raysql 0.5.0

Description:

raysql 0.5.0

RaySQL: DataFusion on Ray
This is a research project to evaluate performing distributed SQL queries from Python, using
Ray and DataFusion.
Goals

Demonstrate how easily new systems can be built on top of DataFusion. See the design documentation
to understand how RaySQL works.
Drive requirements for DataFusion's Python bindings.
Create content for an interesting blog post or conference talk.

Non Goals

Build and support a production system.

Example
Run the following example live in your browser using a Google Colab notebook.
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 a remote context and register a table
ctx = RaySqlContext.remote(workers)
ray.get(ctx.register_csv.remote('tips', 'tips.csv', True))

# Parquet is also supported
# ctx.register_parquet('tips', 'tips.parquet')

result_set = ray.get(ctx.sql.remote('select sex, smoker, avg(tip/total_bill) as tip_pct from tips group by sex, smoker'))
print(result_set)

Status

RaySQL can run 21 of the 22 TPC-H benchmark queries (query 15 needs DDL and that is not yet supported).

Features

Mature SQL support (CTEs, joins, subqueries, etc) thanks to DataFusion
Support for CSV and Parquet files

Limitations

Requires a shared file system currently

Performance
This chart shows the performance of RaySQL compared to Apache Spark for
SQLBench-H at a very small data set (10GB), running on my desktop (Threadripper
with 24 physical cores). Both RaySQL and Spark are configured with 24 executors.
Note that query 15 is excluded from both results since RaySQL does not support DDL yet.
Overall Time
RaySQL is ~65% faster overall for this scale factor and environment.

Per Query Time
Spark is much faster on some queries, likely due to broadcast exchanges, which RaySQL hasn't implemented yet.

Performance Plan
I'm planning on experimenting with the following changes to improve performance:

Make better use of Ray futures to run more tasks in parallel
Use Ray object store for shuffle data transfer to reduce disk I/O cost
Keep upgrading to newer versions of DataFusion to pick up the latest optimizations

Building
# 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.txt

Whenever 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 pytest

Benchmarking
Create a release build when running benchmarks, then use pip to install the wheel.
maturin develop --release

How to update dependencies
To 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.txt

To update dependencies, run with -U
python -m piptools compile -U --generate-hashes -o requirements-310.txt

More details here