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https://issues.apache.org/jira/browse/SPARK-30866?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun resolved SPARK-30866.
-----------------------------------
Fix Version/s: 3.1.0
Resolution: Fixed
Issue resolved by pull request 27620
[https://github.com/apache/spark/pull/27620]
> FileStreamSource: Cache fetched list of files beyond maxFilesPerTrigger as
> unread files
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-30866
> URL: https://issues.apache.org/jira/browse/SPARK-30866
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 3.1.0
> Reporter: Jungtaek Lim
> Assignee: Jungtaek Lim
> Priority: Major
> Fix For: 3.1.0
>
>
> FileStreamSource fetches the available files per batch which is a "heavy
> cost" operation.
> (E.g. It took around 5 seconds to list leaf files for 95 paths which contain
> 674,811 files. It's not even in HDFS path - it's local filesystem.)
> If "maxFilesPerTrigger" is not set, Spark would consume all the fetched
> files. After the batch has been completed, it's obvious for Spark to fetch
> per micro batch.
> If "latestFirst" is true (regardless of "maxFilesPerTrigger"), the files to
> process should be updated per batch, so it's also obvious for Spark to fetch
> per micro batch.
> Except above cases (in short, maxFilesPerTrigger is being set and latestFirst
> is false), the files to process can be "continuous" - we can "cache" the
> fetched list of files and consume until the list has been exhausted.
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