Our Commitment to HDF5’s Diverse Community

David Pearah, The HDF Group

Hello HDF Community!

Thanks for the warm welcome into the HDF family: in my 4+ months as the new CEO, I’ve been blown away by your passion, diversity of interests and applications, and willingness to provide feedback on:  1. why you use HDF5?, and  2. how can HDF5 be improved? I also want to thank my predecessor Mike Folk for his invaluable and ongoing support.

The HDF community is growing fast: when I last checked, there are nearly 700 HDF5 projects in GitHub! I’ve had the privilege of connecting via phone/web with dozens of you over the past few months. Across all of my discussions, one piece of feedback came back loud and clear: The HDF Group needs to be more engaged with its users and help foster the community. We hear you, and here are two actions we’re taking to demonstrate this commitment:   Continue reading

HDF5 and The Big Science of Nuclear Stockpile Stewardship

The August 2016 issue of Physics Today includes a fascinating piece titled, “The Big Science of stockpile stewardship.”1

The article leads with, “In the quarter century since the US last exploded a nuclear weapon, an extensive research enterprise has maintained the resources and know-how needed to preserve confidence in the country’s stockpile.”  It goes on to give the history of how the US Department of Energy (DOE) and its Los Alamos, Sandia and Lawrence Livermore national laboratories pioneered the use of high-performance computing to use computer simulation as a replacement for the actual building and testing of the USA’s nuclear weapons stockpile.

Although HDF5 is not named in this article, the history of The HDF Group and HDF5 are closely linked to this larger story of American science and geopolitics.  In 1993, DOE determined that its computing capabilities would require massive improvements, as the article says, to “ramp up computation speeds by a factor of 10,000 over the highest performing computers at the time, equivalent to a factor of 1 million over computers routinely used for nuclear calculations… To meet the [ten-year] goal, the DOE laboratories had to engage the computer industry in massively parallel processing, a technology that was just becoming available, to develop not just new hardware but new software and visualization techniques.”   Continue reading

HDFql – the new HDF tool that speaks SQL

Rick, HDFql team, HDF guest blogger

HDFql (Hierarchical Data Format query language) was recently released to enable users to handle HDF5 files with a language as easy and powerful as SQL. 

By providing a simpler, cleaner, and faster interface for HDF across C/C++/Java/Python/C#, HDFql aims to ease scientific computing, big data management, and real-time analytics. As the author of HDFql, Rick is collaborating with The HDF Group by integrating HDFql with tools such as HDF Compass, while continuously improving HDFql to feed user needs.

Introducing HDFql

HDFqlIf you’re handling HDF files on a regular basis, chances are you’ve had your (un)fair share of programming headaches. Sure, you might have gotten used to the hassle, but navigating the current APIs probably feels a tad like filing expense reports: rarely a complete pleasure!

If you’re new to HDF, you might seek to avoid the format all together. Even trained users have been known to occasionally scout for alternatives.  One doesn’t have to have a limited tolerance for unnecessary complexity to get queasy around these APIs – one simply needs a penchant for clean and simple data management.

This is what we heard from scientists and data veterans when asked about HDF. It’s what challenged our own synapses and inspired us to create HDFql. Because on the flip-side, we also heard something else:

  • HDF has proven immensely valuable in research and science
  • the data format pushes the boundaries on what is achievable with large and complex datasets
  • and it provides an edge on speed and fast access which is critical in the big data / advanced analytics arena

With an aspiration of becoming the de facto language for HDF, we hope that HDFql will play a vital role in the future of HDF data management by:

  • Enabling current users to arrive at (scientific) insights faster via cleaner data handling experiences
  • Inspiring prospective users to adopt the powerful data format HDF by removing current roadblocks
  • Perhaps even grabbing a few HDF challengers or dissenters along the way…

Continue reading

The HDF Group welcomes new CEO Dave Pearah

HDF
Pearah joins The HDF Group as new Chief Executive Officer

Champaign, IL —  The HDF Group today announced that its Board of Directors has appointed David Pearah as its new Chief Executive Officer. The HDF Group is a software company dedicated to creating high performance computing technology to address many of today’s Big Data challenges.

Pearah replaces Mike Folk upon his retirement after ten years as company President and Board Chair. Folk will remain a member of the Board of Directors, and Pearah will become the company’s Chairman of the Board of Directors.

