wrk2 VS conductor

> This runs a benchmark for 30 seconds, using 2 threads, keeping 100 HTTP connections open, and a constant throughput of 2000 requests per second (total, across all connections combined).

Some distros include `ab`[2] which is also good, but wrk2 improves on it (and on wrk version 1) in multiple ways, so that's what I use myself.

[1] https://github.com/giltene/wrk2

[2] https://httpd.apache.org/docs/2.2/programs/ab.html

I've had my eyes on wrk2 [1]

1. https://github.com/giltene/wrk2

But I am curious, what does HN use? Any tips?

We used wrk2, a fantastic tool to generate stable load on the server. Wrk2 improves on wrk and adds the ability to generate sustained load at a specific rate (-R parameter).

I use tools like vegeta and wrk2 to answer those questions.

Needs (2015).

I loved the talks from Gil Tene.

I always reach for his fork of wrk whenever I need to test throughput:

https://github.com/giltene/wrk2

That's why a lot of people just use something like wrk or wrk2 (highly recommended to run it on a separate machine) and benchmark the ability to serve actual requests.

To get the most out of preload, I preloaded all files that the experimental endpoint needs to include. As a benchmarking tool, I use wrk2 — a more advanced Apache Benchmark analog — to keep it simple and provide more flexibility to generate loads similar to a real-life one.

i use https://github.com/giltene/wrk2 pretty regularly.

it has decent lua hooks to customize behavior but i use it in the dumbest way possible to hammer a server at a fixed rate with the same payload over and over.

i run it by hand after a big change to the server to make sure nothing obviously regressed. i used to run it nightly in a jenkins job but 99% of the time no one looked at results. it was nice to see if assumptions on load a single node could handle didn't hold anymore.

I use wrk2 because it overcomes coordinated omission.

We celebrated a remarkable milestone in September when the Netflix Conductor GitHub repository reached 10k stars. It was a momentous achievement for our DevRel team. Just a month later, we're thrilled to announce that we've surpassed 12k stars! ⭐🎉

Also, don’t forget to give us a ⭐ on our Netflix Conductor repo.

One of my favorite workflow engines that has a really simple way to do things was not listed here, so I'll call it out - Netflix Conductor (https://github.com/Netflix/conductor).

Its capabilities comes to light when you model really complex workflows and one real value is how its all very visual not just during modeling but when running it. The history remains visible and you can even see how the whole flow evolved.

Yet another significant milestone on our journey: we've proudly reached the 10,000-star mark on our Netflix Conductor GitHub repository! 🌟

Give something like https://github.com/Netflix/conductor a try to solve this -- makes it very easy to do what you are trying to achieve.

Checkout Conductor https://github.com/Netflix/conductor which is far more scalable and easy on the resources with its own Celery like queues. Fully supports writing task workers in python: