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https://issues.apache.org/jira/browse/SPARK-4019?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14177617#comment-14177617
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Josh Rosen commented on SPARK-4019:
-----------------------------------
This issue is caused by a bug in HighlyCompressedMapStatus. I think that's
we're compressing a bunch of blocks whose average size is small, so this gets
averaged down to zero. As a result, we skip these blocks as empty even though
they contain data.
I'm going to work on a fix ASAP, but first I'm going to use ScalaCheck to write
a property-based test that would have caught this. The invariant that we need
to maintain: "if an uncompressed map output size is greater than zero, then
compressing and decompressing should continue to report the map output as
non-empty."
> Repartitioning with more than 2000 partitions drops all data
> ------------------------------------------------------------
>
> 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.
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