fix(engine): materialize Spark DataFrames in the pandas fallback - #425
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fd.clean(spark_df) raised TypeError: cannot materialize source of type DataFrame. The balanced-strategy pandas fallback probed to_pandas (polars) and df (duckdb) but not a Spark DataFrame's toPandas(), so any Spark input crashed before cleaning. Add a toPandas() probe. Found by the production-readiness campaign (FDC-L1a-001).
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Authored-code reduction (Metric 6)
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Summary
fd.clean(spark_df)raisedTypeError: cannot materialize source of type DataFrame. Under the defaultstrategy="balanced", a non-pandas source is materialized to pandas, butmaterialize_to_pandas(execution/backends/_pandas.py) only probedto_pandas(polars) anddf(DuckDB). A Spark DataFrame exposestoPandas(), so any Spark input crashed before cleaning ran.Change
Add a
toPandas()probe after the DuckDBdfprobe (collecting to the driver, which is what the balanced-strategy fallback does). Latent since the multi-engine dispatch landed; not a regression.Tests
tests/test_execution/test_dispatch_and_config.py: a fake Spark-style frame exposing onlytoPandasmaterializes correctly. (The real-pyspark testsimportorskipand cover the end-to-end path in CI's extras lanes.)Verification
ruff checkclean.Found by the production-readiness test campaign (lane L1a, FDC-L1a-001).