Hierarchical Data Format
|Latest release||5-1.8.13 / May 15, 2014|
|Type of format||scientific data format|
Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of numerical data. Originally developed at the National Center for Supercomputing Applications, it is supported by the non-profit HDF Group, whose mission is to ensure continued development of HDF5 technologies, and the continued accessibility of data stored in HDF.
In keeping with this goal, the HDF format, libraries and associated tools are available under a liberal, BSD-like license for general use. HDF is supported by many commercial and non-commercial software platforms, including Java, MATLAB/Scilab, Octave, IDL, Python, and R. The freely available HDF distribution consists of the library, command-line utilities, test suite source, Java interface, and the Java-based HDF Viewer (HDFView).
The current version, HDF5, differs significantly in design and API from the major legacy version HDF4.
HDF4 is the older version of the format, although still actively supported by the HDF Group. It supports a proliferation of different data models, including multidimensional arrays, raster images, and tables. Each defines a specific aggregate data type and provides an API for reading, writing, and organizing the data and metadata. New data models can be added by the HDF developers or users.
HDF is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. Users can create their own grouping structures called "vgroups."
The HDF4 format has many limitations. It lacks a clear object model, which makes continued support and improvement difficult. Supporting many different interface styles (images, tables, arrays) leads to a complex API. Support for metadata depends on which interface is in use; SD (Scientific Dataset) objects support arbitrary named attributes, while other types only support predefined metadata. Perhaps most importantly, the use of 32-bit signed integers for addressing limits HDF4 files to a maximum of 2 GB, which is unacceptable in many modern scientific applications.
The HDF5 format is designed to address some of the limitations of the HDF4 library, and to address current and anticipated requirements of modern systems and applications. In 2002 it won an R&D 100 Award.
HDF5 simplifies the file structure to include only two major types of object:
- Datasets, which are multidimensional arrays of a homogeneous type
- Groups, which are container structures which can hold datasets and other groups
This results in a truly hierarchical, filesystem-like data format. In fact, resources in an HDF5 file are even accessed using the POSIX-like syntax /path/to/resource. Metadata is stored in the form of user-defined, named attributes attached to groups and datasets. More complex storage APIs representing images and tables can then be built up using datasets, groups and attributes.
In addition to these advances in the file format, HDF5 includes an improved type system, and dataspace objects which represent selections over dataset regions. The API is also object-oriented with respect to datasets, groups, attributes, types, dataspaces and property lists.
The latest version of NetCDF, version 4, is based on HDF5.
Because it uses B-trees to index table objects, HDF5 works well for time series data such as stock price series, network monitoring data, and 3D meteorological data. The bulk of the data goes into straightforward arrays (the table objects) that can be accessed much more quickly than the rows of a SQL database, but B-Tree access is available for non-array data. The HDF5 data storage mechanism can be simpler and faster than an SQL star schema.
Officially supported APIs
- Fortran, Fortran 90
- CLI - .Net
- HDF5 Lite (H5LT) – a light-weight interface for C
- HDF5 Image (H5IM) – a C interface for images or rasters
- HDF5 Table (H5TB) – a C interface for tables
- HDF5 Packet Table (H5PT) – interfaces for C and C++ to handle "packet" data, accessed at high-speeds
- HDF5 Dimension Scale (H5DS) – allows dimension scales to be added to HDF5; to be introduced in the HDF5-1.8 release
- GNU Data Language
- Huygens Software uses HDF5 as primary storage format since version 3.5
- JHDF5, an alternative Java binding that takes a different approach from the official HDF5 Java binding which some users find simpler
- Julia provides HDF5 support through the HDF5 package.
- LabVIEW can gain HDF support through third-party libraries, such as h5labview.
- MATLAB, Scilab or Octave – use HDF5 as primary storage format in recent releases
- Mathematica immediate analysis of HDF and HDF5 data
- Python supports HDF5 via h5py (both high- and low-level access to HDF5 abstractions) and via PyTables (a high-level interface with advanced indexing and database-like query capabilities).
- CGNS uses HDF5 as main storage
- R offers support in the rhdf5 package.
- Go - kisielk's go-hdf5 package is based on sbinet's go-hdf5 package.
- IGOR Pro offers full support of HDF5 files.
- Common Data Format (CDF)
- FITS, a data format used in astronomy
- GRIB (GRIdded Binary), a data format used in meteorology
- HDF Explorer
- NetCDF, The Netcdf Java library reads HDF5, HDF4, HDF-EOS and other formats using pure Java
- Protocol Buffers - Google's data interchange format
- Official website The HDF Group
- What is HDF5?
- A presentation on how to handle large datasets in Quantum Chemistry using hdf5
- NASA HDF file example, its structure generated and shown online as CreativeCommons image
- HDF Explorer A data visualization program that reads the HDF, HDF5 and netCDF data file formats
- HDFView A browser and editor for HDF files
- ViTables A browser and editor for HDF5 and PyTables files written in Python