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https://issues.apache.org/jira/browse/SPARK-22062?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wenchen Fan resolved SPARK-22062.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.3.0
Issue resolved by pull request 19476
[https://github.com/apache/spark/pull/19476]
> BlockManager does not account for memory consumed by remote fetches
> -------------------------------------------------------------------
>
> Key: SPARK-22062
> URL: https://issues.apache.org/jira/browse/SPARK-22062
> Project: Spark
> Issue Type: Bug
> Components: Block Manager
> Affects Versions: 2.2.0
> Reporter: Sergei Lebedev
> Priority: Minor
> Fix For: 2.3.0
>
>
> We use Spark exclusively with {{StorageLevel.DiskOnly}} as our workloads are
> very sensitive to memory usage. Recently, we've spotted that the jobs
> sometimes OOM leaving lots of byte[] arrays on the heap. Upon further
> investigation, we've found that the arrays come from
> {{BlockManager.getRemoteBytes}}, which
> [calls|https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L638]
> {{BlockTransferService.fetchBlockSync}}, which in its turn would
> [allocate|https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/network/BlockTransferService.scala#L99]
> an on-heap {{ByteBuffer}} of the same size as the block (e.g. full
> partition), if the block was successfully retrieved over the network.
> This memory is not accounted towards Spark storage/execution memory and could
> potentially lead to OOM if {{BlockManager}} fetches too many partitions in
> parallel. I wonder if this is intentional behaviour, or in fact a bug?
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