Operations | Monitoring | ITSM | DevOps | Cloud

A new upstream Kubernetes release, 1.29, is generally available, with significant new features and bugfixes. Canonical closely follows upstream development, harmonising our releases to deliver timely and up-to-date enhancements backed by our commitment to security and support – which means that MicroK8s 1.29 is now generally available as well and Charmed Kubernetes 1.29 will join shortly.

Azure App Service is a platform as a service (PaaS) that enables developers to make, deploy, and scale web apps, mobile backends, and RESTful APIs efficiently. A powerful component of an organization’s cloud strategy, Azure App Service offers numerous advantages in terms of development, deployment, and operations.

Monitoring the performance of your MySQL database will help identify performance bottlenecks, inefficient queries, and resource-intensive processes. By tracking metrics like query execution times, server load, and resource usage, administrators can optimize configurations and fine-tune the database for better efficiency and speed. Additionally, monitoring any running process allows for the early detection of potential problems such as server overload, disk space shortages, or network issues.

One of the most frustrating moments as a Kubernetes developer is when you go to launch your pod, but it fails to start because of a problem during initialization. Init containers are incredibly useful for setting up a pod before handing it off to the main container, but they introduce an additional point of failure. In this post, we'll take an in-depth look at init containers in Kubernetes: what they are, how they work, how they can fail, and what that means for your Kubernetes deployments.

Managing incoming web traffic for your applications is essential to ensuring optimal performance, preventing abuse, and maintaining the security of your cloud infrastructure. To accomplish this, one of the tools HAProxy Enterprise users have at their disposal is rate limiting—the practice of preventing clients from making too many requests and using system resources unfairly.

In part 1 of this 2 part blog we looked at some common engineering tradeoffs. But how might someone navigate these tradeoffs and build a model that works for their product? Here are some core concepts that can help along the way.