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https://issues.apache.org/jira/browse/SPARK-9103?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16182372#comment-16182372
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Steve Loughran commented on SPARK-9103:
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If it helps, most uses of ByteBuffer in hadoop core & HDFS pool their buffers
through {{org.apache.hadoop.io.ByteBufferPool}} and
{{org.apache.hadoop.util.DirectBufferPool}} ...if some instrumentation could be
added there just to measure pool size, and pools were created with an owner
name, then reporting could help apportion blame. Rather than just say "512MB
were bytebuffers", it could say "DFS Client used 189MB; CryptoInputStream 32MB,
etc.
> Tracking spark's memory usage
> -----------------------------
>
> Key: SPARK-9103
> URL: https://issues.apache.org/jira/browse/SPARK-9103
> Project: Spark
> Issue Type: Umbrella
> Components: Spark Core, Web UI
> Reporter: Zhang, Liye
> Attachments: Tracking Spark Memory Usage - Phase 1.pdf
>
>
> Currently spark only provides little memory usage information (RDD cache on
> webUI) for the executors. User have no idea on what is the memory consumption
> when they are running spark applications with a lot of memory used in spark
> executors. Especially when they encounter the OOM, it’s really hard to know
> what is the cause of the problem. So it would be helpful to give out the
> detail memory consumption information for each part of spark, so that user
> can clearly have a picture of where the memory is exactly used.
> The memory usage info to expose should include but not limited to shuffle,
> cache, network, serializer, etc.
> User can optionally choose to open this functionality since this is mainly
> for debugging and tuning.
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