You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Michael N (JIRA)" <ji...@apache.org> on 2017/09/28 23:56:00 UTC
[jira] [Created] (SPARK-22163) Design Issue of Spark Streaming that
Causes Random Run-time Exception
Michael N created SPARK-22163:
---------------------------------
Summary: Design Issue of Spark Streaming that Causes Random Run-time Exception
Key: SPARK-22163
URL: https://issues.apache.org/jira/browse/SPARK-22163
Project: Spark
Issue Type: Bug
Components: DStreams, Structured Streaming
Affects Versions: 2.2.0
Environment: Spark Streaming
Kafka
Linux
Reporter: Michael N
Priority: Critical
The application objects can contain List and can be modified dynamically as well. However, Spark Streaming framework asynchronously serializes the application's objects as the application runs. Therefore, it causes random run-time exception on the List when Spark Streaming framework happens to serializes the application's objects while the application modifies a List in its own object.
In fact, there are multiple bugs reported about
Caused by: java.util.ConcurrentModificationException
at java.util.ArrayList.writeObject
that are permutation of the same root cause. So the design issue of Spark streaming framework is that it should do this serialization asynchronously. Instead, it should either
1. do this serialization synchronously. This is preferred to eliminate the issue completely. Or
2. Allow it to be configured per application whether to do this serialization synchronously or asynchronously, depending on the nature of each application.
Also, Spark documentation should describe the conditions that trigger Spark to do this type of serialization asynchronously, so the applications can work around them until the fix is provided.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org