Guido van RossumEvery podcast appearance, updated as new ones drop

Rock icon Rivers Cuomo goes deep on coding talk with Python computer language creator and programming icon, Guido Van Rossum. We talk about the history of The Sweater Song, coding vs songwriting, the history of Python and much, much more!

Lexman and van Rossum discuss the relationship between Leibniz and poetastering.

Lexman interviews the creator of Python, Guido van Rossum. They discuss the drugget, quittors, granger and cingulum models.

Guido van Rossum is the creator of the Python programming language and a Distinguished Engineer at Microsoft. Apple Podcasts | Spotify | Google Podcasts We discuss Guido’s new work on making CPython faster ( PEP 659 ), Tiers of Python Interpreter Execution , and high impact, low hanging fruit performance improvements. Highlights (an edited summary) [00:21] What got you interested in working on Python performance? Guido: In some sense, it was probably a topic that was fairly comfortable to me because it means working with a core of Python, where I still feel I know my way around. When I started at Microsoft, I briefly looked at Azure but realized I never enjoyed that kind of work at Google or Dropbox. Then I looked at Machine Learning, but it would take a lot of time to do something interested with the non-Python, and even Python-related bits. [02:31] What was different about the set of Mark Shannon’s ideas on Python performance that convinced you to go after them? Guido: I liked how he was thinking about the problem. Most of the other approaches around Python performance like PyPy and Cinder are not suitable for all use cases since they aren’t backward compatible with extension modules. Mark has the perspective and experience of a CPython developer, as well as a viable approach that would maintain backward compatibility, which is the hardest problem to solve. The Python Bytecode interpreter is modified often across minor releases (for eg: 3.8 → 3.9) for various reasons like new opcodes, so modifying that is a relatively safe approach. Utsav: [09:45] Could you walk us through the idea of the tiers of execution of the Python Interpreter ? Guido: When you execute a program, you don't know if it's going to crash after running a fraction of a millisecond, or whether it's going to be a three-week-long computation. Because it could be the same code, just in the first case, it has a bug. And so, if it takes three weeks to run the program, maybe it would make sense to spend half an hour ahead of time optimizing all the code that's going to be run. But obviously, especially in dynamic languages like Python, where we do as much as we can without asking the user to tell us exactly how they need it done, you just want to start executing code as quickly as you can. So that if it's a small script, or a large program that happens to fail early, or just exits early for a good reason, you don't spend any time being distracted by optimizing all that code. So, what we try to do there is keep the bytecode compiler simple so that we get to execute the beginning of the code as soon as possible. If we see that certain functions are being executed many times over, then we call that a hot function, and some definition of “hot”. For some purposes, maybe it's a hot function if it gets called more than once, or more than twice, or more than 10 times. For other purposes, you want to be more conservative, and you ca

See the full show notes for this episode on the website at talkpython.fm/100