Despite the siren song of AI in the keynotes, visitors were far more focused on solving real-world problems. These are the issues that have plagued IT practitioners for years, if not decades: troubleshooting and validating performance and availability of their applications, services, and infrastructure.

Table of contents This is the seventh part of our 12-day Advent of Monitoring series. In this series, Checkly's engineers will share practical monitoring tips from their own experience. At Checkly, we manage various scheduled jobs, some of which play a crucial role in our application's functionality, and others exist to support different teams within Checkly.

When it comes to the best cloud monitoring tools, there are various services you can rely on or choose to support your IT infrastructure. After all, cloud monitoring is critical to ensure the performance, uptime, and overall health of cloud services. Numerous teams in modern IT, SaaS, and app development companies, including DevOps, SREs, and security analysts depend on cloud monitoring solutions.

Your images are 404ing all over the place. You’ve got an angry email from a client. Their site is “broken”, images aren’t loading, cumulative layout shift is running riot, and everything is messed up. The crowds are mocking your broken code on Twitter. A fun GIF loaded via a Giphy URL no longer exists. And someone has accidentally deleted an image from the CMS.

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.

Last week, I attended the Gartner IT Infrastructure, Operations & Cloud Strategies Conference (IOCS). Gartner IOCS is my favorite conference every year because of the quality and level of the presentations. Gartner analysts deliver most sessions and put a lot of effort into the presentations and supporting research. ‍ I’d like to highlight two sessions that I found to be very informative.

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.

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.

Large Language Models (LLMs) are advanced artificial intelligence models designed to comprehend and generate human-like language. With millions or even billions of [parameters, these models, like GPT-3, excel in natural language processing, understanding context, and generating coherent and contextually relevant text across various applications.

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.