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https://issues.apache.org/jira/browse/SPARK-19233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15830989#comment-15830989
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Shixiong Zhu commented on SPARK-19233:
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I don't think filtering "generatedRDDs" will work. "generateJobs" will change
states of DStreamGraph and DStreams. Filtering "generatedRDDs" will make the
checkpoint inconsistent and break a lot of things.
Will this behavior affect correctness?
> Inconsistent Behaviour of Spark Streaming Checkpoint
> ----------------------------------------------------
>
> Key: SPARK-19233
> URL: https://issues.apache.org/jira/browse/SPARK-19233
> Project: Spark
> Issue Type: Improvement
> Components: DStreams
> Affects Versions: 2.0.0, 2.0.1, 2.0.2, 2.1.0
> Reporter: Nan Zhu
>
> When checking one of our application logs, we found the following behavior
> (simplified)
> 1. Spark application recovers from checkpoint constructed at timestamp 1000ms
> 2. The log shows that Spark application can recover RDDs generated at
> timestamp 2000, 3000
> The root cause is that generateJobs event is pushed to the queue by a
> separate thread (RecurTimer), before doCheckpoint event is pushed to the
> queue, there might have been multiple generatedJobs being processed. As a
> result, when doCheckpoint for timestamp 1000 is processed, the generatedRDDs
> data structure containing RDDs generated at 2000, 3000 is serialized as part
> of checkpoint of 1000.
> It brings overhead for debugging and coordinate our offset management
> strategy with Spark Streaming's checkpoint strategy when we are developing a
> new type of DStream which integrates Spark Streaming with an internal message
> middleware.
> The proposed fix is to filter generatedRDDs according to checkpoint timestamp
> when serializing it.
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