#459 Inverted dependency trees

WEBVTT
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Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds.
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This is episode 459, recorded November 24th, 2025.
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And I am Brian Okken.
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And I am Michael Kennedy.
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And we want to thank everybody that supports us through the stuff we sell to you guys
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It's just the show notes, really.
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What do you got for us up first, Michael?
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Actually, we have some pre-topics.
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We have a topic zero topic.
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How about that?
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Yeah.
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Topic zero.
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So I don't know if people notice.
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I mean, if you've read your email or listened to the radio or anything like that, you might know that it is the week of Black Friday.
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And I think there's a lot of restraint on our end, Brian, by keeping it to the week.
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Like I was getting Black Friday sales in October and this is wrong.
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This is disrespecting Halloween amongst other things.
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Yeah.
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Anyway, Black Friday, I launched the Black Friday sale over at Talk Python.
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And this year is different than the last year.
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Traditionally, I just sort of done, hey, get the everything bundle, get all the courses
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on in forever sort of thing.
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But I wanted to do a couple of different deals.
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So now there's three options you can pick from.
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you can pick i have what's called the ai python bundle so two of our most popular courses agentic
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ai programming with python the one that i wrote and that's if you want to use things like water
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cursor or junior or whatever to build applications with alongside you and we have llm building blocks
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in python by vincent mormonam which is uh basically if i'm going to build something that uses an llm
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as an API kind of really cool stuff in both courses they're both really well reviewed and
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received you can get that for like half off as a special mini bundle then the whole library thing
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is still on you can get it at at super discounts and I'm also offering the talk python in production
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book it's doing really well for a while it's been a good while at number one in software engineering
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on Amazon which is amazing it's nice it's got pure five star reviews on gumroad at least as of
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yesterday. I haven't looked today, but I think that's still true. So really well received.
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That's also on sale at a discount. So that's Black Friday at Talk Python. talkpython.fm
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slash Black-Friday. Check it out, please. It's, as you said, at the top brain, it's a really
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great way to support a show. Make sure that we keep doing what we're doing here on the podcast.
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And with that, how about you? Well, I was actually not planning on doing a Black Friday thing,
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but I saw your topic in there. So I thought, yeah, why not? I'll do a Black Friday thing too.
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So the complete pytest course bundle, I put that on sale. And if you go, so you have to use a coupon
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code, the coupon code, if you go to the, if you just say, yeah, I want to get this. If you type
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in Black Friday, hit apply, then it's a 20% discount. But yeah, I don't think that looks
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big enough um i think i think we'll also do a save 50 so if you do save 50 um and apply that
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that's better that's 29 bucks now um so you get half off um 20 off or half off your choice
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um so uh doing that it'll be in the show notes uh but i kind of want okay so i was trying to match
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michael because he was doing 20 on some of the stuff so i did i did the black friday for 20
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But then I read my newsletter from Pragmatic, and Pragmatic is doing a 50% off for Black Friday.
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So you can get all Pragmatic books.
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If you save 50, you get 50% off.
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And that includes Python testing with pytest.
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So you can grab the video course or the book or both.
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So those are my Black Friday deals.
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Awesome.
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And yeah, I don't, I mean, I know sometimes it feels overly salesy, but I did not do Black Friday one year and I got a ton of email like, Michael, where's your Black Friday sales? I've been hoping that you would do one. So I know at least a subset of people genuinely look forward to it.
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And I personally, if there's something I really want for my learning path, I try to get it whenever I get it.
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But there's some things that are like, it depends on your budget.
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And there's some things that I don't need to have right away and I'd like to use.
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And there's a lot of stuff that I've purchased on Black Friday sales.
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And I appreciate that independent retailers and independent people like you and me do the Black Friday thing.
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people that the full price is just a smidge out of their price range that's good yeah absolutely
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all right let's talk about python stuff i mean that was python stuff but you know other stuff
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yeah pure python and i'm talking a language level python okay there's a lot more to python than just
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the language i think some people get confused i saw this comment on reddit of all places somebody
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said hey what podcast should i listen to to learn python like why would you do that that's stupid
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why would you listen to a podcast like just it's a simple language just go here and take the
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I was like, a little bit broader.
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But today, no, actually, we're talking built-ins.
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We're talking runtime.
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So PEP 814, this one is a really natural addition now that Python 3.14 came out.
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This is add frozen dict as a built-in type.
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Comes from Victor Stinner, who does a ton of performance stuff as a core developer.
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Thank you for that, Victor.
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And the idea is that when you have a frozen type, you get a lot of guarantees that you
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can work against that make your life easier, your bugs, et cetera, right? With a regular dictionary,
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you might say, oh, I need to return this dictionary to a thing. And you don't do a copy,
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you just hand it over. And then somebody changes it. You're like, wait, you aren't supposed to
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change it. I'm just trying to give you some data, right? So these frozen types are great for if you
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don't want something to change, it's not going to change. Also, in the code that would try to change
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it would get an error, right? So you're like, no, you're not supposed to do this. Figure something
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else out, right? The intention is sort of conveyed through the exceptions, I guess, of a cause.
