Table of Contents
This chapter is the Not-So-Quick start guide to HBase configuration.
Please read this chapter carefully and ensure that all requirements have been satisfied. Failure to do so will cause you (and us) grief debugging strange errors and/or data loss.
HBase uses the same configuration system as 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.
Just like Hadoop, HBase requires java 6 from Oracle. Usually you'll want to use the latest version available except the problematic u18 (u24 is the latest version as of this writing).
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").
HBase uses the local hostname to self-report it's IP address. Both forward and reverse DNS resolving should work.
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.
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!
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 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
[2]
[3].
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. [4]
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!
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.
This version of HBase will only run on Hadoop
0.20.x. It will not run on hadoop 0.21.x (but may run on 0.22.x/0.23.x).
HBase will lose data unless it is running on an HDFS that has a durable
sync
. Hadoop 0.20.2, Hadoop 0.20.203.0, and Hadoop 0.20.204.0
DO NOT have this attribute.
Currently only Hadoop versions 0.20.205.x or any release in excess of this
version has a durable sync. You have to explicitly enable it though by
setting dfs.support.append
equal to true on both
the client side -- in hbase-site.xml
though it should
be on in your base-default.xml
file -- and on the
serverside in hdfs-site.xml
(You will have to restart
your cluster after setting this configuration). Ignore the chicken-little
comment you'll find in the hdfs-site.xml
in the
description for this configuration; it says it is not enabled because there
are “... bugs in the 'append code' and is not supported in any production
cluster.” because it is not true (I'm sure there are bugs but the
append code has been running in production at large scale deploys and is on
by default in the offerings of hadoop by commercial vendors)
[5]
[6][7].
Or use the Cloudera or MapR distributions. Cloudera' CDH3 is Apache Hadoop 0.20.x plus patches including all of the branch-0.20-append additions needed to add a durable sync. Use the released, most recent version of CDH3.
MapR includes a commercial, reimplementation of HDFS. It has a durable sync as well as some other interesting features that are not yet in Apache Hadoop. Their M3 product is free to use and unlimited.
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.
HBase will run on any Hadoop 0.20.x that incorporates Hadoop security features -- e.g. Y! 0.20S or CDH3B3 -- as long as you do as suggested above and replace the Hadoop jar that ships with HBase with the secure version.
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:
<property> <name>dfs.datanode.max.xcievers</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. 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:
java.io.IOException: No live nodes contain current block. Will get new
block locations from namenode and retry...
[8]
HBase has two run modes: Section 1.4.1, “Standalone HBase” and Section 1.4.2, “Distributed”. Out of the box, HBase runs in
standalone mode. To set up a distributed deploy, you will need to
configure HBase by editing files in the HBase conf
directory.
Whatever your mode, you will need to 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.
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.
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].
Distributed modes require an instance of the Hadoop Distributed File System (HDFS). See the Hadoop requirements and instructions for how to set up a HDFS. Before proceeding, ensure you have an appropriate, working 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 1.4.3, “Running and Confirming Your Installation”. The same verification script applies to both deploy types.
A pseudo-distributed mode is simply a 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.
Once you have confirmed your HDFS setup, edit
conf/hbase-site.xml
. This is the file into
which you add local customizations and overrides for
<xreg></xreg> and Section 1.4.2.2.3, “HDFS Client Configuration”. Point HBase at the running Hadoop HDFS
instance by setting the hbase.rootdir
property.
This property points HBase at the Hadoop filesystem instance to use.
For example, adding the properties below to your
hbase-site.xml
says that HBase should use the
/hbase
directory in the HDFS whose namenode is
at port 8020 on your local machine, and that it should run with one
replica only (recommended for pseudo-distributed mode):
<configuration> ... <property> <name>hbase.rootdir</name> <value>hdfs://localhost:8020/hbase</value> <description>The directory shared by RegionServers. </description> </property> <property> <name>dfs.replication</name> <value>1</value> <description>The replication count for HLog and HFile storage. Should not be greater than HDFS datanode count. </description> </property> ... </configuration>
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).
