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https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16296028#comment-16296028
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Sahil Takiar commented on HIVE-17684:
-------------------------------------
Thanks for taking this on [~mi...@cloudera.com]. Looks like Hive QA is failing
due to:
{code}
[ERROR] Failed to execute goal on project hive-shims-common: Could not resolve
dependencies for project
org.apache.hive.shims:hive-shims-common:jar:3.0.0-SNAPSHOT: The following
artifacts could not be resolved: org.apache.hadoop:hadoop-common:jar:3.0.0,
org.apache.hadoop:hadoop-auth:jar:3.0.0,
org.apache.hadoop:hadoop-hdfs-client:jar:3.0.0,
org.apache.hadoop:hadoop-yarn-api:jar:3.0.0,
org.apache.hadoop:hadoop-yarn-client:jar:3.0.0,
org.apache.hadoop:hadoop-mapreduce-client-core:jar:3.0.0,
org.apache.hadoop:hadoop-yarn-common:jar:3.0.0,
org.apache.hadoop:hadoop-annotations:jar:3.0.0: Could not find artifact
org.apache.hadoop:hadoop-common:jar:3.0.0 in datanucleus
{code}
Maybe the Hadoop 3.0.0 artifacts haven't propagated to datanucleus yet. Can you
build your patch locally?
> 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
> Attachments: HIVE-17684.01.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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