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But also, it's really good for concurrency, which is the link back to 3.14.
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Now that we have true parallelism, we, I don't know how many people are doing this,
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but find yourself a re-entered lock and use it in your code if you're writing true parallel code, right?
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Because race conditions, deadlocks, well, it starts with race conditions, ends with deadlocks, and hopefully fix.
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Anyway, you want to do that, and you've got to lock mutable code.
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But if you're reading, two things are reading from the same data structure, and it's not changing,
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well, there ain't no problem with that.
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You can read with a hundred threads in parallel and they're all going to get the same answers
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deterministically, right?
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So having frozen dict adds dictionaries in as a type that is basically purely thread safe
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if you use a frozen dict instead of a regular one.
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Cool, right?
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That is cool.
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I wonder if this will ripple up into other data types like classes and stuff.
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That is a very good question.
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So I have an answer for you as well.
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So there's some interesting things.
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I linked to the discussion, not the PEP.
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And there's like a really thoughtful conversation by lots of people that you know from the show.
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It's like, well, okay, that's cool.
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Can I equate a dictionary with a frozen dict?
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Because what you'll see from the PEP is frozen dict actually derives from something that is not a dictionary.
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So it's not, if you said, is instance a dictionary frozen dict, it would say no.
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I'm not so sure how I feel about that, but that's how it is.
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So the question is, well, can you say equal, equal?
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And is it ordered and so on?
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It is ordered, but is that order dependent and the equals and so on, right?
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So there's a lot of stuff coming along.
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And here you can go see all the conversation there.
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So I also, just like you, Brian, like, well, what else is frozen in Python?
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Like, what do we already have?
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You know, if we didn't have a frozen dictionary.
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So I put together a list of existing frozen types and structures in Python.
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Tuple, frozen set.
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Frozen set was the motivation for frozen dict.
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Like, well, if we got a frozen set, why can't we have a frozen dictionary?
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You know what I mean?
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String, obviously, bytes, ranges, memory views.
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Memory views are interesting to put over like a buffer, but you don't want it to be written to.
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but you've also got mapping proxy types you've got a record class in the form of data classes
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and then data classes you can say frozen is true so this is as close as you're going to get probably
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to what you're asking for right frozen classes you technically could more or less do that if you had
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private variables and only read-only properties but that's a lot of work you might as well just
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create a frozen data class i don't know but yeah so there's a whole bunch of stuff that i put in
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here that people can check out.
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I put a little extra data sheet because I'm like, huh,
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I also want to know this.
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Yeah.
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Yeah. All right.
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I'm looking forward to Python 3.15 being more frozen.
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How about that?
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More frozen. Yeah.
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More frozen.
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Might be faster too.
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Yeah. It definitely has a possibility for faster,
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safer code more easily.
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Yeah.
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Document it though.
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Yeah, got to document it.
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I'm bringing up an interesting topic that this was suggested by John Hagen.
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Thanks, John.
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Material for MkDocs.
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So I use MkDocs and I use Material for MkDocs.
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Material for MkDocs is a theme for MkDocs,
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but it also adds some cool stuff to it.
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So it's like a theme++ or something.
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And I use it for both personal projects and for at work
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because it's just as easy to work with.
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Anyway, there's some news about Material.
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So the news that came out is that the team behind it
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is putting the Material for MkDocs project in maintenance mode.
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So for new updates, I don't know because I didn't really fully read this,
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but I'm guessing that they're going to, let's see, changes the issue tracker.
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Are they encouraged to open bug reports following?
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okay, they'll fix critical bugs and security vulnerabilities.
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That's right.
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That's a good, that's fair.
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Because a lot of people use it, including me.
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So I care about that.
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But why are they putting it in maintenance mode?
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It's because the efforts that they're putting in this,
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they're switching over to a new project called Zensical.
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Zensical?
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Anyway, interesting name.
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Modern static site generator built on material for MkDocs team,
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built by the material for MkDocs team.
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So it's a backwards compatibility, and we're going to link to a post that is actually all the, it's like a four post series talking about the transition because it affects a lot of people.
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So they're transforming Material for MkDocs, they're starting Zensical, and they've got a Material for MkDocs insiders program, and they're closing down GitHub discussions on Material for MkDocs.
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So it doesn't seem like it's profit-driven
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because they were actually making a decent amount
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off of GitHub sponsorships for this,
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but maybe that's part of it.
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But the gist of it is at the top,
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they said they want Zensical
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to overcome technical limitations of MkDocs.
