GoldenMatch

v1.16.0 Breaking

This release includes 2 breaking changes for platform teams planning a safe upgrade.

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

Backend switch from chunked path to bucket reduces memory usage, fixing the 63 GB plateau.

Full changelog

5M dedupe in 9.94 min wall, 6.4 GB peak RSS on a 16c/64GB Linux runner. New backend=bucket replaces the chunked path that was hanging at 63 GB plateau on the same fixture. Distributed Plan v1 ray auto-pick soft-reverted (pass GOLDENMATCH_ENABLE_DISTRIBUTED_RAY=1 to opt back in). See CHANGELOG.md and examples/at_scale_bucket_backend.py for details. PRs #310-#326.

Breaking Changes

Removed the chunked path implementation; new backend=bucket is now the default.
Minimum runtime: Linux runner with at least 64 GB RAM to support bucket backend.

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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.