WEBVTT 00:00:00.001 --> 00:00:05.080 Hello and welcome to Python Bytes, where we deliver Python news and headlines directly to your earbuds. 00:00:05.080 --> 00:00:10.080 This is episode 149, recorded September 18th, 2019. 00:00:10.080 --> 00:00:11.080 I'm Michael Kennedy. 00:00:11.080 --> 00:00:12.120 And I am Brian Okken. 00:00:12.120 --> 00:00:13.800 And this episode is brought to you by Datadog. 00:00:13.800 --> 00:00:15.100 I'll tell you more about them later. 00:00:15.100 --> 00:00:25.240 Brian, this first item that you have here, it actually sparked some philosophical sort of challenge to my way of seeing the world here. 00:00:25.240 --> 00:00:28.280 So why don't you run it by and I'll tell you about my problem. 00:00:28.280 --> 00:00:29.440 Maybe you can help me through it. 00:00:29.440 --> 00:00:30.940 I'm curious about this now. 00:00:30.940 --> 00:00:31.340 Yes. 00:00:31.340 --> 00:00:44.180 I'm pretty sure we've covered this before, but Dropbox is kind of behind a lot of the push to do different type checking or type hinting and checking those type hints within Python. 00:00:44.180 --> 00:00:48.400 The mypy project is, I think, spearheaded by Dropbox. 00:00:48.400 --> 00:00:48.740 Yes. 00:00:48.740 --> 00:00:54.200 There's an article that they put out called Our Journey to Type Checking 4 Million Lines of Python. 00:00:54.200 --> 00:00:55.460 Wow. 00:00:55.460 --> 00:00:56.520 4 million lines. 00:00:56.520 --> 00:00:57.800 That's a big code base. 00:00:57.800 --> 00:00:59.300 That's a lot of Python. 00:00:59.380 --> 00:00:59.780 Yeah. 00:00:59.780 --> 00:01:01.660 I wonder how much of it's interconnected. 00:01:01.660 --> 00:01:06.660 You know, like, you've got all these little utilities and nothing actually depends on it directly. 00:01:06.660 --> 00:01:08.040 Maybe they depend on the output. 00:01:08.040 --> 00:01:14.300 On the other hand, there could be like a super complicated sort of monolith thing. 00:01:14.560 --> 00:01:16.320 It's interesting to think about that much code. 00:01:16.320 --> 00:01:17.200 That is a ton. 00:01:17.200 --> 00:01:19.980 They're leading a lot of stuff, but one of the – I like this. 00:01:19.980 --> 00:01:20.660 So why? 00:01:20.660 --> 00:01:21.780 I mean, that's not free. 00:01:21.780 --> 00:01:25.060 You don't have a huge code base and move it to type checking. 00:01:25.060 --> 00:01:26.840 You don't get that for free. 00:01:26.960 --> 00:01:27.960 So there has to be benefits to this cost. 00:01:27.960 --> 00:01:29.620 So there has to be benefits to this cost. 00:01:29.620 --> 00:01:31.000 And that's one of the things. 00:01:31.000 --> 00:01:37.180 So this article does talk about their – kind of their – does go through some of their story of how they did it. 00:01:37.540 --> 00:01:39.560 What I really liked is it covered some of the benefits. 00:01:39.560 --> 00:01:41.680 And this isn't even that surprising. 00:01:41.680 --> 00:01:51.420 It says experience tells us that understanding code becomes the key to maintaining developer productivity, and that grows with a larger code base. 00:01:51.420 --> 00:02:04.240 So without type of annotation, basic reasoning such as figuring out what the valid arguments to a function are or the return types – that's a key one for me – becomes kind of a hard problem. 00:02:04.240 --> 00:02:10.180 And just answering those questions quickly, more quickly, what does this function return? 00:02:10.180 --> 00:02:11.900 Does it return none sometimes? 00:02:11.900 --> 00:02:12.920 Can it return none? 00:02:12.920 --> 00:02:14.080 Things like that. 00:02:14.080 --> 00:02:17.860 These become more and more of a drain as you're looking at a larger code base. 00:02:17.860 --> 00:02:19.440 I mean, that's definitely true. 00:02:19.440 --> 00:02:21.600 You spend more time reading code than writing it. 00:02:21.600 --> 00:02:29.980 So thinking about the types as you're writing it and putting those in place, especially for interfaces to functions, those are an easy win. 00:02:29.980 --> 00:02:31.000 I like it. 00:02:31.000 --> 00:02:38.640 They talked about some of the other benefits that the type checker is actually finding subtle bugs that they wouldn't have caught easily without it. 00:02:39.260 --> 00:02:40.580 Refactoring becomes easier. 00:02:40.580 --> 00:02:48.080 And then running the type checking is faster than running the suite of unit tests, so the feedback can be faster. 00:02:48.080 --> 00:02:51.100 And I didn't think about that aspect of it. 00:02:51.100 --> 00:02:51.960 That's pretty interesting. 00:02:51.960 --> 00:02:54.960 To include type checking as part of like a TDD flow. 00:02:55.500 --> 00:02:57.080 I haven't tried that. 00:02:57.080 --> 00:02:57.960 That would be kind of fun. 00:02:57.960 --> 00:03:13.580 And then one of the things I do know is that the IDEs such as Visual Studio Code and PyCharm allow for better completion and static error checking and a whole bunch of goodies that you get from the IDEs if you have type hints in there. 00:03:14.180 --> 00:03:21.960 But anyway, the other part of the story that I think is they talk about is the improvements to mypy to fit their needs. 00:03:21.960 --> 00:03:28.360 And so if you like mypy now, it's probably it's because Dropbox needed it to be really good. 00:03:28.360 --> 00:03:30.240 So anyway, it's a good article. 