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https://issues.apache.org/jira/browse/SPARK-26089?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-26089:
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Assignee: (was: Apache Spark)
> Handle large corrupt shuffle blocks
> -----------------------------------
>
> Key: SPARK-26089
> URL: https://issues.apache.org/jira/browse/SPARK-26089
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Shuffle, Spark Core
> Affects Versions: 2.4.0
> Reporter: Imran Rashid
> Priority: Major
>
> We've seen a bad disk lead to corruption in a shuffle block, which lead to
> tasks repeatedly failing after fetching the data with an IOException. The
> tasks get retried, but the same corrupt data gets fetched again, and the
> tasks keep failing. As there isn't a fetch-failure, the jobs eventually
> fail, spark never tries to regenerate the shuffle data.
> This is the same as SPARK-4105, but that fix only covered small blocks.
> There was some discussion during that change about this limitation
> (https://github.com/apache/spark/pull/15923#discussion_r88756017) and
> followups to cover larger blocks (which would involve spilling to disk to
> avoid OOM), but it looks like that never happened.
> I can think of a few approaches to this:
> 1) wrap the shuffle block input stream with another input stream, that
> converts all exceptions into FetchFailures. This is similar to the fix of
> SPARK-4105, but that reads the entire input stream up-front, and instead I'm
> proposing to do it within the InputStream itself so its streaming and does
> not have a large memory overhead.
> 2) Add checksums to shuffle blocks. This was proposed
> [here|https://github.com/apache/spark/pull/15894] and abandoned as being too
> complex.
> 3) Try to tackle this with blacklisting instead: when there is any failure in
> a task that is reading shuffle data, assign some "blame" to the source of the
> shuffle data, and eventually blacklist the source. It seems really tricky to
> get sensible heuristics for this, though.
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