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Apache HBase - Snappy Compression

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[数据库(综合) 所属分类 数据库(综合) | 发布者 店小二05 | 时间 2018 | 作者 红领巾 ] 0人收藏点击收藏
Overview

Apache HBase provides the ability to perform realtime random read/write access to large datasets. HBase is built on top of Apache Hadoop and can scale to billions of rows and millions of columns. One of the features of HBase is to enable different types of compression for a column family. It is recommended that testing be done for your use case, but this blog shows how Snappy compression can reduce storage needs while keeping the same query performance.

Evidence

Below are some images from some clusters where testing was done with Snappy compression. The charts show a variety of metrics from storage size to system metrics.


Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Apache HBase - Snappy Compression
Conclusion

The charts above show >80% storage saving while only seeing a slight bump in mutate latencies. The clusters that this was tested on were loaded with simulated data and load. The production data matched this when deployed as well. This storage savings also helped backups and disaster recovery since we didn’t need to move as much data across the wire. References for implementing this yourself with more options for testing are below.

References https://community.hortonworks.com/articles/54761/compression-in-hbase.html http://hadoop-hbase.blogspot.com/2016/02/hbase-compression-vs-blockencoding_17.html https://blogs.apache.org/hbase/entry/the_effect_of_columnfamily_rowkey https://db-blog.web.cern.ch/blog/zbigniew-baranowski/2017-01-performance-comparison-different-file-formats-and-storage-engines http://blog.erdemagaoglu.com/post/4605524309/lzo-vs-snappy-vs-lzf-vs-zlib-a-comparison-of https://hbase.apache.org/book.html#compression https://hbase.apache.org/book.html#data.block.encoding.enable

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