Above we bind to localhost
. This means
that a remote client cannot connect. Amend accordingly, if you
want to connect from a remote location.
Now skip to Section 1.4.3, “Running and Confirming Your Installation” for how to start and verify your pseudo-distributed install. [10]
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>
In addition, a fully-distributed mode requires that you
modify conf/regionservers
. The
Section 1.7.1.2, “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.
See section Section 1.5, “ZooKeeper” for ZooKeeper setup for HBase.
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.
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.shRun 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
master.example.org
on the default port, to see the
Master's homepage you'd point your browser at
http://master.example.org:60010
.
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.
A distributed HBase depends on a running ZooKeeper cluster.
All participating nodes and clients need to be able to access the
running ZooKeeper ensemble. HBase by default manages a ZooKeeper
"cluster" for you. It will start and stop the ZooKeeper ensemble
as part of the HBase start/stop process. You can also manage the
ZooKeeper ensemble independent of HBase and just point HBase at
the cluster it should use. To toggle HBase management of
ZooKeeper, use the HBASE_MANAGES_ZK
variable in
conf/hbase-env.sh
. This variable, which
defaults to true
, tells HBase whether to
start/stop the ZooKeeper ensemble servers as part of HBase
start/stop.
When HBase manages the ZooKeeper ensemble, you can specify
ZooKeeper configuration using its native
zoo.cfg
file, or, the easier option is to
just specify ZooKeeper options directly in
conf/hbase-site.xml
. A ZooKeeper
configuration option can be set as a property in the HBase
hbase-site.xml
XML configuration file by
prefacing the ZooKeeper option name with
hbase.zookeeper.property
. For example, the
clientPort
setting in ZooKeeper can be changed
by setting the
hbase.zookeeper.property.clientPort
property.
For all default values used by HBase, including ZooKeeper
configuration, see Section 1.6.1.1, “HBase Default Configuration”. Look for the
hbase.zookeeper.property
prefix [11]
You must at least list the ensemble servers in
hbase-site.xml
using the
hbase.zookeeper.quorum
property. This property
defaults to a single ensemble member at
localhost
which is not suitable for a fully
distributed HBase. (It binds to the local machine only and remote
clients will not be able to connect).
You can run a ZooKeeper ensemble that comprises 1 node only but in production it is recommended that you run a ZooKeeper ensemble of 3, 5 or 7 machines; the more members an ensemble has, the more tolerant the ensemble is of host failures. Also, run an odd number of machines. There can be no quorum if the number of members is an even number. Give each ZooKeeper server around 1GB of RAM, and if possible, its own dedicated disk (A dedicated disk is the best thing you can do to ensure a performant ZooKeeper ensemble). For very heavily loaded clusters, run ZooKeeper servers on separate machines from RegionServers (DataNodes and TaskTrackers).
For example, to have HBase manage a ZooKeeper quorum on
nodes rs{1,2,3,4,5}.example.com, bound to
port 2222 (the default is 2181) ensure
HBASE_MANAGE_ZK
is commented out or set to
true
in conf/hbase-env.sh
and then edit conf/hbase-site.xml
and set
hbase.zookeeper.property.clientPort
and
hbase.zookeeper.quorum
. You should also set
hbase.zookeeper.property.dataDir
to other than
the default as the default has ZooKeeper persist data under
/tmp
which is often cleared on system
restart. In the example below we have ZooKeeper persist to
/user/local/zookeeper
.
