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Mar
31
2016

We are excited and pleased to announce HDF5-1.10.0, the most powerful version of our flagship software ever.> This major new release of HDF5 is more powerful than ever before and packed with new capabilities that address important data challenges faced by our user community. HDF5 1.10.0 contains many important new features and changes, including those listed below. The features marked with * use new extensions to the HDF5 file format. The Single-Writer / Multiple-Reader or SWMR feature enables users to read data while concurrently writing it. * The virtual dataset (VDS) feature enables users to access data in a collection of HDF5 files as a single HDF5 dataset and to use the HDF5 APIs to work with that dataset. *   (NOTE:...

Feb
18
2016

Francesc Alted, Freelance Consultant, HDF guest blogger

The HDF Group has a long history of collaboration with Francesc Alted, creator of PyTables.  Francesc was one of the first HDF5 application developers who successfully employed external compressions in an HDF5 application (PyTables). The first two compression methods that were registered with The HDF Group were LZO and BZIP2 implemented in PyTables; when Blosc was added to PyTables, it became a winner.

While HDF5 and PyTables address data organization and I/O needs for many applications, solutions like the Blosc meta-compressor presented in this blog, are simpler, achieve great I/O performance, and are alternative solutions to HDF5 in cases when portability and data organization are not critical, but compression is still desired.  Enjoy the read!

Why compression?

Compression is a hot topic in data handling. The largest database players have recently (or not-so-recently) implemented support for different kinds of compression libraries. Why is that? It’s all about efficiency: modern CPUs are so fast in comparison with storage write speeds that compression not only offers the opportunity to store more with less space, but to improve storage bandwidth also:

The HDF5 library is an excellent example of a data container that supported out-of-the-box compression in the very first release of HDF5 in November 1998. Their innovation was to introduce support for compression of chunked datasets in a way that permitted the developer to apply compression to each of the chunks individually, resulting in reasonably fast and transparent compression using different codecs. HDF5 also introduced pluggable compression filters that allowed external developers to implement support for different codecs for HDF5. Then with release 1.8.11, they added the ability to discover, load and register filters at run time. More recently, in release 1.8.15 (and fully documented in 1.8.16), HDF5 has a new Plugin Interface that provides a complete programmatic control of dynamically loaded plugins. HDF5’s filter features now offer much-desired flexibility, giving users the freedom to choose the codec that best suits their needs.

Why Blosc?

In the last decade the trend has been to implement faster codecs at the expense of reduced compression ratios. The idea is to reduce compression/decompression time overhead

Feb
01
2016

Lindsay Powers, The HDF Group

The HDF Group is collaborating with the University of California, Santa Barbara and Data Observation Network for Earth (DataONE), to help scientific research communities enhance the consistency and quality of their metadata, to foster discovery, access and understanding of data resources. As part of this collaboration, on February 9, 2016, The HDF Group’s Ted Habermann, Director of Earth Science, and Lindsay Powers, Deputy Director of Earth Science will co-lead a webinar “Sharing Data Through Guided Metadata Improvement” along with Matthew Jones, Director of Informatics Research at the National Center for Ecological Analysis and Synthesis. 

Jan
27
2016

Gerd Heber, The HDF Group and Haymo Kutschbach,* ILNumerics

Metaphorically speaking, this blog post is about a frog trying to climb out of a well, a damp and unsightly corner of the HDF5 ecosystem called HDF5.NET. People who know more about its genesis tell us that it was never intended as what it became to be perceived as, an “aspirational” .NET interface for HDF5 that would one day be complete and fully supported. Be that as it may, it’s important to ask, “What can we do today to better serve the needs of the .NET community?” We believe, as the title suggests, we need to take a step back to move forward. 

Jan
20
2016

Ted Habermann, The HDF Group

The first week of January brought leaders of the Earth Science informatics community together in Washington DC for the winter meeting of the Federation of  Earth Science Information Partners (ESIP Federation). This year NOAA’s Environmental Data Management Workshop was co-located with ESIP for five exciting days of data management ideas and discussions. The HDF Group was well represented at both of these meetings with presentations on metadata and data containers.

The ESIP Federation comes together twice each year to discuss topics around changing technology, data, information and knowledge in support of society. ESIP meetings are interdisciplinary and inclusive. Among the attendees are Earth science data and information technology practitioners; researchers representing a variety of scientific domains that include land, atmosphere, ocean, solid earth, ecology, data and social sciences; science educators; and anyone working in science and technology-related fields who is interested in advancing Earth science information best practices in an open and transparent fashion. 

Jan
06
2016

Elena Pourmal, The HDF Group

UPDATE January 19, 2016: The HDF5-1.10.0-alpha1 release is now available, adding Collective Metadata I/O to these features:

– Concurrent Access to an HDF5 File: Single Writer / Multiple Reader (SWMR)
– Virtual Dataset (VDS)
– Scalable Chunk Indexing
– Persistent Free File Space Tracking

We’re pleased to announce the release of HDF5 1.10.0-alpha0.

HDF5 1.10.0, planned for release in Spring, 2016, is a major release containing many new features.  On January 6, 2016 we announced the release of the first alpha version of the software.

The alpha0 release contains some (but not all) of the features that will be in HDF5 1.10.0.  The Single Writer/Multiple Reader and Virtual Data Set features, below, are both contained in this alpha release as are scalable chunk indexing and persistent free file space tracking.  More features, such as enhancements to  parallel HDF5 and support for compressing contiguous datasets will be added in upcoming alpha releases.

Here are the highlights:   

Dec
17
2015

John Readey, The HDF Group

Editor’s Note: Since this post was written in 2015, The HDF Group has developed the Highly Scalable Data Service (HSDS) which addresses the challenges of adapting large scale array-based computing to the cloud and object storage while intelligently handling the full data management life cycle. Learn more about HSDS and The HDF Group’s related services.

HDF Server is a new product from The HDF Group which enables HDF5 resources to be accessed and modified using Hypertext Transfer Protocol (HTTP).

HDF Server [1], released in February 2015, was first developed as a proof of concept that enabled remote access to HDF5 content using a RESTful API.  HDF Server version 0.1.0 wasn’t yet intended for use in a production environment since it didn’t initially provide a set of security features and controls.  Following its successful debut, The HDF Group incorporated additional planned features.  The newest version of HDF Server provides exciting capabilities for accessing HDF5 data in an easy and secure way.

Nov
20
2015

We are currently planning for a Q2 2016 release of the product. In the meantime, we are working with a few early adopters on finalizing the initial feature set. If you have additional questions about HDF5/ODBC, or if you would like to become an early adopter, please contact us ...