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GoldenMatch

vgoldencheck-v3.0.0 scope: goldencheck Breaking

This release includes 4 breaking changes for platform teams planning a safe upgrade.

Published 15d 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

ReleasePort's take

Light signal
editorial:auto 14d

The `scan_file`, `scan_dataframe`, and CLI `check` commands now operate using Arrow‑native pyarrow.Table without Polars, reflecting a shift in runtime dependencies.

Why it matters: All scanning APIs and the check command no longer depend on Polars; operators must ensure pyarrow is installed for continued functionality.

Summary

AI summary

Updates Breaking, Install, and pl.DataFrame across a mixed release.

Changes in this release

Breaking High

`scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.

`scan_file`, `scan_dataframe`, and CLI `check` now run without Polars, using Arrow-native pyarrow.Table.

Source: llm_adapter@2026-07-12

Confidence: high

 —
Breaking Medium

'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.

'inferred_type' now emits a neutral dtype vocabulary instead of raw Polars dtype strings.

Source: llm_adapter@2026-07-12

Confidence: low

 —
Breaking Low

`inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.

`inferred_type` emits neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtypes.

Source: granite4.1:30b@2026-07-13-audit

Confidence: low

 —
Feature Low

Optional install extras `[native]`, `[baseline]`, and `[polars]` provide compiled kernel accelerator, scipy+Polars stats, and Polars convenience overload respectively.

Optional install extras `[native]`, `[baseline]`, and `[polars]` provide compiled kernel accelerator, scipy+Polars stats, and Polars convenience overload respectively.

Source: llm_adapter@2026-07-12

Confidence: high

 —
Dependency Medium

'pyarrow' is now a base dependency; 'polars' moved to optional extras.

'pyarrow' is now a base dependency; 'polars' moved to optional extras.

Source: llm_adapter@2026-07-12

Confidence: high

 —
Bugfix Medium

Sampling is now an owned deterministic sample, stable across runs and workers.

Sampling is now an owned deterministic sample, stable across runs and workers.

Source: llm_adapter@2026-07-12

Confidence: high

 —
Full changelog

The Flip: the default scan path is now Arrow-native and Polars-free.

Breaking

  • scan_file / scan_dataframe / the CLI check run WITHOUT Polars. The scan frame is a pyarrow.Table; profiling routes through the fused Rust/Arrow kernels (with a pyarrow.compute fallback when the native kernel is absent).
  • pyarrow is now a BASE dependency. polars is NOT: it moved to the [baseline] extra (the opt-in scipy stat/drift/correlation subsystems still use it) and the [polars] extra (the scan_dataframe(pl.DataFrame) convenience overload).
  • inferred_type now emits a neutral dtype vocabulary (str/int/uint/float/date/datetime/bool/other) instead of raw Polars dtype strings.
  • Sampling is now an owned deterministic sample (was the Polars-PRNG sample(seed=42)), stable across runs and workers.

Contract

Non-stat findings are byte-identical to the 2.x Polars path (verified by a finding-set differential: strict Jaccard 1.000 across a 9-dataset corpus, 0 kernel-bug divergences). The only intended differences are the neutral dtype strings and the owned sample.

Install

  • pip install goldencheck -- Arrow-native scanning, no Polars.
  • pip install goldencheck[native] -- adds the compiled kernel accelerator.
  • pip install goldencheck[baseline] -- adds the scipy + Polars stat/drift/correlation features.

See packages/python/goldencheck/CHANGELOG.md for details.

Breaking Changes

  • `scan_file`, `scan_dataframe`, and CLI `check` now operate without Polars, using a pyarrow.Table scan frame and fused Rust/Arrow kernels (fallback to pyarrow.compute when native kernel absent).
  • `pyarrow` is promoted from an optional to a BASE dependency; `polars` is removed from the base install and moved to the `[baseline]` extra for scipy stat/drift/correlation features and the `[polars]` extra for `scan_dataframe(pl.DataFrame)` convenience overload.
  • `inferred_type` output changes from raw Polars dtype strings to a neutral vocabulary (str/int/uint/float/date/datetime/bool/other).
  • Sampling is now an owned deterministic sample, replacing the previous Polars‑PRNG `sample(seed=42)` implementation.
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Related context

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Earlier breaking changes

  • v3.1.0 `GOLDENMATCH_FRAME=polars` now requires the `[polars]` extra; raises error without it.
  • v3.0.0 Result frames now return pyarrow.Table instead of Polars DataFrame.
  • vgoldencheck-v2.0.0 Polars moved from base dependency to optional `[polars]` extra.
  • v2.0.0 Removes internal `_scale_aware_backend` backend selector.
  • v2.0.0 Removes `RunHistory.cheapest_healthy()` method.

Beta — feedback welcome: [email protected]