<configuration> ... <property> <name>hbase.zookeeper.property.clientPort</name> <value>2222</value> <description>Property from ZooKeeper's config zoo.cfg. The port at which the clients will connect. </description> </property> <property> <name>hbase.zookeeper.quorum</name> <value>rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com</value> <description>Comma separated list of servers in the ZooKeeper Quorum. 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 quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh this is the list of servers which we will start/stop ZooKeeper on. </description> </property> <property> <name>hbase.zookeeper.property.dataDir</name> <value>/usr/local/zookeeper</value> <description>Property from ZooKeeper's config zoo.cfg. The directory where the snapshot is stored. </description> </property> ... </configuration>
To point HBase at an existing ZooKeeper cluster, one that
is not managed by HBase, set HBASE_MANAGES_ZK
in conf/hbase-env.sh
to false
... # Tell HBase whether it should manage it's own instance of Zookeeper or not. export HBASE_MANAGES_ZK=false
Next set ensemble locations
and client port, if non-standard, in
hbase-site.xml
, or add a suitably
configured zoo.cfg
to HBase's
CLASSPATH
. HBase will prefer the
configuration found in zoo.cfg
over any
settings in hbase-site.xml
.
When HBase manages ZooKeeper, it will start/stop the ZooKeeper servers as a part of the regular start/stop scripts. If you would like to run ZooKeeper yourself, independent of HBase start/stop, you would do the following
${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper
Note that you can use HBase in this manner to spin up a
ZooKeeper cluster, unrelated to HBase. Just make sure to set
HBASE_MANAGES_ZK
to false
if you want it to stay up across HBase restarts so that when
HBase shuts down, it doesn't take ZooKeeper down with it.
For more information about running a distinct ZooKeeper cluster, see the ZooKeeper Getting Started Guide. Additionally, see the ZooKeeper Wiki or the ZooKeeper documentation for more information on ZooKeeper sizing.
Just as in Hadoop where you add site-specific HDFS configuration
to the hdfs-site.xml
file,
for HBase, site specific customizations go into
the file conf/hbase-site.xml
.
For the list of configurable properties, see
Section 1.6.1.1, “HBase Default Configuration”
below or view the raw hbase-default.xml
source file in the HBase source code at
src/main/resources
.
Not all configuration options make it out to
hbase-default.xml
. Configuration
that it is thought rare anyone would change can exist only
in code; the only way to turn up such configurations is
via a reading of the source code itself.
Currently, changes here will require a cluster restart for HBase to notice the change.
The documentation below is generated using the default hbase configuration file,
hbase-default.xml
, as source.
hbase.rootdir
The directory shared by region servers and into which HBase persists. The URL should be 'fully-qualified' to include the filesystem scheme. For example, to specify the HDFS directory '/hbase' where the HDFS instance's namenode is running at namenode.example.org on port 9000, set this value to: hdfs://namenode.example.org:9000/hbase. By default HBase writes into /tmp. Change this configuration else all data will be lost on machine restart.
Default: file:///tmp/hbase-${user.name}/hbase
hbase.master.port
The port the HBase Master should bind to.
Default: 60000
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.tmp.dir
Temporary directory on the local filesystem. Change this setting to point to a location more permanent than '/tmp' (The '/tmp' directory is often cleared on machine restart).
Default: /tmp/hbase-${user.name}
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: 60010
hbase.master.info.bindAddress
The bind address for the HBase Master web UI
Default: 0.0.0.0
hbase.client.write.buffer
Default size of the HTable clien 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.regionserver.port
The port the HBase RegionServer binds to.
Default: 60020
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: 60030
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.info.bindAddress
The address for the HBase RegionServer web UI
Default: 0.0.0.0
hbase.regionserver.class
The RegionServer interface to use. Used by the client opening proxy to remote region server.
Default: org.apache.hadoop.hbase.ipc.HRegionInterface
hbase.client.pause
General client pause value. Used mostly as value to wait before running a retry of a failed get, region lookup, etc.
Default: 1000
hbase.client.retries.number
Maximum retries. Used as maximum for all retryable operations such as fetching of the root region from root region server, getting a cell's value, starting a row update, etc. Default: 10.
Default: 10
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.
Default: 0
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.regionserver.lease.period
Default: 1
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.regionserver.lease.period
HRegion server lease period in milliseconds. Default is 60 seconds. Clients must report in within this period else they are considered dead.
