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From Yash Sharma <yash...@gmail.com>
Subject Re: Quick question on spark performance
Date Sat, 21 May 2016 01:10:21 GMT
Am going with the default java opts for emr-
-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70
-XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled
-XX:OnOutOfMemoryError='kill -9 %p'

The data is not partitioned. Its 6Tb data of around 400 Megs gz files. The
workload is a scan/filter/reduceBy which needs to scan the entire data.



On Sat, May 21, 2016 at 11:07 AM, Yash Sharma <yash360@gmail.com> wrote:

> The median GC time is 1.3 mins for a median duration of 41 mins. What
> parameters can I tune for controlling GC.
>
> Other details, median Peak execution memory of 13 G and input records of
> 2.3 gigs.
> 180-200 executors launched.
>
> - Thanks, via mobile,  excuse brevity.
> On May 21, 2016 10:59 AM, "Reynold Xin" <rxin@databricks.com> wrote:
>
>> It's probably due to GC.
>>
>> On Fri, May 20, 2016 at 5:54 PM, Yash Sharma <yash360@gmail.com> wrote:
>>
>>> Hi All,
>>> I am here to get some expert advice on a use case I am working on.
>>>
>>> Cluster & job details below -
>>>
>>> Data - 6 Tb
>>> Cluster - EMR - 15 Nodes C3-8xLarge (shared by other MR apps)
>>>
>>> Parameters-
>>> --executor-memory 10G \
>>> --executor-cores 6 \
>>> --conf spark.dynamicAllocation.enabled=true \
>>> --conf spark.dynamicAllocation.initialExecutors=15 \
>>>
>>> Runtime : 3 Hrs
>>>
>>> On monitoring the metrics I notices 10G for executors is not required
>>> (since I don't have lot of groupings)
>>>
>>> Reducing to --executor-memory 3G, Runtime reduced to: 2 Hrs
>>>
>>> Question:
>>> On adding more nodes now has absolutely no effect on the runtime. Is
>>> there anything I can tune/change/experiment with to make the job faster.
>>>
>>> Workload: Mostly reduceBy's and scans.
>>>
>>> Would appreciate any insights and thoughts. Best Regards
>>>
>>>
>>>
>>

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