README.md

zarrs

zarrs is a Rust library for the Zarr storage format for multidimensional arrays and metadata. It supports Zarr V3 and a V3 compatible subset of Zarr V2.

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.

Tip

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

Getting Started

• Review the implementation status (zarr version support, array support, storage support, and the zarrs ecosystem).
• Read The zarrs Book.
• View the examples and the example below.
• Read the documentation.

Example

use zarrs::group::GroupBuilder;
use zarrs::array::{ArrayBuilder, DataType, FillValue, ZARR_NAN_F32};
use zarrs::array::codec::GzipCodec; // requires gzip feature
use zarrs::array_subset::ArraySubset;
use zarrs::storage::ReadableWritableListableStorage;
use zarrs::filesystem::FilesystemStore; // requires filesystem feature

// Create a filesystem store
let store_path: PathBuf = "/path/to/hierarchy.zarr".into();
let store: ReadableWritableListableStorage =
    Arc::new(FilesystemStore::new(&store_path)?);

// Write the root group metadata
GroupBuilder::new()
    .build(store.clone(), "/")?
    // .attributes(...)
    .store_metadata()?;

// Create a new V3 array using the array builder
let array = ArrayBuilder::new(
    vec![3, 4], // array shape
    DataType::Float32,
    vec![2, 2].try_into()?, // regular chunk shape (non-zero elements)
    FillValue::from(ZARR_NAN_F32),
)
.bytes_to_bytes_codecs(vec![
    Arc::new(GzipCodec::new(5)?),
])
.dimension_names(["y", "x"].into())
.attributes(serde_json::json!({"Zarr V3": "is great"}).as_object().unwrap().clone())
.build(store.clone(), "/array")?; // /path/to/hierarchy.zarr/array

// Store the array metadata
array.store_metadata()?;
println!("{}", serde_json::to_string_pretty(array.metadata())?);
// {
//     "zarr_format": 3,
//     "node_type": "array",
//     ...
// }

// Perform some operations on the chunks
array.store_chunk_elements::<f32>(
    &[0, 1], // chunk index
    &[0.2, 0.3, 1.2, 1.3]
)?;
array.store_array_subset_ndarray::<f32, _>(
    &[1, 1], // array index (start of subset)
    ndarray::array![[-1.1, -1.2], [-2.1, -2.2]]
)?;
array.erase_chunk(&[1, 1])?;

// Retrieve all array elements as an ndarray
let array_ndarray = array.retrieve_array_subset_ndarray::<f32>(&array.subset_all())?;
println!("{array_ndarray:4}");
// [[ NaN,  NaN,  0.2,  0.3],
//  [ NaN, -1.1, -1.2,  1.3],
//  [ NaN, -2.1,  NaN,  NaN]]

zarrs Ecosystem

Core

• zarrs: The core library for manipulating Zarr hierarchies.
• zarrs_data_type: Zarr data types (re-exported as zarrs::data_type).
• zarrs_metadata: Zarr metadata support (re-exported as zarrs::metadata).
• zarrs_storage: The storage API for zarrs (re-exported as zarrs::storage).

Stores

• zarrs_filesystem: A filesystem store (re-exported as zarrs::filesystem).
• zarrs_object_store: object_store store support.
• zarrs_opendal: opendal store support.
• zarrs_http: A synchronous http store.
• zarrs_zip: A storage adapter for zip files.
• zarrs_icechunk: icechunk store support.

Bindings

• zarrs-python: A high-performance codec pipeline for zarr-python.
• zarrs_ffi: A subset of zarrs exposed as a C/C++ API.

Zarr Metadata Conventions

• ome_zarr_metadata: A library for OME-Zarr (previously OME-NGFF) metadata.

Tools

• zarrs_tools: Various tools for creating and manipulating Zarr V3 data with the zarrs rust crate
  • A reencoder that can change codecs, chunk shape, convert Zarr V2 to V3, etc.
  • Create an OME-Zarr hierarchy from a Zarr array.
  • Transform arrays: crop, rescale, downsample, gradient magnitude, gaussian, noise filtering, etc.
  • Benchmarking tools and performance benchmarks of zarrs.

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.