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HDF Server

Announcing HDF Server (h5serv) 0.2.0

We are proud to announce the availability of HDF Server (h5serv) 0.2.0!

HDF Server is a Python-based web service that can be used to send and receive HDF5 data using an HTTP-based REST interface. HDF Server supports CRUD (create, read, update, delete) operations on the full spectrum of HDF5 objects including: groups, links, datasets, attributes, and committed data types. As a REST service a variety of clients can be developed in JavaScript, Python, C, and other common languages.

The HDF Server extends the HDF5 data model to efficiently store large data objects (e.g. up to multi-TB data arrays) and access them over the web using a RESTful API. As datasets get larger and larger, it becomes impractical to download files to access data. Using HDF Server, data can be kept in one central location and content vended via well-defined URIs. This enables exploration and analysis of the data while minimizing the number of bytes that need to be transmitted over the network.

Since HDF Server supports both reading and writing of data, it enables some interesting scenarios such as:

In addition to these, we would like to hear your ideas of how HDF Server could be utilized (as well as any other feedback you might have). Additional information follows below.

Thanks to everyone who helped and advised on this project.



The HDF REST API enables access to HDF5 data objects via a combination of standard HTTP verbs: GET, PUT, POST, DELETE acting on HDF5 objects identified via URIs (Uniform Resource Identifier). For example, POST /groups creates a new group. Collections of HDF objects (that would normally be stored in a file) are mapped as Internet domains.

To see how this works, you can try this out using an instance of the server we set up on AWS. Go to this link: https://data.hdfgroup.org:7258/?host=tall.data.hdfgroup.org and you should see text that provides basic information about the data collection "tall" (originally from the file tall.h5 that is included in the HDF5 library release). The text displayed is the JSON representation of the domain (if you install a JSON plugin for your browser that will "prettify" the rendering). As you can see from the somewhat cryptic response text, the server is designed to be used as a Web API rather than a Web UI (though it is certainly possible to write a Web UI using the server as the data source).

Similarly, each group, dataset, and attribute is accessible as a URI within that domain. For example, to read an attribute you would use a URL like this: https://data.hdfgroup.org:7258/groups/4af80138-3e8a-11e6-a48f-0242ac110003/attributes/attr2?host=tall.data.hdfgroup.org. When reading the dataset values, you may desire to only request a sub-region of the dataset. To do so, use a select query that specifies that start, stop, and step values that defines the region to be returned. E.g.: this request: https://data.hdfgroup.org:7258/datasets/4af8bc72-3e8a-11e6-a48f-0242ac110003/value?host=tall.data.hdfgroup.org&select=[0:4,0:4] returns a 4x4 subset of a 10x10 array.

As you may have noticed from the URLs above, objects are identified via a UUID (universally unique identifier) rather than (potentially ambiguous) path names. This has the advantage that each group and dataset object has a unique representation. HDF5 links can then reference the UUID of the linked object. Furthermore, this uniformity of the URL structure decouples server and client, since no a priori knowledge of the HDF5 path name space is necessary.

Besides reading data, HDF Server supports the full set of create, update, and delete operations (comparable to what you would find in the HDF5 library) using respectively the http POST, PUT, and DELETE operations. Though PUT, POST, and DELETE operations can't be invoked directly from the browser address bar, the release includes a large set of test programs that provides examples of how to use all the operations supported by the server.


HDF Server uses JSON as the default representation for requests and responses, so one aspect of this project was defining JSON representations of HDF5 objects. We were able to utilize this code to create utilities which can convert from HDF5 to JSON formats and vice-versa (these are included with the project). An HDF5 JSON grammar can be found here: http://hdf5-json.readthedocs.org/.

AWS Instance

The HDF Group is running an instance of the service on Amazon AWS reachable via the endpoint: data.hdgroup.org:7253 for non-SSL or data.hdgroup.org:7258 for SSL. This endpoint can be used to read and write data using the REST API (for experimentation purposes only!).

Web UI

An experimental AJAX_based web UI is available at: https://data.hdfgroup.org. The Web UI provides an HDFView like interface in a browser for viewing data that is hosted by the HDF Server. In addition to a grid view, simple line, image, and 3D plots can be displayed. For example:

Install Locally

As a self-contained web service, HDF Server is quite easy to install and run on your own system (no Apache server required!). Complete instructions can be found here:


What's new in HDF Server (h5serv) 0.2.0

Previous Release - HDF Server (h5serv) 0.1.0

This was the first release of HDF Server. Major aspects of this release are:

Note: This release of h5serv should be viewed as a reference release of the HDF5 REST API, and not suitable for production use on the public Internet.

Features not included in this release (but planned for future releases)


We would like to hear your thoughts on HDF Server, including:

Please submit feedback using one of the links below.

Watch https://www.hdfgroup.org/blog for previous and upcoming posts about HDF Server.


Download:   https://github.com/HDFGroup/h5serv
Documentation:   http://h5serv.readthedocs.org/en/latest/index.html
Report bugs at:   https://github.com/HDFGroup/h5serv/issues
Submit fixes/enhancements at:   https://github.com/HDFGroup/h5serv/pulls

General comments or feedback should be sent to the HDF-Forum. See the Community Support page for more details on joining and viewing HDF-Forum messages.

- - Last modified: 12 October 2016