GoldenMatch

vgoldenflow-native-v0.24.0 scope: goldenflow-native Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 19d Data Pipelines & ETL

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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 an optional compiled runtime with native Arrow Column and CSV transform pipeline.

Full changelog

Optional compiled runtime for GoldenFlow. First publish since 0.15.0, bringing the Polars-eviction columnar substrate to PyPI (0.16-0.24): arrow-free list chain + native Arrow Column (pyarrow-free); native CSV transform pipeline (parallel read/write, Polars-matching float formatter); auto-routed total/nullable string chains; numeric columnar execution (f64 + i64 parsers + array ops, CSV + in-memory); multi-output splits. Byte-identical to the Polars engine, parity-gated; older callers degrade gracefully via capability probes.

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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.