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https://issues.apache.org/jira/browse/SPARK-4633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15484488#comment-15484488
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Adam Roberts commented on SPARK-4633:
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Very interested in this and I know Nasser Ebrahim is also (full disclosure that
we both work for IBM).
https://www.rootusers.com/gzip-vs-bzip2-vs-xz-performance-comparison/ shows
promising results
Would be interesting to code up a quick prototype (perhaps based on the pull
request here) and to see what performance difference we can gain, looks like
Takeshi has done the starting work for us
> 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...".
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