Profiling.sampling – Statistical Profiler
docs.python.org
Profiling.sampling – Statistical Profiler
1–10 of 26 posts
Re: Profiling.sampling – Statistical Profiler
#2wiwm i'd imagine it has a 3rd party tool. very nice to get it out of the box in 3.15
Re: Profiling.sampling – Statistical Profiler
#3What I like about profiling.tracing is how you can control it in the same code base that you are profiling. That turns tracing into a feature you can ship, not just a tool you can attach. Profiling.sampling doesn't seem to provide anything similar, it has to run as a separate process
Re: Profiling.sampling – Statistical Profiler
#4This sounds a lot like py-spy. How does it compare?
Re: Profiling.sampling – Statistical Profiler
#5I'm not trying to criticize, but Python is known to be much slower than eg. Java or Go etc. So for performance-critcal code, why use Python? I find Python to be very good because it is concise and simple, but I have not used it for production so far.
Re: Profiling.sampling – Statistical Profiler
#6I'm not trying to criticize, but Python is known to be much slower than eg. Java or Go etc. So for performance-critcal code, why use Python? I find Python to be very good because it is concise and simple, but I have not used it for production so far.
Sometimes you get there by accident. You make a thing, it grows or is used in unexpected ways, now suddenly performance matters.
Sometimes Python is just the language used in the domain. Lots of sciences live on Python because it is easy to teach to grad students and the package ecosystem is strong.
Re: Profiling.sampling – Statistical Profiler
#7I'm not trying to criticize, but Python is known to be much slower than eg. Java or Go etc. So for performance-critcal code, why use Python? I find Python to be very good because it is concise and simple, but I have not used it for production so far.
Even when performance is not critical it’s possible to write (or vibe?) disastrously slow code, so having profiler handy is always a plus. It might be a deciding factor between “we must rewrite it all in Rust” and “oh we added exponential complexity by accident” and save a ton of time
Re: Profiling.sampling – Statistical Profiler
#8What I like about profiling.tracing is how you can control it in the same code base that you are profiling. That turns tracing into a feature you can ship, not just a tool you can attach. Profiling.sampling doesn't seem to provide anything similar, it has to run as a separate process
It’s not a downside perhaps but a tradeoff. Implementing internal sampling profiler is not impossible in general (though it will depend on exact python implementation) but it would impose different overhead on the target and might skew results a lot more than external one.
Re: Profiling.sampling – Statistical Profiler
#9I'm not trying to criticize, but Python is known to be much slower than eg. Java or Go etc. So for performance-critcal code, why use Python? I find Python to be very good because it is concise and simple, but I have not used it for production so far.
Because your colleagues insist on it and now you're stuck with slow Python despite telling them that this is exactly what would happen...
Re: Profiling.sampling – Statistical Profiler
#10This sounds a lot like py-spy. How does it compare?
While there are some implementation differences (py-spy is written in rust, profiling.sampling is a mix of python and C etc), the end result seems pretty similar to me.
One thing to note is that there are some differences in blocking behaviour of the target process. Py-spy blocks by default and profiling.sampling doesn't. I wrote a bit about why py-spy blocks by default here https://www.benfrederickson.com/why-python-needs-paused-duri... - the first version of py-spy also didn't block and since we got incredibly misleading results at times this was one of the first changes I made to py-spy