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From vincent gromakowski <vincent.gromakow...@gmail.com>
Subject Re: Spark Optimization
Date Thu, 26 Apr 2018 16:40:06 GMT
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>
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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