rustytree-xarray 0.1.0

Rust-backed xarray DataTree backend for fast Zarr access from object storage

rustytree

Rust-backed xarray DataTree backend for fast Zarr (incl. icechunk) access from object storage.

rustytree registers as xr.open_datatree(engine="rustytree") and walks Zarr v3 hierarchies — both icechunk-backed and vanilla — concurrently in async across one FFI crossing. The goal is a drop-in faster replacement for xr.open_datatree(engine="zarr"), especially on icechunk repos served from object storage where xarray's current per-group sequential decoding is the dominant cost.

Status

Alpha. xr.open_datatree(..., engine="rustytree") works end-to-end against both icechunk repositories (pass session.store) and vanilla Zarr v3 stores (path or s3:// URL). Lazy chunk reads via RustyBackendArray, CF decoding (incl. metadata-only datetime dtype inference), the recursive multi-node walk, glob group= filtering, and non-recursive single-Dataset opens are all in. See CHANGELOG.md for the per-PR breakdown.

Install from PyPI (the import name stays rustytree; the distribution name on PyPI is rustytree-xarray because rustytree collides with an unrelated dormant package):

pip install rustytree-xarray

Or install from source per Quick start below.

Example

Open the public anonymous NEXRAD KLOT icechunk repo on S3:

import icechunk
import xarray as xr

storage = icechunk.s3_storage(
    bucket="nexrad-arco", prefix="KLOT",
    region="us-east-1", anonymous=True,
)
session = icechunk.Repository.open(storage).readonly_session("main")

# Open the full DataTree (107 nodes — every VCP × sweep combination).
dt = xr.open_datatree(session.store, engine="rustytree")

# Or grab one specific sweep as a flat Dataset (skips walking siblings):
ds = xr.open_dataset(session.store, engine="rustytree",
                     group="/VCP-12/sweep_0")

# Or apply a glob pattern. "Give me sweep_0 from every VCP" is the
# canonical radar workflow — returns a tree filtered to those matches,
# with the VCP container groups auto-included as ancestors.
sweeps_0 = xr.open_datatree(session.store, engine="rustytree",
                            group="/*/sweep_0")

engine="rustytree" is a drop-in replacement for engine="zarr" — same xr.open_datatree / xr.open_dataset entry points, same storage_options / decode_* kwargs. The full demo (with side-by-side timings against engine="zarr") is in notebooks/klot_demo.ipynb.

Quick start

rustytree is a Rust extension built via maturin; uv sync handles the venv, Python deps, and the Rust build (via maturin's PEP 517 hook) in one command.

# Clone + enter the repo
git clone https://github.com/aladinor/rustytree.git
cd rustytree

# Install everything: venv, deps, and the compiled Rust extension.
uv sync --extra dev

# Confirm the install
.venv/bin/pytest tests/

# Launch Jupyter and open the demo notebook
.venv/bin/jupyter lab notebooks/klot_demo.ipynb

The notebooks and tests don't require AWS credentials — KLOT is read anonymously via icechunk's anonymous S3 path. Network speed determines cold-cache timings (expect ~1–3 s for engine="rustytree" on KLOT from a home connection; ~50 s for engine="zarr" on the same).

Documentation

• docs/architecture.md — what the project is and why the Rust backend wins.
• docs/usage.md — how to install, run the test suite, and the API surface (inputs, group= patterns, lazy reads, errors).
• docs/contributing.md — branching, commit conventions, validation gates, per-PR audit.
• docs/release-process.md — versioning, tag, CHANGELOG roll.

License

Apache License 2.0 (Apache-2.0).

Acknowledgements

Built on zarrs + zarrs_icechunk + icechunk. Sibling project radish proved the PyO3 + xarray-backend pattern. The bottlenecks this project sets out to collapse are tracked in xarray PRs #10742, #11304, and #11302.