Deployment controllers are a type of Pod controller in Kubernetes. They provide fine-grained control over how its pods are configured, how updates are performed, how many pods should run, and when pods should be terminated. There are many resources available for how to configure basic deployments, but it can be difficult to understand how each option impacts how rolling updates are performed.
Unresponsive and slow pages are both terrible for any website. Even with the best user interface (UI), unresponsive and slow pages negatively affect the customer experience and the brand's reputation. Research from the Nielsen Norman Group has determined that the average user will leave a site after about 10 seconds of waiting for a page to load. If your page takes longer than a few seconds to load, it's time you check your IIS server logs.
Today, we’re bringing you an update of the performance/exceptions sample page. This update includes a number of improvements that will help you navigate and filter the available samples faster and more smoothly. We’re bringing these changes as an iteration of sample navigation improvements that we launched a while ago. We received valuable feedback from our users: the overlay made the navigation choppy instead of fluent.
Grafana dashboards can do a lot, but do you know how much more you can get out of them by configuring them as code? That was the topic of a recent FOSDEM 2020 talk by Grafana software developer Malcolm Holmes and Julien Pivotto, an open source consultant at Inuits. In their presentation, the pair discussed Grafonnet (a Jsonnet library to generate Grafana dashboards), provided tips and tricks about how to use it efficiently, and explained how to fully manage your Grafana instances from code.
Kubernetes is the world’s leading container orchestration platform. Its cloud agnostic status enables you to manage your workloads with ease, whether they reside in the cloud or on-premises. It has reduced the necessity of being locked into services provided by a cloud provider as well as the need for an entire operations team to manage large workloads on-premises on virtualization platforms.
This article is a continuation of Part I (A comprehensive guide to migrating from Python 2(Legacy Python) to Python 3), which details the changes, and improvements in Python 3, and why they are essential. The rest of the article describes automated tools, strategies, and the role of testing in the migration from Python 2 to 3.
Back in the early days of software development, having multiple developers working on the same application was a tough challenge. That’s why VCS (Version Control System) like Git was created and methodology like Feature Branching was introduced. The basic idea of working per git branch (also known as Feature Branching) is that when you start to work on a feature, you take a branch of your repository (e.g: git) to work on that feature.
As part of the re-affirmed commitment to customers as announced on January 30, Ivanti launched an Independent Business Unit (IBU) consisting of proven solutions with a large customer following (dare I say: fans?) that deserve some TLC. One of the solutions managed by this dedicated team is Ivanti Device & Application Control, powered by HEAT. So, where is Ivanti Device & Application Control at these days? The dedicated team hasn’t been sitting still.
IT industry research has repeatedly shown that DevOps-oriented teams that can ship software quickly and effectively routinely outperform their slower counterparts in terms of company profitability, market share, and just about every competitive business metric that matters. That sort of success comes from restructuring teams in ways that empower them to move faster and get closer to their customers.
