When building cloud-based applications, managing the infrastructure becomes a bigger challenge as you scale. Kubernetes brings order to the chaos, letting you control and automate the containers used to deploy your application. Debugging in the cloud presents further challenges, and the complexities of distributed applications make it hard for many debugging setups to keep pace. Tools designed to run locally aren't effective. However, there are Kubernetes debugging tools that can handle the shift in paradigm. In this article, you'll read about several options that make debugging Kubernetes applications much easier.
By combining traffic replay capabilities from Speedscale with observability from Datadog, SRE Teams can deploy with confidence. It makes sense to centralize your monitoring data into as few silos as possible. With this integration, Speedscale will push the results of various traffic replay conditions into Datadog so it can be combined with the other observability data. Being able to preview application performance by simulating production conditions allows better release decisions. Moreover, a baseline to compare production metrics can provide even earlier signals on degradation and scale problems. Speedscale joined the Datadog Marketplace so customers can shift-left the discovery of performance issues.
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.
