufficient (On mac os x its 256). Any significant amount of loading will lead you to ???. 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
        

Do yourself a favor and change the upper bound on the number of file descriptors. Set it to north of 10k. The math runs roughly as follows: per ColumnFamily there is at least one StoreFile and possibly up to 5 or 6 if the region is under load. Multiply the average number of StoreFiles per ColumnFamily times the number of 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, config files, etc.)

You should also up the hbase users' nproc setting; under load, a low-nproc setting could manifest as OutOfMemoryError. [3] [4]

To be clear, upping the file descriptors and nproc for the user who is running the HBase process is an operating system configuration, not an HBase configuration. Also, a common mistake is that administrators will up the file descriptors for a particular user but for whatever reason, HBase will be running as some one else. HBase prints in its logs as the first line the ulimit its seeing. Ensure its correct. [5]

1.1.2.5.1. ulimit on Ubuntu

If you are on Ubuntu you will need to make the following changes:

In the file /etc/security/limits.conf add a line like:

hadoop  -       nofile  32768

Replace hadoop with whatever user is running Hadoop and HBase. If you have separate users, you will need 2 entries, one for each user. In the same file set nproc hard and soft limits. For example:

hadoop soft/hard nproc 32000

In the file /etc/pam.d/common-session add as the last line in the file:

session required  pam_limits.so

Otherwise the changes in /etc/security/limits.conf won't be applied.

Don't forget to log out and back in again for the changes to take effect!

1.1.2.6. Windows

Previous to hbase-0.96.0, Apache HBase was little tested running on Windows. Running a production install of HBase on top of Windows is not recommended.

If you are running HBase on Windows pre-hbase-0.96.0, you must install Cygwin to have a *nix-like environment for the shell scripts. The full details are explained in the Windows Installation guide. Also search our user mailing list to pick up latest fixes figured by Windows users.

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

1.1.3. Hadoop

The below table shows some information about what versions of Hadoop are supported by various HBase versions. Based on the version of HBase, you should select the most appropriate version of Hadoop. We are not in the Hadoop distro selection business. You can use Hadoop distributions from Apache, or learn about vendor distributions of Hadoop at http://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support

Hadoop 2.x is better than Hadoop 1.x

Hadoop 2.x is faster, with more features such as short-circuit reads which will help improve your HBase random read profile as well important bug fixes that will improve your overall HBase experience. You should run Hadoop 2 rather than Hadoop 1. HBase 0.98 deprecates use of Hadoop1. HBase 1.0 will not support Hadoop1.

Use the following legend to interpret this table:

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

Table 1.1. Hadoop version support matrix

HBase-0.92.xHBase-0.94.xHBase-0.96.xHBase-0.98.x[a]HBase-1.0.x[b]
Hadoop-0.20.205SXXXX
Hadoop-0.22.x SXXXX
Hadoop-1.0.0-1.0.2[c] XXXXX
Hadoop-1.0.3+SSSXX
Hadoop-1.1.x NTSSXX
Hadoop-0.23.x XSNTXX
Hadoop-2.0.x-alpha XNTXXX
Hadoop-2.1.0-beta XNTSXX
Hadoop-2.2.0 XNT [d]SSNT
Hadoop-2.3.xXNTSSNT
Hadoop-2.4.xXNTSSS
Hadoop-2.5.xXNTSSS

[a] Support for Hadoop 1.x is deprecated.

[b] Hadoop 1.x is NOT supported

[c] HBase requires hadoop 1.0.3 at a minimum; there is an issue where we cannot find KerberosUtil compiling against earlier versions of Hadoop.

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

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

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

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

  • Type the following commands:

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

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

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

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

Replace the Hadoop Bundled With HBase!