Default: 60000
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 is 10.
Default: 10
hbase.regionserver.msginterval
Interval between messages from the RegionServer to Master in milliseconds.
Default: 3000
hbase.regionserver.optionallogflushinterval
Sync the HLog to the HDFS after this interval if it has not accumulated enough entries to trigger a sync. Default 1 second. Units: milliseconds.
Default: 1000
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 set to 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.SequenceFileLogReader
hbase.regionserver.hlog.writer.impl
The HLog file writer implementation.
Default: org.apache.hadoop.hbase.regionserver.wal.SequenceFileLogWriter
hbase.regionserver.nbreservationblocks
The number of resevoir blocks of memory release on OOME so we can cleanup properly before server shutdown.
Default: 4
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.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.master.dns.interface
The name of the Network Interface from which a master should report its IP address.
Default: default
hbase.master.dns.nameserver
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
hbase.balancer.period
Period at which the region balancer runs in the Master.
Default: 300000
hbase.regions.slop
Rebalance if any regionserver has average + (average * slop) regions. Default is 20% slop.
Default: 0.2
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.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.TimeToLiveLogCleaner
hbase.regionserver.global.memstore.upperLimit
Maximum size of all memstores in a region server before new updates are blocked and flushes are forced. Defaults to 40% of heap
Default: 0.4
hbase.regionserver.global.memstore.lowerLimit
When memstores are being forced to flush to make room in memory, keep flushing until we hit this mark. Defaults to 35% of heap. This value equal to hbase.regionserver.global.memstore.upperLimit causes the minimum possible flushing to occur when updates are blocked due to memstore limiting.
Default: 0.35
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.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
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: 1G.
Default: 1073741824
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.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: 7
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: 90 seconds.
Default: 90000
hbase.hstore.compaction.max
Max number of HStoreFiles to compact per 'minor' compaction.
Default: 10
hbase.hregion.majorcompaction
The time (in miliseconds) between 'major' compactions of all HStoreFiles in a region. Default: 1 day. Set to 0 to disable automated major compactions.
Default: 86400000
hbase.mapreduce.hfileoutputformat.blocksize
The mapreduce HFileOutputFormat writes storefiles/hfiles. This is the minimum hfile blocksize to emit. Usually in hbase, writing hfiles, the blocksize is gotten from the table schema (HColumnDescriptor) but in the mapreduce outputformat context, we don't have access to the schema so get blocksize from Configuation. The smaller you make the blocksize, the bigger your index and the less you fetch on a random-access. Set the blocksize down if you have small cells and want faster random-access of individual cells.
Default: 65536
hfile.block.cache.size
Percentage of maximum heap (-Xmx setting) to allocate to block cache used by HFile/StoreFile. Default of 0.25 means allocate 25%. Set to 0 to disable but it's not recommended.
Default: 0.25
hbase.hash.type
The hashing algorithm for use in HashFunction. Two values are supported now: murmur (MurmurHash) and jenkins (JenkinsHash). Used by bloom filters.
Default: murmur
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. Set this to 1 to test backwards-compatibility. The default value of this option should be consistent with FixedFileTrailer.MAX_VERSION.
Default: 2
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
io.storefile.bloom.cacheonwrite
Enables cache-on-write for inline blocks of a compound Bloom filter.
Default: false
hbase.rs.cacheblocksonwrite
Whether an HFile block should be added to the block cache when the block is finished.
Default: false
hbase.rpc.engine
Implementation of org.apache.hadoop.hbase.ipc.RpcEngine to be used for client / server RPC call marshalling.
Default: org.apache.hadoop.hbase.ipc.WritableRpcEngine
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
zookeeper.session.timeout
ZooKeeper session timeout. HBase passes this to the zk quorum as suggested maximum time for a session (This setting becomes zookeeper's 'maxSessionTimeout'). See http://hadoop.apache.org/zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions "The client sends a requested timeout, the server responds with the timeout that it can give the client. " In milliseconds.
