# Squish: Near-Optimal Compression for Archival of Relational Datasets

arXiv: https://arxiv.org/abs/1602.04256 · HTML: https://mlsanity.com/paper/1602.04256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [LeCo: Lightweight Compression via Learning Serial Correlations](https://mlsanity.com/paper/2306.15374/) (2023)
- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)

## Abstract (arXiv)

Relational datasets are being generated at an alarmingly rapid rate across organizations and industries. Compressing these datasets could significantly reduce storage and archival costs. Traditional compression algorithms, e.g., gzip, are suboptimal for compressing relational datasets since they ignore the table structure and relationships between attributes. We study compression algorithms that leverage the relational structure to compress datasets to a much greater extent. We develop Squish, a system that uses a combination of Bayesian Networks and Arithmetic Coding to capture multiple kinds of dependencies among attributes and achieve near-entropy compression rate. Squish also supports user-defined attributes: users can instantiate new data types by simply implementing five functions for a new class interface. We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effectiveness of our system: Squish achieves a reduction of over 50% in storage size relative to systems developed in prior work on a variety of real datasets.