The main idea behind Kubernetes is to create a standardized approach to running containers in the cloud. Whether you’re running AKS on Azure or EKS on Amazon, your cluster should still behave in more or less the same way. But that’s not to say you’re locked in to doing things one way; Kubernetes still offers a lot of flexibility in many cases. This is what experienced engineers take advantage of when trying to optimize Kubernetes performance.

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.

Testing Kubernetes Ingress resources can be tricky, and can lead to frustration when bugs pop up in production that weren’t caught during testing. This can happen for a variety of reasons, but with Ingress specifically, it often has to do with a misalignment between the data used in testing and the traffic generated in production. Tools like Postman can be a great way of generating traffic, but they have the drawback of being manually created.

Testing in production carries a lot of risk, like possibly causing downtime for users. However, the advantages of using real user traffic are many, which has led to the popularity of traffic mirroring. Mirroring can be implemented as part of pre-deployment testing, as well as other parts of the developer experience like the development itself. But, how do you get started with it?

Lately, in this Continuous Integration and Continuous Delivery (CI/CD) domain, containerization has gained a lot of popularity. Implementing containerization was mainly focused on the development phase. In the recent past, the use of containerization for testing has been gaining prominence as it helps resolve a lot of test environment related issues. In this blog, you will learn how to integrate Docker and Selenium technologies to perform more effective and hassle-free tests. Table Of Contents.

A container comprises no operating system images in contrast to a server or virtualized machine. Due to this, they are lighter, more portable, and have less overhead. By using containers, operating systems can be virtualized. Microservices, software processes, and applications may all be run in one container. Among the files in a container are executables, binary code, libraries, and configuration files.

As an application developer, have you ever had to troubleshoot an issue that only happens in production? Bugs can occur when your application gets released into the wild, and they can be extremely difficult to debug when you cannot reproduce without production data. In this blog, I am going to show you how to safely send your production data to development applications deployed using a service mesh to help you better debug and build production proof releases.

Organizations are starting to realize that simply writing tests to generate traffic is simply not good enough. Rather, production traffic replication is now necessary, where you record traffic from your production environment and then replay it in your development environment. To match the modern principles of this testing methodology, it makes sense to also utilize modern infrastructure, like Kubernetes.

One of the major factors that come into play when deciding on a load testing tool is whether it can perform as you expect it to. There are many ways to measure how well a load testing tool performs, with the amount of requests per second undoubtedly being one of the main ways. Speedscale creates load tests from recorded traffic, so generating load is at the core of the tool.