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There's so many people that were using it, though,
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and not using any other form of MkDocs,
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but in part of this discussion they were talking about
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that a lot of the stuff in the forum and a lot of stuff in just GitHub issues were really not their problem.
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There were like really issues with MkDocs or issues with some other dependencies that they were using.
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So Zensical is going to just wrap up.
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It's going to be a replacement for MkDocs and MkDocs, material for MkDocs.
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And I think a few of the other plugins that used to work with all of this, they're pulling those in to have a support model so that they can actually support everything that matters to people that use it.
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The new website, Zensical.org, is just fun to look at.
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There's this cool animation thing.
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Yeah, it's really cool.
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So for people listening, you got to check it out.
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Zensical.org.
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It's a black background with this kind of a yellow light laser thing going around in circles.
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I don't know what that shape is.
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Like a donut.
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Thank you, Henry Schreiner.
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It's also a...
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Henry jumped in the show.
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It's also a rust rewrite, and it's five times faster.
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So a lot faster.
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I'm pretty excited to try that out.
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I haven't tried this yet, but since I'm a heavy material for MkDocs user, I'll definitely be checking this out.
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So one of the things I was curious about is the whole, like, how does this work for, like, is it going to go away or what's the open source model?
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It's still going to be open source and MIT licensed.
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And there's a compatibility back.
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They're trying to make it backwards compatible.
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So when you switch over, you can use your make ducks YAML file just as is.
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And hopefully it works.
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I haven't tried this yet.
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And I, you know, I actually haven't like customized it too much.
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So it's probably going to work fine for me.
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But I do want to check out this, this compatible backwards compatibility thing to switch it over.
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I like some of the cool features that they're, they're trying to build in and faster is great.
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The other, the other thing I was, so how are they making money?
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is the Zensical Spark model.
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So this is going to be,
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so what's Zensical Spark?
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It's for basically,
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actually, I think it's more than this,
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but part of it is companies that use it
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or groups, teams that use it, even startups.
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It's not just for enterprise people,
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but groups can go ahead and sign up for this
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and have better support model.
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So they can prioritize supporting people
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that are paying them, which makes sense.
00:14:52.320 --> 00:14:55.280
But those fixes will help everybody.
00:14:55.720 --> 00:14:56.120
So that's good.
00:14:57.460 --> 00:15:02.180
One of the cool things that they're doing also during the transition is material for
00:15:02.260 --> 00:15:08.140
MkDocs, one of the ways they made money is they had both an open version that's just
00:15:08.200 --> 00:15:08.760
free for everybody.
00:15:09.180 --> 00:15:13.160
And then they would release new features to an insiders group.
00:15:13.460 --> 00:15:15.160
So you could get new features right away.
00:15:15.620 --> 00:15:19.620
And then they would sort of trickle into the general population features.
00:15:19.780 --> 00:15:25.100
they're getting um i don't know if there's going to be something similar for the spark thing but
00:15:25.400 --> 00:15:29.740
for material for make docs they've just opened up the floodgates anything that was in the insiders
00:15:29.820 --> 00:15:35.120
is now part of uh the open source thing so if you want to stick to that you get all the features
00:15:35.640 --> 00:15:43.280
so anyway just news for people that use make docs yeah nice i'm i'm optimistic here if their website's
00:15:43.400 --> 00:15:49.040
anything to go by no it's i'm optimistic i think it will it's the kind of thing that'll make it
00:15:49.020 --> 00:15:52.560
really polished and really nice if they get a decent number of customers.
00:15:52.730 --> 00:15:53.380
So good.
00:15:53.660 --> 00:15:59.120
And I do like that they're just actually putting the people on the people with their pictures.
00:15:59.470 --> 00:16:01.060
She just, these are the people behind it.
00:16:01.270 --> 00:16:07.460
It's not something just no faceless people making your money off you.
00:16:07.780 --> 00:16:08.260
Exactly.
00:16:08.770 --> 00:16:08.940
Cool.
00:16:09.220 --> 00:16:09.600
That's right.
00:16:09.980 --> 00:16:10.980
Keep Python independent.
00:16:11.110 --> 00:16:11.260
Okay.
00:16:11.960 --> 00:16:17.800
So I want to talk about something that was sent in to us by Gerber Decken.
00:16:18.340 --> 00:16:21.000
And this is sort of the hat trick, if you will, Brian.
00:16:21.400 --> 00:16:26.160
Two weeks ago, you talked about pip-dep-tree and UV-pip-tree, right?
00:16:26.520 --> 00:16:29.020
And then the last week, you talked about dep-tree.
00:16:29.899 --> 00:16:32.900
This time, I want to talk about a couple of things.
00:16:33.000 --> 00:16:34.620
I want to talk about uv.
00:16:35.520 --> 00:16:36.740
I mean, I got to look at this.
00:16:36.760 --> 00:16:38.980
This is a bit of a statement here.
