In this article, you’ll be introduced to two tools, Speedscale and JMeter. While they both provide the same functionality, namely the capability of load testing applications, they go about it in very different ways. This post will compare their ease of setup, the development experience, their integration into CI/CD, and how well their documentation is structured.
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.
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.
Mock APIs in Kubernetes are used to simulate actual API requests. This is useful for detecting issues early in development, improving application performance, speeding up development, performing integration tests, and checking the behavior of new features in a controlled environment. You may be wondering, If using mocks is so advantageous, why do some developers hesitate to use this technique?
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.
There are lot of steps that are involved in creating an application in Kubernetes. First, you need to develop your container, get it through QA, and then deploy it. In some organizations there are even more steps like security scans and peer reviews that need to be added. One of the steps that many companies decide to leave out until very late in their deployment process, if they even include it at all, is kubernetes load testing.
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.
