Thanks Daniel! I've been wondering that for ages!
IE where my JDBC sourced datasets are coming up with 200 partitions on write to S3. What do you mean for (except for the initial read)? Can you explain that a bit further? Gary Lucas On 26 October 2017 at 11:28, Daniel Siegmann <dsiegm...@securityscorecard.io > wrote: > When working with datasets, Spark uses spark.sql.shuffle.partitions. It > defaults to 200. Between that and the default parallelism you can control > the number of partitions (except for the initial read). > > More info here: http://spark.apache.org/docs/latest/sql-programming-guide. > html#other-configuration-options > > I have no idea why it defaults to a fixed 200 (while default parallelism > defaults to a number scaled to your number of cores), or why there are two > separate configuration properties. > > > -- > Daniel Siegmann > Senior Software Engineer > *SecurityScorecard Inc.* > 214 W 29th Street, 5th Floor > <https://maps.google.com/?q=214+W+29th+Street,+5th+FloorNew+York,+NY+10001&entry=gmail&source=g> > New York, NY 10001 > <https://maps.google.com/?q=214+W+29th+Street,+5th+FloorNew+York,+NY+10001&entry=gmail&source=g> > > > On Thu, Oct 26, 2017 at 9:53 AM, Deepak Sharma <deepakmc...@gmail.com> > wrote: > >> I guess the issue is spark.default.parallelism is ignored when you are >> working with Data frames.It is supposed to work with only raw RDDs. >> >> Thanks >> Deepak >> >> On Thu, Oct 26, 2017 at 10:05 PM, Noorul Islam Kamal Malmiyoda < >> noo...@noorul.com> wrote: >> >>> Hi all, >>> >>> I have the following spark configuration >>> >>> spark.app.name=Test >>> spark.cassandra.connection.host=127.0.0.1 >>> spark.cassandra.connection.keep_alive_ms=5000 >>> spark.cassandra.connection.port=10000 >>> spark.cassandra.connection.timeout_ms=30000 >>> spark.cleaner.ttl=3600 >>> spark.default.parallelism=4 >>> spark.master=local[2] >>> spark.ui.enabled=false >>> spark.ui.showConsoleProgress=false >>> >>> Because I am setting spark.default.parallelism to 4, I was expecting >>> only 4 spark partitions. But it looks like it is not the case >>> >>> When I do the following >>> >>> df.foreachPartition { partition => >>> val groupedPartition = partition.toList.grouped(3).toList >>> println("Grouped partition " + groupedPartition) >>> } >>> >>> There are too many print statements with empty list at the top. Only >>> the relevant partitions are at the bottom. Is there a way to control >>> number of partitions? >>> >>> Regards, >>> Noorul >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org >>> >>> >> >> >> -- >> Thanks >> Deepak >> www.bigdatabig.com >> www.keosha.net >> > >