00:16:39.200 --> 00:16:39.960
I know it's rough.
00:16:40.560 --> 00:16:42.000
Rough, analyze, graph.
00:16:42.500 --> 00:16:47.659
And you give it a virtual environment and some place to start or file
00:16:47.700 --> 00:16:52.740
or something like that. And it will generate basically an architectural layer diagram.
00:16:53.390 --> 00:16:59.180
This portion of your application depends on these other modules, which depend on those modules. And
00:16:59.540 --> 00:17:03.440
is there any form of circular weirdness or whatever, right? So if you want to understand
00:17:03.590 --> 00:17:09.180
how that works, this is like, it'll give you just a list. So this thing tech, which I'm covering,
00:17:09.250 --> 00:17:14.819
he sent in is a Python tool to visualize and enforce dependencies using this architecture.
00:17:14.839 --> 00:17:17.839
and it basically takes that output that I described
00:17:18.020 --> 00:17:20.260
and then turns it into a UI, okay?
00:17:20.680 --> 00:17:20.880
Okay.
00:17:21.199 --> 00:17:25.280
So there's a little graph that shows how it works,
00:17:25.329 --> 00:17:27.620
but basically you just create a TOML file
00:17:28.060 --> 00:17:30.220
and then you run a command and it will scheme your code.
00:17:30.640 --> 00:17:33.440
It will generate basically that, more or less, that output.
00:17:33.810 --> 00:17:37.380
And then you can upload that, not your code,
00:17:37.640 --> 00:17:40.060
just this little TOML file that basically has module names
00:17:40.110 --> 00:17:40.940
and nothing else in it,
00:17:41.500 --> 00:17:43.760
to this service that will turn that into a graph, okay?
00:17:43.880 --> 00:17:49.940
So I did that by creating one for a project that I'm not talking about yet, but I'm working on here.
00:17:50.330 --> 00:17:56.060
And you can see we've got this cool little thing and it shows like, how are you, how is it put together?
00:17:56.210 --> 00:17:57.740
What pieces depend on what, right?
00:17:57.900 --> 00:17:59.320
What depends on services?
00:17:59.450 --> 00:18:02.860
And then as you interact with it, you can click on like, I want to see what views depend upon.
00:18:03.100 --> 00:18:03.860
Oh, that's a lot.
00:18:04.360 --> 00:18:08.140
But if I click on view models, you can see there's a nice layered architecture, right?
00:18:08.280 --> 00:18:15.580
view models depend on services infrastructure modules and db models but it doesn't depend on
00:18:15.680 --> 00:18:20.000
the stuff above it that depends on it so you don't get these weird cycles and other things and there's
00:18:20.040 --> 00:18:24.820
like a little cms component it's used by the views it's used by the services and nothing else so you
00:18:24.880 --> 00:18:29.860
can get this like cool little understanding of different parts of your app what do you think
00:18:30.340 --> 00:18:36.299
this is cool so this is um maybe because we were talking about pipped up tree before this is not
00:18:36.380 --> 00:18:42.680
the third-party stuff is just this is your yeah this is your own code so how does your own code
00:18:42.760 --> 00:18:48.220
within itself like a set of sub modules depend upon each other because if you look at this um
00:18:48.540 --> 00:18:53.440
you actually look at the code this view model section probably has 30 or 50 classes in it
00:18:53.760 --> 00:18:59.520
this infrastructure bit has maybe 20 different things like utilities for web stuff urls and so
00:18:59.520 --> 00:19:04.840
on and text parsing and um numeric converting libraries and you know stuff like that this dv
00:19:04.800 --> 00:19:08.700
Everything has, I think, five database models and a helper class or something.
00:19:09.180 --> 00:19:10.720
But they just show up as these blocks.
00:19:10.880 --> 00:19:13.600
So you can kind of get a sense of like, how do these things interact?
00:19:14.180 --> 00:19:17.360
The lines are drawn by analyzing all the individual files, I believe,
00:19:17.920 --> 00:19:20.300
and seeing which ones point to things from other categories.
00:19:20.900 --> 00:19:22.200
Oh, nice. This is cool.
00:19:22.560 --> 00:19:24.940
Yeah. So I uploaded this picture.
00:19:25.900 --> 00:19:27.520
I shared this. I create these shareable graphs.
00:19:27.620 --> 00:19:29.740
So you can actually play with this graph yourself as well.
00:19:30.040 --> 00:19:33.340
Yes. And then Christian asks, is this unmaintained?
00:19:33.380 --> 00:19:37.600
Yes, I believe TAC has become unmaintained, but you can still run it.
00:19:37.610 --> 00:19:38.540
It still looks like it's okay.
00:19:38.820 --> 00:19:39.840
So that's a good point.
