[SPARK-54182][SQL][PYTHON] Optimize non-arrow conversion of df.toPandas
#52897
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HyukjinKwon
changed the title
df.toPandasdf.toPandas
Yicong-Huang
changed the title
df.toPandasdf.toPandas
Yicong-Huang
changed the title
df.toPandasdf.toPandas
Yicong-Huang
changed the title
df.toPandasdf.toPandas
zhengruifeng
approved these changes
Nov 11, 2025
| timestamp_utc_localized=False, | ||
| )(pser) | ||
| for (_, pser), field in zip(pdf.items(), self.schema.fields) | ||
| )(pd.Series(col_data, dtype=object)) |
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why is dtype=object necessary?
| )(pser) | ||
| for (_, pser), field in zip(pdf.items(), self.schema.fields) | ||
| )(pd.Series(col_data, dtype=object)) | ||
| for col_data, field in zip(columns_data, self.schema.fields) |
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can we avoid creating columns_data: list[list] ?
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What changes were proposed in this pull request?
Following up with #52680, this PR optimizes the non-Arrow path of
toPandas()to eliminate intermediate DataFrame creation.Key optimizations:
Avoid intermediate DataFrame copy
pd.DataFrame.from_records(rows)→ Direct column extraction viazip(*rows)Optimize column-by-column conversion (especially for wide tables)
dtype=objectpd.concat(axis="columns")+ column rename →pd.concat(axis=1, keys=columns)Why are the changes needed?
Problem: Current flow creates DataFrame twice:
rows→pd.DataFrame.from_records()→ temporary DataFrame →pd.concat()→ final DataFrameThe intermediate DataFrame is immediately discarded, wasting memory. This is especially inefficient for wide tables where column-by-column overhead is significant.
Does this PR introduce any user-facing change?
No. This is a pure performance optimization with no API or behavior changes.
How was this patch tested?
Benchmark setup:
spark.sql.execution.arrow.pyspark.enabled=false(testing non-Arrow path)Performance Results
General Benchmark (10 iterations):
Column Width Benchmark (100K rows, 10 iterations):
Was this patch authored or co-authored using generative AI tooling?
Yes. Co-Generated-by Cursor