[jira] [Assigned] (SPARK-42577) A large stage could run indefinitely due to executor lost
[ https://issues.apache.org/jira/browse/SPARK-42577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Mridul Muralidharan reassigned SPARK-42577: --- Assignee: Tengfei Huang > A large stage could run indefinitely due to executor lost > - > > Key: SPARK-42577 > URL: https://issues.apache.org/jira/browse/SPARK-42577 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 3.0.3, 3.1.3, 3.2.3, 3.3.2 >Reporter: wuyi >Assignee: Tengfei Huang >Priority: Major > > When a stage is extremely large and Spark runs on spot instances or > problematic clusters with frequent worker/executor loss, the stage could run > indefinitely due to task rerun caused by the executor loss. This happens, > when the external shuffle service is on, and the large stages runs hours to > complete, when spark tries to submit a child stage, it will find the parent > stage - the large one, has missed some partitions, so the large stage has to > rerun. When it completes again, it finds new missing partitions due to the > same reason. > We should add a attempt limitation for this kind of scenario. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-42577) A large stage could run indefinitely due to executor lost
[ https://issues.apache.org/jira/browse/SPARK-42577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-42577: Assignee: (was: Apache Spark) > A large stage could run indefinitely due to executor lost > - > > Key: SPARK-42577 > URL: https://issues.apache.org/jira/browse/SPARK-42577 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 3.0.3, 3.1.3, 3.2.3, 3.3.2 >Reporter: wuyi >Priority: Major > > When a stage is extremely large and Spark runs on spot instances or > problematic clusters with frequent worker/executor loss, the stage could run > indefinitely due to task rerun caused by the executor loss. This happens, > when the external shuffle service is on, and the large stages runs hours to > complete, when spark tries to submit a child stage, it will find the parent > stage - the large one, has missed some partitions, so the large stage has to > rerun. When it completes again, it finds new missing partitions due to the > same reason. > We should add a attempt limitation for this kind of scenario. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-42577) A large stage could run indefinitely due to executor lost
[ https://issues.apache.org/jira/browse/SPARK-42577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-42577: Assignee: Apache Spark > A large stage could run indefinitely due to executor lost > - > > Key: SPARK-42577 > URL: https://issues.apache.org/jira/browse/SPARK-42577 > Project: Spark > Issue Type: Improvement > Components: Spark Core >Affects Versions: 3.0.3, 3.1.3, 3.2.3, 3.3.2 >Reporter: wuyi >Assignee: Apache Spark >Priority: Major > > When a stage is extremely large and Spark runs on spot instances or > problematic clusters with frequent worker/executor loss, the stage could run > indefinitely due to task rerun caused by the executor loss. This happens, > when the external shuffle service is on, and the large stages runs hours to > complete, when spark tries to submit a child stage, it will find the parent > stage - the large one, has missed some partitions, so the large stage has to > rerun. When it completes again, it finds new missing partitions due to the > same reason. > We should add a attempt limitation for this kind of scenario. -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
