[jira] [Updated] (SPARK-24565) Add API for in Structured Streaming for exposing output rows of each microbatch as a DataFrame

["Tathagata Das (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-24565?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Tathagata Das updated SPARK-24565:
----------------------------------
    Description: 
Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have a better sense of building a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the micro-batch output as a dataframe is useful. 
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exist (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.

The proposal is to add a method {{foreachBatch(f: Dataset[T] => Unit)}} to Scala/Java/Python {{DataStreamWriter}}.


  was:
Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have a better sense of building a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the micro-batch output as a dataframe is useful. 
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exist (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.

The proposal is to add a method {{foreachBatch(f: Dataset[T] => Unit)}} to Scala/Java/Python `DataStreamWriter`.



> Add API for in Structured Streaming for exposing output rows of each microbatch as a DataFrame
> ----------------------------------------------------------------------------------------------
>
>                 Key: SPARK-24565
>                 URL: https://issues.apache.org/jira/browse/SPARK-24565
>             Project: Spark
>          Issue Type: Improvement
>          Components: Structured Streaming
>    Affects Versions: 2.3.0
>            Reporter: Tathagata Das
>            Assignee: Tathagata Das
>            Priority: Major
>
> Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have a better sense of building a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the micro-batch output as a dataframe is useful. 
> - Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
> - Reuse batch data sources for output whose streaming version does not exist (e.g. redshift data source).
> - Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.
> The proposal is to add a method {{foreachBatch(f: Dataset[T] => Unit)}} to Scala/Java/Python {{DataStreamWriter}}.



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