HBASE-19023 Replace hbase-server with hbase-mapreduce for HBase and MapReduce chapter

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[appendix]
[[compression]]
== Compression and Data Block Encoding In HBase(((Compression,Data BlockEncoding)))
:doctype: book
:numbered:
:toc: left
:icons: font
:experimental:

NOTE: Codecs mentioned in this section are for encoding and decoding data blocks or row keys.
For information about replication codecs, see <<cluster.replication.preserving.tags,cluster.replication.preserving.tags>>.

Some of the information in this section is pulled from a link:http://search-hadoop.com/m/lL12B1PFVhp1/v=threaded[discussion] on the HBase Development mailing list.

HBase supports several different compression algorithms which can be enabled on a ColumnFamily.
Data block encoding attempts to limit duplication of information in keys, taking advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys and the schema of a given table.
Compressors reduce the size of large, opaque byte arrays in cells, and can significantly reduce the storage space needed to store uncompressed data.

Compressors and data block encoding can be used together on the same ColumnFamily.

.Changes Take Effect Upon Compaction
If you change compression or encoding for a ColumnFamily, the changes take effect during compaction.

Some codecs take advantage of capabilities built into Java, such as GZip compression. Others rely on native libraries. Native libraries may be available as part of Hadoop, such as LZ4. In this case, HBase only needs access to the appropriate shared library.

Other codecs, such as Google Snappy, need to be installed first.
Some codecs are licensed in ways that conflict with HBase's license and cannot be shipped as part of HBase.

This section discusses common codecs that are used and tested with HBase.
No matter what codec you use, be sure to test that it is installed correctly and is available on all nodes in your cluster.
Extra operational steps may be necessary to be sure that codecs are available on newly-deployed nodes.
You can use the <<compression.test,compression.test>> utility to check that a given codec is correctly installed.

To configure HBase to use a compressor, see <<compressor.install,compressor.install>>.
To enable a compressor for a ColumnFamily, see <<changing.compression,changing.compression>>.
To enable data block encoding for a ColumnFamily, see <<data.block.encoding.enable,data.block.encoding.enable>>.

.Block Compressors
* none
* Snappy
* LZO
* LZ4
* GZ

.Data Block Encoding Types
Prefix::
  Often, keys are very similar. Specifically, keys often share a common prefix and only differ near the end. For instance, one key might be `RowKey:Family:Qualifier0` and the next key might be `RowKey:Family:Qualifier1`.
  +
In Prefix encoding, an extra column is added which holds the length of the prefix shared between the current key and the previous key.
Assuming the first key here is totally different from the key before, its prefix length is 0.
+
The second key's prefix length is `23`, since they have the first 23 characters in common.
+
Obviously if the keys tend to have nothing in common, Prefix will not provide much benefit.
+
The following image shows a hypothetical ColumnFamily with no data block encoding.
+
.ColumnFamily with No Encoding
image::data_block_no_encoding.png[]
+
Here is the same data with prefix data encoding.
+
.ColumnFamily with Prefix Encoding
image::data_block_prefix_encoding.png[]

Diff::
  Diff encoding expands upon Prefix encoding.
  Instead of considering the key sequentially as a monolithic series of bytes, each key field is split so that each part of the key can be compressed more efficiently.
+
Two new fields are added: timestamp and type.
+
If the ColumnFamily is the same as the previous row, it is omitted from the current row.
+
If the key length, value length or type are the same as the previous row, the field is omitted.
+
In addition, for increased compression, the timestamp is stored as a Diff from the previous row's timestamp, rather than being stored in full.
Given the two row keys in the Prefix example, and given an exact match on timestamp and the same type, neither the value length, or type needs to be stored for the second row, and the timestamp value for the second row is just 0, rather than a full timestamp.
+
Diff encoding is disabled by default because writing and scanning are slower but more data is cached.
+
This image shows the same ColumnFamily from the previous images, with Diff encoding.
+
.ColumnFamily with Diff Encoding
image::data_block_diff_encoding.png[]

Fast Diff::
  Fast Diff works similar to Diff, but uses a faster implementation. It also adds another field which stores a single bit to track whether the data itself is the same as the previous row. If it is, the data is not stored again.
+
Fast Diff is the recommended codec to use if you have long keys or many columns.
+
The data format is nearly identical to Diff encoding, so there is not an image to illustrate it.


