Handling 100 Requests per Second with Python and Django

100 requests per second? That is not too impressive. Yes, I know Python is their tool of choice, but it is probably the wrong tool for the problem at hand.<p>There are frameworks that can handle over a million requests per second (simple json output) or at least several 10,000 requests per second if DB queries are performed (even though on different hardware, but just compare the scale).<p><a href="https:&#x2F;&#x2F;www.techempower.com&#x2F;benchmarks&#x2F;#section=data-r20&amp;hw=ph&amp;test=json" rel="nofollow">https:&#x2F;&#x2F;www.techempower.com&#x2F;benchmarks&#x2F;#section=data-r20&amp;hw=...</a> <a href="https:&#x2F;&#x2F;www.techempower.com&#x2F;benchmarks&#x2F;#section=data-r20&amp;hw=ph&amp;test=query" rel="nofollow">https:&#x2F;&#x2F;www.techempower.com&#x2F;benchmarks&#x2F;#section=data-r20&amp;hw=...</a><p>I think, if performance is a strong requirement, they were better off with another programming language.

One thing that jumps out at me is that it seems they didn&#x27;t use any Postgres connection pooling. They mention that going above 100 concurrent connection would bump them into a more expensive Postgres plan, which really only makes sense if you don&#x27;t use a pool.<p>My first instinct is that the number of requests seems really low, but I have no idea about the complexity of each request. To me that is kind of a crucial information to actually evaluate anything in the blog post.

A blog post we recently published talking about scaling up our operations. I love using standard tools (Django, Python, Postgres) to achieve this. Definitely shows that you don&#x27;t need fancy tools until you get huge -- and here is a longer version of that story.

Coming from a node.js world, I thought they meant 100k and the title had a typo.

&gt; A few months after rolling things out to production we encountered some issues with AppService.<p>I’d be curious to hear more about this. Was is just degraded performance or something else?

Forgive me for my ignorance (I&#x27;m not in the saas biz) but $300-$500&#x2F;mo seems eye wateringly expensive for what is essentially static content + abuse detection and metering.<p>less than $100&#x2F;mo buys you an absolute beast of a VPS at some hosting provider that could alone handle way more than that.

I built a project in python using django that handles 11k&#x2F;second. Requires a load balancer and a few machines of course.. :D

Interesting read.. Have you tried using waitress[0]?<p>We got some better performance for an internal service than Gunicorn after trying different gunicorn configurations to improve the performance of our workloads. We now mostly run (read depend) one instance of that service instead of 3 like we used to before.<p>[0]: <a href="https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;waitress&#x2F;" rel="nofollow">https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;waitress&#x2F;</a>

I wonder how much Django&#x27;s async views and ASGI support would affect the request rate.

if you do blocking DB operations before serving ads, latency won&#x27;t be great. Assuming you don&#x27;t have to wait for transactions to commit, you can probably push required operations onto a queue (Redis would be fine for that) and serve the content immediately?

Note: across multiple machines.

Don&#x27;t most major cloud providers offer multi-region replication to start solving the Europe&#x2F;Asia latency issue?

Why dont use just use Java?