GoldenMatch

vgoldenmatch-native-v0.1.0 scope: goldenmatch-native Feature

This release adds 3 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 Rust/PyO3 acceleration runtime for goldenmatch.

Full changelog

First release of goldenmatch-native, the optional compiled (Rust/PyO3 abi3)
acceleration runtime for goldenmatch.

This is not a standalone package. Install it via:

pip install "goldenmatch[native]"

goldenmatch stays a pure-Python wheel; this ships the compiled _native kernel
and goldenmatch discovers it automatically (the polars / polars-runtime split).
With the runtime present, 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). Set GOLDENMATCH_PLANNER_BUCKET=0 to force the pure-Python path.

Wheels: linux x86_64 + aarch64 (manylinux 2_28), windows x64, macOS x86_64 +
arm64, plus sdist. abi3 (CPython 3.11+).

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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.