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.
API Observability isn't exactly new, however it's popularity has seen rapid growth in the past few years in terms of popularity. API Observability using open source is different from regular API monitoring, as it allows you to get deeper and extract more valuable insights. Although it takes a bit more effort to set up, once you've got an observability infrastructure running it can be immensely helpful not only in catching errors and making debugging easier, but also in finding areas that can be optimized.
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.
It's impossible to learn about containerization without hearing about Docker and Kubernetes. These two tools together dominate the world of containers, both being the de-facto standard in what they each do. When you're first getting started learning about containers, it can be quite a challenge to figure out what the differences are between these two tools.