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

1.1.3.1. Apache HBase 0.92 and 0.94

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

1.1.3.2. Apache HBase 0.96

As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required. Hadoop 2 is strongly encouraged (faster but also has fixes that help MTTR). We will no longer run properly on older Hadoops such as 0.20.205 or branch-0.20-append. Do not move to Apache HBase 0.96.x if you cannot upgrade your Hadoop.[6]

1.1.3.3. Hadoop versions 0.20.x - 1.x

HBase will lose data unless it is running on an HDFS that has a durable sync implementation. DO NOT use Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0 which DO NOT have this attribute. Currently only Hadoop versions 0.20.205.x or any release in excess of this version -- this includes hadoop-1.0.0 -- have a working, durable sync [7]. Sync has to be explicitly enabled by setting dfs.support.append equal to true on both the client side -- in hbase-site.xml -- and on the serverside in hdfs-site.xml (The sync facility HBase needs is a subset of the append code path).

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

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

1.1.3.4. Apache HBase on Secure Hadoop

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

1.1.3.5. 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 ??? and note that this property was previously known as dfs.datanode.max.xcievers (e.g. Hadoop HDFS: Deceived by Xciever).

1.2. HBase run modes: Standalone and Distributed

HBase has two run modes: Section 1.2.1, “Standalone HBase” and Section 1.2.2, “Distributed”. Out of the box, HBase runs in standalone mode. Whatever your mode, you will need to configure HBase by editing files in the HBase conf directory. At a minimum, you must edit conf/hbase-env.sh to tell HBase which java to use. In this file you set HBase environment variables such as the heapsize and other options for the JVM, the preferred location for log files, etc. Set JAVA_HOME to point at the root of your java install.

1.2.1. Standalone HBase

This is the default mode. Standalone mode is what is described in the ??? 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.

1.2.2. Distributed

Distributed mode can be subdivided into distributed but all daemons run on a single node -- a.k.a pseudo-distributed-- and fully-distributed where the daemons are spread across all nodes in the cluster [8].

Pseudo-distributed mode can run against the local filesystem or it can run against an instance of the Hadoop Distributed File System (HDFS). Fully-distributed mode can ONLY run on HDFS. See the Hadoop requirements and instructions for how to set up HDFS.

Below we describe the different distributed setups. Starting, verification and exploration of your install, whether a pseudo-distributed or fully-distributed configuration is described in a section that follows, Section 1.2.3, “Running and Confirming Your Installation”. The same verification script applies to both deploy types.

1.2.2.1. Pseudo-distributed

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

First, if you want to run on HDFS rather than on the local filesystem, setup your HDFS. You can set up HDFS also in pseudo-distributed mode (TODO: Add pointer to HOWTO doc; the hadoop site doesn't have any any more). Ensure you have a working HDFS before proceeding.

Next, configure HBase. Edit conf/hbase-site.xml. This is the file into which you add local customizations and overrides. At a minimum, you must tell HBase to run in (pseudo-)distributed mode rather than in default standalone mode. To do this, set the hbase.cluster.distributed property to true (Its default is false). The absolute bare-minimum hbase-site.xml is therefore as follows:

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

        

With this configuration, HBase will start up an HBase Master process, a ZooKeeper server, and a RegionServer process running against the local filesystem writing to wherever your operating system stores temporary files into a directory named hbase-YOUR_USER_NAME.

Such a setup, using the local filesystem and writing to the operating systems's temporary directory is an ephemeral setup; the Hadoop local filesystem -- which is what HBase uses when it is writing the local filesytem -- would lose data unless the system was shutdown properly in versions of HBase before 0.98.4 and 1.0.0 (see HBASE-11218 Data loss in HBase standalone mode). Writing to the operating system's temporary directory can also make for data loss when the machine is restarted as this directory is usually cleared on reboot. For a more permanent setup, see the next example where we make use of an instance of HDFS; HBase data will be written to the Hadoop distributed filesystem rather than to the local filesystem's tmp directory.

In this conf/hbase-site.xml example, the hbase.rootdir property points to the local HDFS instance homed on the node h-24-30.example.com.

Let HBase create ${hbase.rootdir}

Let HBase create the hbase.rootdir directory. If you don't, you'll get warning saying HBase needs a migration run because the directory is missing files expected by HBase (it'll create them if you let it).

