Getting Started

presto-query-predictor is a Python module introducing machine learning techniques to the Presto ecosystem. It contains a machine learning pipeline for the model training/evaluation and a query predictor web service to predict CPU and memory usages of Presto queries.

Installation

After cloning the GitHub repository,

pip3 install -e .  # Installs the presto-query-predictor package locally
pip3 install -r requirements.txt  # Installs dependencies

An alternative way is to install the package from PyPi,

pip3 install presto-query-predictor

Note

We recommend installing the package in a Python virtual environment instead of installing it globally.

Examples

The query_predictor/ folder contains the core of the package. We have prepared some examples in the example/ folder, including

• load_data.py - An example to load the embedded fake TPCH-based dataset.
• transform.py - An example to transform datasets for further training.
• train.py - An example to train CPU and memory models.
• tune.py - An example to tune classification algorithms.
• app.py - An example to create a query predictor web service.

Training

A simple way to get a sense of the CPU and memory model training is running the examples in the example/ folder.

cd examples
python3 transform.py
python3 train.py

Warning

The presto-query-predictor package can only be executed in a Python 3 environment. It does not support Python 2.

Afterward, the trained models should be generated in the models folder, including

models/
    vec-cpu.bin
    vec-memory.bin
    model-cpu.bin
    model-memory.bin

By default, the vectorizers are trained from the TF-IDF algorithm, and the models are trained from XGBoost classifiers. The dataset used for training is a faked dataset based on the TPC-H benchmark with only 22 samples.

Serving

After running

python3 app.py

A Flask web application should be created at http://0.0.0.0:8000/. There is a web UI for the application where you can fill in the form with a query for resources prediction.