GoldenMatch

v1.21.0 Feature

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

Published 2mo 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

Adds optional native acceleration runtime and a cross‑language record fingerprint function.

Full changelog

Optional native acceleration runtime and the cross-language record fingerprint.

Added - optional native acceleration:

pip install "goldenmatch[native]"

Pulls goldenmatch-native, a separately distributed compiled (Rust/PyO3 abi3)
runtime (the polars / polars-runtime split). goldenmatch stays a pure-Python
wheel; the native runtime is discovered automatically when present, and the
pure-Python paths run unchanged without it. With the runtime installed, the
auto-config planner routes simple/fast-box plans through the native Arrow
block-scorer (1.7-3.7x faster at 1k-60k rows, identical clusters). Opt out with
GOLDENMATCH_PLANNER_BUCKET=0.

Added - record_fingerprint(record) in the public API (and the TypeScript port):
a canonical, cross-language SHA-256 over a type-tagged, key-sorted byte
canonicalization, stable across Python/TypeScript/SQL surfaces.

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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.