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From Umesh Kacha <umesh.ka...@gmail.com>
Subject Re: Why dataframe.persist(StorageLevels.MEMORY_AND_DISK_SER) hangs for long time?
Date Sun, 11 Oct 2015 05:30:30 GMT
Hi Alex thanks for the response. I am using 40 executor with 30 gb
including 5 gb menoryOverhead and 4 cores. My cluster has around 100 nodes
with 30 gig and 8 cores.
On Oct 11, 2015 06:54, "Alex Rovner" <alex.rovner@magnetic.com> wrote:

> How many executors are you running with? How many nodes in your cluster?
>
> On Thursday, October 8, 2015, unk1102 <umesh.kacha@gmail.com> wrote:
>
>> Hi as recommended I am caching my Spark job dataframe as
>> dataframe.persist(StorageLevels.MEMORY_AND_DISK_SER) but what I see in
>> Spark
>> job UI is this persist stage runs for so long showing 10 GB of shuffle
>> read
>> and 5 GB of shuffle write it takes to long to finish and because of that
>> sometimes my Spark job throws timeout or throws OOM and hence executors
>> gets
>> killed by YARN. I am using Spark 1.4.1. I am using all sort of
>> optimizations
>> like Tungsten, Kryo I have given storage.memoryFraction as 0.2 and
>> storage.shuffle as 0.2 also. My data is huge around 1 TB I am using
>> default
>> 200 partitions for spark.sql.shuffle.partitions. Please help me I am
>> clueless please guide.
>>
>>
>>
>> --
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>>
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>
> --
> *Alex Rovner*
> *Director, Data Engineering *
> *o:* 646.759.0052
>
> * <http://www.magnetic.com/>*
>
>

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