[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16056118#comment-16056118 ] Thomas Graves commented on SPARK-21082: --- Yes executor should not oom if you are try

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16050016#comment-16050016 ] Saisai Shao commented on SPARK-21082: - That's fine if the storage memory is not enoug

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16049965#comment-16049965 ] DjvuLee commented on SPARK-21082: - Data locality, input size for task, scheduling order a

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16049940#comment-16049940 ] Saisai Shao commented on SPARK-21082: - Fast node actually equals to idle node, since

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16049933#comment-16049933 ] DjvuLee commented on SPARK-21082: - Not a really fast node and slow node problem. Even al

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16049906#comment-16049906 ] Saisai Shao commented on SPARK-21082: - Is it due to fast node and slow node problem?

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048835#comment-16048835 ] DjvuLee commented on SPARK-21082: - Yes, one of the reason why Spark do not balance tasks

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048821#comment-16048821 ] Sean Owen commented on SPARK-21082: --- This doesn't address my question about locality. I

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048654#comment-16048654 ] DjvuLee commented on SPARK-21082: - My idea is try to consider the BlockManger information

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048653#comment-16048653 ] DjvuLee commented on SPARK-21082: - [~srowen] This situation occurred when the partition n

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048270#comment-16048270 ] Sean Owen commented on SPARK-21082: --- I don't see how this would interact with, for exam

[jira] [Commented] (SPARK-21082) Consider Executor's memory usage when scheduling task

[ https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048146#comment-16048146 ] DjvuLee commented on SPARK-21082: - If this feature is a good suggestion(we encounter this