BryanCutler commented on a change in pull request #24981: [SPARK-27463][PYTHON]
Support Dataframe Cogroup via Pandas UDFs
URL: https://github.com/apache/spark/pull/24981#discussion_r312265921
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File path: python/pyspark/serializers.py
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@@ -401,6 +427,22 @@ def __repr__(self):
return "ArrowStreamPandasUDFSerializer"
+class InterleavedArrowStreamPandasSerializer(ArrowStreamPandasUDFSerializer):
+
+ def __init__(self, timezone, safecheck, assign_cols_by_name):
+ super(InterleavedArrowStreamPandasSerializer, self).__init__(timezone,
safecheck, assign_cols_by_name)
+
+ def load_stream(self, stream):
+ """
+ Deserialize ArrowRecordBatches to an Arrow table and return as a list
of pandas.Series.
+ """
+ reader = InterleavedArrowReader(stream)
Review comment:
`ArrowStreamSerializer` doesn't need to know how many streams in total are
being sent. Calling `ArrowStreamSerializer.load_stream` will return an iterator
that loads 1 single Arrow stream and then stops. It's basically what you are
already doing in `InterleavedArrowReader._read_df`. You would just wrap in a
while loop. Taking what you have already, for example:
```python
def load_stream(self, stream):
dataframes_in_group = None
while dataframes_in_group is None or dataframes_in_group > 0:
dataframes_in_group = read_int(self._stream)
if dataframes_in_group == 2:
batch1 = [batch for batch in
ArrowStreamSerializer.load_stream(stream)]
batch2 = [batch for batch in
ArrowStreamSerializer.load_stream(stream)]
yield ([self.arrow_to_pandas(c) for c in
pa.Table.from_batches(batch1).itercolumns()],
[self.arrow_to_pandas(c) for c in
pa.Table.from_batches(batch2).itercolumns()])
elif dataframes_in_group != 0:
raise ValueError(
'Received Invalid number of dataframes in group
{0}'.format(dataframes_in_group))
```
I think this is the same as what's in `InterleavedArrowReader`, just
condensed a little
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