alterlab-zarr

Zarr Python

Overview

Zarr is a Python library for storing large N-dimensional arrays with chunking and compression. Apply this skill for efficient parallel I/O, cloud-native workflows, and seamless integration with NumPy, Dask, and Xarray.

Quick Start

Installation

uv pip install zarr

Requires Python 3.11+ and Zarr v3 (zarr>=3). For cloud storage support, install the matching fsspec backend:

uv pip install s3fs   # For S3
uv pip install gcsfs  # For Google Cloud Storage

Basic Array Creation

import zarr
import numpy as np

# Create a 2D array with chunking and compression
z = zarr.create_array(
    store="data/my_array.zarr",
    shape=(10000, 10000),
    chunks=(1000, 1000),
    dtype="f4"
)

# Write data using NumPy-style indexing
z[:, :] = np.random.random((10000, 10000))

# Read data
data = z[0:100, 0:100]  # Returns NumPy array

Core Workflow

1. Create or open an array/group, picking a store appropriate to the environment (local, in-memory, ZIP, S3/GCS).
2. Choose chunking aligned to your access pattern (aim for 1-10 MB chunks; rows-first → chunks span columns, and vice versa). This is the single biggest performance lever.
3. Pick compression via compressors= based on workload — Zstandard (the default), Blosc+LZ4 (fast), Gzip (max ratio); compressors=None to disable.
4. Read/write with NumPy-style indexing; resize/append as data grows.
5. Scale out with Dask (lazy, out-of-core, parallel) or label with Xarray for climate/geospatial data.
6. For cloud and many-array stores, consolidate metadata and consider sharding to cut object/file count.

# Minimal end-to-end
import zarr, numpy as np
z = zarr.create_array(store="data/my_array.zarr", shape=(10000, 10000),
                      chunks=(1000, 1000), dtype="f4")
z[:, :] = np.random.random((10000, 10000))
sub = z[0:100, 0:100]            # returns a NumPy array

Routing — where to look

You need…	Go to
Array create/open, read/write, resize/append, attributes, groups & hierarchies, consolidated metadata	references/array_operations.md
Chunk-size guidelines, aligning chunks to access patterns, sharding, compression codecs & tips	references/chunking_compression.md
Local / in-memory / ZIP / S3 / GCS stores and cloud best practices	references/storage_backends.md
NumPy / Dask / Xarray integration, thread- and process-safe parallel writes	references/integration.md
Performance checklist, profiling, common patterns (time series, large matrices, cloud-native, format conversion), troubleshooting	references/patterns_performance.md
Full API surface	references/api_reference.md