When building performance tests, we all understand the value of using properties or variables to store static values outside of our tests. This ensures that any changes to these values need only be made in one place rather than having to make these changes in many tests. Sometime though you may have inherited a suite of JMeter tests, or you were ** under pressure to develop these tests** and in order to do so you hardcoded values in your tests.

This tutorial will demonstrate Keptn, a CNCF Incubator Project’s integration with k6. Keptn is a cloud-native application delivery and operations platform. We will use the Job Executor Service to execute k6 performance testing in a Keptn project. We'll start with running a k6 script and how the logs look. And then we'll modify the k6 script to see the behavior when it fails.

At ObservabilityCON, we announced our first step towards launching a native integration between Grafana k6 load testing and Grafana Tempo tracing (k6 x Tempo) in Grafana Cloud. We created k6 x Tempo to help dev, testing, and operation teams analyze their performance test results more effectively and proactively improve the reliability of their business-critical applications.

When performance testing you need a set of requirements to measure your response times against. When defining these you should do so with your end users or business teams. It is relatively easy to predict volumes, load and users that will use your application as you will no doubt have some data based on your current systems. It is a lot harder to agree on what the response times of your application should be.

Loadero is a versatile tool that can be used for different types of web application tests. In order to provide a comfortable way of running the tests that you need, we have different test modes. In this blog post, we describe each of those in detail so you can pick the appropriate one every time you are launching a test run. There are currently 3 available test modes to choose from when creating your own Loadero test.

In this Blog Post we are going to discuss performance testing in production. Now before you think we have gone mad and lost our minds completely this is not as crazy as it sounds. Production is an environment that: All the things you spend a significant amount of time getting right in your performance testing environment and that can be difficult to achieve. Therefore, it seems like the perfect environment to performance test in.

The growth of a code base is unpredictable. To account for this uncertainty, we call on everything we've learnt in the last x years about how to scale an application effectively: adopting naming conventions, creating file and folder structures, using the latest patterns, and producing sensible abstractions. These actions add up over time. Eventually, you'll mentally high-five your past self for having taken the time to do them ✋.