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https://issues.apache.org/jira/browse/SPARK-4452?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14222572#comment-14222572
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Sandy Ryza commented on SPARK-4452:
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[~tianshuo], I took a look at the patch, and the general approach looks
reasonable to me.
A couple additional thoughts that apply both to the current approach and
Tianshuo's patch:
* When we chain an ExternalAppendOnlyMap to an ExternalSorter for processing
combined map outputs in sort based shuffle, we end up double counting, no? Both
data structures will be holding references to the same objects and estimating
their size based on these objects.
* We could make the in-memory iterators destructive as well right? I.e. if the
data structures can release references to objects as they yield them, then we
can give memory back to the shuffle memory manager and make it available to
other data structures in the same thread.
If we can avoid double and holding on to unneeded objects, it would obviate
some of the need for intra-thread limits / forced spilling.
> Shuffle data structures can starve others on the same thread for memory
> ------------------------------------------------------------------------
>
> Key: SPARK-4452
> URL: https://issues.apache.org/jira/browse/SPARK-4452
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.1.0
> Reporter: Tianshuo Deng
> Assignee: Tianshuo Deng
> Priority: Critical
>
> When an Aggregator is used with ExternalSorter in a task, spark will create
> many small files and could cause too many files open error during merging.
> Currently, ShuffleMemoryManager does not work well when there are 2 spillable
> objects in a thread, which are ExternalSorter and ExternalAppendOnlyMap(used
> by Aggregator) in this case. Here is an example: Due to the usage of mapside
> aggregation, ExternalAppendOnlyMap is created first to read the RDD. It may
> ask as much memory as it can, which is totalMem/numberOfThreads. Then later
> on when ExternalSorter is created in the same thread, the
> ShuffleMemoryManager could refuse to allocate more memory to it, since the
> memory is already given to the previous requested
> object(ExternalAppendOnlyMap). That causes the ExternalSorter keeps spilling
> small files(due to the lack of memory)
> I'm currently working on a PR to address these two issues. It will include
> following changes:
> 1. The ShuffleMemoryManager should not only track the memory usage for each
> thread, but also the object who holds the memory
> 2. The ShuffleMemoryManager should be able to trigger the spilling of a
> spillable object. In this way, if a new object in a thread is requesting
> memory, the old occupant could be evicted/spilled. Previously the spillable
> objects trigger spilling by themselves. So one may not trigger spilling even
> if another object in the same thread needs more memory. After this change The
> ShuffleMemoryManager could trigger the spilling of an object if it needs to.
> 3. Make the iterator of ExternalAppendOnlyMap spillable. Previously
> ExternalAppendOnlyMap returns an destructive iterator and can not be spilled
> after the iterator is returned. This should be changed so that even after the
> iterator is returned, the ShuffleMemoryManager can still spill it.
> Currently, I have a working branch in progress:
> https://github.com/tsdeng/spark/tree/enhance_memory_manager. Already made
> change 3 and have a prototype of change 1 and 2 to evict spillable from
> memory manager, still in progress. I will send a PR when it's done.
> Any feedback or thoughts on this change is highly appreciated !
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