hexz 0.6.0

High-performance snapshot storage library with compression and encryption

hexz

Python library for reading, writing, and streaming Hexz snapshots — a seekable, deduplicated compression format built in Rust.

pip install hexz

Quick Start

Reading snapshots

import hexz

with hexz.open("data.hxz") as reader:
    data = reader.read()           # read entire snapshot
    chunk = reader.read(4096)      # read 4KB from current position
    reader.seek(1024)              # seek to offset
    block = reader[100:200]        # slice notation

Writing snapshots

import hexz

with hexz.open("output.hxz", mode="w", compression="lz4") as writer:
    writer.add_file("disk.img")
    writer.add_bytes(b"extra data")

Building from files

import hexz

# Build with a profile preset (ml, eda, embedded, generic, archival)
metadata = hexz.build("source.img", "output.hxz", profile="ml")

Converting from other formats

import hexz

# Convert tar, HDF5, or WebDataset archives
hexz.convert("dataset.tar", "dataset.hxz")
hexz.convert("data.h5", "data.hxz")       # requires pip install hexz[hdf5]

Remote storage

import hexz

# Stream from S3 (only fetches needed blocks)
reader = hexz.open("s3://bucket/data.hxz", s3_region="us-east-1")
chunk = reader.read(4096)

# HTTP streaming
reader = hexz.open("https://example.com/data.hxz")

API

Core I/O

Function / Class	Description
hexz.open(path, mode="r", **opts)	Open a snapshot for reading or writing
Reader(path, ...)	Read snapshots with file-like interface (seek, read, tell, slice)
AsyncReader.create(path, ...)	Async reader for asyncio workflows
Writer(path, ...)	Create new snapshots with compression and deduplication

Data operations

Function	Description
build(source, output, profile, ...)	Build snapshot from files with preset profiles
convert(input, output, format, ...)	Convert tar/HDF5/WebDataset to Hexz
inspect(path)	Get snapshot metadata (compression, size, block count)
verify(path, ...)	Verify integrity and optional cryptographic signature

Array support

Function / Class	Description
read_array(source, offset, shape, dtype)	Read NumPy array from snapshot
write_array(dest, array, ...)	Write NumPy array to snapshot
ArrayView(path, shape, dtype)	Memory-mapped array access with slicing

ML integration

Class	Description
Dataset(path, ...)	PyTorch Dataset with caching and prefetching

from hexz import Dataset
from torch.utils.data import DataLoader

dataset = Dataset("s3://bucket/train.hxz", cache_size_mb=512)
loader = DataLoader(dataset, batch_size=32, num_workers=4, shuffle=True)

for batch in loader:
    train_step(batch)

Cryptographic operations

import hexz

hexz.keygen("private.key", "public.key")   # generate Ed25519 keypair
hexz.sign("snapshot.hxz", "private.key")   # sign a snapshot
hexz.verify("snapshot.hxz", "public.key")  # verify signature

Optional dependencies

pip install hexz[numpy]       # NumPy array support
pip install hexz[torch]       # PyTorch Dataset integration
pip install hexz[hdf5]        # HDF5 conversion (h5py)
pip install hexz[ml]          # NumPy + PyTorch
pip install hexz[full]        # All optional dependencies

Compression

LZ4 is always available. Zstd and S3 streaming are included by default in PyPI wheels.

Algorithm	Speed	Ratio	Default
LZ4	Fast (~2-3 GB/s)	Moderate	Always included
Zstd	Moderate	High	Yes

Requirements

• Python 3.8+
• Linux (x86_64, aarch64), macOS (x86_64, Apple Silicon), or Windows (x86_64)

Links

• GitHub
• Documentation
• CLI Tool — hexz command-line binary