cchighman opened a new pull request #28841:
URL: https://github.com/apache/spark/pull/28841
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### What changes were proposed in this pull request?
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A new option, _fileModifiedDate_ , is provided expecting a value in
'YYYY-MM-DD HH:mm:ss' format. _InMemoryFileIndex_ considers this option during
the process of checking checking for files, just before considering applied
_PathFilters_. In order to filter file results, a new PathFilter class was
derived for this purpose. General house-keeping around classes extending
PathFilter was performed for neatness. It became apparent support was needed
to handle multiple potential path filters. Logic was introduced for this
purpose and the associated tests written. A new method signature was created
in order to maintain backwards compatibility and ensure safety of other
features.
This PR presents a very clean way to minimize complexity under various file
data source loading scenarios. It's also compatible with structured streaming
requiring just a handful of small additions to move forward there. Looking to
complete that in a separate PR.
Example Usage:
spark.read.format("csv").option("fileModifiedDate","2020-06-15T05:00:00")
### Why are the changes needed?
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When loading files from a data source, there can often times be thousands of
file within a respective file path. In many cases I've seen, we want to start
loading from a folder path and ideally be able to begin loading files having
modification dates past a certain point. This would mean out of thousands of
potential files, only the ones with modification dates greater than the
specified timestamp would be considered. This saves a ton of time
automatically and reduces significant complexity managing this in code.
### Does this PR introduce _any_ user-facing change?
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This PR introduces an option that can be used with Spark file data sources
similar to the _latestFirst_ option in structured streaming. An documentation
update was made to reflect an example and usage of the new data source option.
### How was this patch tested?
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A handful of new unit tests were written and passing. The package was
tested locally as well as in a live Databricks environment as well.
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