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
1.3.4. 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 mimimum 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/ -- 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.

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. Java 7 should work and can even be faster than Java 6, but almost all testing has been done on the latter at this point.

1.1.2. Operating System 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. 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. Loopback IP

HBase expects the loopback IP address to be See Section, “Loopback IP” 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!  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...

      2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream
      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] 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

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! Windows

Apache HBase has been 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, 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.

1.1.3. Hadoop

Selecting a Hadoop version is critical for your HBase deployment. 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

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. if you can.

Table 1.1. Hadoop version support matrix

Hadoop-0.22.x SXX
Hadoop-1.0.0-1.0.2[a] SSX
Hadoop-1.1.x NTSS
Hadoop-0.23.x XSNT
Hadoop-2.0.x-alpha XNTX
Hadoop-2.1.0-beta XNTS
Hadoop-2.x XNTS

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


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

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. 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) Apache HBase 0.96

Apache HBase 0.96.0 requires Apache Hadoop 1.x at a minimum, and it can run equally well on hadoop-2.0. As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required. 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]. 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, and Hadoop 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 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).


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 configuration. 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 ???. dfs.datanode.max.xcievers

An Hadoop HDFS datanode has an upper bound on the number of files that it will serve at any one time. The upper bound parameter is called xcievers (yes, this is misspelled). Again, before doing any loading, make sure you have configured Hadoop's conf/hdfs-site.xml setting the xceivers value to at least the following:


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

Not having this configuration in place makes for strange looking failures. Eventually you'll see a complain in the datanode logs complaining about the xcievers exceeded, but on the run up to this one manifestation is 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: No live nodes contain current block. Will get new block locations from namenode and retry... [8]

See also ???

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/ 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 [9].

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. 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. 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:


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 does not support sync so unless the system is shutdown properly, the data will be lost. 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

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).


Now skip to Section 1.2.3, “Running and Confirming Your Installation” for how to start and verify your pseudo-distributed install. [10] Pseudo-distributed Extras Startup

To start up the initial HBase cluster...

% bin/

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

% bin/ start 1

... the '1' means use ports 60001 & 60011, and this backup master's logfile will be at logs/hbase-${USER}-1-master-${HOSTNAME}.log.

To startup multiple backup masters run...

% bin/ start 2 3

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

To start up more regionservers...

% bin/ start 1

where '1' means use ports 60201 & 60301 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/ start 2 3 4 5

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

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

% cat /tmp/hbase-${USER} |xargs kill -9

Note that bin/ stop 1 will try to stop the cluster along with the master.

To stop an individual regionserver, run...

% bin/ stop 1

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 on port 8020 and you wanted to home your HBase in HDFS at /hbase, make the following configuration.

    <description>The directory shared by RegionServers.
    <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
</configuration> regionservers

In addition, a fully-distributed mode requires that you modify conf/regionservers. The Section, “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. ZooKeeper and HBase

See section ??? for ZooKeeper setup for HBase. 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

  • 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/ 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:

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 60010 (HBase RegionServers listen on port 60020 by default and put up an informational http server at 60030). If the Master were running on a host named on the default port, to see the Master's homepage you'd point your browser at

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/
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 Section, “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

HBase Default Configuration

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


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

Default: ${}/hbase-${}


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 on port 9000, set this value to: hdfs:// 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


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


Comma separated list of servers in the ZooKeeper ensemble (This config. should have been named hbase.zookeeper.ensemble). For example, ",,". 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 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


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

Default: ${hbase.tmp.dir}/local/


The port the HBase Master should bind to.

Default: 60000

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

Default: 60010

The bind address for the HBase Master web UI



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


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

Default: 600000


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


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

Default: 600000

If abort immediately for the expired master without trying to recover its zk session.

Default: false


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

Default: default


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

Default: default


The port the HBase RegionServer binds to.

Default: 60020

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

Default: 60030

The address for the HBase RegionServer web UI


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

Default: false


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

Default: 30


Interval between messages from the RegionServer to Master in milliseconds.

Default: 3000


Sync the HLog to the HDFS after this interval if it has not accumulated enough entries to trigger a sync. Units: milliseconds.

Default: 1000


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


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

Default: 3600000


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


The HLog file reader implementation.

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


The HLog file writer implementation.

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

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

Default: 0.4

Maximum size of all memstores in a region server before flushes are forced. Defaults to 38% of heap. This value equal to causes the minimum possible flushing to occur when updates are blocked due to memstore limiting.

Default: 0.38


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


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

Default: 600000


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

Default: default


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


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 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 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


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


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


Root ZNode for access control lists.

Default: acl


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

Default: default


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


Port used by ZooKeeper peers to talk to each other. See for more information.

Default: 2888


Port used by ZooKeeper for leader election. See for more information.

Default: 3888


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

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

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

Default: 10

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

Default: 5

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

Default: ${hbase.tmp.dir}/zookeeper

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

Default: 2181

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


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


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


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

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

Default: 100


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

Default: 5


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


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


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


Client scanner lease period in milliseconds.

Default: 60000


Default: 2


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


Period at which the region balancer runs in the Master.

Default: 300000


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

Default: 0.2


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

Default: 10000


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


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


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


Block updates if memstore has hbase.hregion.block.memstore time hbase.hregion.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: 2


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


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


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


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


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


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


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


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

Default: 10


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

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

Default: false

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

Default: 10


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


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

Default: false


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


The HFile format version to use for new files. Set this to 1 to test backwards-compatibility. The default value of this option should be consistent with FixedFileTrailer.MAX_VERSION.

Default: 2


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

Default: false


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

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

Default: false


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

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


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


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


Set no delay on rpc socket connections. See

Default: true


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



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



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


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.



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

Default: 86400000


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

Default: 604800000


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


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.


The port for the HBase REST server.

Default: 8080

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

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

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


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


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.



Set to true to cause the hosting server (master or regionserver) to abort if a coprocessor throws a Throwable object that is not IOException or a subclass of IOException. Setting it to true might be useful in development environments where one wants to terminate the server as soon as possible to simplify coprocessor failure analysis.

Default: false

Set true to enable online schema changes.

Default: true


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


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

Default: 16


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


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


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


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


Default frame size when using framed transport

Default: 2


Use Thrift TCompactProtocol binary serialization protocol.

Default: false


The amount of off heap space to be allocated towards the experimental off heap cache. If you desire the cache to be disabled, simply set this value to 0.

Default: 0

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

Default: false

File permissions that should be used to write data files when is true

Default: 000


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, 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


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


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

Default: true


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

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

Default: hbase-failsafe-{}-{restore.timestamp}


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

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

Default: 900000

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.socket.timeout. See the end of HBASE-8389 for more.

Default: 64000

If the DFSClient configuration 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


If set to true, HBase will read data and then verify checksums for hfile blocks. Checksum verification inside HDFS will be switched off. If the hbase-checksum verification fails, then it will switch back to using HDFS checksums.

Default: true


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

Default: 16384


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

Default: CRC32


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


Implementation of the status publication with a multicast message.

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


Implementation of the status listener with a multicast message.

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


Multicast address to use for the status publication by multicast.



Multicast port to use for the status publication by multicast.

Default: 6100


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

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

Default: simple

Servlet filters for REST service.



Class used to execute the regions balancing when the period occurs. See the class comment for more on how it works It replaces the DefaultLoadBalancer as the default (since renamed as the SimpleLoadBalancer).

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


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 garbarge 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/ 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.


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"?>
    <description>The directory shared by region servers.
</configuration> 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 -- found in the HBase conf directory might look like. hbase-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
    <description>The directory shared by RegionServers.
    <description>Property from ZooKeeper's config zoo.cfg.
    The directory where the snapshot is stored.
    <description>The directory shared by RegionServers.
    <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
</configuration> regionservers

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


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

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

 # 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. 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. 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 ZooKeeper Configuration 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. Number of ZooKeeper Instances

See ???. HDFS Configurations dfs.datanode.failed.volumes.tolerated

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

Rather than let HBase auto-split your Regions, manage the splitting manually [11]. With growing amounts of data, splits will continually be needed. Since you always know exactly what regions you have, long-term debugging and profiling is much easier with manual splits. It is hard to trace the logs to understand region level problems if it keeps splitting and getting renamed. Data offlining bugs + unknown number of split regions == oh crap! If an HLog or StoreFile was mistakenly unprocessed by HBase due to a weird bug and you notice it a day or so later, you can be assured that the regions specified in these files are the same as the current regions and you have less headaches trying to restore/replay your data. You can finely tune your compaction algorithm. With roughly uniform data growth, it's easy to cause split / compaction storms as the regions all roughly hit the same data size at the same time. With manual splits, you can let staggered, time-based major compactions spread out your network IO load.

How do I turn off automatic splitting? Automatic splitting is determined by the configuration value hbase.hregion.max.filesize. It is not recommended that you set this to Long.MAX_VALUE in case you forget about manual splits. A suggested setting is 100GB, which would result in > 1hr major compactions if reached.

What's the optimal number of pre-split regions to create? Mileage will vary depending upon your application. You could start low with 10 pre-split regions / server and watch as data grows over time. It's better to err on the side of too little regions and rolling split later. A more complicated answer is that this depends upon the largest storefile in your region. With a growing data size, this will get larger over time. You want the largest region to be just big enough that the Store compact selection algorithm only compacts it due to a timed major. If you don't, your cluster can be prone to compaction storms as the algorithm decides to run major compactions on a large series of regions all at once. Note that compaction storms are due to the uniform data growth, not the manual split decision.

If you pre-split your regions too thin, you can increase the major compaction interval by configuring HConstants.MAJOR_COMPACTION_PERIOD. If your data size grows too large, use the (post-0.90.0 HBase) org.apache.hadoop.hbase.util.RegionSplitter script to perform a network IO safe rolling split of all regions. 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 ??? 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 and mapred.reduce.tasks.speculative.execution to false.

1.5.3. Other Configurations 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. 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). 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. Better Mean Time to Recover (MTTR)

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.

[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] See Hadoop HDFS: Deceived by Xciever for an informative rant on xceivering.

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

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

[11] What follows is taken from the javadoc at the head of the org.apache.hadoop.hbase.util.RegionSplitter tool added to HBase post-0.90.0 release.

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