[ambivalence]

In short: static analysis, unit tests, staging environments, effective log aggregation.

The first one is a big productivity booster as it shows you bugs before you commit them.

Here's a post about this, not exactly new but still describing the general principles very well: https://instagram-engineering.com/static-analysis-at-scale-a...

[Yoric]

Thanks. For context, I happen to have a PhD in static analysis, so I'm not entirely stranger to the general idea :)

Sadly, all the attempts I've had with static analysis in Python screamed that while the language and tools make a valiant effort at supporting a reasonable set of annotations, 8-9 years after PEP 484, the libraries are simply not yet ready for it (not even, in many cases, the standard library).

Unit tests, staging environment, effective log aggregation are all important tools, of course.

[VWWHFSfQ]

I would say that those things don't really contribute to the actual runtime scalability of the application. Those things ensure that the code is at least correct for what it's supposed to do, not necessarily whether it's performant. Even the most perfectly-written Python code will still be dog-slow and require an enormous budget to actually run at scale. It's obviously fine to do that and there are many success stories, but all of them required massive infrastructure investment.

[ambivalence]

> Even the most perfectly-written Python code will still be dog-slow and require an enormous budget to actually run at scale.

Nah, asyncio on uvloop is plenty fast.

[Yoric]

For context, I was asking about scaling code complexity, not scaling performance.

That being said, my personal experience suggests that both are really hard in Python, and not independent.