Virtual Datasets (VDS)

Starting with version 2.9, h5py includes high-level support for HDF5 ‘virtual datasets’. The VDS feature is available in version 1.10 of the HDF5 library; h5py must be built with a new enough version of HDF5 to create or read virtual datasets.

What are virtual datasets?

Virtual datasets allow a number of real datasets to be mapped together into a single, sliceable dataset via an interface layer. The mapping can be made ahead of time, before the parent files are written, and is transparent to the parent dataset characteristics (SWMR, chunking, compression etc…). The datasets can be meshed in arbitrary combinations, and even the data type converted.

Once a virtual dataset has been created, it can be read just like any other HDF5 dataset.


Virtual dataset files cannot be opened with versions of the hdf5 library older than 1.10.

The HDF Group has documented the VDS features in detail on the website: Virtual Datasets (VDS) Documentation.

Creating virtual datasets in h5py

To make a virtual dataset using h5py, you need to:

  1. Create a VirtualLayout object representing the dimensions and data type of the virtual dataset.
  2. Create a number of VirtualSource objects, representing the datasets the array will be built from. VirtualSource objects can be created either from an h5py Dataset, or from a filename, dataset name and shape. This can be done even before the file exists.
  3. Map slices from the sources into the layout.
  4. Convert the VirtualLayout object into a virtual dataset in an HDF5 file.

The following snippet creates a virtual dataset to stack together four 1D datasets from separate files into a 2D dataset:

layout = h5py.VirtualLayout(shape=(4, 100), dtype='i4')

for n in range(1, 5):
    filename = "{}.h5".format(n)
    vsource = h5py.VirtualSource(filename, 'data', shape=(100,))
    layout[n - 1] = vsource

# Add virtual dataset to output file
with h5py.File("VDS.h5", 'w', libver='latest') as f:
    f.create_virtual_dataset('data', layout, fillvalue=-5)

This is an extract from the example in the examples folder.


In addition to the above example snippet, a few more complete examples can be found in the examples folder:

  • is a self-contained, runnable example which creates four source files, and then maps them into a virtual dataset as shown above.
  • A number of examples are based on the sample use cases presented in the virtual datasets RFC:,, and