zarrista
zarrista-bounded is a fork of zarrista 0.1.0 (
pip install zarrista-bounded, stillimport zarrista) whose only change is a fixed zarrs filesystem store: withexperimental_partial_encoding=True, inner chunks are appended to a shard in place instead of rewriting the whole shard file, so a shard of any size is written in memory bounded by an inner chunk. It will be retired once the fix is released upstream (zarrs/zarrs#482). Do not install it next tozarrista: both provide thezarristapackage.
A fast, low-level Zarr API for Python, powered from Rust by Zarrs.
Initial benchmarks suggest Zarrista is 1.9x to 2.7x faster than Zarr-Python for reading compressed or compressed+sharded data, respectively, from a local file system. We expect future async-focused benchmarks to be even faster.
While Zarrista will exist as a standalone Python library and can be used directly, the goal is to integrate Zarrista directly into Zarr-Python so that existing users can get improved performance out of the box and to avoid fracturing the ecosystem.
This library is beta-quality. The underlying Zarrs library is reliable and broadly used. The way we expose a Python API from it may change in the future.
Documentation
Features
- High-performance Rust core
- Encoded chunk access allows explicitly managing IO-bound data access and CPU-bound decoding separately.
- Sync/Async support
- Synchronous
ArrayandGroupfor local file system access - Asynchronous counterparts for remote data access through Obstore
- Synchronous
- NumPy integration
- Zero-copy data exchange via DLPack, the buffer protocol, and Arrow.
- Broad data type support including variable-length string/bytes and the machine-learning float and sub-byte integer types (integrating with
ml_dtypes). - Broad codec support, including all in the Zarr v3 spec.
- Full type hinting for all operations.
- Icechunk integration
Benchmarks
In our PR to zarr_benchmarks, Zarrista is the fastest Python chunked array library, in line with Google's Tensorstore. It's surpassed only by the Rust Zarrs library, which Zarrista uses internally.
Read All
The minimum time and peak memory usage to read an entire dataset into memory.
Read Chunk-By-Chunk
The minimum time and peak memory usage to read a dataset chunk-by-chunk into memory.
Read Subchunk-By-Subchunk
The minimum time and peak memory usage to read a dataset subchunk-by-subchunk into memory.
Example
Open a store, then open an Array from it:
from zarrista import Array
from zarrista.store import FilesystemStore
store = FilesystemStore("data/example.zarr")
array = Array.open(store, path="/temperature")
Inspect the array's metadata:
array.shape
# [720, 1440]
array.dtype
# DataType(float32 / <f4)
array.dimension_names
# ["lat", "lon"]
Read a subset of the array. Indexing returns a Tensor, which converts to a NumPy array:
data = array[0:128, 0:128]
arr = data.to_numpy()
arr.shape
# (128, 128)
You can also read individual chunks by their grid index:
data = array.retrieve_chunk([0, 0])
AI Usage
Zarrista's source code is minimally vibe-coded.
Most of the library code was written by hand by Kyle, sometimes resulting from a conversation with Claude.
Documentation and type stubs are mixed. Much documentation is written by hand but Python type stubs are partially kept up to date via Claude.
Almost all current tests were written by Claude.
1 maintainer
Metadata
1 maintainer
Release files for zarrista-bounded 0.1.0
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