Hierarchical Data Format - HDF5

HDF5

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 homogenous 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.