Neither of those statements are true. You need more receivers if you want more parallelism. You don't have to manage offset positioning with the direct stream if you don't want to, as long as you can accept the limitations of Spark checkpointing.
On Fri, Oct 2, 2015 at 10:52 AM, <nib...@free.fr> wrote: > From my understanding as soon as I use YARN I don't need to use > parrallelisme (at least for RDD treatment) > I don't want to use direct stream as I have to manage the offset > positionning (in order to be able to start from the last offset treated > after a spark job failure) > > > ----- Mail original ----- > De: "Cody Koeninger" <c...@koeninger.org> > À: "Nicolas Biau" <nib...@free.fr> > Cc: "user" <user@spark.apache.org> > Envoyé: Vendredi 2 Octobre 2015 17:43:41 > Objet: Re: Spark Streaming over YARN > > > If you're using the receiver based implementation, and want more > parallelism, you have to create multiple streams and union them together. > > > Or use the direct stream. > > > On Fri, Oct 2, 2015 at 10:40 AM, < nib...@free.fr > wrote: > > > Hello, > I have a job receiving data from kafka (4 partitions) and persisting data > inside MongoDB. > It works fine, but when I deploy it inside YARN cluster (4 nodes with 2 > cores) only on node is receiving all the kafka partitions and only one node > is processing my RDD treatment (foreach function) > How can I force YARN to use all the resources nodes and cores to process > the data (receiver & RDD treatment) > > Tks a lot > Nicolas > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > > >
