zarrs

zarrs is a Rust library for the Zarr storage format for multidimensional arrays and metadata.

[!TIP] If you are a Python user, check out zarrs-python. It includes a high-performance codec pipeline for the reference zarr-python implementation.

zarrs supports Zarr V3 and a V3 compatible subset of Zarr V2. It is fully up-to-date and conformant with the Zarr 3.1 specification with support for:

• all core extensions (data types, codecs, chunk grids, chunk key encodings, storage transformers),
• all accepted Zarr Enhancement Proposals (ZEPs) and several draft ZEPs:
  • ZEP 0003: Variable chunking
  • ZEP 0007: Strings
  • ZEP 0009: Zarr Extension Naming
• various registered extensions from zarr-extensions,
• experimental extensions intended for future registration, and
• user-defined custom extensions and stores.

A changelog can be found here. Correctness issues with past versions are detailed here.

Developed at the Department of Materials Physics, Australian National University, Canberra, Australia.

Getting Started

• Read the documentation (docs.rs), which details:
  • Zarr version support,
  • array extension support (codecs, data types, chunk grids, etc.),
  • storage support,
  • examples of how to use zarrs, and
  • an overview of the zarrs ecosystem including supporting crates and Python and C/C++ bindings.
• Read The zarrs Book.
• Review benchmarks of zarrs and zarrs-python compared to zarr-python and tensorstore.
• Try the command line tools in zarrs_tools:
  • zarrs_reencode: a reencoder that can change codecs, chunk shape, convert Zarr V2 to V3, etc.
  • zarrs_ome: create an OME-Zarr hierarchy from a Zarr array.
  • zarrs_filter: transform arrays: crop, rescale, downsample, gradient magnitude, gaussian, noise filtering, etc.

Licence

zarrs is licensed under either of

• the Apache License, Version 2.0 LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0 or
• the MIT license LICENSE-MIT or http://opensource.org/licenses/MIT, at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.