In the last post we covered the industry shift towards ARM machines for both local and production software engineering. Last time we learned how to create Docker images that would work on multiple architectures for dev machines. Now we want to take this portability and leverage it for cost savings in production. You may be able to transition some of your services into multi-architecture builds.

In this article, you’ll be introduced to two different load testing tools that are both able to work with Kubernetes; Speedscale and K6. Throughout this post you’ll be given a comparative view of how each tool performs in five different categories: Ease of setup, developer experience, working with the CLI, creating tests, and integration into CI/CD pipelines.

With ARM based dev machines and servers becoming more common, it is become increasingly important to build Docker images that support multiple architectures. This guide will show you how to build these Docker images on any machine of your choosing.

In this blog, understand why your pod has OOMKilled errors when provisioning Kubernetes resources and how Speedscale can aid with automated testing. When creating production-level applications, enterprises want to ensure the high availability of services. This often results in a lengthy development process that requires extensive testing for the applications or a new release.

Nate Lee here, and I’m one of the founders of Speedscale. The founding team’s worked at several observability and testing companies like New Relic, Observe Inc, and iTKO over the last decade. Speedscale traffic replay was borne out of a frustration from reacting to problems (even if they were minor) that could have been prevented with better testing.

Kubernetes shouldn’t be reserved for production. Using local Kubernetes in development means you can build and test your service using the same technologies as your live deployments. Some organizations provide a shared Kubernetes cluster for development activities. Others offer on-demand virtual clusters that serve staging environments for significant changes.

Mocking APIs is an increasingly popular trend, with more and more developers seeing the advantages of mocking dependencies rather than spinning up actual duplicates of resources. On a high-level, a mock API means that you have a service that returns static data, which in turn is based on a real API.