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From Jörn Franke <jornfra...@gmail.com>
Subject Re: Spark hive overwrite is very very slow
Date Sun, 20 Aug 2017 16:42:38 GMT
Have you made sure that the saveastable stores them as parquet?

> On 20. Aug 2017, at 18:07, KhajaAsmath Mohammed <mdkhajaasmath@gmail.com> wrote:
> 
> we are using parquet tables, is it causing any performance issue?
> 
>> On Sun, Aug 20, 2017 at 9:09 AM, Jörn Franke <jornfranke@gmail.com> wrote:
>> Improving the performance of Hive can be also done by switching to Tez+llap as an
engine.
>> Aside from this : you need to check what is the default format that it writes to
Hive. One issue for the slow storing into a hive table could be that it writes by default
to csv/gzip or csv/bzip2
>> 
>> > On 20. Aug 2017, at 15:52, KhajaAsmath Mohammed <mdkhajaasmath@gmail.com>
wrote:
>> >
>> > Yes we tried hive and want to migrate to spark for better performance. I am
using paraquet tables . Still no better performance while loading.
>> >
>> > Sent from my iPhone
>> >
>> >> On Aug 20, 2017, at 2:24 AM, Jörn Franke <jornfranke@gmail.com> wrote:
>> >>
>> >> Have you tried directly in Hive how the performance is?
>> >>
>> >> In which Format do you expect Hive to write? Have you made sure it is in
this format? It could be that you use an inefficient format (e.g. CSV + bzip2).
>> >>
>> >>> On 20. Aug 2017, at 03:18, KhajaAsmath Mohammed <mdkhajaasmath@gmail.com>
wrote:
>> >>>
>> >>> Hi,
>> >>>
>> >>> I have written spark sql job on spark2.0 by using scala . It is just
pulling the data from hive table and add extra columns , remove duplicates and then write
it back to hive again.
>> >>>
>> >>> In spark ui, it is taking almost 40 minutes to write 400 go of data.
Is there anything that I need to improve performance .
>> >>>
>> >>> Spark.sql.partitions is 2000 in my case with executor memory of 16gb
and dynamic allocation enabled.
>> >>>
>> >>> I am doing insert overwrite on partition by
>> >>> Da.write.mode(overwrite).insertinto(table)
>> >>>
>> >>> Any suggestions please ??
>> >>>
>> >>> Sent from my iPhone
>> >>> ---------------------------------------------------------------------
>> >>> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>> >>>
> 

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