Prefix Tree::
  Prefix tree encoding was introduced as an experimental feature in HBase 0.96.
  It provides similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides faster random access at a cost of slower encoding speed.
  It was removed in hbase-2.0.0. It was a good idea but little uptake. If interested in reviving this effort, write the hbase dev list.

[[data.block.encoding.types]]
=== Which Compressor or Data Block Encoder To Use

The compression or codec type to use depends on the characteristics of your data. Choosing the wrong type could cause your data to take more space rather than less, and can have performance implications.

In general, you need to weigh your options between smaller size and faster compression/decompression. Following are some general guidelines, expanded from a discussion at link:http://search-hadoop.com/m/lL12B1PFVhp1[Documenting Guidance on compression and codecs].

* If you have long keys (compared to the values) or many columns, use a prefix encoder.
  FAST_DIFF is recommended, as more testing is needed for Prefix Tree encoding.
* If the values are large (and not precompressed, such as images), use a data block compressor.
* Use GZIP for [firstterm]_cold data_, which is accessed infrequently.
  GZIP compression uses more CPU resources than Snappy or LZO, but provides a higher compression ratio.
* Use Snappy or LZO for [firstterm]_hot data_, which is accessed frequently.
  Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high of a compression ratio.
* In most cases, enabling Snappy or LZO by default is a good choice, because they have a low performance overhead and provide space savings.
* Before Snappy became available by Google in 2011, LZO was the default.
  Snappy has similar qualities as LZO but has been shown to perform better.

[[hadoop.native.lib]]
=== Making use of Hadoop Native Libraries in HBase

The Hadoop shared library has a bunch of facility including compression libraries and fast crc'ing -- hardware crc'ing if your chipset supports it.
To make this facility available to HBase, do the following. HBase/Hadoop will fall back to use alternatives if it cannot find the native library
versions -- or fail outright if you asking for an explicit compressor and there is no alternative available.

First make sure of your Hadoop. Fix this message if you are seeing it starting Hadoop processes:
----
16/02/09 22:40:24 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
----
It means is not properly pointing at its native libraries or the native libs were compiled for another platform.
Fix this first.

Then if you see the following in your HBase logs, you know that HBase was unable to locate the Hadoop native libraries:
[source]
----
2014-08-07 09:26:20,139 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
----
If the libraries loaded successfully, the WARN message does not show. Usually this means you are good to go but read on.

Let's presume your Hadoop shipped with a native library that suits the platform you are running HBase on.
To check if the Hadoop native library is available to HBase, run the following tool (available in  Hadoop 2.1 and greater):
[source]
----
$ ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:15:38,717 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Native library checking:
hadoop: false
zlib:   false
snappy: false
lz4:    false
bzip2:  false
2014-08-26 13:15:38,863 INFO  [main] util.ExitUtil: Exiting with status 1
----
Above shows that the native hadoop library is not available in HBase context.

The above NativeLibraryChecker tool may come back saying all is hunky-dory
-- i.e. all libs show 'true', that they are available -- but follow the below
presecription anyways to ensure the native libs are available in HBase context,
when it goes to use them.

To fix the above, either copy the Hadoop native libraries local or symlink to them if the Hadoop and HBase stalls are adjacent in the filesystem.
You could also point at their location by setting the `LD_LIBRARY_PATH` environment variable in your hbase-env.sh.