Default: 180000
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.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.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.zookeeper.quorum
Comma separated list of servers in the ZooKeeper Quorum. 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 quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh this is the list of servers which we will start/stop ZooKeeper on.
Default: localhost
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.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.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.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 (@@@VERSION@@@), this version is X.X.X-SNAPSHOT"
Default: false
hbase.coprocessor.abortonerror
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
hbase.online.schema.update.enable
Set true to enable online schema changes. This is an experimental feature. There are known issues modifying table schemas at the same time a region split is happening so your table needs to be quiescent or else you have to be running with splits disabled.
Default: false
dfs.support.append
Does HDFS allow appends to files? This is an hdfs config. set in here so the hdfs client will do append support. You must ensure that this config. is true serverside too when running hbase (You will have to restart your cluster after setting it).
Default: true
hbase.offheapcache.percentage
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
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/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.
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.
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 the hbase, hadoop, log4j, commons-logging, commons-lang,
and ZooKeeper jars in its CLASSPATH
connecting to a cluster.
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>
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.
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.
<?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>
In this file you list the nodes that will run RegionServers.
In our case we run RegionServers on all but the head node
example1
which is carrying the HBase Master and
the HDFS namenode
example1 example3 example4 example5 example6 example7 example8 example9
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.
Below we list what the important Configurations. We've divided this section into required configuration and worth-a-look recommended configs.
Review the Section 1.2, “Operating System” and Section 1.3, “Hadoop” sections.
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.
This setting defines the number of threads that are kept open to answer incoming requests to user tables. The default of 10 is rather low in order to prevent users from killing their region servers when using large write buffers with a high number of concurrent clients. 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).
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.
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.
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.
Consider going to larger regions to cut down on the total number of regions
on your cluster. Generally less Regions to manage makes for a smoother running
cluster (You can always later manually split the big Regions should one prove
hot and you want to spread the request load over the cluster). By default,
regions are 256MB in size. You could run with
1G. Some run with even larger regions; 4G or even larger. Adjust
hbase.hregion.max.filesize
in your hbase-site.xml
.
Rather than let HBase auto-split your Regions, manage the splitting manually
[12].
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.
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 Long.MAX_VALUE
.
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.
The balancer is periodic operation 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.
io.hfile.bloom.enabled
in
Configuration
serves as the kill switch in case
something goes wrong. Default = true
.
io.hfile.bloom.error.rate
= average false
positive rate. Default = 1%. Decrease rate by ½ (e.g. to .5%) == +1
bit per bloom entry.
io.hfile.bloom.max.fold
= guaranteed minimum
fold rate. Most people should leave this alone. Default = 7, or can
collapse to at least 1/128th of original size. See the
Development Process section of the document BloomFilters
in HBase for more on what this option means.
[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] See Jack Levin's major hdfs issues note up on the user list.
[3] The requirement that a database requires upping of system limits is not peculiar to HBase. See for example the section Setting Shell Limits for the Oracle User in Short Guide to install Oracle 10 on Linux.
[4] A useful read setting config on you hadoop cluster is Aaron Kimballs' Configuration Parameters: What can you just ignore?
[5] Until recently only the branch-0.20-append branch had a working sync but no official release was ever made from this branch. You had to build it yourself. Michael Noll wrote a detailed blog, Building an Hadoop 0.20.x version for HBase 0.90.2, on how to build an Hadoop from branch-0.20-append. Recommended.
[6] Praveen Kumar has written a complimentary article, Building Hadoop and HBase for HBase Maven application development.
[7] dfs.support.append
[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 Pseudo-distributed mode extras for notes on how to start extra Masters and RegionServers when running pseudo-distributed.
[11] For the full list of ZooKeeper configurations, see
ZooKeeper's zoo.cfg
. HBase does not ship
with a zoo.cfg
so you will need to browse
the conf
directory in an appropriate
ZooKeeper download.
[12] 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.