Starting in v0.2.0, OpenZL ships its own native LZ engine, which can offer better performance than using Zstandard or LZ4. While OpenZL's main target is structured data, LZ is still a core backend compression technique used in nearly all compression graphs, applied after the higher-order structure has been removed. Additionally, there are use cases where the structure of the data is unknown, and LZ is a good default.
Letting a neural network build the compression graph
OpenZL compresses data by chaining processing layers in any order. This is powerful, but requires careful configuration: choosing the right combination of codecs for a given input has always been its greatest challenge. The Compression Transformer now makes that choice automatically, building the compression graph on the fly, one decision at a time. It needs no per-source training, no manual tuning, and no change on the decompression side. It is available from both the API and the CLI.