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GoldenMatch

vgolden-suite-v0.2.0 scope: golden-suite Breaking

This release includes 3 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

GoldenSuite v0.2.0 raises the GoldenMatch minimum version to ≥3.0, enabling Arrow‑native results and defaulting to the Arrow frame backend which delivers ~36% faster performance on a 100K benchmark.

Why it matters: The release enforces a ≥3.0 GoldenMatch floor; adopting it yields ~36% speed gains on large workloads, directly impacting latency metrics for data‑intensive pipelines.

Summary

AI summary

Raises minimum GoldenMatch version to >=3.0, using Arrow-native results and enabling the Arrow frame backend by default for ~36% faster performance.

Changes in this release

Feature Medium

Provides opt-out environment variable GOLDENMATCH_FRAME=polars to disable the default Arrow frame backend.

Provides opt-out environment variable GOLDENMATCH_FRAME=polars to disable the default Arrow frame backend.

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

Confidence: low

Performance Medium

Raises goldenmatch floor to >=3.0, enabling Arrow-native results and default Arrow frame backend (~36% faster on 100K benchmark).

Raises goldenmatch floor to >=3.0, enabling Arrow-native results and default Arrow frame backend (~36% faster on 100K benchmark).

Source: llm_adapter@2026-07-12

Confidence: high

Performance Low

Raises goldenmatch floor to >=3.0, enabling Arrow-native results (pyarrow.Table) via pl.from_arrow.

Raises goldenmatch floor to >=3.0, enabling Arrow-native results (pyarrow.Table) via pl.from_arrow.

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

Confidence: low

Full changelog

Raises the goldenmatch floor to >=3.0: Arrow-native results (pyarrow.Table; migrate with pl.from_arrow) and the Arrow frame backend by default (~36% faster on the 100K zero-config benchmark; GOLDENMATCH_FRAME=polars is the opt-out). See goldenmatch's migrating-to-v3 guide.

Breaking Changes

  • Minimum required GoldenMatch version raised to >=3.0
  • Default frame backend switched to Arrow; opt-out via GOLDENMATCH_FRAME=polars
  • Result format changed to pyarrow.Table (migrate with pl.from_arrow)

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Related context

Related tools

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.

Beta — feedback welcome: [email protected]