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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 articles 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.

Block Compressors

  • none
  • Snappy
  • LZO
  • LZ4
  • GZ


Unfortunately, HBase cannot ship with LZO because of the licensing issues; HBase is Apache-licensed, LZO is GPL. Therefore LZO install is to be done post-HBase install. See the Using LZO Compression wiki page for how to make LZO work with HBase.

A common problem users run into when using LZO is that while initial setup of the cluster runs smooth, a month goes by and some sysadmin goes to add a machine to the cluster only they'll have forgotten to do the LZO fixup on the new machine. In versions since HBase 0.90.0, we should fail in a way that makes it plain what the problem is, but maybe not.


GZIP will generally compress better than LZO though slower. For some setups, better compression may be preferred. Java will use java's GZIP unless the native Hadoop libs are available on the CLASSPATH; in this case it will use native compressors instead (If the native libs are NOT present, you will see lots of Got brand-new compressor reports in your logs


If snappy is installed, HBase can make use of it (courtesy of hadoop-snappy [32]).

  1. Build and install snappy on all nodes of your cluster (see below)
  2. Use CompressionTest to verify snappy support is enabled and the libs can be loaded ON ALL NODES of your cluster:
    $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy
  3. Create a column family with snappy compression and verify it in the hbase shell:
    $ hbase> create 't1', { NAME => 'cf1', COMPRESSION => 'SNAPPY' }
    hbase> describe 't1'

    In the output of the "describe" command, you need to ensure it lists "COMPRESSION => 'SNAPPY'"


You will find the snappy library file under the .libs directory from your Snappy build (For example /home/hbase/snappy-1.0.5/.libs/). The file is called where 1.x.x is the version of the snappy code you are building. You can either copy this file into your hbase directory under name, or simply create a symbolic link to it.

The second file you need is the hadoop native library. You will find this file in your hadoop installation directory under lib/native/Linux-amd64-64/ or lib/native/Linux-i386-32/. The file you are looking for is Again, you can simply copy this file or link to it, under the name

At the end of the installation, you should have both and links or files present into lib/native/Linux-amd64-64 or into lib/native/Linux-i386-32

To point hbase at snappy support, in set

export HBASE_LIBRARY_PATH=/pathtoyourhadoop/lib/native/Linux-amd64-64<br>	

In /pathtoyourhadoop/lib/native/Linux-amd64-64 you should have something like:


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 beRowKey: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.

  • 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.
  • 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 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. Prefix Tree may be appropriate for applications that have high block cache hit ratios. It introduces new 'tree' fields for the row and column. The row tree field contains a list of offsets/references corresponding to the cells in that row. This allows for a good deal of compression.

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 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 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 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.
If you have long keys (compared to the values) or many columns, use a prefix encoder. FAST_DIFF is recommended

Sorry, this post is few months old, the above sentence mean it is recommended to use FAST_DIFF over PREFIX (not PREFIX_TREE) right?