[jira] [Commented] (HADOOP-11644) Contribute CMX compression

["Hadoop QA (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HADOOP-11644?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15946188#comment-15946188 ] 

Hadoop QA commented on HADOOP-11644:
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|| Vote || Subsystem || Runtime || Comment ||
| {color:blue}0{color} | {color:blue} reexec {color} | {color:blue}  0m  0s{color} | {color:blue} Docker mode activated. {color} |
| {color:red}-1{color} | {color:red} patch {color} | {color:red}  0m  7s{color} | {color:red} HADOOP-11644 does not apply to trunk. Rebase required? Wrong Branch? See https://wiki.apache.org/hadoop/HowToContribute for help. {color} |
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|| Subsystem || Report/Notes ||
| JIRA Issue | HADOOP-11644 |
| JIRA Patch URL | https://issues.apache.org/jira/secure/attachment/12743192/HADOOP-11644.002.patch |
| Console output | https://builds.apache.org/job/PreCommit-HADOOP-Build/11971/console |
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This message was automatically generated.



> 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, HADOOP-11644.002.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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