What is HDF5?

HDF5 (Hierarchical Data Format version 5) is a container format designed for storing massive scientific datasets with complex hierarchical structures. It can store heterogeneous data (arrays, tables, images, metadata) in a file-system-like structure with groups and datasets. HDF5 supports multi-dimensional arrays up to 32 dimensions, chunking for partial I/O, compression (gzip, LZF, Blosc), and parallel I/O for supercomputers.

HDF5 is the standard format in scientific computing, used by NASA for satellite data, CERN for particle physics, genomics research (10X Genomics), machine learning (Keras/TensorFlow model storage), climate modeling, and medical imaging. Popular with Python (h5py, PyTables), MATLAB, R, and Julia for handling large datasets that don't fit in memory. Used when you need efficient random access to terabyte-scale arrays.

Did you know? NASA stores petabytes of Earth observation data in HDF5 format!

History

The HDF Group at the National Center for Supercomputing Applications (NCSA) developed HDF5 to handle large scientific datasets with complex structures.

Key Milestones

  • 1988: HDF4 predecessor created
  • 1998: HDF5 1.0 released
  • 2006: NASA adopts for Earth science
  • 2013: Keras uses for model weights
  • 2018: HDF5 1.10 performance boost
  • Present: Scientific computing standard

Key Features

Core Capabilities

  • Hierarchical: Group/dataset structure
  • Multi-Dimensional: Up to 32 dimensions
  • Chunking: Partial array access
  • Compression: gzip, LZF, Blosc
  • Metadata: Rich attribute storage
  • Parallel I/O: MPI support

Common Use Cases

Research

Scientific datasets

Machine Learning

Neural network weights

Aerospace

NASA satellite data

Genomics

DNA sequencing data

Advantages

  • Handles massive datasets efficiently
  • Complex hierarchical structures
  • Partial data access (chunking)
  • Excellent compression support
  • Rich metadata capabilities
  • Parallel I/O for HPC
  • Wide scientific software support

Disadvantages

  • Complex format specification
  • Steep learning curve
  • Can corrupt with improper closure
  • Not human-readable
  • Overkill for simple data
  • Requires specialized libraries

Technical Information

Format Specifications

Specification Details
File Extension .hdf5, .h5, .hdf
MIME Type application/x-hdf5
Max Dimensions 32 dimensions
Compression gzip, szip, LZF, Blosc
Parallel I/O MPI support
License BSD-style (free)

Common Tools

  • Python: h5py, PyTables, Keras (model storage)
  • MATLAB: Built-in HDF5 support
  • R: rhdf5, hdf5r packages
  • Tools: HDFView (GUI), h5dump, h5ls