This post is about Kafka and the process I have been through recently writing a performance test for an application that subscribes to messages from this technology. The test I ended up with was in the end very straightforward but there were several hurdles that took a while to resolve. I hope that reading this post will hopefully help you avoid them.

It’s been some time since our latest major release, in fact OctoPerf v12 is probably our biggest/longest coming release to date. There’s of course been a couple of minor versions this summer and we’ll also cover them but first let’s focus on the new killer features: the scheduler, alerting through slack/mail and a better UI for the menus. The scheduler is the first item left on our original roadmap (back when OctoPerf was still called jellly.io).

“JMeter is old technology”, I hear this a lot. “Let’s use this tool or that tool instead of JMeter as it’s the latest”, I hear this a lot. “We need a lightweight tool without the GUI interface to write our tests as that will make us more agile”, I hear this a lot.

This article is the fourth part of a series of tutorials dedicated to Gatling Load Testing. Kraken is used to ease the debugging of Gatling simulations and to speed up the process of load testing a fake e-commerce website: PetStore. We will focuse on POST requests and script modularization: In the previous blog post we created a realistic Virtual User that browses the store without buying anything.

While developing Kraken’s frontend I quickly stumbled upon performance issues with Angular Material tree when too many nodes where opened. Kraken is an open source load testing IDE. As such, it displays a tree of directories and files used to script the load testing scenarios: You can have a look by creating a free account on the demo or check the source code of the UI on GitHub. It uses the latest version of Angular and components provided by Angular Material such as the tree.

This third version of Kraken represents one more step towards a load testing solution suitable to teams and enterprises. Kraken can already be installed on your own Kubernetes cluster thanks to Helm charts: You own all data and can handle the security inhouse. But until now it was lacking users management, making it cumbersome to use it for a team of performance testers. This point is now addressed in the version 3.0 thanks to Keycloak.

OctoPerf’s report engine provides many graphs to sort and presents test metrics in a comprehensive way. We’ve tried to improve it over the years so that you can access critical information very quickly. But requirements vary from one project to the other. In this post we will look at how you can configure the report to show you preferred metrics, and also all the shortcuts you can take to achieve this goal.

You may have spent a considerable amount of time configuring Postman requests for your in-house API tests, and you wish to use them without having to create them again from scratch on Octoperf. That’s one of the many situations where Octoperf’s compatibility with Jmeter is going to come in a handy.

JMeter has many plug-ins and downloadable JAR Files you can use to support your testing, it is also possible to write your own custom Jar files and use these as well. Why would you want to do that, you may say, well whilst it is unlikely that you would need to create anything too complicated. You may feel the need to create utilities to support your own in-house performance testing or just something to make your testing process easier.

Here we are for yet another new release of OctoPerf. We’ve actually released two minor versions since the last update post, but this time we will also release a long awaited feature, Microsoft Azure on demand load generators!