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From "Hive QA (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-19937) Intern fields in MapWork on deserialization
Date Fri, 20 Jul 2018 07:41:00 GMT

    [ https://issues.apache.org/jira/browse/HIVE-19937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16550341#comment-16550341
] 

Hive QA commented on HIVE-19937:
--------------------------------



Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12932285/HIVE-19937.4.patch

{color:red}ERROR:{color} -1 due to build exiting with an error

Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/12721/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/12721/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-12721/

Messages:
{noformat}
Executing org.apache.hive.ptest.execution.TestCheckPhase
Tests exited with: Exception: Patch URL https://issues.apache.org/jira/secure/attachment/12932285/HIVE-19937.4.patch
was found in seen patch url's cache and a test was probably run already on it. Aborting...
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12932285 - PreCommit-HIVE-Build

> 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, HIVE-19937.4.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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