This is an experimental research project to evaluate the concept of performing distributed SQL queries from Python, using Ray and DataFusion.
See examples/tips.py.
import ray
from raysql.context import RaySqlContext
from raysql.worker import Worker
# Start our cluster
ray.init()
# create some remote Workers
workers = [Worker.remote() for i in range(2)]
# create context and plan a query
ctx = RaySqlContext(workers)
ctx.register_csv('tips', 'tips.csv', True)
ctx.sql('select sex, smoker, avg(tip/total_bill) as tip_pct from tips group by sex, smoker')- Proof-of-concept. Not producing correct results yet.
- Mature SQL support (CTEs, joins, subqueries, etc) thanks to DataFusion
- Support for CSV and Parquet files
- Simplistic shuffle mechanism that produces lots of files
- Requires a shared file system currently
# prepare development environment (used to build wheel / install in development)
python3 -m venv venv
# activate the venv
source venv/bin/activate
# update pip itself if necessary
python -m pip install -U pip
# install dependencies (for Python 3.8+)
python -m pip install -r requirements-in.txtWhenever rust code changes (your changes or via git pull):
# make sure you activate the venv using "source venv/bin/activate" first
maturin develop
python -m pytestTo change test dependencies, change the requirements.in and run
# install pip-tools (this can be done only once), also consider running in venv
python -m pip install pip-tools
python -m piptools compile --generate-hashes -o requirements-310.txtTo update dependencies, run with -U
python -m piptools compile -U --generate-hashes -o requirements-310.txtMore details here