HeartSaVioR commented on code in PR #40561:
URL: https://github.com/apache/spark/pull/40561#discussion_r1159326934
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python/pyspark/sql/dataframe.py:
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@@ -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
+ than max timestamp differences among duplicated events. In addition,
too late data older
+ than watermark will be dropped.
+
+ .. versionadded:: 3.5.0
+
+ Parameters
+ ----------
+ subset : List of column names, optional
+ List of columns to use for duplicate comparison (default All
columns).
+
+ Returns
+ -------
+ :class:`DataFrame`
+ DataFrame without duplicates.
+
+ Examples
+ --------
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([
+ ... Row(name='Alice', age=5, height=80),
+ ... Row(name='Alice', age=5, height=80),
+ ... Row(name='Alice', age=10, height=80)
+ ... ])
+
+ Deduplicate the same rows.
+
+ >>> df.dropDuplicatesWithinWatermark().show()
Review Comment:
Updated. I just removed the example (test) for batch case since it sounds to
be obvious. I can split example for batch and streaming and add back batch
queries if we want to.
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