Not for the stage that fails, all it does is read and write - the number of tasks is # of cores * # of executor instances. For us that is 60 (3 cores 20 executors)
The input partition size for the failing stage, when spark reads the 20 files each 132M, it comes out to be 40 partitions. On Fri, Dec 20, 2019 at 4:40 PM Chris Teoh <chris.t...@gmail.com> wrote: > If you're using Spark SQL, that configuration setting causes a shuffle if > the number of your input partitions to the write is larger than that > configuration. > > Is there anything in the executor logs or the Spark UI DAG that indicates > a shuffle? I don't expect a shuffle if it is a straight write. What's the > input partition size? > > On Sat, 21 Dec 2019, 10:24 am Ruijing Li, <liruijin...@gmail.com> wrote: > >> Could you explain why shuffle partitions might be a good starting point? >> >> Some more details: when I write the output the first time after logic is >> complete, I repartition the files to 20 after having >> spark.sql.shuffle.partitions = 2000 so we don’t have too many small files. >> Data is small about 130MB per file. When spark reads it reads in 40 >> partitions and tries to output that to the different cluster. Unfortunately >> during that read and write stage executors drop off. >> >> We keep hdfs block 128Mb >> >> On Fri, Dec 20, 2019 at 3:01 PM Chris Teoh <chris.t...@gmail.com> wrote: >> >>> spark.sql.shuffle.partitions might be a start. >>> >>> Is there a difference in the number of partitions when the parquet is >>> read to spark.sql.shuffle.partitions? Is it much higher than >>> spark.sql.shuffle.partitions? >>> >>> On Fri, 20 Dec 2019, 7:34 pm Ruijing Li, <liruijin...@gmail.com> wrote: >>> >>>> Hi all, >>>> >>>> I have encountered a strange executor OOM error. I have a data pipeline >>>> using Spark 2.3 Scala 2.11.12. This pipeline writes the output to one HDFS >>>> location as parquet then reads the files back in and writes to multiple >>>> hadoop clusters (all co-located in the same datacenter). It should be a >>>> very simple task, but executors are being killed off exceeding container >>>> thresholds. From logs, it is exceeding given memory (using Mesos as the >>>> cluster manager). >>>> >>>> The ETL process works perfectly fine with the given resources, doing >>>> joins and adding columns. The output is written successfully the first >>>> time. *Only when the pipeline at the end reads the output from HDFS >>>> and writes it to different HDFS cluster paths does it fail.* (It does >>>> a spark.read.parquet(source).write.parquet(dest)) >>>> >>>> This doesn't really make sense and I'm wondering what configurations I >>>> should start looking at. >>>> >>>> -- >>>> Cheers, >>>> Ruijing Li >>>> -- >>>> Cheers, >>>> Ruijing Li >>>> >>> -- >> Cheers, >> Ruijing Li >> > -- Cheers, Ruijing Li
