Free-threaded Python (Interview)
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What up, Python nerds? I'm Jared, and you are listening to The Change Log, where each and every week we sit down with the hackers, the leaders, and the innovators of the software world to pick their brain, to learn from their mistakes, to get inspired by their accomplishments, and to have a lot of fun along the way. On this episode, I'm joined by the co-hosts of the Core.py podcast, Pablo Galindo and Lukas Langa, whose name I will pronounce Lukas from here on out because it's just a lot easier for me. On Core.py, they talk about Python internals because they work on Python internals. And today we're talking about Python 3.13, which is right around the corner. When we recorded this conversation last week, it was slated to be released on October 1st, but now they are targeting October 7th. So if you're listening to this in the future, 3.13 is fully baked. But if you are listening right after we hit publish, wait a week or grab the release candidate, which is 99% baked. Why are we all so excited about Python 3.13?
Well, the global interpreter lock, aka the GIL, is now experimentally optional. This is a huge deal, as Python is finally free-threaded and able to run with true parallelism. There's more, of course, and we get into all the details. I think you'll enjoy it, even if, like me, you aren't a regular Pythonista. But first, a mention of our partners at Fly.io. Over 3 million apps have launched on Fly, including ours. And you can too, in less than five minutes, learn how at Fly.io. Okay, free threaded Python on the changelog. Let's do this.
Hey friends, I'm here with Dave Rosenthal, CTO of Sentry. So Dave, I know lots of developers know about Sentry, know about the platform, because hey, we use Sentry and we love Sentry. And I know tracing is one of the next big frontiers for Sentry. Why add tracing to the platform? Why tracing and why now?
When we first launched the ability to collect tracing data, we were really emphasizing the performance aspect of that, the kind of application performance monitoring aspect, you know, because you have these things that are spans that measure how long something takes. And so the natural thing is to try to graph their durations and think about their durations and, you know, warn somebody if the durations are getting too long. But what we've realized is that the performance stuff ends up being just a bunch of gauges to look at. And it's not super actionable, right? Sentry is all about this notion of debug ability and actually making it easier to fix the problem, not just sort of giving you more gauges. A lot of what we're trying to do now is focus a little bit less on the sort of just the performance monitoring side of things and turn tracing into a tool that actually aids the debug ability of problems.
I love it. Okay, so they mean it when they say code breaks. Fix it faster with Sentry. More than 100,000 growing teams use Sentry to find problems fast, and you can too. Learn more at Sentry.io. That's S-E-N-T-R-Y.io. And use our code CHANGELOG. Get $100 off the team plan. That's almost four months free for you to try out Sentry. Once again, Sentry.io.
Today I'm joined by Pablo Galindo and Lucas Longa from Core.py, a podcast all about Python internals because these two work on Python internals. Welcome to the show guys.
Thank you very much for having us. Happy to be here.
Now your podcast, is it usually just the two of you talking to each other or do you have a third party ever?
Every now and again, we would invite a guest, but mostly it's just the two of us.
Yeah, when people are fed up of listening to us, and then we reach someone to...
Now, we've been asked to start a Python podcast a few times, as well as an Elixir podcast and a Rust podcast. And we don't usually feel like we have anything to add to the conversation, except when it came to Python, I remember telling our friend Brett Cannon, I said, you guys should have a podcast where it's like the people who work on Python talking about Python, not...
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