You definitely wouldn't use Locust to test a UI, or even to test your API responds correctly, but you'd use it to know how many requests per second that API can serve without falling over.
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Two key differentiators (advantages?) Locust has over ab are:
+ It's scriptable, so you can design complex interactions like described in the blog post (or even something as simple as logging in prior to hitting the app).
+ It supports clustering. It's easy to setup a multi-host Locust cluster, and results are aggregated to your locust master.
I don't have an affiliation with Locust, but I did find it extremely easy to setup and use when I needed it (for HTTP load tests with ~20K RPS).
I do recall a few scale issues (e.g. running out of client ports would break the clustering), but with a bit of fine-tuning, it runs like a charm.
Locust is a loadtesting tool. I have been using it for years now and from what I can see there is no reasonable alternative. If there is one, I would love to hear about it.
That's hardly an issue as you can trivially build multi machine swarms with Locust.
https://github.com/simple10/guides/blob/master/load_testing....
Having the power of Python for scripting my loadtest was probably what sealed the deal for me. It allowed me to create a very powerful/reusable loadtesting framework that I can easily adapt very quickly to any app. Python scripting also allowed me to add features not build in to Locust's core itself like additional logging or metrics reporting and reading API endpoints from file/Redis/...
Also, being able to completely test all endpoints of an app at once is really useful. Most of the time, what I really want to know is can the whole backend handle the full traffic I'm expecting. Having clustering built-in helped a lot here. I was able to scale the loadtest to several thousands of RPS by just adding a couple more slaves...
The only feature I would like to see added is built-in Vegeta style graphs of latency/RPS over time. By default, Locust only gives you real time stat of the last second during the load test and a final average results in csv files. Combining metrics reporting with a graphing engine like Graphite can fix that though.
I highly recommend.
