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https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16385978#comment-16385978
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Daniel Voros commented on HIVE-17684:
-------------------------------------
I've opened HIVE-18858 to track the issues caused by Hadoop not resolving
system properties.
> 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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