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https://issues.apache.org/jira/browse/HIVE-19937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16537362#comment-16537362
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Misha Dmitriev commented on HIVE-19937:
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The analysis above looks good. I remember seeing similar issues with Kryo (and
in general, all other kinds of serialization) in the past, and dealing with
them in a similar way.
The new patch looks good to me. I assume that you analyzed a heap dump before
and after this change and verified that the problems that you wanted to address
indeed went away. A few small comments/nits:
* MapOperator.java, 'contexts = new LinkedHashMap<Operator<?>, MapOpCtx>()' -
if this code has any idea of the expected size of LinkedHashMap, you may want
to create it with the appropriate capacity. This is especially relevant when
such maps are small - then the default capacity of 16 makes them waste a lot of
memory.
* MapWork.java, 'if (includedBuckets != null) { this.includedBuckets = ...' -
you can make this code a bit shorter using the conditional operator, i.e.
'this.includedBuckets = (includedBuckets != null) ? includedBuckets : null'
Same in several other methods.
> Use BeanSerializer for MapWork to carry calls to String.intern
> --------------------------------------------------------------
>
> Key: HIVE-19937
> URL: https://issues.apache.org/jira/browse/HIVE-19937
> Project: Hive
> Issue Type: Improvement
> Components: Spark
> Reporter: Sahil Takiar
> Assignee: Sahil Takiar
> Priority: Major
> Attachments: HIVE-19937.1.patch, HIVE-19937.2.patch, report.html
>
>
> When fixing HIVE-16395, we decided that each new Spark task should clone the
> {{JobConf}} object to prevent any {{ConcurrentModificationException}} from
> being thrown. However, setting this variable comes at a cost of storing a
> duplicate {{JobConf}} object for each Spark task. These objects can take up a
> significant amount of memory, we should intern them so that Spark tasks
> running in the same JVM don't store duplicate copies.
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