[TEST](pyspark) Batch per-test DataFrames into one collect#577
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[TEST](pyspark) Batch per-test DataFrames into one collect#577Seth Fitzsimmons (sethfitz) wants to merge 1 commit into
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1.5X-3X test speed-up. I'll take it!
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test_column_patterns.py and test_constraint_expressions.py each built a DataFrame and collected once per test -- ~200 Spark jobs between them. Both now use a case registry: each _Case carries its input column, the check over it, and a predicate on the result; a module-scoped fixture packs every case's input into one wide single-row DataFrame, applies every check in a single select, and collects once. This is the batch-once pattern the generated conformance harness uses. Every prior assertion is preserved. Timings (remaining floor is Spark session startup): test_column_patterns.py 6.4s -> 4.0s (33 -> 37 cases) test_constraint_expressions.py 16.9s -> 5.1s (164 -> 165 cases) The extra cases come from splitting valid/invalid two-row tests into explicit per-input cases; no coverage dropped. Signed-off-by: Seth Fitzsimmons <seth@mojodna.net>
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Follow-up to #572, stacked on its
generalize-field-pathbranch (the column_patterns cases use thenested_map_{keys,values}_checkhelpers #572 adds). I'll rebase onto the integration branch once #572 merges.Closes #576.
What changed
test_column_patterns.pyandtest_constraint_expressions.pyeach built a DataFrame andcollect()ed once per test — roughly 200 Spark jobs between them. Both now use a case registry: each_Casecarries its input column, the check over it, and a predicate on the result; a module-scoped fixture packs every case's input into one wide single-row DataFrame, applies every check in a singleselect, and collects once. This is the batch-once pattern the generated conformance harness already uses.Effect
Every prior assertion is preserved — the distinct asserted literals all survive and the parametrized cases stay green.
test_column_patterns.pytest_constraint_expressions.pyThe extra cases come from splitting valid/invalid two-row tests into explicit per-input cases; no coverage is dropped. The remaining floor is Spark session startup.