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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:16:43 UTC
[jira] [Resolved] (SPARK-19233) Inconsistent Behaviour of Spark
Streaming Checkpoint
[ https://issues.apache.org/jira/browse/SPARK-19233?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-19233.
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Resolution: Incomplete
> 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
> Priority: Major
> Labels: bulk-closed
>
> 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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