zsxwing commented on code in PR #40561:
URL: https://github.com/apache/spark/pull/40561#discussion_r1160479372
##########
python/pyspark/sql/dataframe.py:
##########
@@ -3928,6 +3928,71 @@ def dropDuplicates(self, subset: Optional[List[str]] =
None) -> "DataFrame":
jdf = self._jdf.dropDuplicates(self._jseq(subset))
return DataFrame(jdf, self.sparkSession)
+ def dropDuplicatesWithinWatermark(self, subset: Optional[List[str]] =
None) -> "DataFrame":
+ """Return a new :class:`DataFrame` with duplicate rows removed,
+ optionally only considering certain columns, within watermark.
+
+ For a static batch :class:`DataFrame`, it just drops duplicate rows.
For a streaming
+ :class:`DataFrame`, this will keep all data across triggers as
intermediate state to drop
+ duplicated rows. The state will be kept to guarantee the semantic,
"Events are deduplicated
+ as long as the time distance of earliest and latest events are smaller
than the delay
+ threshold of watermark." The watermark for the input
:class:`DataFrame` must be set via
+ :func:`withWatermark`. Users are encouraged to set the delay threshold
of watermark longer
Review Comment:
It sounds weird to me that a method is called
dropDuplicates**WithinWatermark** but I don't need to set the watermark.
> For batch, there are a bunch of tools to perform deduplication, distinct /
dropDuplicates / dropDuplicatesWithinWatermark. Most of batch use cases don't
need to come up with using dropDuplicatesWithinWatermark.
I think the common use case for `dropDuplicatesWithinWatermark` in batch is:
develop the code in batch mode and switch to streaming later. In this case,
catching potential issues in batch mode is better.
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