zeruibao commented on code in PR #52391:
URL: https://github.com/apache/spark/pull/52391#discussion_r2396438513
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -1630,8 +1633,23 @@ def row_stream():
yield (batch_key, row)
for batch_key, group_rows in groupby(row_stream(), key=lambda x:
x[0]):
- df = pd.DataFrame([row for _, row in group_rows])
- yield (batch_key, df)
+ rows = []
+ accumulate_size = 0
+ for _, row in group_rows:
+ rows.append(row)
+ # Short circuit batch size calculation if the batch size is
+ # unlimited as computing batch size is computationally
expensive.
+ if self.arrow_max_bytes_per_batch != 2**31 - 1:
Review Comment:
Since we already leverage the generator-based approach to optimize for
high-cardinality scenarios, switching to `applyInPandasWithState` would not be
a trivial change. I would prefer to retaining the current implementation.
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