<configuration>
  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://h-24-30.sfo.stumble.net:8020/hbase</value>
  </property>
  <property>
    <name>hbase.cluster.distributed</name>
    <value>true</value>
  </property>
</configuration>
        

Now skip to Section 1.2.3, “Running and Confirming Your Installation” for how to start and verify your pseudo-distributed install. [9]

1.2.2.1.1. Pseudo-distributed Extras
1.2.2.1.1.1. Startup

To start up the initial HBase cluster...

% bin/start-hbase.sh

To start up an extra backup master(s) on the same server run...

% bin/local-master-backup.sh start 1

... the '1' means use ports 16001 & 16011, and this backup master's logfile will be at logs/hbase-${USER}-1-master-${HOSTNAME}.log.

To startup multiple backup masters run...

% bin/local-master-backup.sh start 2 3

You can start up to 9 backup masters (10 total).

To start up more regionservers...

% bin/local-regionservers.sh start 1

... where '1' means use ports 16201 & 16301 and its logfile will be at `logs/hbase-${USER}-1-regionserver-${HOSTNAME}.log.

To add 4 more regionservers in addition to the one you just started by running...

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

This supports up to 99 extra regionservers (100 total).

1.2.2.1.1.2. Stop

Assuming you want to stop master backup # 1, run...

% cat /tmp/hbase-${USER}-1-master.pid |xargs kill -9

Note that bin/local-master-backup.sh stop 1 will try to stop the cluster along with the master.

To stop an individual regionserver, run...

% bin/local-regionservers.sh stop 1

1.2.2.2. Fully-distributed

For running a fully-distributed operation on more than one host, make the following configurations. In hbase-site.xml, add the property hbase.cluster.distributed and set it to true and point the HBase hbase.rootdir at the appropriate HDFS NameNode and location in HDFS where you would like HBase to write data. For example, if you namenode were running at namenode.example.org on port 8020 and you wanted to home your HBase in HDFS at /hbase, make the following configuration.

<configuration>
  ...
  <property>
    <name>hbase.rootdir</name>
    <value>hdfs://namenode.example.org: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>

        
1.2.2.2.1. regionservers

In addition, a fully-distributed mode requires that you modify conf/regionservers. The Section 1.4.1.2, “regionservers file lists all hosts that you would have running HRegionServers, one host per line (This file in HBase is like the Hadoop slaves file). All servers listed in this file will be started and stopped when HBase cluster start or stop is run.

1.2.2.2.2. ZooKeeper and HBase

See section ??? for ZooKeeper setup for HBase.

1.2.2.2.3. HDFS Client Configuration

Of note, if you have made HDFS client configuration on your Hadoop cluster -- i.e. configuration you want HDFS clients to use as opposed to server-side configurations -- HBase will not see this configuration unless you do one of the following:

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

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

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

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

1.2.3. Running and Confirming Your Installation

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

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

Start HBase with the following command:

bin/start-hbase.sh

Run the above from the HBASE_HOME directory.

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

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

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

Once HBase has started, see the ??? 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.

1.3. Configuration Files

1.3.1. hbase-site.xml and hbase-default.xml

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

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

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

HBase Default Configuration

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

hbase.tmp.dir

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

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

hbase.rootdir

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

Default. ${hbase.tmp.dir}/hbase

hbase.cluster.distributed

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

Default. false

hbase.zookeeper.quorum

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

Default. localhost

hbase.local.dir

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

Default. ${hbase.tmp.dir}/local/

hbase.master.info.port

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

Default. 16010

hbase.master.info.bindAddress

The bind address for the HBase Master web UI

Default. 0.0.0.0

hbase.master.logcleaner.plugins

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

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

hbase.master.logcleaner.ttl

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

Default. 600000

hbase.master.hfilecleaner.plugins

A comma-separated list of HFileCleanerDelegate 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 HFileCleanerDelegate, just put it in HBase's classpath and add the fully qualified class name here. Always add the above default log cleaners in the list as they will be overwritten in hbase-site.xml.

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

hbase.master.catalog.timeout

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

Default. 600000

hbase.master.infoserver.redirect

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

Default. true

hbase.regionserver.port

The port the HBase RegionServer binds to.

