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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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