00:19:39.960 --> 00:19:46.280
I did notice after I put this together that somewhere it's got like a, although the GitHub
00:19:46.530 --> 00:19:51.140
repo, no, it's not TAC that is at least doesn't have an explicit statement that has gone into
00:19:51.260 --> 00:19:51.880
maintenance mode or anything.
00:19:52.640 --> 00:19:56.620
But if you look at TAC, it goes to gauge.sh.
00:19:56.640 --> 00:20:06.220
And if you go to gauge.sh, this product is changed and it's becoming some AI thing that means nothing to me.
00:20:07.180 --> 00:20:08.160
So I don't know.
00:20:08.360 --> 00:20:11.160
But I imagine they'll probably keep it running.
00:20:11.500 --> 00:20:13.740
And apparently it's also open source.
00:20:14.060 --> 00:20:16.380
So you could go and fork it and just run it yourself or whatever.
00:20:16.650 --> 00:20:16.920
I don't know.
00:20:17.230 --> 00:20:22.520
So there is some component in this whole chain that is, it's the visualization of it, not the parsing.
00:20:22.820 --> 00:20:25.240
I think TAC, which does the parsing, still works fine.
00:20:25.340 --> 00:20:26.480
but then it uploads the gauge.
00:20:26.570 --> 00:20:28.320
And the gauge thing is in question
00:20:28.630 --> 00:20:30.140
about whether that will still work.
00:20:30.660 --> 00:20:32.160
But, you know, you could always go
00:20:32.210 --> 00:20:33.800
and make yourself, you know, fork it.
00:20:34.180 --> 00:20:35.280
I mean, how hard would it be to make,
00:20:36.140 --> 00:20:38.500
given the file, which I don't have it easy,
00:20:38.560 --> 00:20:39.060
I can pull it up,
00:20:39.200 --> 00:20:40.900
but given a file that lists these relationships,
00:20:41.420 --> 00:20:42.620
how hard is it to graph it, right?
00:20:42.700 --> 00:20:44.420
You could AI your way to something
00:20:44.490 --> 00:20:46.340
that draws your pictures out of tech
00:20:46.990 --> 00:20:48.600
in like an afternoon if you really had to.
00:20:49.400 --> 00:20:51.800
This visualization left is an exercise for the reader.
00:20:52.140 --> 00:20:52.460
Exactly.
00:20:52.710 --> 00:20:53.799
Like you could visualize it
00:20:54.020 --> 00:20:57.020
if you knew the right series of prompts, I don't know, or you wanted to write it or something.
00:20:57.340 --> 00:21:00.460
No, anyway, that's a good point, Christian. Thank you. Over to you, Brian.
00:21:00.880 --> 00:21:08.400
Well, I wanted to talk about upcoming speedups in Python. So there's a couple news articles that
00:21:08.520 --> 00:21:15.280
came by that I noticed talking about some efforts to speed up Python in the future. So we're at
00:21:15.460 --> 00:21:22.300
Python 3.14 right now. 3.15 is in the works and 3.16 later, right? So one of the things,
00:21:22.400 --> 00:21:30.140
This is a post by Ken Jin, a plan for 5% to 10% faster free-threaded JIT by Python 3.16.
00:21:30.560 --> 00:21:36.480
And the highlight is 5% faster for 3.15, 10% faster for 3.16.
00:21:37.480 --> 00:21:42.060
And some of the names popped out as why I should pay attention to this.
00:21:42.600 --> 00:21:48.100
This was brought up during the Python Core Dev Sprint in Cambridge hosted by ARM.
00:21:48.860 --> 00:21:50.160
They planned this project.
00:21:50.520 --> 00:22:06.260
The planners of the project that were present is Savannah Ostrowski, Mark Shannon, Ken Jen, this person, Diego Russo, and Brent Butcher, accompanied by other CPython core team members as well.
00:22:07.620 --> 00:22:12.060
And also, I'm like, well, 5% to 10%, that's not that big of a deal.
00:22:12.400 --> 00:22:15.020
It says, you might wonder, 5% seems awfully conservative.
00:22:15.280 --> 00:22:18.520
However, note that this figure is a geometric mean.
00:22:18.540 --> 00:22:21.840
the number can range from slower to significantly faster.
00:22:22.210 --> 00:22:24.760
All numbers are high performance figures.
00:22:26.050 --> 00:22:29.080
So I think that means they're optimistically thinking
00:22:29.360 --> 00:22:30.960
it's going to be a lot faster.
00:22:32.880 --> 00:22:36.440
So it's interesting that the LLM involvement here
00:22:36.560 --> 00:22:37.160
is a little interesting.
00:22:37.880 --> 00:22:43.520
Plan for 3.15 profiling support via LLVM21.
00:22:43.800 --> 00:22:44.740
Oh, not LLVM.
00:22:44.740 --> 00:22:48.340
That's probably performance-driven compiler optimization.
