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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2022/08/03 00:17:00 UTC

[jira] [Commented] (SPARK-39955) Improve LaunchTask process to avoid Stage failures caused by fail-to-send LaunchTask messages

    [ https://issues.apache.org/jira/browse/SPARK-39955?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17574432#comment-17574432 ] 

Apache Spark commented on SPARK-39955:
--------------------------------------

User 'kevin85421' has created a pull request for this issue:
https://github.com/apache/spark/pull/37384

> Improve LaunchTask process to avoid Stage failures caused by fail-to-send LaunchTask messages
> ---------------------------------------------------------------------------------------------
>
>                 Key: SPARK-39955
>                 URL: https://issues.apache.org/jira/browse/SPARK-39955
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 3.4.0
>            Reporter: Kai-Hsun Chen
>            Priority: Major
>
> There are two possible reasons, including Network Failure and Task Failure, to make RPC failures.
> (1) Task Failure: The network is good, but the task causes the executor's JVM crash. Hence, RPC fails.
> (2) Network Failure: The executor works well, but the network between Driver and Executor is broken. Hence, RPC fails.
> We should handle these two different kinds of failure in different ways. First, if the failure is Task Failure, we should increment the variable `{{{}numFailures`{}}}. If the value of {{`numFailures`}} is larger than a threshold, Spark will label the job failed. Second, if the failure is Network Failure, we will not increment the variable `{{{}numFailures`{}}}. We will just assign the task to a new executor. Hence, the job will not be recognized as failed due to Network Failure.
> However, currently, Spark recognizes every RPC failure as Task Failure. Hence, it will cause extra Spark job failures.



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