Where the JVM looks to find native libraries is "system dependent" (See `java.lang.System#loadLibrary(name)`). On linux, by default, is going to look in _lib/native/PLATFORM_ where `PLATFORM`      is the label for the platform your HBase is installed on.
On a local linux machine, it seems to be the concatenation of the java properties `os.name` and `os.arch` followed by whether 32 or 64 bit.
HBase on startup prints out all of the java system properties so find the os.name and os.arch in the log.
For example:
[source]
----
...
2014-08-06 15:27:22,853 INFO  [main] zookeeper.ZooKeeper: Client environment:os.name=Linux
2014-08-06 15:27:22,853 INFO  [main] zookeeper.ZooKeeper: Client environment:os.arch=amd64
...
----
So in this case, the PLATFORM string is `Linux-amd64-64`.
Copying the Hadoop native libraries or symlinking at _lib/native/Linux-amd64-64_     will ensure they are found.
Rolling restart after you have made this change.

Here is an example of how you would set up the symlinks.
Let the hadoop and hbase installs be in your home directory. Assume your hadoop native libs
are at ~/hadoop/lib/native. Assume you are on a Linux-amd64-64 platform. In this case,
you would do the following to link the hadoop native lib so hbase could find them.
----
...
$ mkdir -p ~/hbaseLinux-amd64-64 -> /home/stack/hadoop/lib/native/lib/native/
$ cd ~/hbase/lib/native/
$ ln -s ~/hadoop/lib/native Linux-amd64-64
$ ls -la
# Linux-amd64-64 -> /home/USER/hadoop/lib/native
...
----

If you see PureJavaCrc32C in a stack track or if you see something like the below in a perf trace, then native is not working; you are using the java CRC functions rather than native:
----
  5.02%  perf-53601.map      [.] Lorg/apache/hadoop/util/PureJavaCrc32C;.update
----
See link:https://issues.apache.org/jira/browse/HBASE-11927[HBASE-11927 Use Native Hadoop Library for HFile checksum (And flip default from CRC32 to CRC32C)],
for more on native checksumming support. See in particular the release note for how to check if your hardware to see if your processor has support for hardware CRCs.
Or checkout the Apache link:https://blogs.apache.org/hbase/entry/saving_cpu_using_native_hadoop[Checksums in HBase] blog post.

Here is example of how to point at the Hadoop libs with `LD_LIBRARY_PATH`      environment variable:
[source]
----
$ LD_LIBRARY_PATH=~/hadoop-2.5.0-SNAPSHOT/lib/native ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker
2014-08-26 13:42:49,332 INFO  [main] bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native
2014-08-26 13:42:49,337 INFO  [main] zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
Native library checking:
hadoop: true /home/stack/hadoop-2.5.0-SNAPSHOT/lib/native/libhadoop.so.1.0.0
zlib:   true /lib64/libz.so.1
snappy: true /usr/lib64/libsnappy.so.1
lz4:    true revision:99
bzip2:  true /lib64/libbz2.so.1
----
Set in _hbase-env.sh_ the LD_LIBRARY_PATH environment variable when starting your HBase.

=== Compressor Configuration, Installation, and Use

[[compressor.install]]
==== Configure HBase For Compressors

Before HBase can use a given compressor, its libraries need to be available.
Due to licensing issues, only GZ compression is available to HBase (via native Java libraries) in a default installation.
Other compression libraries are available via the shared library bundled with your hadoop.
The hadoop native library needs to be findable when HBase starts.
See

.Compressor Support On the Master

A new configuration setting was introduced in HBase 0.95, to check the Master to determine which data block encoders are installed and configured on it, and assume that the entire cluster is configured the same.
This option, `hbase.master.check.compression`, defaults to `true`.
This prevents the situation described in link:https://issues.apache.org/jira/browse/HBASE-6370[HBASE-6370], where a table is created or modified to support a codec that a region server does not support, leading to failures that take a long time to occur and are difficult to debug.

If `hbase.master.check.compression` is enabled, libraries for all desired compressors need to be installed and configured on the Master, even if the Master does not run a region server.

