# BIT-DataLab/LakeBench

Repository: https://github.com/BIT-DataLab/LakeBench
Canonical: https://ross.abutalabs.com/products/lakebench
Language: Python
License Family: other
Last push: 2025-05-25T05:59:15+00:00

## Health v2 (maintenance only)
Score: 37/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 23, release rhythm 35, longevity 74
- inputs: {"age_days": 1036, "days_push": 465, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1211, forks 36 (observed 2026-08-28T04:04:00.123968+00:00)

## What it is
LakeBench is a large-scale benchmark for evaluating table discovery methods (joinable and unionable table search) in data lakes, containing over 16 million real tables and 10 thousand queries. It includes implementations of state-of-the-art join and union search algorithms for comparison.

## Use cases
- benchmark table discovery algorithms on large data lakes
- evaluate joinable table search methods
- evaluate unionable table search methods
- compare state-of-the-art table search systems like DeepJoin and Starmie
- research scalable schema matching in data lakes

## When to choose
- you need a realistic large-scale benchmark for table discovery research
- you want to evaluate join or union search algorithms at scale
- you need reproducible baselines for data lake table search

## When to avoid
- you need a production table discovery system rather than a benchmark
- your data lake is small and existing smaller benchmarks suffice
- you need a non-Python or non-PyTorch evaluation environment

## Facets
- artifact type: dataset
- maturity: active
- function: benchmarking, search-engine, data-science, machine-learning
- domain: databases, data-science, big-data, analytics
- platform: python, cross-platform
- tags: table-discovery, data-lakes, join-search, union-search, benchmark-dataset

## Member repositories
- BIT-DataLab/LakeBench (main) score 37

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:00.123968+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:18:02.070987+00:00, confidence not recorded.
  - readme: https://github.com/BIT-DataLab/LakeBench (fetched 2026-08-28T04:04:00.123968+00:00, sha 242c9dfc15bb)
- Data as of 2026-08-30T08:39:29.467469+00:00.