[ https://issues.apache.org/jira/browse/SPARK-12554?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15074543#comment-15074543 ]
Lijie Xu commented on SPARK-12554: ---------------------------------- I agree that it is a design problem. {{spark.scheduler.maxRegisteredResourcesWaitingTime}} is a powerful parameter, but it still suffers from two problems. (1) Although case 2 is rare, it cannot be resolved by {{spark.scheduler.maxRegisteredResourcesWaitingTime}}. The reason is that {{keepScheduling}} is FALSE at the first time of allocation and no executor will be allocated (i.e., canLaunchExecutor) to the app even the cluster has enough cores. (2) {{spark.scheduler.maxRegisteredResourcesWaitingTime}} can resolve case 1. However, The app needs to wait 30s and then wait for reusing the executor whose tasks have finished. As a result, the app's performance is not very well. The dilemma is that whether we should allocate a new executor to the *extra* cores (i.e., {{spark.cores.max % spark.executor.cores}}). Maybe we can design a flexible algorithm for the tradeoff between performance and resources. For example, if {{extra cores / spark.executor.cores >= 0.75}} or {{spark.executor.cores <= 8}, we can allocate a new executor to the app. > 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* > Case 1: > 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. > Case 2: 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