HyukjinKwon commented on code in PR #36683:
URL: https://github.com/apache/spark/pull/36683#discussion_r894062038
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python/pyspark/sql/pandas/conversion.py:
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@@ -596,7 +596,7 @@ def _create_from_pandas_with_arrow(
]
# Slice the DataFrame to be batched
- step = -(-len(pdf) // self.sparkContext.defaultParallelism) # round
int up
+ step = self._jconf.arrowMaxRecordsPerBatch()
Review Comment:
BTW, just to extra clarify, when the pandas DataFrame is small (lower than
the threshold), the number of partitions remains same (configured by
`spark.sql.leafNodeDefaultParallelism` that falls back to
`sparkContext.defaultParallelism` if not set).
The number of partitions is only different when the input DataFrame is
large, which I think makes more sense in general ..
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