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https://issues.apache.org/jira/browse/SPARK-4019?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Josh Rosen updated SPARK-4019:
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Summary: Shuffling with more than 2000 reducers may drop all data when
partitons are mostly empty or cause deserialization errors if at least one
partition is empty (was: Shuffling with more than 2000 map partitions may drop
all data when partitions are mostly empty or cause deserialization errors if at
least one partition is empty)
> Shuffling with more than 2000 reducers may drop all data when partitons 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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