wrk2 VS orkes-queues

> 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.

Postgres is used for indexing of workflow data. Beyond the disk storage requirements, scaling postgres requires two factors 1) adequate CPU and 2) IOPS required (especially on cloud environment) to handle the writes under heavy workloads. Writes to postgres are asynchronous done using durable queues (check out orkes-queues) but longer delays means completed workflows remain in Redis for longer periods of time, requiring larger Redis memory.