Default. 16020

hbase.regionserver.info.port

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

Default. 16030

hbase.regionserver.info.bindAddress

The address for the HBase RegionServer web UI

Default. 0.0.0.0

hbase.regionserver.info.port.auto

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

Default. false

hbase.regionserver.handler.count

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

Default. 30

hbase.regionserver.msginterval

Interval between messages from the RegionServer to Master in milliseconds.

Default. 3000

hbase.regionserver.regionSplitLimit

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

Default. 2147483647

hbase.regionserver.logroll.period

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

Default. 3600000

hbase.regionserver.logroll.errors.tolerated

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

Default. 2

hbase.regionserver.hlog.reader.impl

The HLog file reader implementation.

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

hbase.regionserver.hlog.writer.impl

The HLog file writer implementation.

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

hbase.master.distributed.log.replay

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

Default. true

hbase.regionserver.global.memstore.size

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

Default. 0.4

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

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

Default. 0.95

hbase.regionserver.global.memstore.size

Maximum size of all memstores in a region server before new updates are blocked and flushes are forced. Defaults to 40% of heap (0.4). Updates are blocked and region level flushes are forced until size of all memstores in a region server hits hbase.regionserver.global.memstore.lowerLimit.

Default. 0.4

hbase.regionserver.optionalcacheflushinterval

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

Default. 3600000

hbase.regionserver.catalog.timeout

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

Default. 600000

hbase.regionserver.dns.interface

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

Default. default

hbase.regionserver.dns.nameserver

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

Default. default

hbase.regionserver.region.split.policy

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

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

zookeeper.session.timeout

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

Default. 90000

zookeeper.znode.parent

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

Default. /hbase

zookeeper.znode.rootserver

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

Default. root-region-server

zookeeper.znode.acl.parent

Root ZNode for access control lists.

Default. acl

hbase.zookeeper.dns.interface

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

Default. default

hbase.zookeeper.dns.nameserver

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

Default. default

hbase.zookeeper.peerport

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

Default. 2888

hbase.zookeeper.leaderport

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

Default. 3888

hbase.zookeeper.useMulti

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

Default. false

hbase.config.read.zookeeper.config

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

Default. false

hbase.zookeeper.property.initLimit

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

Default. 10

hbase.zookeeper.property.syncLimit

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

Default. 5

hbase.zookeeper.property.dataDir

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

Default. ${hbase.tmp.dir}/zookeeper

hbase.zookeeper.property.clientPort

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

Default. 2181

hbase.zookeeper.property.maxClientCnxns

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

Default. 300

hbase.client.write.buffer

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

Default. 2097152

hbase.client.pause

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

Default. 100

hbase.client.retries.number

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

Default. 35

hbase.client.max.total.tasks

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

Default. 100

hbase.client.max.perserver.tasks

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

Default. 5

hbase.client.max.perregion.tasks

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

Default. 1

hbase.client.scanner.caching

Number of rows that will be fetched when calling next on a scanner if it is not served from (local, client) memory. Higher caching values will enable faster scanners but will eat up more memory and some calls of next may take longer and longer times when the cache is empty. Do not set this value such that the time between invocations is greater than the scanner timeout; i.e. hbase.client.scanner.timeout.period

Default. 100

hbase.client.keyvalue.maxsize

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

Default. 10485760

hbase.client.scanner.timeout.period

Client scanner lease period in milliseconds.

Default. 60000

hbase.client.localityCheck.threadPoolSize

Default. 2

hbase.bulkload.retries.number

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

Default. 0

hbase.balancer.period

Period at which the region balancer runs in the Master.

Default. 300000

hbase.balancer.backupMasterWeight

Used to control how many regions the region balancer can assign to backup Masters, compared to normal region servers. The default value 1 means a backup Master can host as many regions as a normal region server. The bigger the weight, the less the regions a backup Master will host. If the weight is less than 1, the balancer will not assign any region to any backup Master

Default. 1

hbase.regions.slop

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

Default. 0.2

hbase.server.thread.wakefrequency

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

Default. 10000

hbase.server.versionfile.writeattempts

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

Default. 3

hbase.hregion.memstore.flush.size

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

Default. 134217728

hbase.hregion.preclose.flush.size

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

Default. 5242880

hbase.hregion.memstore.block.multiplier

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

Default. 4

hbase.hregion.memstore.mslab.enabled

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

Default. true

hbase.hregion.max.filesize

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

Default. 10737418240

hbase.hregion.majorcompaction

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

Default. 604800000

hbase.hregion.majorcompaction.jitter

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

Default. 0.50

hbase.hstore.compactionThreshold

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

Default. 3

hbase.hstore.flusher.count

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

Default. 2

hbase.hstore.blockingStoreFiles

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

Default. 10

hbase.hstore.blockingWaitTime

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

Default. 90000

hbase.hstore.compaction.max

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

Default. 10

hbase.hstore.compaction.kv.max

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

Default. 10

hbase.storescanner.parallel.seek.enable

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

Default. false

hbase.storescanner.parallel.seek.threads

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

Default. 10

hfile.block.cache.size

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

Default. 0.4

hfile.block.index.cacheonwrite

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

Default. false

hfile.index.block.max.size

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

Default. 131072

hfile.format.version

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

Default. 3

hfile.block.bloom.cacheonwrite

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

Default. false

io.storefile.bloom.block.size

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

Default. 131072

hbase.rs.cacheblocksonwrite

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

Default. false

hbase.rpc.server.engine

Implementation of org.apache.hadoop.hbase.ipc.RpcServerEngine to be used for server RPC call marshalling.

Default. org.apache.hadoop.hbase.ipc.ProtobufRpcServerEngine

hbase.rpc.timeout

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

Default. 60000

hbase.rpc.shortoperation.timeout

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

Default. 10000

hbase.ipc.client.tcpnodelay

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

Default. true

hbase.master.keytab.file

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

Default. 

hbase.master.kerberos.principal

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

Default. 

hbase.regionserver.keytab.file

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

Default. 

hbase.regionserver.kerberos.principal

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

Default. 

hadoop.policy.file

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

Default. hbase-policy.xml

hbase.superuser

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

Default. 

hbase.auth.key.update.interval

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

Default. 86400000

hbase.auth.token.max.lifetime

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

Default. 604800000

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

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

Default. false

hbase.coprocessor.region.classes

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

Default. 

hbase.rest.port

The port for the HBase REST server.

Default. 8080

hbase.rest.readonly

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

Default. false

hbase.rest.threads.max

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

Default. 100

hbase.rest.threads.min

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

Default. 2

hbase.rest.support.proxyuser

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

Default. false

hbase.defaults.for.version.skip

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

Default. false

hbase.coprocessor.master.classes

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

Default. 

hbase.coprocessor.abortonerror

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

Default. true

hbase.online.schema.update.enable

Set true to enable online schema changes.

Default. true

hbase.table.lock.enable

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

Default. true

hbase.table.max.rowsize

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

Default. 1073741824

hbase.thrift.minWorkerThreads

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

Default. 16

hbase.thrift.maxWorkerThreads

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

Default. 1000

hbase.thrift.maxQueuedRequests

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

Default. 1000

hbase.thrift.htablepool.size.max

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

Default. 1000

hbase.regionserver.thrift.framed

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

Default. false

hbase.regionserver.thrift.framed.max_frame_size_in_mb

Default frame size when using framed transport

Default. 2

hbase.regionserver.thrift.compact

Use Thrift TCompactProtocol binary serialization protocol.

Default. false

hbase.offheapcache.percentage

The percentage of the off heap space (-XX:MaxDirectMemorySize) to be allocated towards the experimental off heap "SlabCache" (This is different to the BucketCache -- see the package javadoc for org.apache.hadoop.hbase.io.hfile for more on your options). If you desire the cache to be disabled, simply set this value to 0.

