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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5944) Flink should support reading Snappy Files
Date Mon, 25 Sep 2017 06:32:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5944?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16178593#comment-16178593
] 

ASF GitHub Bot commented on FLINK-5944:
---------------------------------------

Github user haohui commented on a diff in the pull request:

    https://github.com/apache/flink/pull/4683#discussion_r140696992
  
    --- Diff: flink-core/pom.xml ---
    @@ -52,6 +52,12 @@ under the License.
     			<artifactId>flink-shaded-asm</artifactId>
     		</dependency>
     
    +		<dependency>
    +			<groupId>org.apache.flink</groupId>
    +			<artifactId>flink-shaded-hadoop2</artifactId>
    +			<version>${project.version}</version>
    +		</dependency>
    --- End diff --
    
    
    Internally we have several users that try Flink to read the files generated by Hadoop
(e.g. lz4 / gz / snappy). I think the support of Hadoop is quite important.
    
    I'm not sure supporting the xerial snappy format is a good idea. The two file formats
are actually incompatible -- it would be quite confusing for the users to find out that they
can't access the files using Spark / MR / Hive due to a missed configuration.
    
    I suggest at least we should make the Hadoop file format as the default -- or to just
get rid of the xerial version of the file format.
    
    Putting the dependency in provided sounds fine to me -- if we need even tighter controls
on the dependency, we can start thinking about having a separate module for it.
    
    What do you think?



> Flink should support reading Snappy Files
> -----------------------------------------
>
>                 Key: FLINK-5944
>                 URL: https://issues.apache.org/jira/browse/FLINK-5944
>             Project: Flink
>          Issue Type: New Feature
>          Components: Batch Connectors and Input/Output Formats
>            Reporter: Ilya Ganelin
>            Assignee: Mikhail Lipkovich
>              Labels: features
>
> Snappy is an extremely performant compression format that's widely used offering fast
decompression/compression. 
> This can be easily implemented by creating a SnappyInflaterInputStreamFactory and updating
the initDefaultInflateInputStreamFactories in FileInputFormat.
> Flink already includes the Snappy dependency in the project. 
> There is a minor gotcha in this. If we wish to use this with Hadoop, then we must provide
two separate implementations since Hadoop uses a different version of the snappy format than
Snappy Java (which is the xerial/snappy included in Flink). 



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