This release adds 2 notable features for engineering teams evaluating rollout.
✓ No known CVEs patched in this version
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Summary
AI summaryAdded field-level provenance support with new config and batch function.
Full changelog
Field-level golden-record provenance at scale.
Added - build_golden_records_batch(..., provenance=True): each field dict gains
source_row_id (the row_id of the record whose value won survivorship for
that field), while preserving the single-group_by-per-column vectorization.
Added - config.output.lineage_provenance (default False, opt-in): when enabled,
the lineage sidecar ({run_name}_lineage.json) gains a golden_records section
with per-field provenance (value, source_row_id, strategy, confidence) for
every cluster. Default off because at large scale this materializes one
provenance object per cluster plus a large JSON sidecar; the vectorized builder
is what makes it feasible. golden_records_to_provenance adapts the batch
builder output to the ClusterProvenance shape lineage consumes.
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About GoldenMatch
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Earlier breaking changes
- v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
- vgoldencheck-v3.0.0 `inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.
- vgoldencheck-v3.0.0 'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.
- vgoldencheck-v3.0.0 `scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.
- v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.
Beta — feedback welcome: [email protected]