T3: Tuple Time Tree - Accurate and Fast Performance Prediction

This repository contains the code to reproduce all results of our paper about T3.

Installation With Docker

Download the image from Docker Hub: First clone this repository and move into the cloned directory.

sudo docker pull tupletimetree/t3
sudo docker run -v $(pwd):/app -it tupletimetree/t3

Or build the image yourself: First clone this repository and move into the cloned directory.

sudo docker build -t t3 .
sudo docker run -v $(pwd):/app -it t3

Native Installation

General Requirements:

sudo apt install lz4 python3 python3-venv python3-pip

Install python requirements: First clone this repository and move into the cloned directory.

python -m venv venv
. venv/bin/activate
pip install -r requirements.txt

On MacOS you might need to

brew install libomp

Reproduce all figures of the paper:

. venv/bin/activate
python main.py

Additional Benchmarks

The master script reproduces most results by default. However, some parts of the project are not portable. Most notably our database system only works on x86_64 Linux:

• Join Order Microbenchmark and model latency: Compiling our C++ benchmark file is only tested on x86_64 Linux. You can run this benchmark by adding the flag -c
• Join Order Microbenchmark Query Testing: Benchmarking the generated queries with different join orderings requires to run the database system. This only works on x86_64 Linux. You can run this benchmark by adding the flag -j This will download (about 6 GB) and generate (about 300 GB) the csv data and load it into the database (about 500 GB). Total required storage is about (800 GB).
• Reproducing the full database benchmarks: Creating the full dataset of benchmarked queries requires to run the database system. This only works on x86_64 Linux. You can run this benchmark by adding the flag -b This will download (about 6 GB) and generate (about 300 GB) the csv data and load it into the database (about 500 GB). Total required storage is about (800 GB). Benchmarks will take a while (about 8 hours on a 16 core machine)

The best way to run these additional benchmarks is to use the provided Dockerfile. To reproduce all results run:

sudo docker run -v $(pwd):/app -it tupletimetree/t3 -c -j

To re-create all results from scratch run:

sudo docker run -v $(pwd):/app -it tupletimetree/t3 -c -j -b

Individual Figures

Each figure script has its own main function. These have to be run from the root of this directory. For example

. venv/bin/activate
python src/figures/latency_accuracy.py