Hi, Both jobs use spark.dynamicAllocation.enabled so there's no need to change the number of executors. There are 702 executors in the Dataproc cluster so this is not the problem. About number of partitions - this I didn't change and it's still 400. While writing this now, I am realising that I have more partitions than executors, but the same situation applies to EMR.
I am observing 1 task in the final stage also on EMR. The difference is that on EMR that task receives 50K volume of data and on Dataproc it receives 700gb. I don't understand why it's happening. It can mean that the graph is different. But the job is exactly the same. Could it be because the minor version of Spark is different? On Wed, May 25, 2022 at 12:27 AM Ranadip Chatterjee <ranadi...@gmail.com> wrote: > Hi Ori, > > A single task for the final step can result from various scenarios like an > aggregate operation that results in only 1 value (e.g count) or a key based > aggregate with only 1 key for example. There could be other scenarios as > well. However, that would be the case in both EMR and Dataproc if the same > code is run on the same data in both cases. > > On a separate note, since you have now changed the size and number of > nodes, you may need to re-optimize the number and size of executors for the > job and perhaps the number of partitions as well to optimally use the > cluster resources. > > Regards, > Ranadip > > On Tue, 24 May 2022, 10:45 Ori Popowski, <ori....@gmail.com> wrote: > >> Hello >> >> I migrated a job from EMR with Spark 2.4.4 to Dataproc with Spark 2.4.8. >> I am creating a cluster with the exact same configuration, where the only >> difference is that the original cluster uses 78 workers with 96 CPUs and >> 768GiB memory each, and in the new cluster I am using 117 machines with 64 >> CPUs and 512GiB each, to achieve the same amount of resources in the >> cluster. >> >> The job is run with the same configuration (num of partitions, >> parallelism, etc.) and reads the same data. However, something strange >> happens and the job takes 20 hours. What I observed is that there is a >> stage where the driver instantiates a single task, and this task never >> starts because the shuffle of moving all the data to it takes forever. >> >> I also compared the runtime configuration and found some minor >> differences (due to Dataproc being different from EMR) but I haven't found >> any substantial difference. >> >> In other stages the cluster utilizes all the partitions (400), and it's >> not clear to me why it decides to invoke a single task. >> >> Can anyone provide an insight as to why such a thing would happen? >> >> Thanks >> >> >> --------------------------------------------------------------------- >> To unsubscribe e-mail: user-unsubscr...@spark.apache.org > >
