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🟡 Changes recommended
Unresolved Spark inference and casting defects can lose numeric precision, overwrite input columns, and produce incorrect categories, missing-value counts, and duplicate detection.
5 open issues
What changed in this PR
This PR enables type inference for PySpark profiling, using the existing typeset’s precedence and shared thresholds to align Spark behavior with pandas.
Changes:
- Enable Spark inference by default and cast inferred columns for summaries and downstream analysis.
- Share cardinality rules between backends and document Spark-specific parsing limitations.
- Add Spark inference and casting tests, and consolidate pandas inference regression tests.
| File | Description |
|---|---|
| tests/unit/test_typeset_sampling.py | Consolidates sampling tests and expands cast-equivalence checks. |
| tests/unit/test_typeset_precedence.py | Consolidates precedence and traversal tests. |
| tests/backends/spark_backend/test_typeset_spark.py | Adds backend parity and inferred-schema tests. |
| src/data_profiling/model/typeset_relations.py | Uses shared cardinality rules for pandas inference. |
| src/data_profiling/model/type_rules.py | Defines backend-independent cardinality decisions. |
| src/data_profiling/model/spark/typeset_spark.py | Implements Spark inference, parsing, and casting. |
| src/data_profiling/model/spark/summary_spark.py | Applies inference before Spark column summaries. |
| src/data_profiling/model/describe.py | Casts Spark columns before downstream analysis. |
| src/data_profiling/config.py | Enables inference by default in Spark settings. |
| docs/features/big_data.md | Documents Spark inference, limitations, and opt-out configuration. |
Review effort: Balanced
| # DataFrame, so cast its columns to the inferred types. | ||
| from data_profiling.model.spark.typeset_spark import cast_frame | ||
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| df = cast_frame(config, df, variables) |
| """ | ||
| # Java-style suffixes ("1.0d", "2f") are numbers to Spark, not to Python. | ||
| return F.when(value.rlike(r"[0-9.][dDfF]\s*$"), F.lit(None)).otherwise( | ||
| F.expr(f"try_cast({_quote(cleaned_name)} AS DOUBLE)") |
Comment on lines
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| cleaned = f"__dp_number_{i}" | ||
| work = work.withColumn(cleaned, _cleaned_number(value)) |
| column = _date(value) | ||
| elif is_string and target == "Boolean": | ||
| column = _boolean(value, config) | ||
| elif not is_string and target == "Categorical": |
| elif is_string and target == "Boolean": | ||
| column = _boolean(value, config) | ||
| elif not is_string and target == "Categorical": | ||
| column = value.cast("string") |
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