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From "Misha Dmitriev (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-15882) HS2 generating high memory pressure with many partitions and concurrent queries
Date Thu, 23 Feb 2017 22:17:45 GMT

    [ https://issues.apache.org/jira/browse/HIVE-15882?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15881399#comment-15881399
] 

Misha Dmitriev commented on HIVE-15882:
---------------------------------------

I've just measured the CPU performance impact of my changes using the same benchmark with
the same high heap size (-Xmx3g) to exclude effects of excessive GC. I've measured the total
time spent in all beeline clients. To do that, I ran beeline clients with /usr/bin/time as

{code}
for i in `seq 1 50`; do /usr/bin/time -p -o hive-timings-withchanges.txt --append beeline
-u jdbc:hive2://localhost:10000 -n admin -p admin -e "select count(i_f_1) from misha_table;"
& done
{code}

I then calculated the sum of all timings in the file with another fun bash script:

{code}
sum=0; for s in `grep real hive-timings-withchanges.txt`; do t=${s/real/}; t=${t/\.*/}; echo
$t; sum=$((sum+t)); done; echo $sum
{code}

The result is:
- before my changes: 17401s
- after my changes: 17012s

So, my changes have no negative CPU impact, and may even result in 1-2% CPU time improvement.
This is not surprising given that my changes reduce the number of objects in memory, and thus
ultimately reduce GC time.

Do I really need another JIRA ticket to post a patch that covers my other change (interning
Properties objects in PartitionDesc)?

> HS2 generating high memory pressure with many partitions and concurrent queries
> -------------------------------------------------------------------------------
>
>                 Key: HIVE-15882
>                 URL: https://issues.apache.org/jira/browse/HIVE-15882
>             Project: Hive
>          Issue Type: Improvement
>          Components: HiveServer2
>            Reporter: Misha Dmitriev
>            Assignee: Misha Dmitriev
>         Attachments: HIVE-15882.01.patch, hs2-crash-2000p-500m-50q.txt
>
>
> I've created a Hive table with 2000 partitions, each backed by two files, with one row
in each file. When I execute some number of concurrent queries against this table, e.g. as
follows
> {code}
> for i in `seq 1 50`; do beeline -u jdbc:hive2://localhost:10000 -n admin -p admin -e
"select count(i_f_1) from misha_table;" & done
> {code}
> it results in a big memory spike. With 20 queries I caused an OOM in a HS2 server with
-Xmx200m and with 50 queries - in the one with -Xmx500m.
> I am attaching the results of jxray (www.jxray.com) analysis of a heap dump that was
generated in the 50queries/500m heap scenario. It suggests that there are several opportunities
to reduce memory pressure with not very invasive changes to the code:
> 1. 24.5% of memory is wasted by duplicate strings (see section 6). With String.intern()
calls added in the ~10 relevant places in the code, this overhead can be highly reduced.
> 2. Almost 20% of memory is wasted due to various suboptimally used collections (see section
8). There are many maps and lists that are either empty or have just 1 element. By modifying
the code that creates and populates these collections, we may likely save 5-10% of memory.
> 3. Almost 20% of memory is used by instances of java.util.Properties. It looks like these
objects are highly duplicate, since for each Partition each concurrently running query creates
its own copy of Partion, PartitionDesc and Properties. Thus we have nearly 100,000 (50 queries
* 2,000 partitions) Properties in memory. By interning/deduplicating these objects we may
be able to save perhaps 15% of memory.
> So overall, I think there is a good chance to reduce HS2 memory consumption in this scenario
by ~40%.



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