[jira] [Updated] (SPARK-4737) Prevent serialization errors from ever crashing the DAG scheduler
[ https://issues.apache.org/jira/browse/SPARK-4737?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Patrick Wendell updated SPARK-4737: --- Affects Version/s: 1.0.2 1.1.1 > Prevent serialization errors from ever crashing the DAG scheduler > - > > Key: SPARK-4737 > URL: https://issues.apache.org/jira/browse/SPARK-4737 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 1.0.2, 1.1.1, 1.2.0 >Reporter: Patrick Wendell >Assignee: Matthew Cheah >Priority: Blocker > > Currently in Spark we assume that when tasks are serialized in the > TaskSetManager that the serialization cannot fail. We assume this because > upstream in the DAGScheduler we attempt to catch any serialization errors by > serializing a single partition. However, in some cases this upstream test is > not accurate - i.e. an RDD can have one partition that can serialize cleanly > but not others. > Do do this in the proper way we need to catch and propagate the exception at > the time of serialization. The tricky bit is making sure it gets propagated > in the right way. -- 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
[jira] [Updated] (SPARK-4737) Prevent serialization errors from ever crashing the DAG scheduler
[ https://issues.apache.org/jira/browse/SPARK-4737?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Andrew Or updated SPARK-4737: - Affects Version/s: 1.2.0 > Prevent serialization errors from ever crashing the DAG scheduler > - > > Key: SPARK-4737 > URL: https://issues.apache.org/jira/browse/SPARK-4737 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 1.2.0 >Reporter: Patrick Wendell >Assignee: Matthew Cheah >Priority: Blocker > > Currently in Spark we assume that when tasks are serialized in the > TaskSetManager that the serialization cannot fail. We assume this because > upstream in the DAGScheduler we attempt to catch any serialization errors by > serializing a single partition. However, in some cases this upstream test is > not accurate - i.e. an RDD can have one partition that can serialize cleanly > but not others. > Do do this in the proper way we need to catch and propagate the exception at > the time of serialization. The tricky bit is making sure it gets propagated > in the right way. -- 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
[jira] [Updated] (SPARK-4737) Prevent serialization errors from ever crashing the DAG scheduler
[ https://issues.apache.org/jira/browse/SPARK-4737?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Patrick Wendell updated SPARK-4737: --- Component/s: Spark Core > Prevent serialization errors from ever crashing the DAG scheduler > - > > Key: SPARK-4737 > URL: https://issues.apache.org/jira/browse/SPARK-4737 > Project: Spark > Issue Type: Bug > Components: Spark Core >Reporter: Patrick Wendell >Assignee: Matthew Cheah >Priority: Blocker > > Currently in Spark we assume that when tasks are serialized in the > TaskSetManager that the serialization cannot fail. We assume this because > upstream in the DAGScheduler we attempt to catch any serialization errors by > serializing a single partition. However, in some cases this upstream test is > not accurate - i.e. an RDD can have one partition that can serialize cleanly > but not others. > Do do this in the proper way we need to catch and propagate the exception at > the time of serialization. The tricky bit is making sure it gets propagated > in the right way. -- 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
