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From "Jungtaek Lim (Jira)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-27648) In Spark2.4 Structured Streaming:The executor storage memory increasing over time
Date Tue, 01 Oct 2019 02:27:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-27648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16941474#comment-16941474
] 

Jungtaek Lim commented on SPARK-27648:
--------------------------------------

I'll push the branch-2.4 version of code in my repo and comment. Btw, there's some discussion
on how to deal with this issue (and other issue) so the patch may not be shipped as it is.
Please refer https://github.com/apache/spark/pull/25779

> In Spark2.4 Structured Streaming:The executor storage memory increasing over time
> ---------------------------------------------------------------------------------
>
>                 Key: SPARK-27648
>                 URL: https://issues.apache.org/jira/browse/SPARK-27648
>             Project: Spark
>          Issue Type: Bug
>          Components: Structured Streaming
>    Affects Versions: 2.4.0
>            Reporter: tommy duan
>            Priority: Major
>         Attachments: houragg(1).out, houragg_filter.csv, houragg_with_state1_state2.csv,
houragg_with_state1_state2.xlsx, image-2019-05-09-17-51-14-036.png, image-2019-05-10-17-49-42-034.png,
image-2019-05-24-10-20-25-723.png, image-2019-05-27-10-10-30-460.png, image-2019-06-02-19-43-21-652.png
>
>
> *Spark Program Code Business:*
>  Read the topic on kafka, aggregate the stream data sources, and then output it to another
topic line of kafka.
> *Problem Description:*
>  *1) Using spark structured streaming in CDH environment (spark 2.2)*, memory overflow
problems often occur (because of too many versions of state stored in memory, this bug has
been modified in spark 2.4).
> {code:java}
> /spark-submit \
> --conf “spark.yarn.executor.memoryOverhead=4096M”
> --num-executors 15 \
> --executor-memory 3G \
> --executor-cores 2 \
> --driver-memory 6G \{code}
> {code}
> Executor memory exceptions occur when running with this submit resource under SPARK 2.2
and the normal running time does not exceed one day.
> The solution is to set the executor memory larger than before 
> {code:java}
>  My spark-submit script is as follows:
> /spark-submit\
> conf "spark. yarn. executor. memoryOverhead = 4096M"
> num-executors 15\
> executor-memory 46G\
> executor-cores 3\
> driver-memory 6G\
> ...{code}
> In this case, the spark program can be guaranteed to run stably for a long time, and
the executor storage memory is less than 10M (it has been running stably for more than 20
days).
> *2) From the upgrade information of Spark 2.4, we can see that the problem of large memory
consumption of state storage has been solved in Spark 2.4.* 
>  So we upgraded spark to SPARK 2.4 under CDH, tried to run the spark program, and found
that the use of memory was reduced.
>  But a problem arises, as the running time increases, the storage memory of executor
is growing (see Executors - > Storage Memory from the Spark on Yarn Resource Manager UI).
>  This program has been running for 14 days (under SPARK 2.2, running with this submit
resource, the normal running time is not more than one day, Executor memory abnormalities
will occur).
>  The script submitted by the program under spark2.4 is as follows:
> {code:java}
> /spark-submit \
>  --conf “spark.yarn.executor.memoryOverhead=4096M”
>  --num-executors 15 \
>  --executor-memory 3G \
>  --executor-cores 2 \
>  --driver-memory 6G 
> {code}
> Under Spark 2.4, I counted the size of executor memory as time went by during the running
of the spark program:
> |Run-time(hour)|Storage Memory size(MB)|Memory growth rate(MB/hour)|
> |23.5H|41.6MB/1.5GB|1.770212766|
> |108.4H|460.2MB/1.5GB|4.245387454|
> |131.7H|559.1MB/1.5GB|4.245254366|
> |135.4H|575MB/1.5GB|4.246676514|
> |153.6H|641.2MB/1.5GB|4.174479167|
> |219H|888.1MB/1.5GB|4.055251142|
> |263H|1126.4MB/1.5GB|4.282889734|
> |309H|1228.8MB/1.5GB|3.976699029|



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