Python code optimization may seem easy or hard depending on the performance target. If the target is “best effort”, carefully choosing the algorithm and applying well-known common practices is usually enough. If the target is dictated by the UX, you have to go down a few abstraction layers and hack the system sometimes. Or rewrite the underlying libraries. Or change the language, really. This post is about our experience in Python code optimizations when whatever you do is not fast enough.

While Go provides a testing package and a go test command, the former only offers basic testing capabilities. The package also has some drawbacks, such as missing assertions and increasing repetition with large-scale tests. As a result, several Go testing frameworks have been created to augment it. Go testing frameworks consist of tools and resources for creating and designing tests. Some of these frameworks incorporate the testing package and go test command, while others take a different approach.

One of Checkly's strengths is the capability to monitor key transactions on your site. It'd be missed opportunity if we didn't reuse it to monitor our own product! But for some important flows that comes with a couple of pitfalls. In this post, we'll take a closer look at how we monitor one of our top key flows: signup.

The focus of the 2.5 version was on expanding support for HAProxy configuration keywords, and that’s where most of the effort during this release cycle was spent. We will continue doing that during the next couple of versions to gain complete feature parity with both the HAProxy configuration and Runtime API so that you can use the Data Plane API as a full-featured way to configure HAProxy.