A format-aware compression framework that generates specialized compressors for specific data formats, achieving high ratios with high speed.
OpenZL is a format-aware compression framework that generates specialized compressors for specific data formats. It solves the problem of achieving both high compression ratios and high speed, which is challenging for generic compressors, particularly for large, specialized datasets like those in AI workloads.
Engineers and developers who deal with large quantities of specialized datasets, such as AI workloads, and require high-speed processing pipelines where compression performance is critical.
Developers choose OpenZL because it offers a unique approach: by understanding the data format, it creates optimized compressors that outperform generic ones in both compression ratio and speed, making it ideal for performance-sensitive applications.
A novel data compression framework
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By taking a description of your data, OpenZL builds specialized compressors that outperform generic ones in both speed and compression ratio, as highlighted in its whitepaper for structured datasets like AI workloads.
All generated compressors are compatible with a single decompressor, simplifying deployment and ensuring backward compatibility across different specialized formats.
The core library is used extensively in production at Meta, indicating reliability for critical workloads despite ongoing development.
Payloads compressed with release-tagged versions remain decompressible by new releases for several years, providing confidence for data archival and forward compatibility.
The project is under active development with the API, compressed format, and codecs subject to change, which can break integrations and require frequent updates.
Requires C11 and C++17 compilers, modern CMake, and has nuanced build modes like DEV or OPT, making setup more involved than plug-and-play libraries.
MSVC has limited C11 support, forcing users to use clang-cl or MinGW as recommended in the README, adding complexity for Windows-based teams.
Users must provide detailed descriptions of their data formats to generate compressors, adding an extra step compared to generic compressors that work out-of-the-box.