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https://issues.apache.org/jira/browse/SPARK-4073?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15111607#comment-15111607
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Juliet Hougland commented on SPARK-4073:
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For those playing along at home-- the solution for me was to set
spark.executor.extraJavaOptions=-XX:MaxDirectMemorySize=4G so that worker JVMs
had a much, much higher off heap memory limit.
> Parquet+Snappy can cause significant off-heap memory usage
> ----------------------------------------------------------
>
> Key: SPARK-4073
> URL: https://issues.apache.org/jira/browse/SPARK-4073
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.1.0
> Reporter: Patrick Wendell
> Priority: Critical
>
> The parquet snappy codec allocates off-heap buffers for decompression[1]. In
> one cases the observed size of these buffers was high enough to add several
> GB of data to the overall virtual memory usage of the Spark executor process.
> I don't understand enough about our use of Snappy to fully grok how much data
> we would _expect_ to be present in these buffers at any given time, but I can
> say a few things.
> 1. The dataset had individual rows that were fairly large, e.g. megabytes.
> 2. Direct buffers are not cleaned up until GC events, and overall there was
> not much heap contention. So maybe they just weren't being cleaned.
> I opened PARQUET-118 to see if they can provide an option to use on-heap
> buffers for decompression. In the mean time, we could consider changing the
> default back to gzip, or we could do nothing (not sure how many other users
> will hit this).
> [1]
> https://github.com/apache/incubator-parquet-mr/blob/master/parquet-hadoop/src/main/java/parquet/hadoop/codec/SnappyDecompressor.java#L28
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