[ https://issues.apache.org/jira/browse/SPARK-4962?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-4962: ----------------------------------- Assignee: (was: Apache Spark) > Put TaskScheduler.start back in SparkContext to shorten cluster resources > occupation period > ------------------------------------------------------------------------------------------- > > Key: SPARK-4962 > URL: https://issues.apache.org/jira/browse/SPARK-4962 > Project: Spark > Issue Type: Improvement > Components: Scheduler > Affects Versions: 1.0.0 > Reporter: YanTang Zhai > Priority: Minor > > When SparkContext object is instantiated, TaskScheduler is started and some > resources are allocated from cluster. However, these > resources may be not used for the moment. For example, > DAGScheduler.JobSubmitted is processing and so on. These resources are wasted > in > this period. Thus, we want to put TaskScheduler.start back to shorten cluster > resources occupation period specially for busy cluster. > TaskScheduler could be started just before running stages. > We could analyse and compare the resources occupation period before and > after optimization. > TaskScheduler.start execution time: [time1__] > DAGScheduler.JobSubmitted (excluding HadoopRDD.getPartitions or > TaskScheduler.start) execution time: [time2_] > HadoopRDD.getPartitions execution time: [time3___] > Stages execution time: [time4_____] > The cluster resources occupation period before optimization is > [time2_][time3___][time4_____]. > The cluster resources occupation period after optimization > is....[time3___][time4_____]. > In summary, the cluster resources > occupation period after optimization is less than before. > If HadoopRDD.getPartitions could be put forward (SPARK-4961), the period may > be shorten more which is [time4_____]. > The resources saving is important for busy cluster. -- 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