GoldenMatch

v1.3.2 Bugfix

This release fixes issues for SREs watching stability and regressions.

Published 3mo 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

Embedding scorer fallback to token_sort prevents crashes when models fail to load.

Full changelog

Fixes

Cross-Source Blocking Overlap Detection

Auto-config now checks whether blocking key values overlap between sources when __source__ column is present. Keys with zero overlap are skipped with a warning.

Before: DBLP-ACM dataset — auto-config picked venue for blocking, but DBLP uses "VLDB" while ACM uses "Very Large Data Bases". Zero overlap = zero cross-source matches.

After: venue skipped with warning, year selected instead. Pipeline completes with 92% F1.

Embedding Scorer Fallback

When embedding models fail to load (HuggingFace auth, Vertex AI quota, missing dependency, CUDA OOM), the scorer now falls back to token_sort instead of crashing. Both _fuzzy_score_matrix and record_embedding paths are covered.

Full Changelog: https://github.com/benzsevern/goldenmatch/compare/v1.3.1...v1.3.2

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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.