.Install GZ Support Via Native Libraries

HBase uses Java's built-in GZip support unless the native Hadoop libraries are available on the CLASSPATH.
The recommended way to add libraries to the CLASSPATH is to set the environment variable `HBASE_LIBRARY_PATH` for the user running HBase.
If native libraries are not available and Java's GZIP is used, `Got brand-new compressor` reports will be present in the logs.
See <<brand.new.compressor,brand.new.compressor>>).

[[lzo.compression]]
.Install LZO Support

HBase cannot ship with LZO because of incompatibility between HBase, which uses an Apache Software License (ASL) and LZO, which uses a GPL license.
See the link:https://github.com/twitter/hadoop-lzo/blob/master/README.md[Hadoop-LZO at Twitter] for information on configuring LZO support for HBase.

If you depend upon LZO compression, consider configuring your RegionServers to fail to start if LZO is not available.
See <<hbase.regionserver.codecs,hbase.regionserver.codecs>>.

[[lz4.compression]]
.Configure LZ4 Support

LZ4 support is bundled with Hadoop.
Make sure the hadoop shared library (libhadoop.so) is accessible when you start HBase.
After configuring your platform (see <<hadoop.native.lib,hadoop.native.lib>>), you can make a symbolic link from HBase to the native Hadoop libraries.
This assumes the two software installs are colocated.
For example, if my 'platform' is Linux-amd64-64:
[source,bourne]
----
$ cd $HBASE_HOME
$ mkdir lib/native
$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64
----
Use the compression tool to check that LZ4 is installed on all nodes.
Start up (or restart) HBase.
Afterward, you can create and alter tables to enable LZ4 as a compression codec.:
----
hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'}
----

[[snappy.compression.installation]]
.Install Snappy Support

HBase does not ship with Snappy support because of licensing issues.
You can install Snappy binaries (for instance, by using +yum install snappy+ on CentOS) or build Snappy from source.
After installing Snappy, search for the shared library, which will be called _libsnappy.so.X_ where X is a number.
If you built from source, copy the shared library to a known location on your system, such as _/opt/snappy/lib/_.

In addition to the Snappy library, HBase also needs access to the Hadoop shared library, which will be called something like _libhadoop.so.X.Y_, where X and Y are both numbers.
Make note of the location of the Hadoop library, or copy it to the same location as the Snappy library.

[NOTE]
====
The Snappy and Hadoop libraries need to be available on each node of your cluster.
See <<compression.test,compression.test>> to find out how to test that this is the case.

See <<hbase.regionserver.codecs,hbase.regionserver.codecs>> to configure your RegionServers to fail to start if a given compressor is not available.
====

Each of these library locations need to be added to the environment variable `HBASE_LIBRARY_PATH` for the operating system user that runs HBase.
You need to restart the RegionServer for the changes to take effect.

[[compression.test]]
.CompressionTest

You can use the CompressionTest tool to verify that your compressor is available to HBase:

----

 $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
----

[[hbase.regionserver.codecs]]
.Enforce Compression Settings On a RegionServer

You can configure a RegionServer so that it will fail to restart if compression is configured incorrectly, by adding the option hbase.regionserver.codecs to the _hbase-site.xml_, and setting its value to a comma-separated list of codecs that need to be available.
For example, if you set this property to `lzo,gz`, the RegionServer would fail to start if both compressors were not available.
This would prevent a new server from being added to the cluster without having codecs configured properly.

[[changing.compression]]
==== Enable Compression On a ColumnFamily

To enable compression for a ColumnFamily, use an `alter` command.
You do not need to re-create the table or copy data.
If you are changing codecs, be sure the old codec is still available until all the old StoreFiles have been compacted.

.Enabling Compression on a ColumnFamily of an Existing Table using HBaseShell
====
----

hbase> disable 'test'
hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'}
hbase> enable 'test'
----
====

.Creating a New Table with Compression On a ColumnFamily
====
----

hbase> create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' }
----
====

.Verifying a ColumnFamily's Compression Settings
====
----

hbase> describe 'test'
DESCRIPTION                                          ENABLED
 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false
 ', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0',
 VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS
 => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa
 lse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B
 LOCKCACHE => 'true'}
1 row(s) in 0.1070 seconds
----
====

==== Testing Compression Performance

HBase includes a tool called LoadTestTool which provides mechanisms to test your compression performance.
You must specify either `-write` or `-update-read` as your first parameter, and if you do not specify another parameter, usage advice is printed for each option.

