OpenZL: Using Graphs to Compress Smaller and Faster

TL;DR

OpenZL introduces a graph-based compression framework enabling rapid development of application-specific compressors that outperform general-purpose methods in speed and ratio, suitable for real-world data.

Contribution

It proposes a novel graph model for compression, allowing quick creation of tailored compressors with a universal decoder, improving practicality and performance.

Findings

OpenZL achieves better compression ratios and speeds than state-of-the-art general-purpose compressors.

OpenZL is competitive with deep-learning compressors in ratio but much faster.

Meta deployments show reduced development time and improved data size and speed.

Abstract

In the last few decades, research techniques have improved lossless compression ratios by significantly increasing processing time. However, these techniques have not gained popularity in industry because production systems require high throughput and low resource utilization. Instead, real world improvements in compression are increasingly realized by building application-specific compressors which can exploit knowledge about the structure and semantics of the data being compressed. Application-specific compressor systems outperform even the best generic compressors, but these techniques have severe drawbacks -- they are inherently limited in applicability, are hard to develop, and are difficult to maintain and deploy. In this work, we show that these challenges can be overcome with a new compression strategy. We propose the "graph model" of compression, a new theoretical framework for representing compression as a directed acyclic graph of modular codecs. OpenZL implements this framework and compresses data into a self-describing wire format, any configuration of which can be decompressed by a universal decoder. OpenZL's design enables rapid development of application-specific compressors with minimal code. Experimental results demonstrate that OpenZL achieves superior compression ratios and speeds compared to state-of-the-art general-purpose compressors on a variety of real-world datasets. Compared to ratio-focused deep-learning compressors, OpenZL is competitive on ratio while being many orders of magnitude faster. Internal deployments at Meta have also shown consistent improvements in size and/or speed, with development timelines reduced from months to days. OpenZL thus represents a significant advance in practical, scalable, and maintainable data compression for modern data-intensive applications.