[jira] [Commented] (HIVE-17684) HoS memory issues with MapJoinMemoryExhaustionHandler

["Daniel Voros (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16376846#comment-16376846 ] 

Daniel Voros commented on HIVE-17684:
-------------------------------------

[~aihuaxu], [~stakiar] are the test failures due to Hadoop not resolving system properties something you're actively working on? Should I open a new ticket for that?

> 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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