00:22:48.400 --> 00:22:50.040
stuff. Yeah. Yeah.
00:22:50.350 --> 00:22:51.940
Where you profile it and then the
00:22:52.640 --> 00:22:54.260
compiler says let me look at the profiler
00:22:54.680 --> 00:22:56.340
output and then actually re-optimize
00:22:56.640 --> 00:22:58.340
based on how you actually ran. You know, like
00:22:58.350 --> 00:22:59.840
that sort of fact. I bet it's something like that.
00:23:00.260 --> 00:23:02.460
Yeah, but it's also looking forward to
00:23:03.180 --> 00:23:04.420
some support from
00:23:05.280 --> 00:23:06.400
LLVM20 and then LLVM21.
00:23:07.780 --> 00:23:08.500
So some
00:23:08.950 --> 00:23:10.540
of those lower
00:23:10.670 --> 00:23:12.140
level parsing improvements
00:23:12.550 --> 00:23:14.240
are going to help with the JIT. So
00:23:14.290 --> 00:23:15.980
trace recording, better machine code,
00:23:16.360 --> 00:23:18.660
Register allocation, top of stack caching,
00:23:19.140 --> 00:23:20.080
reference count elimination.
00:23:20.800 --> 00:23:21.660
That's an interesting one.
00:23:22.370 --> 00:23:25.380
More constant promotion and basic free threading support.
00:23:26.480 --> 00:23:30.180
So I'm looking forward to seeing there's some graphs
00:23:30.430 --> 00:23:33.480
and some more information that's really above my pay grade.
00:23:35.620 --> 00:23:36.500
That just-
00:23:36.500 --> 00:23:37.940
Bars and stuff, they must be in things.
00:23:37.940 --> 00:23:39.580
We don't know.
00:23:39.580 --> 00:23:41.000
Yeah, it looks like,
00:23:41.000 --> 00:23:44.300
it's got like those bar graphs with the eyes,
00:23:44.300 --> 00:23:45.240
like max admin and stuff.
00:23:45.960 --> 00:23:46.740
Oh, wow.
00:23:47.000 --> 00:23:48.540
Yeah, so the potential.
00:23:48.940 --> 00:23:49.420
Yeah, anyway.
00:23:50.260 --> 00:23:50.620
Interesting.
00:23:50.840 --> 00:23:54.220
I'm glad that we're getting some JIT speed up.
00:23:54.260 --> 00:23:54.840
So this is cool.
00:23:55.360 --> 00:23:58.880
Another speed up is, this is from Emma's blog.
00:24:00.540 --> 00:24:04.440
I forgot to grab what Emma's last name is, but Emma's blog.
00:24:05.180 --> 00:24:09.920
Decompression is up to 30% faster in CPython 3.15.
00:24:11.040 --> 00:24:11.900
This is cool.
00:24:12.000 --> 00:24:21.660
So, you know, and this is based on the Z standard being added to the standard library.
00:24:22.160 --> 00:24:27.140
So really, I didn't really get why we cared about the Z standard before.
00:24:27.390 --> 00:24:30.820
But hey, if we can decompress that much faster, that's great.
00:24:31.340 --> 00:24:37.000
There's compression and decompression happening all over the place that you don't even really know is going on.
00:24:37.240 --> 00:24:52.920
So, you know, I actually ended up for the very first time, I think ever using compression in my Python app for a non, not because I somehow received a zip file or a tarball or something and wanted to process it, but like as a part of operation of the app.
00:24:52.920 --> 00:24:54.760
So I'll run this by you, tell me what you think.
00:24:55.340 --> 00:25:06.740
So this project that I'm working on that, that shall not be named has to store text and it has to store about 250 K of text per database record for like a certain thing.
00:25:07.040 --> 00:25:08.080
But you never search it.
00:25:08.330 --> 00:25:09.280
You never index it.
00:25:09.560 --> 00:25:12.300
You mean you don't have a database index, like you find it through other
00:25:12.460 --> 00:25:13.700
keys and you just want to process it.
00:25:13.700 --> 00:25:17.180
And I'm like, well, how long does it take to compress and decompress that?
00:25:17.440 --> 00:25:18.780
How much smaller does it get?
00:25:18.990 --> 00:25:23.720
So I used XZ, which is also built in a Python and it goes down to about a
00:25:23.900 --> 00:25:24.640
10th of the size.
00:25:24.920 --> 00:25:29.900
So for caching and database records, it's instead of being 250 K it's like 25 K
00:25:30.300 --> 00:25:32.220
and you want to process it as part of a web request.
00:25:32.340 --> 00:25:34.980
You're like, yeah, let me just unzip it and send it back to you or whatever.
00:25:35.540 --> 00:25:36.780
And yeah, I found it really useful.
00:25:36.880 --> 00:25:40.700
So I'm all of a sudden super excited about fast decompression inside the, inside of CPython.
