GoldenMatch

v1.2.5 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

Fixed auto-configure misclassification of CamelCase identifier columns such as SalesID, MachineID, ModelID, RecordId.

Full changelog

Bug Fix

ID pattern now matches CamelCase columns — SalesID, MachineID, ModelID, RecordId are now correctly classified as identifiers by auto-configure. Previously, these were misclassified as "phone" (numeric strings) because _ID_PATTERNS only matched _id$ (with underscore).

Uses case-sensitive (?:ID|Id)$ suffix pattern to avoid false positives on words like "valid" and "rapid".

View diff on GitHub

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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.