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From "Sahil Takiar (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-17684) HoS memory issues with MapJoinMemoryExhaustionHandler
Date Tue, 11 Sep 2018 15:11:00 GMT

    [ https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16610774#comment-16610774
] 

Sahil Takiar commented on HIVE-17684:
-------------------------------------

Sounds good to me.

> 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, HIVE-17684.03.patch, HIVE-17684.04.patch,
HIVE-17684.05.patch, HIVE-17684.06.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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