You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "IsisPolei (Jira)" <ji...@apache.org> on 2025/04/24 01:18:00 UTC
[jira] [Resolved] (SPARK-48334) NettyServer doesn't shutdown if SparkContext initialize failed
[ https://issues.apache.org/jira/browse/SPARK-48334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
IsisPolei resolved SPARK-48334.
-------------------------------
Fix Version/s: 4.0.0
Resolution: Fixed
This issue does't exist in the master branch. The SparkEnv changed the initialization logic since 4.x, the memoryManager instanced alternatively . If anyone still use spark 3.x and encounter the same issue this change may give some ideas.
https://github.com/apache/spark/pull/50661
> NettyServer doesn't shutdown if SparkContext initialize failed
> --------------------------------------------------------------
>
> Key: SPARK-48334
> URL: https://issues.apache.org/jira/browse/SPARK-48334
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.1.3
> Reporter: IsisPolei
> Priority: Critical
> Labels: pull-request-available
> Fix For: 4.0.0
>
>
> When obtaining a SparkContext instance using SparkContext.getOrCreate(), if an exception occurs during initialization (such as using incorrect Spark parameters, e.g., spark.executor.memory=1 without units), the RpcServer started during this period will not be shut down, resulting in the port being occupied indefinitely.
> The action to close the RpcServer happens in _env.stop(), where rpcEnv.shutdown() is executed, but this action only occurs when _env != null (SparkContext.scala:2106, version 3.1.3). However, the error occurs during initialization, and _env is not instantiated, so _env.stop() will not be executed, leading to the RpcServer not being closed.
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org