Al M created SPARK-24474: ---------------------------- Summary: Cores are left idle when there are a lot of stages to run Key: SPARK-24474 URL: https://issues.apache.org/jira/browse/SPARK-24474 Project: Spark Issue Type: Bug Components: Scheduler Affects Versions: 2.2.0 Reporter: Al M
I've observed an issue happening consistently when: * A job contains a join of two datasets * One dataset is much larger than the other * Both datasets require some processing before they are joined What I have observed is: * 2 stages are initially active to run processing on the two datasets ** These stages are run in parallel ** One stage has significantly more tasks than the other (e.g. one has 30k tasks and the other has 2k tasks) ** Spark allocates a similar (though not exactly equal) number of cores to each stage * First stage completes (for the smaller dataset) ** Now there is only one stage running ** It still has many tasks left (usually > 20k tasks) ** Around half the cores are idle (e.g. Total Cores = 200, active tasks = 103) ** This continues until the second stage completes * Second stage completes, and third begins (the stage that actually joins the data) ** This stage works fine, no cores are idle (e.g. Total Cores = 200, active tasks = 200) Other interesting things about this: * It seems that when we have multiple stages active, and one of them finishes, it does not actually release any cores to other stages * I can't reproduce this locally on my machine, only on a cluster with YARN enabled -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org