Default. 0

hbase.data.umask.enable

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

Default. false

hbase.data.umask

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

Default. 000

hbase.metrics.showTableName

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

Default. true

hbase.metrics.exposeOperationTimes

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

Default. true

hbase.snapshot.enabled

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

Default. true

hbase.snapshot.restore.take.failsafe.snapshot

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

Default. true

hbase.snapshot.restore.failsafe.name

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

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

hbase.server.compactchecker.interval.multiplier

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

Default. 1000

hbase.lease.recovery.timeout

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

Default. 900000

hbase.lease.recovery.dfs.timeout

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

Default. 64000

hbase.column.max.version

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

Default. 1

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

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

Default. 131072

hbase.regionserver.checksum.verify

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

Default. true

hbase.hstore.bytes.per.checksum

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

Default. 16384

hbase.hstore.checksum.algorithm

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

Default. CRC32

hbase.status.published

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

Default. false

hbase.status.publisher.class

Implementation of the status publication with a multicast message.

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

hbase.status.listener.class

Implementation of the status listener with a multicast message.

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

hbase.status.multicast.address.ip

Multicast address to use for the status publication by multicast.

Default. 226.1.1.3

hbase.status.multicast.address.port

Multicast port to use for the status publication by multicast.

Default. 16100

hbase.dynamic.jars.dir

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

Default. ${hbase.rootdir}/lib

hbase.security.authentication

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

Default. simple

hbase.rest.filter.classes

Servlet filters for REST service.

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

hbase.master.loadbalancer.class

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

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

hbase.security.exec.permission.checks

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

Default. false

hbase.procedure.regionserver.classes

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

Default. 

hbase.procedure.master.classes

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

Default. 

hbase.coordinated.state.manager.class

Fully qualified name of class implementing coordinated state manager.

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

hbase.http.filter.initializers

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

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

hbase.http.max.threads

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

Default. 10

hbase.http.staticuser.user

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

Default. dr.stack

1.3.2. hbase-env.sh

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

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

1.3.3. log4j.properties

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

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

1.3.4. Client configuration and dependencies connecting to an HBase cluster

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

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

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

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

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

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

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

1.3.4.1. Java client configuration

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

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

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

1.4. Example Configurations

1.4.1. Basic Distributed HBase Install

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

1.4.1.1. hbase-site.xml


<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
  <property>
    <name>hbase.zookeeper.quorum</name>
    <value>example1,example2,example3</value>
    <description>The directory shared by RegionServers.
    </description>
  </property>
  <property>
    <name>hbase.zookeeper.property.dataDir</name>
    <value>/export/zookeeper</value>
    <description>Property from ZooKeeper's 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>

        

1.4.1.2. regionservers

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

example1
example2
example3
example4
example5
example6
example7
example8
example9
        

1.4.1.3. hbase-env.sh

Below we use a diff to show the differences from default in the hbase-env.sh file. Here we are setting the HBase heap to be 4G instead of the default 1G.


$ git diff hbase-env.sh
diff --git a/conf/hbase-env.sh b/conf/hbase-env.sh
index e70ebc6..96f8c27 100644
--- a/conf/hbase-env.sh
+++ b/conf/hbase-env.sh
@@ -31,7 +31,7 @@ export JAVA_HOME=/usr/lib//jvm/java-6-sun/
 # export HBASE_CLASSPATH=

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

 # Extra Java runtime options.
 # Below are what we set by default.  May only work with SUN JVM.

        

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

1.5. The Important Configurations

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

1.5.1. Required Configurations

Review the Section 1.1.2, “Operating System” and Section 1.1.3, “Hadoop” sections.

1.5.1.1. Big Cluster Configurations

If a cluster with a lot of regions, it is possible if an eager beaver regionserver checks in soon after master start while all the rest in the cluster are laggardly, this first server to checkin will be assigned all regions. If lots of regions, this first server could buckle under the load. To prevent the above scenario happening up the hbase.master.wait.on.regionservers.mintostart from its default value of 1. See HBASE-6389 Modify the conditions to ensure that Master waits for sufficient number of Region Servers before starting region assignments for more detail.

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

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

1.5.2. Recommended Configurations

1.5.2.1. ZooKeeper Configuration

1.5.2.1.1. zookeeper.session.timeout

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

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

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

1.5.2.1.2. Number of ZooKeeper Instances

See ???.

