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From Alex Rovner <alex.rov...@magnetic.com>
Subject Re: How to optimize group by query fired using hiveContext.sql?
Date Sat, 03 Oct 2015 13:41:18 GMT
Can you send over your yarn logs along with the command you are using to
submit your job?

*Alex Rovner*
*Director, Data Engineering *
*o:* 646.759.0052

* <http://www.magnetic.com/>*

On Sat, Oct 3, 2015 at 9:07 AM, Umesh Kacha <umesh.kacha@gmail.com> wrote:

> Hi Alex thanks much for the reply. Please read the following for more
> details about my problem.
>
>
> http://stackoverflow.com/questions/32317285/spark-executor-oom-issue-on-yarn
>
> My each container has 8 core and 30 GB max memory. So I am using
> yarn-client mode using 40 executors with 27GB/2 cores. If I use more cores
> then my job start loosing more executors. I tried to set
> spark.yarn.executor.memoryOverhead around 2 GB even 8 GB but it does not
> help I loose executors no matter what. The reason is my jobs shuffles lots
> of data even 20 GB of data in every job in UI I have seen it. Shuffle
> happens because of group by and I cant avoid it in my case.
>
>
>
> On Sat, Oct 3, 2015 at 6:27 PM, Alex Rovner <alex.rovner@magnetic.com>
> wrote:
>
>> This sounds like you need to increase YARN overhead settings with the "spark.yarn.executor.memoryOverhead"
>> parameter. See http://spark.apache.org/docs/latest/running-on-yarn.html
>> for more information on the setting.
>>
>> If that does not work for you, please provide the error messages and the
>> command line you are using to submit your jobs for further troubleshooting.
>>
>>
>> *Alex Rovner*
>> *Director, Data Engineering *
>> *o:* 646.759.0052
>>
>> * <http://www.magnetic.com/>*
>>
>> On Sat, Oct 3, 2015 at 6:19 AM, unk1102 <umesh.kacha@gmail.com> wrote:
>>
>>> Hi I have couple of Spark jobs which uses group by query which is getting
>>> fired from hiveContext.sql() Now I know group by is evil but my use case
>>> I
>>> cant avoid group by I have around 7-8 fields on which I need to do group
>>> by.
>>> Also I am using df1.except(df2) which also seems heavy operation and does
>>> lots of shuffling please see my UI snap
>>> <
>>> http://apache-spark-user-list.1001560.n3.nabble.com/file/n24914/IMG_20151003_151830218.jpg
>>> >
>>>
>>> I have tried almost all optimisation including Spark 1.5 but nothing
>>> seems
>>> to be working and my job fails hangs because of executor will reach
>>> physical
>>> memory limit and YARN will kill it. I have around 1TB of data to process
>>> and
>>> it is skewed. Please guide.
>>>
>>>
>>>
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>>
>

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