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From Pallavi Singh <pallavi_si...@persistent.com>
Subject RE: Spark Optimization
Date Fri, 27 Apr 2018 05:34:15 GMT
Thanks for your reply.

It is 64GB per node. We will try using UseParallelGC.

From: CPC [mailto:achalil@gmail.com]
Sent: Thursday, April 26, 2018 11:44 PM
To: vincent gromakowski <vincent.gromakowski@gmail.com>
Cc: Pallavi Singh <pallavi_singh@persistent.com>; user <user@spark.apache.org>
Subject: Re: Spark Optimization

I would recommend UseParallelGC since this is a batch job. Parallelization should be 2-3x
of cores. Also if those are physical machines i would recommend 9000 as network mtu. Is 128
gb per node or 64 gb per node?
On Thu, Apr 26, 2018, 7:40 PM vincent gromakowski <vincent.gromakowski@gmail.com<mailto:vincent.gromakowski@gmail.com>>
wrote:
Ideal parallelization is 2-3x the nb of cores. But it depends on the number of partitions
of your source and the operation you use (Shuffle or not). It can be worth paying the extra
cost of an initial repartition to match your cluster but it clearly depends on your DAG.
Optimizing spark apps depends on lots of thing, it's hard to answer
- cluster size
- scheduler
- spark version
- transformation graph (DAG)
...

Le jeu. 26 avr. 2018 à 17:49, Pallavi Singh <pallavi_singh@persistent.com<mailto:pallavi_singh@persistent.com>>
a écrit :
Hi Team,

We are currently working on POC based on Spark and Scala.
we have to read 18million records from parquet file and perform the 25 user defined aggregation
based on grouping keys.
we have used spark high level Dataframe API for the aggregation. On cluster of two node we
could finish end to end job ((Read+Aggregation+Write))in 2 min.

Cluster Information:
Number of Node:2
Total Core:28Core
Total RAM:128GB

Component:
Spark Core

Scenario:
How-to

Tuning Parameter:
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.default.parallelism 24
spark.sql.shuffle.partitions 24
spark.executor.extraJavaOptions -XX:+UseG1GC
spark.speculation true
spark.executor.memory 16G
spark.driver.memory 8G
spark.sql.codegen true
spark.sql.inMemoryColumnarStorage.batchSize 100000
spark.locality.wait 1s
spark.ui.showConsoleProgress false
spark.io.compression.codec org.apache.spark.io.SnappyCompressionCodec
Please let us know, If you have any ideas/tuning parameter that we can use to finish the job
in less than one min.


Regards,
Pallavi
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