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GoldenMatch

vgoldencheck-v2.0.0 scope: goldencheck Breaking

This release includes 3 breaking changes for platform teams planning a safe upgrade.

Published 16d 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

Affected surfaces

breaking_upgrade deps

ReleasePort's take

Light signal
editorial:auto 12d

Polars is now an optional extra; CSV reading will raise ImportError if the Polars extra isn’t installed.

Why it matters: If your project reads CSVs with goldencheck‑v2.0.0, ensure pip installs the `[polars]` extra to avoid ImportError; failure triggers at runtime when parsing CSV data.

Summary

AI summary

Updates Requires `pip install goldencheck[polars, Polars-free, and BREAKING across a mixed release.

Changes in this release

Breaking High

Polars moved from base dependency to optional `[polars]` extra.

Polars moved from base dependency to optional `[polars]` extra.

Source: llm_adapter@2026-07-15

Confidence: high

Feature Medium

Polars‑free scan substrate adds native regex and date kernels for `scan_columns`.

Polars‑free scan substrate adds native regex and date kernels for `scan_columns`.

Source: llm_adapter@2026-07-15

Confidence: low

Dependency Medium

Parquet reading now uses optional `[parquet]` extra (pyarrow).

Parquet reading now uses optional `[parquet]` extra (pyarrow).

Source: llm_adapter@2026-07-15

Confidence: low

Bugfix Medium

CSV reading now requires Polars extra; ImportError guides installation.

CSV reading now requires Polars extra; ImportError guides installation.

Source: llm_adapter@2026-07-15

Confidence: low

Bugfix Medium

Raises a clear ImportError naming `goldencheck[polars]` when a Polars-required operation runs without it.

Raises a clear ImportError naming `goldencheck[polars]` when a Polars-required operation runs without it.

Source: granite4.1:30b@2026-07-15-audit

Confidence: low

Full changelog

goldencheck 2.0.0 (BREAKING): Polars is now optional

polars moved from a base dependency to the [polars] optional extra. pip install goldencheck no longer pulls Polars (~185 MB).

Polars-free (base install)

  • import goldencheck, scan_columns(dict)
  • Parquet + Excel reading via read_columns(path) / scan_file_columns(path) (pyarrow via the new [parquet] extra; openpyxl already base)

Requires pip install goldencheck[polars]

  • CSV reading (Polars' CSV dtype inference is not reproducible without it)
  • The full scan: scan_dataframe / scan_file (Polars-native)

A clear ImportError naming goldencheck[polars] is raised if a Polars-required operation runs without it.

Migration

Users who scan CSVs or use scan_file / scan_dataframe must add the extra: pip install goldencheck[polars]. With [polars] installed, behaviour is byte-identical to 1.4.1.

Completes the goldencheck Polars-eviction: a byte-identical polars-free scan substrate (scan_columns + native regex/date kernels), polars-free Parquet/Excel readers, and this deps-flip.

Breaking Changes

  • Polars moved from base dependency to the `[polars]` optional extra; `pip install goldencheck` no longer installs Polars (~185 MB).
  • `scan_dataframe`, `scan_file`, and CSV reading now require installing `goldencheck[polars]`.
  • ImportError with message naming `goldencheck[polars]` is raised when a Polars‑required operation runs without the extra.

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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]