Quantum Reinforcement Learning for Join Ordering

This repository provides the implementation and artifacts accompanying the following article:

@article{franz:23:qrl_jo,
    author = {Maja Franz AND
              Tobias Winker AND
              Sven Groppe AND
              Wolfgang Mauerer},
    title  = {Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation},
    note   = {under review},
}

A preprint is available on arXiv.

Setup

Python packages

The code can be executed with Python version 3.9. For managing multiple python versions it is recommended to use pyenv. After installing Python 3.9 using pyenv with

pyenv install 3.9

It can be used in the local directory:

pyenv local 3.9

Setting up a new virtual environment using

python -m venv .venv
source .venv/bin/activate

all required python packages can be installed with:

pip install -r requirements.txt

PostgreSQL and the Join Order Benchmark

For the setup of PostgreSQL-V16.0 and the Join Order Benchmark, we provide two setup scripts:

source install_postgres.sh
source setup_JOB.sh

Training

python main.py <configuration_name>

e.g. python main.py example

Available configurations can be found in configs/

A training process can be tracked with TensorBoard:

tensorboard --logdir logs

Additional Material and Hyperparameter Search

Information on the classical baseline replication and on specific hyperparameters can be found in info/supplementary.pdf

Result Sets

All result sets from the paper are stored as CSV files in logs. The tikz plots from the paper can be reproduced with the plots/plot.r script using

Rscript plots/plot.r