Beaver is a holistic framework for evaluating performance on complex, private‑enterprise text‑to‑SQL tasks. This repository includes the full collection of tables. Each table contains:

db: ID of the database the table belongs to
table_name: name of the table in the database
column_names: names of the columns in the table
column_types: data types of the columns in the table
example_rows: example rows of the table
example_columns: example values for each column in the table

Getting started

We use MySQL as the execution engine for running SQL queries. You can download the anonymized MySQL dump here. A free MySQL installation is available here. After installing MySQL, import the dump files to your local MySQL using mysql -u root -p < xxx.sql.

from datasets import load_dataset
import json

domain = 'dw'
data = load_dataset('beaverbench/beaver-table')
json_fields = ['column_names', 'column_types', 'example_rows', 'example_columns']
for sample in data[domain]:
    sample = {k: (json.loads(v) if k in json_fields else v)  for k, v in sample.items()}
    print(json.dumps(sample, indent=2))

Citation

@article{chen2024beaver,
  title={BEAVER: an enterprise benchmark for text-to-sql},
  author={Chen, Peter Baile and Wenz, Fabian and Zhang, Yi and Yang, Devin and Choi, Justin and Tatbul, Nesime and Cafarella, Michael and Demiralp, {\c{C}}a{\u{g}}atay and Stonebraker, Michael},
  journal={arXiv preprint arXiv:2409.02038},
  year={2024}
}