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https://issues.apache.org/jira/browse/SPARK-4019?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14180737#comment-14180737
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Josh Rosen commented on SPARK-4019:
-----------------------------------
This also explains another occurrence of the Snappy PARSING_ERROR(2) error. If
the average block size is non-zero but there is at least one zero-sized block,
then HighlyCompressedMapStatus would cause us to fetch empty blocks, leading to
the PARSING_ERROR(2) when Snappy tries to decompress this empty block.
Thanks to [~ilikerps] for helping to figure this out.
> Repartitioning with more than 2000 partitions may drop all data when
> partitions are mostly empty or cause deserialization errors if at least one
> partition is empty
> -------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-4019
> URL: https://issues.apache.org/jira/browse/SPARK-4019
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.2.0
> Reporter: Xiangrui Meng
> Assignee: Josh Rosen
> Priority: Blocker
>
> {code}
> sc.makeRDD(0 until 10, 1000).repartition(2001).collect()
> {code}
> returns `Array()`.
> 1.1.0 doesn't have this issue. Tried both HASH and SORT manager.
> This problem can also manifest itself as Snappy deserialization errors if the
> average map output status size is non-zero but there is at least one empty
> partition, e.g.
> sc.makeRDD(0 until 100000, 1000).repartition(2001).collect()
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