When you’re trying to optimize your application for performance, it helps to understand not only the number of people affected, but also user conditions of the slowest transactions, such as OS, browser type, and even connection type. When you’re looking at performance data, it can be hard to see the forest through the trees.
Enrique Fueyo Ramírez is the Co-founder and CTO of Lang.ai. Here’s a post on how him and his team at Lang.ai instrumented performance monitoring for GraphQL resolvers.
Slow apps frustrate users, which leads to bad reviews, or customers that swipe left to competition. Unfortunately, seeing and solving performance issues can be a struggle and time-consuming. Most developers use profilers within IDEs like Android Studio or Xcode to hunt for bottlenecks and automated performance tests to catch performance regressions in their code during development. However, testing an application before it ships is not enough.
Welcome to Part 1 of our multipart series on Distributed Tracing for Full Stack Developers. In this series, we’ll be learning the ins-and-outs of distributed tracing and how it can assist you in monitoring the increasingly complex requirements of full stack applications.
Komodor is a Kubernetes-native platform we’ve created to streamline troubleshooting. It was born out of frustrations we felt as developers, when we were required to waste hours of our time on troubleshooting, instead of focusing on what we really wanted to do - creating and innovating. Komodor sits on top of your K8s cluster and integrates with every existing tool you have, be it CI/CD, repo, monitoring, alerting, or communication.
