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 ✋.
Modern applications are constantly evolving, and with the advent of integrated DevOps toolchains applications can be built and deployed at ever-increasing velocities. This puts pressure on quality assurance teams to increase their velocity as well. With NeoLoad RealBrowser technology, we now offer a rapid way to record and immediately execute user paths to validate the performance of application builds as they move through their pipeline.
Speedscale creates load tests from recorded traffic so generating load is pretty core to what we do. As a brief overview, we record traffic from your service in one environment and replay it in another, optionally increasing load several fold. During a replay the Speedscale load generator makes requests against the system under test (SUT), with the responses from external dependencies like APIs or a payment processor optionally mocked out for consistency. Your service is the SUT here. Currently the load generator runs as a single process, usually inside a pod in Kubernetes. So how fast is this thing, and how did we get to where we are today?
As the rate of delivery for applications and services continues to rise, performance testing often takes a backseat in the race to delivering apps on-time. This 2022 holiday season is expected to be bigger than ever, with holiday shopping growing 15.5% to $236 billion. During this make-or-break time of year, companies can’t afford for their apps to become unreliable under heavy load.
API load testing, which identifies how stable your APIs are under different workloads, is a crucial part of performance testing. In this guide you will learn what is API load testing, when to perform load testing, and more. One growing pain of increasing iterative development and shortening release cycles is a growing inability to detect and fix inefficient code – whether in the CI/CD pipeline or in production.
We’ve been working on many new features and improvements for Loadero, both large and small. And now we are excited to share the updates and some information about even bigger updates coming soon. Here is what changed in Loadero recently.
I Google’d the word “reliable” the other day. At the top of the results page was a definition from the Oxford Languages dictionary. I started thinking, what’s “reliable”? I glanced at my mobile phone. I felt it was pretty reliable. I can make and receive calls, I play games, battery life is pretty decent, photos are amazing… But it’s kind of new, so maybe that doesn’t count. It’s not really reliable, the jury’s still out.
Quite often we get requests to provide support to migrate tests from the testRTC testing tool to our service. We try to provide support to our users to help make this transition as easy and flawless as possible. To ease the process further for other users with similar needs this blog post was created.
This article shows some of the complexity that the k6 cloud backend handles when you run a k6 Cloud test.
