h5grove
Core utilities to serve HDF5 file contents
Decision gist · record as of 2026-08-14
Yes, if you are building a backend to serve HDF5 files and want to reuse battle-tested utilities for common problems (link resolution, compression, JSON encoding). No, if you only need to read HDF5 files in a local Python script—h5py alone is sufficient. Yes-with-conditions if you plan to use the web framework integrations: verify that the FastAPI/Flask/Tornado extras match your framework version and production readiness expectations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; h5py requires HDF5 libraries on the system.
- Low friction: pure-Python wheel with five stable runtime dependencies (h5py, numpy, orjson, tifffile, typing-extensions).
- Active maintenance with a release 122 days ago and commits through April 2026.
License · maintenance · safety
permissive license (permissive) — MIT License (permissive): you can use, modify, and distribute h5grove freely in commercial and private projects, provided you include the original copyright notice and license text.
last release 2026-04-14 (122 days) · last repo commit 2026-04-21 · 24 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 125,674 downloads/mo, #11,807 on PyPI
Alternatives
Verify before relying
pip install h5grove
import h5grove
import h5py
with h5py.File('data.h5', 'r') as f:
# Use h5grove utilities to access and serve HDF5 content
pass- Whether the package provides ready-to-use server implementations or only low-level utilities for custom backends.
- Performance characteristics when serving large HDF5 datasets or handling concurrent requests.
- Whether FastAPI, Flask, and Tornado integrations are production-ready or experimental.
What it is and what it does
h5grove is a Python library for building backends that expose HDF5 file contents over the network. Rather than being a complete server, it provides reusable utilities that solve recurring problems in HDF5 serving: resolving external links, handling dataset compression and slicing, and encoding data consistently for JSON transport (including special handling for NaN and Infinity). It wraps h5py for file access and depends on numpy, orjson, tifffile, and typing-extensions.
The package is designed for developers building custom HDF5 backends, with optional integrations for FastAPI, Flask, and Tornado. You install the core package and add framework-specific extras only when needed. It's actively maintained, supports current Python versions (3.10+), and carries no known security vulnerabilities.
Use it for
- Build a REST API to query and download slices of large HDF5 datasets stored on disk.
- Serve scientific instrument data (common in synchrotron facilities) to web clients without reimplementing link resolution and compression logic.
- Create a custom HDF5 viewer backend that handles external file references and encodes numeric data safely for JSON.
- Integrate HDF5 file access into an existing FastAPI or Flask application with minimal boilerplate.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building a backend to serve HDF5 files and want to reuse battle-tested utilities for common problems (link resolution, compression, JSON encoding).
No, if you only need to read HDF5 files in a local Python script—h5py alone is sufficient. Yes-with-conditions if you plan to use the web framework integrations: verify that the FastAPI/Flask/Tornado extras match your framework version and production readiness expectations.
Install
h5grove on PyPI
Before you install
Low friction: pure-Python wheel with five stable runtime dependencies (h5py, numpy, orjson, tifffile, typing-extensions). Active maintenance with a release 122 days ago and commits through April 2026.
Requires Python 3.10 or later; h5py requires HDF5 libraries on the system.
License in practice
MIT License (permissive): you can use, modify, and distribute h5grove freely in commercial and private projects, provided you include the original copyright notice and license text.
Quickstart
pip install h5grove
import h5grove
import h5py
with h5py.File('data.h5', 'r') as f:
# Use h5grove utilities to access and serve HDF5 content
passVerify before relying
- Whether the package provides ready-to-use server implementations or only low-level utilities for custom backends.
- Performance characteristics when serving large HDF5 datasets or handling concurrent requests.
- Whether FastAPI, Flask, and Tornado integrations are production-ready or experimental.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesh5pynumpyorjsontifffiletyping-extensions |
| Maintenance | Actively maintained 122 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 125,674 / month, #11,807 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 |
Evidence: h5grove-4.0.0-py3-none-any.whl
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See also h5py · hdf5plugin · hdmf · h5netcdf · hickle · tables · mat73 · static3 · servestatic · kerchunk