Re: Spark Streaming Shuffle to Disk
how often do you checkpoint? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shuffle-to-Disk-tp25567p25682.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Spark Streaming Shuffle to Disk
UpdateStateByKey and your batch data could be filling up your executor memory and hence it might be hitting the disk, you can verify it by looking at the memory footprint while your job is running. Looking at the executor logs will also give you a better understanding of whats going on. Thanks Best Regards On Fri, Dec 4, 2015 at 8:24 AM, Steven Pearson wrote: > I'm running a Spark Streaming job on 1.3.1 which contains an > updateStateByKey. The job works perfectly fine, but at some point (after a > few runs), it starts shuffling to disk no matter how much memory I give the > executors. > > I have tried changing --executor-memory on > spark-submit, spark.shuffle.memoryFraction, spark.storage.memoryFraction, > and spark.storage.unrollFraction. But no matter how I configure these, it > always spills to disk around 2.5GB. > > What is the best way to avoid spilling shuffle to disk? > >
Spark Streaming Shuffle to Disk
I'm running a Spark Streaming job on 1.3.1 which contains an updateStateByKey. The job works perfectly fine, but at some point (after a few runs), it starts shuffling to disk no matter how much memory I give the executors. I have tried changing --executor-memory on spark-submit, spark.shuffle.memoryFraction, spark.storage.memoryFraction, and spark.storage.unrollFraction. But no matter how I configure these, it always spills to disk around 2.5GB. What is the best way to avoid spilling shuffle to disk? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-Shuffle-to-Disk-tp25567.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Spark Streaming Shuffle to Disk
I'm running a Spark Streaming job on 1.3.1 which contains an updateStateByKey. The job works perfectly fine, but at some point (after a few runs), it starts shuffling to disk no matter how much memory I give the executors. I have tried changing --executor-memory on spark-submit, spark.shuffle.memoryFraction, spark.storage.memoryFraction, and spark.storage.unrollFraction. But no matter how I configure these, it always spills to disk around 2.5GB. What is the best way to avoid spilling shuffle to disk?