[fred123]

Bjoern author here.

The code looks very nice, looks like it’s inspired by bjoern but much cleaner. I’ve wanted to clean up the mess that the bjoern code is for years but never came around to actually doing it.

That being said I don’t think it provides a lot of value in practice. Same with bjoern. With a reasonably fast server implementation around 99% of time is spent in your Python application, not the server. So it doesn’t actually provide much value in practice to optimize the server. But it’s a nice project to learn about how to write a HTTP and WSGI server :)

[james_roberts]

Hey, thanks! Great job on Bjoern by the way!

Yeah, I did get some inspiration from Bjoern for this project. Was also a learning project for me. I used this project to pick up C and learn a bit more about HTTP!

Hoping to add a few more features to the server side of things, still not finished, but I agree, there is only so much you can do to optimize things when you're making calls to Python anyway.

[necovek]

While you are completely right, I applaud both James and you for working to potentially remove one bottleneck. If wins are significant enough and people are not blocked by request processing anymore, we'll see them work on frameworks which are better optimized too.

[whalesalad]

This was my first thought - Python itself is always the bottleneck.

[jacob019]

I'm sick of everyone hating on python speed. IO is the bottleneck. By my estimate, 95% of applications are spending <5% of their response time executing python. Maybe your application is compute heavy and is better written in rust, but python gets shit done with minimal effort and the extra cycles are rarely an issue in most workloads.

[whalesalad]

I write a LOT of Python. I’m not hating on it.

You can definitely outrun I/O but you can never outrun the GIL.

[fault1]

Sure, IO is often the bottleneck, but the python interpreter can most definitely add a lot of overhead, which can add a lot of operational costs. Personally, even in IO bound applications, I prefer something like Go or Elixir, and with those languages, it's not clear what python's productivity advantage is or is not (and I've known python since v1.5.2 so it's not a matter of familiarity).

[simfree]

We had a vendor who implemented their stack with Flask and Postgres on Debian. Their API is consistently slow (seconds to tens of seconds) to the point that we wrote our own app in Dotnet Core (running atop Postgres and Debian) that queries the available content once a day (500k rows of data) with minor refreshes hourly.

We take tens of milliseconds to query Postgres and generate a rendered HTML page for our clients. Showing this to the vendor's devs we got a very surprised response.

Admittedly, we do not operate at their scale, but I am certain this $5 a month droplet will keep running this app for a long time yet even with many users :)

Edit: I did write an MVP in Python atop Sqlalchemy wrapping Postgres, but the performance was still not ideal when rendering hundreds or thousands of rows of data, and the primary developer was already using Dotnet Core.

[jacob019]

Bad code is bad code in in any language. Our ecommerce website jacobsparts.com is written in python on debian. It hits the db and rerenders with every page load. I dare you to call it slow.

[bkovacev]

Awesome stuff! What are some optimizations you have used, if you don't mind me asking? What's the underlying framework?

[radus]

How big are those responses? I’ve seen terrible performance come down to serialization, which can be addressed by swapping in a fast serialization library like orjson (https://github.com/ijl/orjson). Though even then you’d probably have a hard time getting to tens of milliseconds. Other common culprits: poor indexing, n+1 queries.

[JPKab]

At my last company, there was an existing product (an auto ml product for business users) built on Flask with a ton of serialization occurring to populate various charts in the GUI.

After I had left that specific team, I came back and swapped out the json serializer with orjson. It was like 5 lines of code if I recall. The performance skyrocketed. The GUI was noticeably far more responsive in populating the various charts and plots. By "noticeable" I mean it was loading in less than a 1/3 the previous time. Definitely recommend it. It's written in Rust, and it inspired me to start learning the language.

[cmclaughlin]

I’m not familiar with dotnet, but I’m not sure if blaming Python is the problem.

A more even comparable rewrite would have been FastApi with an asynchronous library for Postgres (such as SQL Alchemy or TortoiseORM).

There are probably ways to achieve similar results with Django or Flask, but it’s pretty easy with FastApi.

[necovek]

To any experienced Python dev, it's obvious from their description of what the problem is. And it's understandable that anyone inexperienced with Python would blame Python.

They were returning a large number of rows from Postgres (which, if the DB is properly set up, should take at most tens of ms: of course, depending on the width of the rows too), and most (well, I know of none that don't) Python ORM libraries (SQLAlchemy included) have a huge "serialization" cost (turning raw data from Postgres into objects). I've done a benchmark once, and things like Django-ORM or SQLAlchemy were like 10-50x slower than fetching tuples with psycopg directly. SQLAlchemy-core was fastest when fetching tuples if you wanted to not do raw SQL (IIRC, a performance penalty of at most 100%, translated to a factor, up to 2x slower), but Django's fetch-me-tuples functionality was also a single digit multiple of psycopg.

So, the solution to that problem is to fetch tuples, and then pass them in for rendering the page.

Of course, this also points at the problem with all the ORM implementations in Python: they are being too "smart" and dynamic for their own good (if all are bad at it, it also means that Python is not doing something good either, so criticism is warranted).

[necovek]

That's not what was said. Python application code is the bottleneck. As evident from the benchmarks for FastWSGI, even Flask the framework is a bottleneck: pure Python vs Flask went from 70k RPS to 9k RPS.

Python does have a huge performance penalty for basic computation, which is why it has a bunch of C-based libraries that provide bulk-operations that avoid it. If properly used (you rarely need to roll your own with a number of compiled libraries present), Python itself is not a bottleneck. One can argue if that's still Python, but at the very least, it's idiomatic Python development: I hope you don't use Python to prove that pure dynamic languages can outperform compiled languages, but to develop and deliver applications faster using Python's expressiveness.

People bring up GIL as well: it will affect your application startup time and memory usage since you can trivially avoid it by running multiple Python processes. But performance of executing code itself will only be minimally affected if you switch to multi-processing (of course, if memory pressure is so high that all those Python libraries loaded multiple times in memory is affecting your app, that can hinder performance, but that's going to be pretty rare).