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https://issues.apache.org/jira/browse/SPARK-19659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15964596#comment-15964596
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jin xing commented on SPARK-19659:
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*bytesShuffleToMemory* is different from *bytesInFlight*. *bytesInFlight* is
for only one *ShuffleBlockFetcherIterator* and get decreased when the remote
blocks is received. *bytesShuffleToMemory* is for all
ShuffleBlockFetcherIterators and get decreased only when reference count of
ByteBuf is zero(though the memory maybe still inside cache and not really
destroyed).
If *spark.reducer.maxReqsInFlight* is only for memory control, I think
*spark.reducer.maxBytesShuffleToMemory* is an improvement. In the current PR, I
want to simplify the logic and the memory is tracked by *bytesShuffleToMemory*
and memory usage is not tracked by MemoryManager.
> Fetch big blocks to disk when shuffle-read
> ------------------------------------------
>
> Key: SPARK-19659
> URL: https://issues.apache.org/jira/browse/SPARK-19659
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle
> Affects Versions: 2.1.0
> Reporter: jin xing
> Attachments: SPARK-19659-design-v1.pdf, SPARK-19659-design-v2.pdf
>
>
> Currently the whole block is fetched into memory(offheap by default) when
> shuffle-read. A block is defined by (shuffleId, mapId, reduceId). Thus it can
> be large when skew situations. If OOM happens during shuffle read, job will
> be killed and users will be notified to "Consider boosting
> spark.yarn.executor.memoryOverhead". Adjusting parameter and allocating more
> memory can resolve the OOM. However the approach is not perfectly suitable
> for production environment, especially for data warehouse.
> Using Spark SQL as data engine in warehouse, users hope to have a unified
> parameter(e.g. memory) but less resource wasted(resource is allocated but not
> used),
> It's not always easy to predict skew situations, when happen, it make sense
> to fetch remote blocks to disk for shuffle-read, rather than
> kill the job because of OOM. This approach is mentioned during the discussion
> in SPARK-3019, by [~sandyr] and [~mridulm80]
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