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.

Current upstream: zarr 3.2.1 (released 2026-05-05). Docs: zarr.readthedocs.io. New arrays default to Zarr format 3; set zarr_format=2 for legacy interop. Zarr 3.2 adds rectilinear chunks and continues to refine the v3 codec pipeline. This skill is a community guide maintained by K-Dense Inc., not an official zarr-developers package.

Quick Start

Installation

uv pip install "zarr==3.2.1"

Requires Python 3.12+ and NumPy 2.0+ for current stable Zarr-Python. For remote stores (S3, GCS, HTTP), pin the optional extras/backends in your project lockfile:

uv pip install "zarr[remote]==3.2.1" "s3fs==2026.4.0" "gcsfs==2026.5.0"

Use a version range such as zarr>=3,<4 only when your project has a committed lockfile and compatibility tests. For Zarr-Python 2 / Python 3.10–3.11 workflows, choose an exact zarr==2.x.y patch version from the support-v2 release notes and commit the resulting lockfile.

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 Operations

Creating Arrays

Zarr provides multiple convenience functions for array creation:

# Create empty array
z = zarr.zeros(shape=(10000, 10000), chunks=(1000, 1000), dtype='f4',
               store='data.zarr')

# Create filled arrays
z = zarr.ones((5000, 5000), chunks=(500, 500))
z = zarr.full((1000, 1000), fill_value=42, chunks=(100, 100))

# Create from existing data
data = np.arange(10000).reshape(100, 100)
z = zarr.array(data, chunks=(10, 10), store='data.zarr')

# Create like another array
z2 = zarr.zeros_like(z)  # Matches shape, chunks, dtype of z

Opening Existing Arrays

# Open array (read/write mode by default)
z = zarr.open_array('data.zarr', mode='r+')

# Read-only mode
z = zarr.open_array('data.zarr', mode='r')

# The open() function auto-detects arrays vs groups
z = zarr.open('data.zarr')  # Returns Array or Group

Reading and Writing Data

Zarr arrays support NumPy-like indexing:

# Write entire array
z[:] = 42

# Write slices
z[0, :] = np.arange(100)
z[10:20, 50:60] = np.random.random((10, 10))

# Read data (returns NumPy array)
data = z[0:100, 0:100]
row = z[5, :]

# Advanced indexing
z.vindex[[0, 5, 10], [2, 8, 15]]  # Coordinate indexing
z.oindex[0:10, [5, 10, 15]]       # Orthogonal indexing
z.blocks[0, 0]                     # Block/chunk indexing

Resizing and Appending

# Resize array (v3: pass shape as a tuple)
z.resize((15000, 15000))

# Append data along an axis
z.append(np.random.random((1000, 10000)), axis=0)  # Adds rows

Chunking Strategies

Chunking is critical for performance. Choose chunk sizes and shapes based on access patterns.

Chunk Size Guidelines

• Minimum chunk size: 1 MB recommended for optimal performance
• Balance: Larger chunks = fewer metadata operations; smaller chunks = better parallel access
• Memory consideration: Entire chunks must fit in memory during compression

# Configure chunk size (aim for ~1MB per chunk)
# For float32 data: 1MB = 262,144 elements = 512×512 array
z = zarr.zeros(
    shape=(10000, 10000),
    chunks=(512, 512),  # ~1MB chunks
    dtype='f4'
)

Aligning Chunks with Access Patterns

Critical: Chunk shape dramatically affects performance based on how data is accessed.

# If accessing rows frequently (first dimension)
z = zarr.zeros((10000, 10000), chunks=(10, 10000))  # Chunk spans columns

# If accessing columns frequently (second dimension)
z = zarr.zeros((10000, 10000), chunks=(10000, 10))  # Chunk spans rows

# For mixed access patterns (balanced approach)
z = zarr.zeros((10000, 10000), chunks=(1000, 1000))  # Square chunks

Performance example: For a (200, 200, 200) array, reading along the first dimension:

• Using chunks (1, 200, 200): ~107ms
• Using chunks (200, 200, 1): ~1.65ms (65× faster!)

Rectilinear Chunks and Sharding

Zarr 3.2 supports rectilinear chunks for uneven grids. Pass nested chunk lengths when a dimension has variable tile sizes:

z = zarr.create_array(
    store="rectilinear.zarr",
    shape=(60, 100),
    chunks=([10, 20, 30], [50, 50]),
    dtype="f4",
)

When arrays have millions of small chunks, use sharding to group chunks into larger storage objects:

# Create array with sharding
z = zarr.create_array(
    store='data.zarr',
    shape=(100000, 100000),
    chunks=(100, 100),  # Small chunks for access
    shards=(1000, 1000),  # Groups 100 chunks per shard
    dtype='f4'
)

Benefits:

• Reduces file system overhead from millions of small files
• Improves cloud storage performance (fewer object requests)
• Prevents filesystem block size waste

Important: Entire shards must fit in memory before writing.

Compression

Zarr applies compression per chunk to reduce storage while maintaining fast access.

Configuring Compression

from zarr.codecs import BloscCodec, BloscShuffle, GzipCodec

# Default: Blosc with Zstandard
z = zarr.zeros((1000, 1000), chunks=(100, 100))  # Uses default compression

# Configure Blosc compression
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    compressors=BloscCodec(cname='zstd', clevel=5, shuffle=BloscShuffle.bitshuffle)
)

# Available Blosc compressors: 'blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd'

# Use Gzip compression
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    compressors=GzipCodec(level=6)
)

# Disable compression
z = zarr.create_array(
    store='data.zarr',
    shape=(1000, 1000),
    chunks=(100, 100),
    dtype='f4',
    compressors=None
)

Compression Performance Tips

• Blosc (default): Fast compression/decompression, good for interactive workloads
• Zstandard: Better compression ratios, slightly slower than LZ4
• Gzip: Maximum compression, slower performance
• LZ4: Fastest compression, lower ratios
• Shuffle: Enable shuffle filter for better compression on numeric data

# Optimal for numeric scientific data
compressors=BloscCodec(cname='zstd', clevel=5, shuffle=BloscShuffle.bitshuffle)

# Optimal for speed
compressors=BloscCodec(cname='lz4', clevel=1)

# Optimal for compression ratio
compressors=GzipCodec(level=9)

Storage Backends

Zarr supports multiple storage backends through a flexible storage interface.

Local Filesystem (Default)

from zarr.storage import LocalStore

# Explicit store creation
store = LocalStore('data/my_array.zarr')
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))

# Or use string path (creates LocalStore automatically)
z = zarr.open_array('data/my_array.zarr', mode='w', shape=(1000, 1000),
                    chunks=(100, 100))

In-Memory Storage

from zarr.storage import MemoryStore

# Create in-memory store
store = MemoryStore()
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))

# Data exists only in memory, not persisted

ZIP File Storage

from zarr.storage import ZipStore

# Write to ZIP file
store = ZipStore('data.zip', mode='w')
z = zarr.open_array(store=store, mode='w', shape=(1000, 1000), chunks=(100, 100))
z[:] = np.random.random((1000, 1000))
store.close()  # IMPORTA