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 <mdkhajaasm...@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 <jornfra...@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 <mdkhajaasm...@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-unsubscr...@spark.apache.org >>> --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
