You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2014/11/27 03:03:12 UTC

[jira] [Commented] (SPARK-4633) Support gzip in spark.compression.io.codec

    [ https://issues.apache.org/jira/browse/SPARK-4633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14227133#comment-14227133 ] 

Apache Spark commented on SPARK-4633:
-------------------------------------

User 'maropu' has created a pull request for this issue:
https://github.com/apache/spark/pull/3488

> Support gzip in spark.compression.io.codec
> ------------------------------------------
>
>                 Key: SPARK-4633
>                 URL: https://issues.apache.org/jira/browse/SPARK-4633
>             Project: Spark
>          Issue Type: Improvement
>          Components: Input/Output
>            Reporter: Takeshi Yamamuro
>            Priority: Trivial
>
> gzip is widely used in other frameowrks such as hadoop mapreduce and tez, and also
> I think that gizip is more stable than other codecs in terms of both performance
> and space overheads.
> I have one open question; current spark configuratios have a block size option
> for each codec (spark.io.compression.[gzip|lz4|snappy].block.size).
> As # of codecs increases, the configurations have more options and
> I think that it is sort of complicated for non-expert users.
> To mitigate it, my thought follows;
> the three configurations are replaced with a single option for block size
> (spark.io.compression.block.size). Then, 'Meaning' in configurations
> will describe "This option makes an effect on gzip, lz4, and snappy. 
> Block size (in bytes) used in compression, in the case when these compression
> codecs are used. Lowering...".



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org