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https://issues.apache.org/jira/browse/HIVE-11414?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15470173#comment-15470173
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Wang Zhiqiang commented on HIVE-11414:
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We met the same issue when processing too many small RCFiles. Any one is
working on this?
Set mapred.min.split.size.per.node to a small value can solve it, but we want
to set mapred.min.split.size.per.node to a bigger default value(eg, 32M), for
reducing map overhead.
> Fix OOM in MapTask with many input partitions with RCFile
> ---------------------------------------------------------
>
> Key: HIVE-11414
> URL: https://issues.apache.org/jira/browse/HIVE-11414
> Project: Hive
> Issue Type: Improvement
> Components: File Formats, Serializers/Deserializers
> Affects Versions: 0.11.0, 0.12.0, 0.14.0, 0.13.1, 1.2.0
> Reporter: Zheng Shao
> Priority: Minor
>
> MapTask hit OOM in the following situation in our production environment:
> * src: 2048 partitions, each with 1 file of about 2MB using RCFile format
> * query: INSERT OVERWRITE TABLE tgt SELECT * FROM src
> * Hadoop version: Both on CDH 4.7 using MR1 and CDH 5.4.1 using YARN.
> * MapTask memory Xmx: 1.5GB
> By analyzing the heap dump using jhat, we realized that the problem is:
> * One single mapper is processing many partitions (because of
> CombineHiveInputFormat)
> * Each input path (equivalent to partition here) will construct its own SerDe
> * Each SerDe will do its own caching of deserialized object (and try to reuse
> it), but will never release it (in this case, the
> serde2.columnar.ColumnarSerDeBase has a field cachedLazyStruct which can take
> a lot of space - pretty much the last N rows of a file where N is the number
> of rows in a columnar block).
> * This problem may exist in other SerDe as well, but columnar file format are
> affected the most because they need bigger cache for the last N rows instead
> of 1 row.
> Proposed solution:
> * Remove cachedLazyStruct in serde2.columnar.ColumnarSerDeBase. The cost
> saving of not recreating a single object is too small compared to processing
> N rows.
> Alternative solutions:
> * We can also free up the whole SerDe after processing a block/file. The
> problem with that is that the input splits may contain multiple blocks/files
> that maps to the same SerDe, and recreating a SerDe is a much bigger change
> to the code.
> * We can also move the SerDe creation/free-up to the place when input file
> changes. But that requires a much bigger change to the code.
> * We can also add a "cleanup()" method to SerDe interface that release the
> cached object, but that change is not backward compatible with many SerDes
> that people have wrote.
> * We can make cachedLazyStruct in serde2.columnar.ColumnarSerDeBase a weakly
> referenced object, but that feels like an overkill.
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