[ 
https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16409583#comment-16409583
 ] 

Hive QA commented on HIVE-17684:
--------------------------------



Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12902915/HIVE-17684.02.patch

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

Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/9735/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/9735/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-9735/

Messages:
{noformat}
**** This message was trimmed, see log for full details ****
+ git gc
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+ cp -R . ../yetus_PreCommit-HIVE-Build-9735
+ mkdir /data/hiveptest/logs/PreCommit-HIVE-Build-9735/yetus
+ patchCommandPath=/data/hiveptest/working/scratch/smart-apply-patch.sh
+ patchFilePath=/data/hiveptest/working/scratch/build.patch
+ [[ -f /data/hiveptest/working/scratch/build.patch ]]
+ chmod +x /data/hiveptest/working/scratch/smart-apply-patch.sh
+ /data/hiveptest/working/scratch/smart-apply-patch.sh 
/data/hiveptest/working/scratch/build.patch
error: a/pom.xml: does not exist in index
error: a/ql/src/java/org/apache/hadoop/hive/ql/exec/HashTableSinkOperator.java: 
does not exist in index
error: a/ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java: does not 
exist in index
error: patch failed: pom.xml:144
Falling back to three-way merge...
Applied patch to 'pom.xml' cleanly.
error: patch failed: 
ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java:1554
Falling back to three-way merge...
Applied patch to 'ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java' 
with conflicts.
Going to apply patch with: git apply -p1
error: patch failed: pom.xml:144
Falling back to three-way merge...
Applied patch to 'pom.xml' cleanly.
error: patch failed: 
ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java:1554
Falling back to three-way merge...
Applied patch to 'ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java' 
with conflicts.
U ql/src/java/org/apache/hadoop/hive/ql/exec/Operator.java
'
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12902915 - PreCommit-HIVE-Build

> HoS memory issues with MapJoinMemoryExhaustionHandler
> -----------------------------------------------------
>
>                 Key: HIVE-17684
>                 URL: https://issues.apache.org/jira/browse/HIVE-17684
>             Project: Hive
>          Issue Type: Bug
>          Components: Spark
>            Reporter: Sahil Takiar
>            Assignee: Misha Dmitriev
>            Priority: Major
>         Attachments: HIVE-17684.01.patch, HIVE-17684.02.patch
>
>
> We have seen a number of memory issues due the {{HashSinkOperator}} use of 
> the {{MapJoinMemoryExhaustionHandler}}. This handler is meant to detect 
> scenarios where the small table is taking too much space in memory, in which 
> case a {{MapJoinMemoryExhaustionError}} is thrown.
> The configs to control this logic are:
> {{hive.mapjoin.localtask.max.memory.usage}} (default 0.90)
> {{hive.mapjoin.followby.gby.localtask.max.memory.usage}} (default 0.55)
> The handler works by using the {{MemoryMXBean}} and uses the following logic 
> to estimate how much memory the {{HashMap}} is consuming: 
> {{MemoryMXBean#getHeapMemoryUsage().getUsed() / 
> MemoryMXBean#getHeapMemoryUsage().getMax()}}
> The issue is that {{MemoryMXBean#getHeapMemoryUsage().getUsed()}} can be 
> inaccurate. The value returned by this method returns all reachable and 
> unreachable memory on the heap, so there may be a bunch of garbage data, and 
> the JVM just hasn't taken the time to reclaim it all. This can lead to 
> intermittent failures of this check even though a simple GC would have 
> reclaimed enough space for the process to continue working.
> We should re-think the usage of {{MapJoinMemoryExhaustionHandler}} for HoS. 
> In Hive-on-MR this probably made sense to use because every Hive task was run 
> in a dedicated container, so a Hive Task could assume it created most of the 
> data on the heap. However, in Hive-on-Spark there can be multiple Hive Tasks 
> running in a single executor, each doing different things.



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