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GoldenMatch

v1.10.0 Breaking

This release includes breaking changes for platform teams planning a safe upgrade.

Published 2mo Data Pipelines & ETL
✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ 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

Composite DQbench score improved from 62.87 to 66.91, driven by T2 F1 gain of +10.3 percentage points.

Full changelog

Highlights

  • 5 complexity indicators in core/indicators.py: column priors (identity + corruption scoring), sparse-match estimation, full-population matchkey hits, cross-blocking overlap, corruption score. Cheap two run eagerly; expensive three run lazily via IndicatorContext memoization.
  • IndicatorContext threads indicators through the policy/rule chain. RefitPolicy.propose gains optional ctx kwarg; controller introspects custom-policy signatures via inspect.signature for backward compat.
  • 3 new rules: rule_corruption_normalize adds normalize when blocking column has corruption + identity prior; rule_cross_blocking_disagreement proposes multi-pass when overlap is low; rule_sparse_match_expand lowers threshold + side-channel signal when sample is sparse.
  • GOLDENMATCH_AUTOCONFIG_INDICATOR_BUDGET=fast env var gates the two expensive indicators for users who prefer v1.9 wall-clock.

Benchmarks (zero-config, no LLM)

| Dataset | v1.9.0 | v1.10.0 |
|---|---|---|
| DBLP-ACM | 0.9641 | 0.9641 |
| Febrl3 | 0.9443 | 0.9443 |
| NCVR | 0.9719 | 0.9719 |
| DQbench composite | 62.87 | 66.91 |
| T1 F1 | 89.3% | 88.9% |
| T2 F1 | 58.7% | 69.0% (+10.3pp) |
| T3 F1 | 53.8% | 53.8% |

Composite gain came from T2 recovery via rule_corruption_normalize and rule_cross_blocking_disagreement.

Honest framing

Shipped at fallback target (≥65), not the spec's primary target (≥70). T2 carried the gain. T3 still at 53.8% — needs richer matchkey design (queued for v1.11). T1 slight regression (-0.4pp) within measurement noise.

Breaking changes

None. Adds optional fields with default-None for v1.9 cache compat. RefitPolicy.propose gains optional ctx kwarg (custom 3-arg policies still work via signature introspection).

Full diff: https://github.com/benzsevern/goldenmatch/compare/v1.9.0...v1.10.0

View diff on GitHub

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Related context

Related tools

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]