00:25:40.940 --> 00:25:41.320
Yeah.
00:25:42.860 --> 00:25:45.080
Little, little bits matter if you do the right ones.
00:25:45.620 --> 00:25:46.080
So yeah.
00:25:47.300 --> 00:25:51.400
And by the way, it's, it's like millisecond level type of time to do that.
00:25:51.600 --> 00:25:54.120
So you might think, well, I mean, it's going to make your site slow, right?
00:25:54.360 --> 00:25:57.940
You know, but if you can store 10 times as much data without running out of disk space
00:25:57.980 --> 00:26:00.240
or cash or whatever, like that also helps a lot, you know?
00:26:00.480 --> 00:26:00.740
Yeah.
00:26:00.780 --> 00:26:05.280
It helps with your, your, how much you're paying for database services as well.
00:26:05.600 --> 00:26:05.820
So yeah.
00:26:06.480 --> 00:26:06.720
All right.
00:26:06.880 --> 00:26:09.240
Oh, one more thing for Henry on the compression thing.
00:26:10.220 --> 00:26:12.360
ZSTD is used by Python build standalone.
00:26:12.510 --> 00:26:14.920
So having the ability to handle that without dependencies is great.
00:26:15.240 --> 00:26:16.260
And faster next year too.
00:26:16.440 --> 00:26:17.500
Yeah, thanks for the background info there.
00:26:17.900 --> 00:26:22.320
And, you know, just shout out, like, I'm such a fan of Python build standalone as a way to,
00:26:22.490 --> 00:26:25.820
like getting Python on your, a computer, your computer, server, whatever,
00:26:26.100 --> 00:26:29.980
is just ridiculous with uv plus Python build standalone now, right?
00:26:30.380 --> 00:26:32.280
Seconds, not trouble, right?
00:26:32.380 --> 00:26:34.760
You know, uv, make a virtual environment, desperation, Python.
00:26:35.060 --> 00:26:36.600
Yeah, it just changed.
00:26:37.000 --> 00:26:39.720
It's one of those things that changed everybody's workflow.
00:26:40.820 --> 00:26:45.120
And I don't even think about having to install a different version anymore.
00:26:45.410 --> 00:26:46.020
It just happens.
00:26:46.610 --> 00:26:47.180
And just does.
00:26:47.840 --> 00:26:49.580
All right, you got any extras while you got your screen up?
00:26:49.880 --> 00:26:50.200
Sure.
00:26:51.210 --> 00:26:55.960
The one extra I've got is that the Lean TDD book,
00:26:56.620 --> 00:27:00.500
one of the things I'm putting it out while I'm writing it.
00:27:01.030 --> 00:27:02.980
This week, I'm doing Thanksgiving prep,
00:27:03.200 --> 00:27:04.860
so I'm not going to release a chapter this week.
00:27:04.940 --> 00:27:07.120
but there'll probably be one next week.
00:27:08.020 --> 00:27:09.900
And that one's on building on lean.
00:27:10.900 --> 00:27:12.720
Last one was building on TDD.
00:27:13.820 --> 00:27:15.820
What I wanted to talk about though
00:27:15.960 --> 00:27:17.140
is I appreciate the issues.
00:27:17.460 --> 00:27:20.620
So I decided to just use GitHub repo
00:27:21.120 --> 00:27:22.840
as an issue tracker for this.
00:27:23.260 --> 00:27:26.400
And I've gotten a couple so far,
00:27:26.640 --> 00:27:28.700
not very many, but maybe that's a good thing.
00:27:29.420 --> 00:27:32.400
A typo came in and actually it's one that like was,
00:27:32.640 --> 00:27:33.280
I was just missing.
00:27:33.330 --> 00:27:34.340
I was reading all the time.
00:27:34.440 --> 00:27:35.360
And so I appreciate that.
00:27:36.200 --> 00:27:43.000
And then one of the ones was, was that I really appreciate is like discussion of extra topics
00:27:43.100 --> 00:27:43.920
I should build in.
00:27:43.940 --> 00:27:48.880
So I had, one of the things I've done is like release the headings and subheadings
00:27:49.180 --> 00:27:53.400
for different chapters and, and, and different parts of the chapter.
00:27:53.820 --> 00:27:58.320
And I'm hoping in the hopes of that people will go, oh, you gotta make sure you don't
00:27:58.600 --> 00:27:59.720
forget to talk about this.
00:28:00.160 --> 00:28:03.800
And that's what I got from looks like Jonah.
00:28:04.600 --> 00:28:06.920
I'm not going to try to pronounce that, but I'll say Jonah.
00:28:08.280 --> 00:28:14.000
In the chapter on considering considerations for applications for applying Lean TDD,
00:28:14.880 --> 00:28:22.460
talked about not just technical considerations, but what we're really doing to social considerations.
