在DT大数据时代，海量数据的存储和分析是一个巨大的挑战，给我们的hadoop或者hbase集群添加数据压缩的能力，是必不可少的，通过压缩我们不但能节约磁盘空间，而且也能节省集群间网络带宽的损耗，从而间接提高了集群任务的整体执行效率，hadoop已经自带支持一些比较常用的压缩，如gz，bz等，使用hadoop checknative -a命令可以查看你的hadoop支持几种压缩格式：
Java代码
- 15/12/30 14:51:11 INFO bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native
- 15/12/30 14:51:11 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
- Native library checking:
- hadoop: true /ROOT/server/hadoop/lib/native/libhadoop.so
- zlib: true /lib64/libz.so.1
- snappy: true /ROOT/server/hadoop/lib/native/libsnappy.so.1
- lz4: true revision:99
- bzip2: true /lib64/libbz2.so.1
- openssl: true /usr/lib64/libcrypto.so
15/12/30 14:51:11 INFO bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native 15/12/30 14:51:11 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library Native library checking: hadoop: true /ROOT/server/hadoop/lib/native/libhadoop.so zlib: true /lib64/libz.so.1 snappy: true /ROOT/server/hadoop/lib/native/libsnappy.so.1 lz4: true revision:99 bzip2: true /lib64/libbz2.so.1 openssl: true /usr/lib64/libcrypto.so
当今大多数的互联网公司对于hadoop压缩的选型，通常是Snappy和LZO，两者都有不错的压缩比和解压速度，关于具体的对比，请参考此篇文章：http://www.cnblogs.com/zhengrunjian/p/4527165.html
本篇主要介绍在Hbase中使用snappy压缩，如果你的hadoop已经安装了snappy，那么接下来就会非常简单了，如果你的hadoop集群还不支持snappy压缩，那么也没关系，请参考散仙以前的文章：http://qindongliang.iteye.com/blog/2222145
版本介绍：
Apache Hadoop2.7.1
Apache Hbase0.98.12
安装测试步骤如下：
（1）给hadoop集群正确安装snappy压缩
（2）拷贝hadoop/lib下的native下的所有so文件和hadoop-snappy-0.0.1-SNAPSHOT.jar到hbase/lib下面
（3）如果有多台机器，每台机器都要拷贝分发
（4）拷贝完成后，重启hbase集群
（5）执行命令验证snappy是否验证成功，如果打印succes，即安装成功
Java代码
- [webmaster@Hadoop-0-187 logs]$ hbase org.apache.hadoop.hbase.util.CompressionTest /user/webmaster/word/in/tt2 snappy
- 2015-12-30 15:14:11,460 INFO [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
- SLF4J: Class path contains multiple SLF4J bindings.
- SLF4J: Found binding in [jar:file:/ROOT/server/hbase/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class]
- SLF4J: Found binding in [jar:file:/ROOT/server/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
- SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
- 2015-12-30 15:14:12,607 INFO [main] util.ChecksumType: Checksum using org.apache.hadoop.util.PureJavaCrc32
- 2015-12-30 15:14:12,609 INFO [main] util.ChecksumType: Checksum can use org.apache.hadoop.util.PureJavaCrc32C
- 2015-12-30 15:14:12,916 INFO [main] compress.CodecPool: Got brand-new compressor [.snappy]
- 2015-12-30 15:14:12,923 INFO [main] compress.CodecPool: Got brand-new compressor [.snappy]
- 2015-12-30 15:14:12,932 ERROR [main] hbase.KeyValue: Unexpected getShortMidpointKey result, fakeKey:testkey, firstKeyInBlock:testkey
- 2015-12-30 15:14:13,218 INFO [main] compress.CodecPool: Got brand-new decompressor [.snappy]
- SUCCESS
[webmaster@Hadoop-0-187 logs]$ hbase org.apache.hadoop.hbase.util.CompressionTest /user/webmaster/word/in/tt2 snappy 2015-12-30 15:14:11,460 INFO [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/ROOT/server/hbase/lib/slf4j-log4j12-1.6.4.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/ROOT/server/hadoop/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. 2015-12-30 15:14:12,607 INFO [main] util.ChecksumType: Checksum using org.apache.hadoop.util.PureJavaCrc32 2015-12-30 15:14:12,609 INFO [main] util.ChecksumType: Checksum can use org.apache.hadoop.util.PureJavaCrc32C 2015-12-30 15:14:12,916 INFO [main] compress.CodecPool: Got brand-new compressor [.snappy] 2015-12-30 15:14:12,923 INFO [main] compress.CodecPool: Got brand-new compressor [.snappy] 2015-12-30 15:14:12,932 ERROR [main] hbase.KeyValue: Unexpected getShortMidpointKey result, fakeKey:testkey, firstKeyInBlock:testkey 2015-12-30 15:14:13,218 INFO [main] compress.CodecPool: Got brand-new decompressor [.snappy] SUCCESS
确认成功后，执行hbase shell命令行，采用下面的几个命令，建2个表，一个指定snappy压缩，另外一个不启动压缩，最后插入几十条数据对比结果。
Java代码
- //创建一个表，指定snappy压缩
- create 'tsnappy', { NAME => 'f', COMPRESSION => 'snappy'}
- //创建一个表，不启动压缩
- create 'nosnappy', { NAME => 'f'}
- //查看表描述
- describe 'tsnappy'
- //put一条数据
- put 'tsnappy', 'row1', 'f:col1', 'value'
- //统计hbase表数据
- count 'tsnappy'
- //查看数据
- scan 'tsnappy'
- truncate 'tsnappy' 清空表里面的数据
- //修改已有的表为snappy压缩
- alter apData,{NAME=>'cf1',COMPRESSION=>'snappy'}
- //禁用表
- disable 'my_table'
- //修改支持压缩
- alter 'my_table', {NAME => 'my_column_family', COMPRESSION => 'snappy'}
- //激活表
- enable 'my_table'
//创建一个表，指定snappy压缩
create 'tsnappy', { NAME => 'f', COMPRESSION => 'snappy'}
//创建一个表，不启动压缩
create 'nosnappy', { NAME => 'f'}
//查看表描述
describe 'tsnappy'
//put一条数据
put 'tsnappy', 'row1', 'f:col1', 'value'
//统计hbase表数据
count 'tsnappy'
//查看数据
scan 'tsnappy'
truncate 'tsnappy' 清空表里面的数据
//修改已有的表为snappy压缩
alter apData,{NAME=>'cf1',COMPRESSION=>'snappy'}
//禁用表
disable 'my_table'
//修改支持压缩
alter 'my_table', {NAME => 'my_column_family', COMPRESSION => 'snappy'}
//激活表
enable 'my_table'
然后插入50行数据，一个字段，内容大概4M，附件里面有Hbase操作的Java类，插入数据完整后，执行命令：
hadoop fs -du -s -h /hbase/data/default/*对比查看开启压缩和不开启压缩索占的存储空间，大约为1：9，当然这也和自己的存储的数据有关系，总体来说，还是不错的。
