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https://issues.apache.org/jira/browse/HIVE-3997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13579018#comment-13579018
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Gopal V commented on HIVE-3997:
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All map-tasks happening in a single wave, but some of the hashtable generation
before the map-side task is taking 2x the time it took on the client node.
This is probably because of CPU starvation on the map-task because of too many
parallel tasks - couldn't find a way to tune down the map count per node from
12 (as it is doing now) because the NodeManager does not seem to have a tunable
for it (?).
> Use distributed cache to cache/localize dimension table & filter it in map
> task setup
> -------------------------------------------------------------------------------------
>
> Key: HIVE-3997
> URL: https://issues.apache.org/jira/browse/HIVE-3997
> Project: Hive
> Issue Type: Improvement
> Reporter: Gopal V
> Assignee: Gopal V
>
> The hive clients are not always co-located with the hadoop/hdfs cluster.
> This means that the dimension table filtering, when done on the client side
> becomes very slow. Not only that, the conversion of the small tables into
> hashtables has to be done every single time a query is run with different
> filters on the big table.
> That entire hashtable has to be part of the job, which involves even more
> HDFS writes from the far client side.
> Using the distributed cache also has the advantage that the localized files
> can be kept between jobs instead of firing off an HDFS read for every query.
> Moving the operator pipeline for the hash generation into the map task itself
> has perhaps a few cons.
> The map task might OOM due to this change, but it will take longer to recover
> until all the map attempts fail, instead of being conditional on the client.
> The client has no idea how much memory the hashtable needs and has to rely on
> the disk sizes (compressed sizes, perhaps) to determine if it needs to fall
> back onto a reduce-join instead.
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