in spark, every partition needs to fit in the memory available to the core processing it.
as you coalesce you reduce number of partitions, increasing partition size. at some point the partition no longer fits in memory. On Fri, Feb 12, 2016 at 4:50 PM, Silvio Fiorito < silvio.fior...@granturing.com> wrote: > Coalesce essentially reduces parallelism, so fewer cores are getting more > records. Be aware that it could also lead to loss of data locality, > depending on how far you reduce. Depending on what you’re doing in the map > operation, it could lead to OOM errors. Can you give more details as to > what the code for the map looks like? > > > > > On 2/12/16, 1:13 PM, "Christopher Brady" <christopher.br...@oracle.com> > wrote: > > >Can anyone help me understand why using coalesce causes my executors to > >crash with out of memory? What happens during coalesce that increases > >memory usage so much? > > > >If I do: > >hadoopFile -> sample -> cache -> map -> saveAsNewAPIHadoopFile > > > >everything works fine, but if I do: > >hadoopFile -> sample -> coalesce -> cache -> map -> saveAsNewAPIHadoopFile > > > >my executors crash with out of memory exceptions. > > > >Is there any documentation that explains what causes the increased > >memory requirements with coalesce? It seems to be less of a problem if I > >coalesce into a larger number of partitions, but I'm not sure why this > >is. How would I estimate how much additional memory the coalesce requires? > > > >Thanks. > > > >--------------------------------------------------------------------- > >To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > >For additional commands, e-mail: user-h...@spark.apache.org > > >