1.5.2.2. HDFS Configurations

1.5.2.2.1. dfs.datanode.failed.volumes.tolerated

This is the "...number of volumes that are allowed to fail before a datanode stops offering service. By default any volume failure will cause a datanode to shutdown" from the hdfs-default.xml description. If you have > three or four disks, you might want to set this to 1 or if you have many disks, two or more.

1.5.2.3. hbase.regionserver.handler.count

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

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

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

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

1.5.2.4. Configuration for large memory machines

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

1.5.2.5. Compression

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

See ??? for more information.

1.5.2.6. Configuring the size and number of WAL files

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

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

1.5.2.7. Managed Splitting

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

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

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

Automatic Splitting Is Recommended

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

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

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

1.5.2.8. Managed Compactions

A common administrative technique is to manage major compactions manually, rather than letting HBase do it. By default, HConstants.MAJOR_COMPACTION_PERIOD is one day and major compactions may kick in when you least desire it - especially on a busy system. To turn off automatic major compactions set the value to 0.

It is important to stress that major compactions are absolutely necessary for StoreFile cleanup, the only variant is when they occur. They can be administered through the HBase shell, or via HBaseAdmin.

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

1.5.2.9. Speculative Execution

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

1.5.3. Other Configurations

1.5.3.1. Balancer

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

See ??? for more information on the LoadBalancer.

1.5.3.2. Disabling Blockcache

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

1.5.3.3. Nagle's or the small package problem

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

1.5.3.4. Better Mean Time to Recover (MTTR)

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

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

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

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

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

1.5.3.5. JMX

JMX(Java Management Extensions) provides built-in instrumentation that enables you to monitor and manage the Java VM. To enable monitoring and management from remote systems, you need to set system property com.sun.management.jmxremote.port(the port number through which you want to enable JMX RMI connections) when you start the Java VM. See official document for more information. Historically, besides above port mentioned, JMX opens 2 additional random TCP listening ports, which could lead to port conflict problem.(See HBASE-10289 for details)

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

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

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

Currently it supports Master and RegionServer Java VM. The reason why you only configure coprocessor for 'regionserver' is that, starting from HBase 0.99, a Master IS also a RegionServer. (See HBASE-10569 for more information.) By default, the JMX listens on TCP port 10102, you can further configure the port using below properties:

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

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

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

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

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

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

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

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

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

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

And then update hbase-env.sh like below:

export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=true                         \
                       -Djavax.net.ssl.keyStore=/home/tianq/myKeyStore                 \
                       -Djavax.net.ssl.keyStorePassword=your_password_in_step_#1       \
                       -Dcom.sun.management.jmxremote.authenticate=true                \
                       -Dcom.sun.management.jmxremote.password.file=your_password file \
                       -Dcom.sun.management.jmxremote.access.file=your_access_file"

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

Finally start jconsole on client using the key store:

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

NOTE: for HBase 0.98, To enable the HBase JMX implementation on Master, you also need to add below property in hbase-site.xml:

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

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



[1] Be careful editing XML. Make sure you close all elements. Run your file through xmllint or similar to ensure well-formedness of your document after an edit session.

[2] The hadoop-dns-checker tool can be used to verify DNS is working correctly on the cluster. The project README file provides detailed instructions on usage.

[3] See Jack Levin's major hdfs issues note up on the user list.

[4] The requirement that a database requires upping of system limits is not peculiar to Apache HBase. See for example the section Setting Shell Limits for the Oracle User in Short Guide to install Oracle 10 on Linux.

[5] A useful read setting config on you hadoop cluster is Aaron Kimballs' Configuration Parameters: What can you just ignore?

[7] The Cloudera blog post An update on Apache Hadoop 1.0 by Charles Zedlweski has a nice exposition on how all the Hadoop versions relate. Its worth checking out if you are having trouble making sense of the Hadoop version morass.

[8] The pseudo-distributed vs fully-distributed nomenclature comes from Hadoop.

[9] See Section 1.2.2.1.1, “Pseudo-distributed Extras” for notes on how to start extra Masters and RegionServers when running pseudo-distributed.

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