Hi Jacek. I 'm not using SparkSql, I'm using RDD API directly. I can confirm that the jobs and stages are the same on both executions. In the environment tab of the web UI, when using spark 2.4 spark.default.parallelism=128 is shown while in 2.3.1 is not. But in 2.3.1 should be the same, because 128 is the number of cores of cluster * 2 and it didn't change in the latest version.
In the example I gave, 5580 is the number of parts left by a previous job in S3, in Hadoop sequence files. So the initial RDD has 5580 partitions. While in 2.3.1, RDDs that are created with transformations from the initial RDD conserve the same number of partitions, in 2.4 the number of partitions reset to default. So RDD1, the product of the first mapToPair, prints 5580 when getPartitions() is called in 2.3.1, while prints 128 in 2.4. Regards, Pedro El mar., 12 de feb. de 2019 a la(s) 09:13, Jacek Laskowski (ja...@japila.pl) escribió: > Hi, > > Can you show the plans with explain(extended=true) for both versions? > That's where I'd start to pinpoint the issue. Perhaps the underlying > execution engine change to affect keyBy? Dunno and guessing... > > Pozdrawiam, > Jacek Laskowski > ---- > https://about.me/JacekLaskowski > Mastering Spark SQL https://bit.ly/mastering-spark-sql > Spark Structured Streaming https://bit.ly/spark-structured-streaming > Mastering Kafka Streams https://bit.ly/mastering-kafka-streams > Follow me at https://twitter.com/jaceklaskowski > > > On Fri, Feb 8, 2019 at 5:09 PM Pedro Tuero <tuerope...@gmail.com> wrote: > >> I did a repartition to 10000 (hardcoded) before the keyBy and it ends in >> 1.2 minutes. >> The questions remain open, because I don't want to harcode paralellism. >> >> El vie., 8 de feb. de 2019 a la(s) 12:50, Pedro Tuero ( >> tuerope...@gmail.com) escribió: >> >>> 128 is the default parallelism defined for the cluster. >>> The question now is why keyBy operation is using default parallelism >>> instead of the number of partition of the RDD created by the previous step >>> (5580). >>> Any clues? >>> >>> El jue., 7 de feb. de 2019 a la(s) 15:30, Pedro Tuero ( >>> tuerope...@gmail.com) escribió: >>> >>>> Hi, >>>> I am running a job in spark (using aws emr) and some stages are taking >>>> a lot more using spark 2.4 instead of Spark 2.3.1: >>>> >>>> Spark 2.4: >>>> [image: image.png] >>>> >>>> Spark 2.3.1: >>>> [image: image.png] >>>> >>>> With Spark 2.4, the keyBy operation take more than 10X what it took >>>> with Spark 2.3.1 >>>> It seems to be related to the number of tasks / partitions. >>>> >>>> Questions: >>>> - Is it not supposed that the number of task of a job is related to >>>> number of parts of the RDD left by the previous job? Did that change in >>>> version 2.4?? >>>> - Which tools/ configuration may I try, to reduce this aberrant >>>> downgrade of performance?? >>>> >>>> Thanks. >>>> Pedro. >>>> >>>
