Brian Proffitt is the Principal Community Analyst for Open Source and Standards team at Red Hat, responsible for community metrics, onboarding, and support. A former technology journalist, Brian is also a graduate lecturer at the University of Notre Dame. Follow him on Twitter @TheTechScribe
As part of a modern IT environment, Linux distributions can look to optimizing their size to be better suited for container use. One of the ways this improvement can happen is through reducing the size of a distribution, a process known as minimization. A new tool is being put together that will enable developers and operators to create minimal images of the appropriate size for the container use cases they need.
Continue reading “Size matters: how Fedora approaches minimization”
Operators within Kubernetes are useful tools, designed to extend the container orchestration platform with additional resources. More directly, an Operator, sometimes referred to as custom controllers, is a method of packaging, deploying, and managing a Kubernetes application.
As useful as Operators are, they have had one limitation: originally they all had to be written in the Go programming language. Thanks to the Operator SDK, you do not need to develop your Operators in Go. The Operator SDK has options for Ansible and Helm that may be better suited for the way you or your team work. But, it can still be limiting for dev teams trying to build an operator if they don’t happen to be skilled in Helm or Ansible.
A well-known tactic for figuring out how to identify the root cause of a problem that has caused an outage in a production environment is to go back and see what the environment has been doing so far. Through the analysis of logs, developers and operators alike can determine usage information that ideally reveal what’s wrong with a given application or how it can be improved to work better.
In the early days of logging, there wasn’t a great deal of activity going on, so it was possible for a human being (or two) to examine such logs and figure out what was up. It didn’t hurt that the logs were not only sparse in content, but also not terribly complicated in terms of what they reported. Alerts such as “Help, my processor is melting” really didn’t take a lot to figure out how to fix. Applications now are more distributed and that further complicates the situation. But over time, logs got far more voluminous and more detailed in what they were reporting.
Quick, name some weird stuff that’s happened to your production machines.
Accidentally dropping a production database table? Rolling out a patch that enabled any user to log in with any password? Disabling a load balancer? Using a dictionary to physically keep keyboard keys depressed so “terminals [could] repeatedly [hit] the enter key in order for the logins and print jobs of about 40,000 people to work”?
It’s happened to Alex Corvin, a senior engineer at Red Hat. Well, not that last one. But Corvin has been around long enough in his career to have met Mr. Murphy and his Law: if it can go wrong, it will.
Continue reading “Managing chaos in a containerized environment”
The prospect of true machine learning is a tangible goal for data scientists and researchers. It has been long known that the platform on which such ML apps can run have to be fast and hyper efficient so that learning can be that much faster. This is the motivation for Red Hat engineers in the Office of the CTO who are working to optimize such an open source platform: Open Data Hub.
Open Data Hub is built on Red Hat OpenShift Container Platform, Ceph Object Storage, and Apache Kafka/Strimzi integrated into a collection of open source projects to enable a machine-learning-as-a-service platform. That’s a lot of components to be integrated, and to ensure that their contributions to Open Data Hub perform well, Red Hat engineers have taken the step of creating an Internal Data Hub within Red Hat as a proving ground and learning environment.
Continue reading “How Open Data Hub learners become the teachers”
As machine learning becomes more interesting to technology companies, it is hardly surprising that a company like Red Hat is going to approach the challenges of this aspect of artificial intelligence with an open source methodology in mind.
The immediate benefits to open source machine learning tools are plain as day to anyone familiar with how open source works: lower cost, more flexibility, no vendor lock-in… you know, the usual.
But dig a little deeper and it quickly becomes apparent that open source means more for cutting-edge software than just a faster way to get cheaper software.
Continue reading “Machine Learning with Open Source Infrastructure”
The concept of artificial intelligence, which seemed so much like science fiction a few decades ago, has made real, practical inroads in producing results that organizations can find useful. What’s making those results happen, though, isn’t esoteric pie-in-the-sky theory: it’s creating statistical models that have been trained to make decisions. And trained a lot.
Artificial intelligence itself is a term that, for now, has had less of a focus than the more results-oriented machine learning, where a computer system is given input and output data and then is directed to infer the mathematical rules that govern the transformation of that data.
“It’s like pointing a program to look at the solar system and then have it figure out the laws of motion that govern a planetary system,” explained Sanjay Arora.
Continue reading “Exploring Unsupervised Deep Learning”
It’s no secret that if you want to run containerized applications in a distributed way, then Kubernetes is the platform for you. Kubernetes’ role as an orchestration platform for containers has taken center stage to become a main player for automating deployment, scaling, and management of applications within containers. Red Hat’s own OpenShift Container Platform is a Kubernetes distribution that uses Kubernetes optimized for enterprises.
Storage has been one of the areas of potential optimization. Many containers, by their very nature, are usually small enough to be easily distributed and managed. Containers hold applications, but the data those applications use needs to be held somewhere else, for a number of reasons. Of particular interest in this post, we want to avoid the containers themselves becoming too large and unwieldy to be effectively managed.
Continue reading “Rook Changes the Kubernetes Storage Landscape”
One of the more obscure terms one might hear bandied about in the free and open source software ecosystem is the so-called “bus factor.” The somewhat-informal term refers to the state of a given project based on its sustainability.
Specifically, bus factor is shorthand for the question: what would happen to your open source project if one of your community members were hit by a bus? Would the project survive? Or is so much workflow and institutional knowledge wrapped up in that one person that your project would be damaged, possibly to the point of no recovery?
Continue reading “Consumption is Fractal: Open Source Sustainability”
(There’s a great new conference in the U.S., DevConf.US, returning in 2019 to Boston University (15 to 17 Aug). This highly-technical conference is interested in drawing a diverse group of speakers and attendees, with a specific emphasis on people who are new to speaking and tech conferences in general. Only in its second year, DevConf.US builds on the successful decade-spanning run of DevConf.CZ in Brno, CZ.
Software development has found a niche in almost every aspect of our transactional lives, be it retail, finance, and even academia. This last sector is a particularly strong growth area in the past few years, as more and more coders are looking at universities and colleges as a direct career path.
This isn’t just software for supporting faculty, staff, and student operations (though that’s important too). According to Dr. Andrei Laptets, Associate Professor at Boston University, it also includes software for any scientist and researcher who needs to manage and analyze a wide variety of data-driven projects.
Continue reading “Merging Research and Software with Open Source”