README.md

virtual

A booster 💪 for your Parquet file sizes.

virtual is a lightweight framework that transparently compresses Parquet files by using functions between columns, all while giving you the same familiar interface you are used to. How virtual works is magic, and is described in our recent research papers (see below).

🛠 Build

pip install virtual-parquet

or

git clone https://github.com/utndatasystems/virtual.git && cd virtual
pip install .

🔗 Examples

A demo can be found at examples/demo-parquet.ipynb.

🗜️ Compress

Simply compress a Pandas DataFrame with virtual.to_format(df):

import pandas as pd
import virtual

df = pd.read_csv('file.csv')

...

virtual.to_format(df, 'file_virtual.parquet')

% Virtualization finished: Check out 'file_virtual.parquet'.

🥢 Read

Reading in a virtual compress parquet file with virtual.from_format([path]):

import virtual

df = virtual.from_format('file_virtual.parquet')

📊 Query

Or directly run SQL queries on the virtualized Parquet file via duckdb with virtual.query([SQL]):

import virtual

virtual.query(
  'select avg(price) from read_parquet("file_virtual.parquet") where year >= 2024',
  engine = 'duckdb'
)

Expert-User Features

🔍 Inspect the Functions Found

import pandas as pd
import virtual

df = pd.read_csv('file.csv')

functions = virtual.train(df)

% Functions saved under functions.json.

📚 Citation

Please do cite our (very) cool work if you use virtual in your work.

@inproceedings{
  virtual,
  title={{Lightweight Correlation-Aware Table Compression}},
  author={Mihail Stoian and Alexander van Renen and Jan Kobiolka and Ping-Lin Kuo and Josif Grabocka and Andreas Kipf},
  booktitle={NeurIPS 2024 Third Table Representation Learning Workshop},
  year={2024},
  url={https://openreview.net/forum?id=z7eIn3aShi}
}