GoldenMatch

v1.24.0 Feature

This release adds 2 notable features for engineering teams evaluating rollout.

Published 1mo Data Pipelines & ETL

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✓ No known CVEs patched

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✓ No known CVEs patched in this version

Topics

data-cleaning data-engineering data-matching data-quality deduplication entity-resolution

+14 more

fellegi-sunter fuzzy-matching knowledge-graph llm master-data-management mcp-server polars pprl python record-linkage rust splink typescript zero-config

Summary

AI summary

Performance improvements reduce runtime from 2604s to 502s and lower memory usage.

Full changelog

10M QIS-bucket-realistic: 2604s -> 502s (-81% wall) at F1=0.9886 invariant + 18% RSS reduction. ~15 perf PRs + Chao1 scale-aware cardinality + heuristic rule expansion + diagnostic harness.

See CHANGELOG.md for the full PR list.

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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.