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Posted to common-issues@hadoop.apache.org by "Xabriel J Collazo Mojica (JIRA)" <ji...@apache.org> on 2015/06/25 03:42:05 UTC
[jira] [Updated] (HADOOP-11644) Contribute CMX compression
[ https://issues.apache.org/jira/browse/HADOOP-11644?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xabriel J Collazo Mojica updated HADOOP-11644:
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Attachment: HADOOP-11644.001.patch
> Contribute CMX compression
> --------------------------
>
> Key: HADOOP-11644
> URL: https://issues.apache.org/jira/browse/HADOOP-11644
> Project: Hadoop Common
> Issue Type: Improvement
> Components: io
> Reporter: Xabriel J Collazo Mojica
> Assignee: Xabriel J Collazo Mojica
> Attachments: HADOOP-11644.001.patch
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> Hadoop natively supports four main compression algorithms: BZIP2, LZ4, Snappy and ZLIB.
> Each one of these algorithms fills a gap:
> bzip2 : Very high compression ratio, splittable
> LZ4 : Very fast, non splittable
> Snappy : Very fast, non splittable
> zLib : good balance of compression and speed.
> We think there is a gap for a compression algorithm that can perform fast compress and decompress, while also being splittable. This can help significantly on jobs where the input file sizes are >= 1GB.
> For this, IBM has developed CMX. CMX is a dictionary-based, block-oriented, splittable, concatenable compression algorithm developed specifically for Hadoop workloads. Many of our customers use CMX, and we would love to be able to contribute it to hadoop-common.
> CMX is block oriented : We typically use 64k blocks. Blocks are independently decompressable.
> CMX is splittable : We implement the SplittableCompressionCodec interface. All CMX files are a multiple of 64k, so the splittability is achieved in a simple way with no need for external indexes.
> CMX is concatenable : Two independent CMX files can be concatenated together. We have seen that some projects like Apache Flume require this feature.
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