Squish: Near-Optimal Compression for Archival of Relational Datasets

TL;DR

Squish is a system that leverages relational structure and probabilistic models to achieve near-optimal compression of relational datasets, significantly reducing storage costs.

Contribution

We introduce Squish, a novel compression system using Bayesian Networks and Arithmetic Coding that captures attribute dependencies and supports user-defined data types, proving asymptotic optimality.

Findings

Achieves over 50% reduction in storage size compared to prior systems.

Effectively captures complex attribute dependencies in relational data.

Proven asymptotic optimality of the compression algorithm.

Abstract

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.