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https://issues.apache.org/jira/browse/HIVE-19937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16549704#comment-16549704
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Sahil Takiar commented on HIVE-19937:
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The overheads probably won't grow in proportion to the heap, but the goal is to
allow users to run Hive-on-Spark successfully even with low heap settings (e.g.
1g).
The overheads are more of a function of the workload. In this case, the
workload is TPC-DS (a standard SQL benchmarks). Hive users to run queries that
scan more partitions can expect the overheads to increase.
> Intern fields in MapWork on deserialization
> -------------------------------------------
>
> 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,
> HIVE-19937.3.patch, post-patch-report.html, 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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