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https://issues.apache.org/jira/browse/SPARK-24144?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hemant Bhanawat updated SPARK-24144:
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Priority: Major (was: Minor)
> monotonically_increasing_id on streaming dataFrames
> ---------------------------------------------------
>
> Key: SPARK-24144
> URL: https://issues.apache.org/jira/browse/SPARK-24144
> Project: Spark
> Issue Type: New Feature
> Components: Structured Streaming
> Affects Versions: 2.3.0
> Reporter: Hemant Bhanawat
> Priority: Major
>
> For our use case, we want to assign snapshot ids (incrementing counters) to
> the incoming records. In case of failures, the same record should get the
> same id after failure so that the downstream DB can handle the records in a
> correct manner.
> We were trying to do this by zipping the streaming rdds with that counter
> using a modified version of ZippedWithIndexRDD. There are other ways to do
> that but it turns out all ways are cumbersome and error prone in failure
> scenarios.
> As suggested on the spark user dev list, one way to do this would be to
> support monotonically_increasing_id on streaming dataFrames in Spark code
> base. This would ensure that counters are incrementing for the records of the
> stream. Also, since the counter can be checkpointed, it would work well in
> case of failure scenarios. Last but not the least, doing this in spark would
> be the most performance efficient way.
>
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