Pearah said, “I am honored to have been selected as The HDF Group’s next CEO. It is a privilege to be part of an organization with a nearly 30-year history of delivering innovative technology to meet the Big Data demands of commercial industry, scientific research and governmental clients.”

Industry leaders in fields from aerospace and biomedicine to finance join the company’s client list.  In addition, government entities such as the Department of Energy and NASA, numerous research facilities, and scientists in disciplines from climate study to astrophysics depend on HDF technologies.

Pearah continued, “We are an organization led by a mission to make a positive impact on everyone we engage, whether they are individuals using our open-source software, or organizations who rely on our talented team of scientists and engineers as trusted partners. I will do my best to serve the HDF community by enabling our team to fulfill their passion to make a difference.  We’ve just delivered a major release of HDF5 with many additional powerful features, and we’re very excited about several innovative new products that we’ll soon be making available to our user community.”

“Dave is clearly the leader for HDF’s future, and Continue reading

Easy access to the NASA HDF products via OPeNDAP’s Hyrax

MuQun (Kent) Yang, The HDF Group

Many NASA HDF and HDF5 data products can be visualized via the Hyrax OPeNDAP server through Hyrax’s HDF4 and HDF5 handlers.  Now we’ve enhanced the HDF5 OPeNDAP handler so that SMAP level 1, level 3 and level 4 products can be displayed properly using popular visualization tools.

Organizations in both the public and private sectors use HDF to meet long term, mission-critical data management needs. For example, NASA’s Earth Observing System, the primary data repository for understanding global climate change, uses HDF.  Over the lifetime of the project, which began in 1999, NASA has stored 15 petabytes of satellite data in HDF which will be accessible by NASA data centers and NASA HDF end users for many years to come.

In a previous blog, we discussed the concept of using the Hyrax OPeNDAP web server to serve NASA HDF4 and HDF5 products.  Each year, The HDF Group has enhanced the HDF4 and HDF5 handlers that work within the Hyrax OPeNDAP framework to support all sorts of NASA HDF data products, making them interoperable with popular Earth Science tools such as NASA’s Panoply and UCAR’s IDVThe Hyrax HDF4 and HDF5 handlers make data products display properly using popular visualization tools.  Continue reading

Announcing HDF5 1.10.0

We are excited and pleased to announce HDF5-1.10.0, the most powerful version of our flagship software ever.

HDF5
HDF5 1.10.0 is now available

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: There is a known issue with the h5repack utility when using it to modify the layout of a VDS. We understand the issue and are working on a patch for it.)
  • New indexing structures for chunked datasets were added to support SWMR and to optimize performance. *
  • Persistent free file space can now be managed and tracked for better performance. *
  • The HDF5 Collective Metadata I/O feature has been added to improve performance when reading and writing data collectively with Parallel HDF5.
  • The Java HDF5 JNI has been integrated into HDF5.
  • Changes were made in how autotools handles large file support.
  • New options for the storage and filtering of partial edge chunks have been added for performance tuning.*

* Files created with these new extensions will not be readable by applications based on the HDF5-1.8 library.

We would like to thank you, our user community, for your support, and your input and feedback which helped shape this important release.

The HDF Group

Solutions to Data Challenges

Please refer to the following document which describes the new features in this release:   https://www.hdfgroup.org/HDF5/docNewFeatures/

All new and modified APIs are listed in detail in the “HDF5 Software Changes from Release to Release” document:     https://www.hdfgroup.org/HDF5/doc/ADGuide/Changes.html

For detailed information regarding this release see the release notes:     https://www.hdfgroup.org/ftp/HDF5/releases/hdf5-1.10/hdf5-1.10.0/src/hdf5-1.10.0-RELEASE.txt

For questions regarding these or other HDF issues, contact:      help@hdfgroup.org

Links to the HDF5 1.10.0 source code, documentation, and additional materials can be found on the HDF5 web page at:     https://www.hdfgroup.org/HDF5/

The HDF5 1.10.0 release can be obtained directly from:   https://www.hdfgroup.org/HDF5/release/obtain5110.html

User documentation for 1.10.0 can be accessed from:   https://www.hdfgroup.org/HDF5/doc/

The Blosc meta-compressor

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:compression read speed

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 Continue reading

HDF5 and .NET: One step back, two steps forward

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.  Continue reading