py-spy

> you can either race with the program and hope that you read its memory to get the function stack before it changes what function it's running (and you're likely to win the race, because C is faster than Python), or you can pause the program briefly while taking a sample. Py-spy defaults to blocking because the results can be pretty wrong otherwise: https://github.com/benfred/py-spy/issues/56 . You can see this problem profiling a program like https://github.com/benfred/py-spy/blob/master/tests/scripts/... with or without the nonblocking flag in py-spy - the nonblocking version produces garbage output. Somewhat interestingly, this problem doesn't seem to occur with Ruby - and rbspy can get away without pausing the target program with only minor errors seen when profiling a similar function. I suspect this is because of differences betwee

Two other profilers that also let you spy on a running Python program: - py-spy: https://github.com/benfred/py-spy (written in Rust) - pyflame: https://github.com/uber-archive/pyflame (C++, seems to be not maintained anymore) The "no performance cost" thing is interesting: my experience writing a similar profiler is that there are a couple of things that can affect performance a little bit: 1. You have to make a lot of system calls to read the memory of the target process, and if you want to sample at a high rate then that does use some CPU. This can be an issue if you only have 1 CPU. 2. you have two choices when reading memory from a process: you can either race with the program and hope that you read its memory to get the function stack before it changes what function it's running (and you're likely to win the race, because C is faster

Very cool, py-spy[1] has been an invaluable tool in my development process since jvns blogged[2] about it. The power of being able to visualize where your code is spending its time is so obvious and I'm glad people are building tools to make that easier. As a quick compare and contrast between py-spy and pyinstrument it looks like py-spy has the advantage of being able to attach to an already running process which is super useful when your program is stuck and you don't know why. I haven't used pyinstrument yet but I do like the fact that it can do its flame graph in the console, sometimes I find saving down an svg file and opening up the browser a bit arduous. Excited to give it a try. [1] https://github.com/benfred/py-spy [2] https://jvns.ca/blog/2018/09/08/an-awesome-new-python-profil...

To extend on inspecting stack traces: I found that I often wanted to know the the content of (relevant) (local) variables of a stack trace. Whenever I got a stacktrace, I would often restart it in a debugger, or add print statements on variables. Until I extended the stacktrace to just include this information. The tricky part is that printing all local variables is too much. And also global variables might be involved. Or attributes (e.g. self.x or so). So now I'm parsing all variables and attributes from the source code line from the traceback (in a very simple way) and print only those. This covers about 95% of all cases - i.e. in 95% of the case, the stack trace contains all information I need to understand and fix the problem. I published that here: https://pypi.org/project/better_exchook/ https://github.com/albertz/py_better_exchook

Interesting article. While I definitely think you should be profiling your code to figure out the hot spots, cProfile has some limitations for profiling: cProfile doesn't give you line numbers, doesn’t work with threads, and significantly slows your program down. I wrote a tool py-spy (https://github.com/benfred/py-spy) that is worth checking out if you’re interesting in profiling python programs. Not only does it solve those problems with cProfile - py-spy also lets you generate a flamegraph, profile running programs in production, works with multiprocess python applications, can profile native python extensions etc.

Similar to PyFlame is also py-spy: https://github.com/benfred/py-spy > While pyflame is a great project, it doesn't support Python 3.7 yet and doesn't work on OSX, Windows or FreeBSD. I wonder how the CPU profiling in Scalene is different. It does not mention PyFlame or py-spy at all in the Readme. Of course, the memory profiler is some nice extra.