.+LoadTestTool+ Usage
====
----

$ bin/hbase org.apache.hadoop.hbase.util.LoadTestTool -h
usage: bin/hbase org.apache.hadoop.hbase.util.LoadTestTool <options>
Options:
 -batchupdate                 Whether to use batch as opposed to separate
                              updates for every column in a row
 -bloom <arg>                 Bloom filter type, one of [NONE, ROW, ROWCOL]
 -compression <arg>           Compression type, one of [LZO, GZ, NONE, SNAPPY,
                              LZ4]
 -data_block_encoding <arg>   Encoding algorithm (e.g. prefix compression) to
                              use for data blocks in the test column family, one
                              of [NONE, PREFIX, DIFF, FAST_DIFF, PREFIX_TREE].
 -encryption <arg>            Enables transparent encryption on the test table,
                              one of [AES]
 -generator <arg>             The class which generates load for the tool. Any
                              args for this class can be passed as colon
                              separated after class name
 -h,--help                    Show usage
 -in_memory                   Tries to keep the HFiles of the CF inmemory as far
                              as possible.  Not guaranteed that reads are always
                              served from inmemory
 -init_only                   Initialize the test table only, don't do any
                              loading
 -key_window <arg>            The 'key window' to maintain between reads and
                              writes for concurrent write/read workload. The
                              default is 0.
 -max_read_errors <arg>       The maximum number of read errors to tolerate
                              before terminating all reader threads. The default
                              is 10.
 -multiput                    Whether to use multi-puts as opposed to separate
                              puts for every column in a row
 -num_keys <arg>              The number of keys to read/write
 -num_tables <arg>            A positive integer number. When a number n is
                              speicfied, load test tool  will load n table
                              parallely. -tn parameter value becomes table name
                              prefix. Each table name is in format
                              <tn>_1...<tn>_n
 -read <arg>                  <verify_percent>[:<#threads=20>]
 -regions_per_server <arg>    A positive integer number. When a number n is
                              specified, load test tool will create the test
                              table with n regions per server
 -skip_init                   Skip the initialization; assume test table already
                              exists
 -start_key <arg>             The first key to read/write (a 0-based index). The
                              default value is 0.
 -tn <arg>                    The name of the table to read or write
 -update <arg>                <update_percent>[:<#threads=20>][:<#whether to
                              ignore nonce collisions=0>]
 -write <arg>                 <avg_cols_per_key>:<avg_data_size>[:<#threads=20>]
 -zk <arg>                    ZK quorum as comma-separated host names without
                              port numbers
 -zk_root <arg>               name of parent znode in zookeeper
----
====

.Example Usage of LoadTestTool
====
----

$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000
          -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE
----
====

[[data.block.encoding.enable]]
== Enable Data Block Encoding

Codecs are built into HBase so no extra configuration is needed.
Codecs are enabled on a table by setting the `DATA_BLOCK_ENCODING` property.
Disable the table before altering its DATA_BLOCK_ENCODING setting.
Following is an example using HBase Shell:

.Enable Data Block Encoding On a Table
====
----

hbase>  disable 'test'
hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' }
Updating all regions with the new schema...
0/1 regions updated.
1/1 regions updated.
Done.
0 row(s) in 2.2820 seconds
hbase> enable 'test'
0 row(s) in 0.1580 seconds
----
====

.Verifying a ColumnFamily's Data Block Encoding
====
----

hbase> describe 'test'
DESCRIPTION                                          ENABLED
 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true
 _DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE =>
 '0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS
 IONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS =
 > 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals
 e', BLOCKCACHE => 'true'}
1 row(s) in 0.0650 seconds
----
====

:numbered:

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