[jira] [Reopened] (SPARK-22163) Design Issue of Spark Streaming that Causes Random Run-time Exception

["Michael N (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22163?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael N reopened SPARK-22163:
-------------------------------

Please distinguish between code bug vs design flaws.  That is why this ticket is separate from the other ticket.

The analogy is the design flaw with the older Spark's map framework where it makes a function call for every single object. its code implementation is ok, but its design flaw is that it has massive overhead when there are millions and billions of objects.  On the other hand, the newer flatMap framework make one function call for a list of objects via the Iterator. 

Here are the questions to provide the insights as to why this issue is a design flaw of Spark's framework trying to serialize application objects of a Streaming application that runs continuously.  Until you could provide the answers to the questions and resolve them, please do not close this ticket.

1. In the first place, why does Spark serialize the application objects asynchronously while the streaming application is running continuously from batch to batch ?

2. If Spark needs to do this type of serialization at all, why does it not do at the end of the batch ?


> 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. 



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