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https://issues.apache.org/jira/browse/SPARK-21938?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16156181#comment-16156181
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Abbi McClintic commented on SPARK-21938:
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Unfortunately it appears to unpredictable and we're not able to reliably
reproduce the problem.
It occurs 1-3x/week on a daily job - we're essentially just using
df.write().csv("s3://some-bucket/some_location")
> Spark partial CSV write fails silently
> --------------------------------------
>
> Key: SPARK-21938
> URL: https://issues.apache.org/jira/browse/SPARK-21938
> Project: Spark
> Issue Type: Bug
> Components: Java API, Spark Core
> Affects Versions: 2.2.0
> Environment: Amazon EMR 5.8, varying instance types
> Reporter: Abbi McClintic
>
> Hello,
> My team has been experiencing a recurring unpredictable bug where only a
> partial write to CSV in S3 on one partition of our Dataset is performed. For
> example, in a Dataset of 10 partitions written to CSV in S3, we might see 9
> of the partitions as 2.8 GB in size, but one of them as 1.6 GB. However, the
> job does not exit with an error code.
> This becomes problematic in the following ways:
> 1. When we copy the data to Redshift, we get a bad decrypt error on the
> partial file, suggesting that the failure occurred at a weird byte in the
> file.
> 2. We lose data - sometimes as much as 10%.
> We don't see this problem with parquet, which we also use, but moving all of
> our data to parquet is not currently feasible. We're using the Java API.
> Any help on resolving this would be much appreciated.
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