00:28:23.220 --> 00:28:24.380
And I've faced this too.
00:28:24.540 --> 00:28:26.200
I'm like, oh, yeah, I didn't even think about this.
00:28:26.460 --> 00:28:34.400
There's a lot of people just meant like just opposed to doing test-driven development or opposed to doing different kinds of tests for various reasons.
00:28:35.100 --> 00:28:38.880
And I wasn't planning on talking about that, but I think it's very essential.
00:28:39.140 --> 00:28:41.200
I'm glad that I'm getting people's feedback.
00:28:41.780 --> 00:28:42.480
Could be the other way.
00:28:42.630 --> 00:28:46.700
They only want to do TDD and they want to do integration tests because they're impure.
00:28:47.100 --> 00:28:47.480
Yuck.
00:28:47.840 --> 00:28:50.900
You know, I mean, there's a whole spectrum of things you probably got to speak to.
00:28:51.240 --> 00:28:51.460
Yeah.
00:28:52.360 --> 00:28:52.800
Go ahead.
00:28:52.880 --> 00:29:08.960
And they even brought up some other things, like we don't have time to test. Testing makes it harder to refactor. Your tests are not compatible with our architecture. I did a release, even though the tests are failing because it's too important to get the code out there. Yeah, these are great things to talk about.
00:29:09.460 --> 00:29:21.900
Yeah, for sure. I set up a issue tracker discussion board for the Talk Python in production book too. And it's been pretty active. I really appreciate it. There's a couple of things I could respond to. So if you're waiting on me, I'm forgotten, I'm forgotten.
00:29:21.960 --> 00:29:28.120
And I told you that this was a follow-up to the dependency thing, but here's another.
00:29:28.190 --> 00:29:29.320
This one's by Bob Belderbus.
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I didn't think it was big enough to make its own thing, but check this out.
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Bob says, I need to see which packages were pulling in a certain dependency instead of
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what packages depend.
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So you can actually go and do uv tree and give it a package and say --invert
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to answer who depends upon this.
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And look at this picture.
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I see that's not really helpful.
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But you look at it and it actually says six was pulled in by bleach, which was pulled in by Jenga bleach, which was pulled in PyBytes platform.
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Also six pulled in Python.
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DateUtil, why is that even there?
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So it's like a reverse of your depth tree.
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So you can focus it on a node and basically filter and restrict it just to the stuff that works with that leaf node, I guess.
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So you'd look at it.
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Yeah.
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So if you're thinking about cleaning up your dependencies and say, well, I don't want to pull in six anymore.
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it might not matter because a bunch of your dependencies are already pulling it in.
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Right, exactly.
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And so you could also ask the question like, well, do I really need that dependency?
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Yeah.
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Or could I find something else that would allow me to get rid of this thing,
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this lower level dependency that's causing a problem?
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You know, that's often a question to try and answer.
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All right.
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Well, that is it for...
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Well, now I want an extra feature that says, hey, uv, can you tell me which packages
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don't have any transitive, aren't transitive dependencies?
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Hmm. Yeah. cool. So, okay. Let's talk a joke. So do you remember Brian last week,
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it was like so many things went down. And one of the things that we had, we had AWS go down
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two weeks ago and take out a huge chunk of the internet, I think for 14 hours because of DNS,
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because of course it was, it was DNS to the database, which was the foundation of all the
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other things. And then we had Azure go down in the beginning of the last week. And then was it Friday,
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Thursday I don't know GitHub itself went down not the web page but all the git based operations
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like git pull git push etc that talked to GitHub over the git CLI and other tools was failing for
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hours and so my question or my question my joke is which is a question can GitHub even push fixes
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to GitHub when GitHub is down like what do they do how do you fix GitHub if git is surely and I'm
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oh my gosh surely there's a lot of ways that you deploy code within GitHub by doing some kind of
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git pull or git push with a webhook or something like that might have been a huge portion of the
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problem yeah yeah the fix was probably instantaneously known but you can't roll back
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or you know get rollbacks can't do that um exactly they're like how who is going to log in here and
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fix this i cannot fix this oh no i'm curious how they did it then i mean this is this funny but
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also it probably was an issue exactly so i thought we might have a um an educational joke this week
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can GitHub even push to GitHub when GitHub is down this looks like uh um who is the guy in the
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matrix neo who's that actor keanu reeves yeah this looks like a young keanu reeves i'm not sure who
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this actually is but from like bill and ted days yeah exactly whoa man how'd they even fix it
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anyway i don't know how they fixed it but when it came back i was very excited that it was back
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awesome all right well uh fun episode and i hope everybody everybody in the u.s or anybody that
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celebrates turkey day outside of the u.s i hope you have a wonderful exgiving and um and everybody
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else hope you have a great week also yeah thanks to you next week bye everyone bye