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Andrew Or commented on SPARK-12554: ----------------------------------- I've downgraded the issue. I also think the behavior is by design. The semantics of `spark.executor.cores` is that each executor has exactly N number of cores. Your cluster "hanging" means it doesn't have enough resources to launch the expected number of executors, so you should provision more resources. > Standalone app scheduler will hang when app.coreToAssign < minCoresPerExecutor > ------------------------------------------------------------------------------ > > Key: SPARK-12554 > URL: https://issues.apache.org/jira/browse/SPARK-12554 > Project: Spark > Issue Type: Bug > Components: Deploy, Scheduler > Affects Versions: 1.5.2 > Reporter: Lijie Xu > > In scheduleExecutorsOnWorker() in Master.scala, > *val keepScheduling = coresToAssign >= minCoresPerExecutor* should be changed > to *val keepScheduling = coresToAssign > 0* > Suppose that an app's requested cores is 10 (i.e., spark.cores.max = 10) and > app.coresPerExecutor is 4 (i.e., spark.executor.cores = 4). > After allocating two executors (each has 4 cores) to this app, the > *app.coresToAssign = 2* and *minCoresPerExecutor = coresPerExecutor = 4*, so > *keepScheduling = false* and no extra executor will be allocated to this app. > If *spark.scheduler.minRegisteredResourcesRatio* is set to a large number > (e.g., > 0.8 in this case), the app will hang and never finish. > Another case: if a small app's coresPerExecutor is larger than its requested > cores (e.g., spark.cores.max = 10, spark.executor.cores = 16), *val > keepScheduling = coresToAssign >= minCoresPerExecutor* is always FALSE. As a > result, this app will never get an executor to run. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org