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https://issues.apache.org/jira/browse/SPARK-27808?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-27808:
----------------------------------
    Affects Version/s:     (was: 2.4.3)
                           (was: 2.3.3)
                       3.0.0

> Ability to ignore existing files for structured streaming
> ---------------------------------------------------------
>
>                 Key: SPARK-27808
>                 URL: https://issues.apache.org/jira/browse/SPARK-27808
>             Project: Spark
>          Issue Type: New Feature
>          Components: Structured Streaming
>    Affects Versions: 3.0.0
>            Reporter: Vladimir Matveev
>            Priority: Major
>
> Currently it is not easily possible to make a structured streaming query to 
> ignore all of the existing data inside a directory and only process new 
> files, created after the job was started. See here for example: 
> [https://stackoverflow.com/questions/51391722/spark-structured-streaming-file-source-starting-offset]
>  
> My use case is to ignore everything which existed in the directory when the 
> streaming job is first started (and there are no checkpoints), but to behave 
> as usual when the stream is restarted, e.g. catch up reading new files since 
> the last restart. This would allow us to use the streaming job for continuous 
> processing, with all the benefits it brings, but also to keep the possibility 
> to reprocess the data in the batch fashion by a different job, drop the 
> checkpoints and make the streaming job only run for the new data.
>  
> It would be great to have an option similar to the `newFilesOnly` option on 
> the original StreamingContext.fileStream method: 
> https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.streaming.StreamingContext@fileStream[K,V,F%3C:org.apache.hadoop.mapreduce.InputFormat[K,V]](directory:String,filter:org.apache.hadoop.fs.Path=%3EBoolean,newFilesOnly:Boolean)(implicitevidence$7:scala.reflect.ClassTag[K],implicitevidence$8:scala.reflect.ClassTag[V],implicitevidence$9:scala.reflect.ClassTag[F]):org.apache.spark.streaming.dstream.InputDStream[(K,V])]
> but probably with slightly different semantics, described above (ignore all 
> existing for the first run, catch up for the following runs)>



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