00:03:30.420 --> 00:03:33.420 I'm a big fan of type hinting and stuff. 00:03:33.420 --> 00:03:37.340 I think it all these things here that you've laid out, I definitely think they're all true. 00:03:37.600 --> 00:03:43.200 I would say absolutely the biggest one for me is making the IDEs and the editors just better. 00:03:43.200 --> 00:03:52.640 When I get the return value function that declares its return type and I hit dot on that variable, boom, there's the list of the things that I can do. 00:03:52.640 --> 00:03:54.060 I type one or two characters. 00:03:54.060 --> 00:03:54.920 It autocompletes. 00:03:54.920 --> 00:03:56.100 I just, you know, just flow. 00:03:56.100 --> 00:03:57.600 And yes, it's in the docs. 00:03:57.600 --> 00:03:59.000 What comes back from some of these things? 00:03:59.000 --> 00:04:00.160 Yes, you can go look them up. 00:04:00.160 --> 00:04:03.960 What arguments or what operations you can do on them? 00:04:03.960 --> 00:04:11.780 But if it's one character or two typing and it's just always there, it just massively improves what you're doing and your confidence and the speed. 00:04:11.780 --> 00:04:13.300 And it doesn't take you out of that flow. 00:04:13.300 --> 00:04:15.060 And I really appreciate that aspect of it. 00:04:15.060 --> 00:04:21.580 One of the things I'm embracing more and more is things that can return multiple types because we definitely can do that in Python. 00:04:21.580 --> 00:04:32.380 So things that arguments that can be set to none but are either none or a Boolean or there can be an A element or a list of those types of elements. 00:04:32.820 --> 00:04:40.560 Those sorts of things are great because if they're one of the types most of the time, you don't even really think about making sure that it works for the other one. 00:04:40.560 --> 00:04:40.940 For sure. 00:04:40.940 --> 00:04:43.320 So you want to hear my philosophical dilemma? 00:04:43.320 --> 00:04:44.080 Yeah, I do. 00:04:44.080 --> 00:04:44.400 All right. 00:04:44.400 --> 00:04:52.720 So in that article, it says something to the effect of mypy is an open source project and the core team is employed by Dropbox. 00:04:53.420 --> 00:04:57.840 One of the people who is doing major work on this project is Guido Van Rossum. 00:04:57.840 --> 00:04:58.100 Yeah. 00:04:58.100 --> 00:04:58.440 Yeah. 00:04:58.440 --> 00:05:02.200 I think he did something in Python, like created things like that. 00:05:02.200 --> 00:05:02.400 Right. 00:05:02.400 --> 00:05:04.100 He created the language and whatnot. 00:05:04.100 --> 00:05:13.520 And it wasn't until, gosh, I don't know, well into the 2010s or something like that until type hinting became a thing in the language. 00:05:13.520 --> 00:05:15.700 So Python was created. 00:05:15.700 --> 00:05:20.500 Its sort of core essence is a language without type declarations. 00:05:20.500 --> 00:05:20.860 Right. 00:05:21.220 --> 00:05:22.720 So here's my philosophical debate. 00:05:22.720 --> 00:05:33.400 Like, would Guido have gone back and said in 1991, actually a little bit of type hints should have been how Python originally came into the world? 00:05:33.820 --> 00:05:42.540 Or is this something that you have to go through and you're like, oh, it's fine when you have 100 lines of code that don't have any type information. 00:05:42.540 --> 00:05:48.040 But if you have 4 million, all of a sudden you're in a bad place with 4 million and hundreds of people working on it. 00:05:48.040 --> 00:05:57.120 Well, all of a sudden these types now are super valuable because here he is working explicitly on this thing that, you know, he probably decided not to have in his original language. 00:05:57.120 --> 00:05:58.200 And there's my dilemma. 00:05:58.200 --> 00:06:00.120 I think it's the size thing. 00:06:00.600 --> 00:06:03.740 It's helpful for large projects for tiny little things. 00:06:03.740 --> 00:06:04.280 It's not. 00:06:04.280 --> 00:06:10.320 I mean, has it ever bothered you that there are no type declarations in Bash scripts? 00:06:10.320 --> 00:06:12.060 Yeah, not really, I guess. 00:06:12.060 --> 00:06:14.400 I don't do really huge Bash applications. 00:06:14.400 --> 00:06:18.700 Yeah, that's probably some form of anti-pattern right there, isn't it? 00:06:18.700 --> 00:06:20.180 Yeah, I don't know. 00:06:20.180 --> 00:06:21.620 Maybe it's also the tooling, right? 00:06:21.620 --> 00:06:24.600 Like the editors do a lot more with that information now. 00:06:24.600 --> 00:06:27.920 It is an interesting question of why didn't it have it to begin with? 00:06:28.060 --> 00:06:33.620 If someone else was working on this, sure, okay, these are two philosophies and they kind of come together or don't in different ways. 00:06:33.620 --> 00:06:35.120 But it's the same person, right? 00:06:35.120 --> 00:06:38.920 So that was my thought as I was looking through this article. 00:06:38.920 --> 00:06:39.300 Yeah. 00:06:39.300 --> 00:06:39.600 Yeah. 00:06:39.600 --> 00:06:39.960 But cool. 00:06:39.960 --> 00:06:41.260 I'm happy to see them doing it. 00:06:41.260 --> 00:06:44.820 And I like to bring this sort of stuff into my code as well. 00:06:44.820 --> 00:06:45.560 I think it makes it better. 00:06:45.560 --> 00:06:46.180 All right. 00:06:46.220 --> 00:06:47.320 Well, what do you got for us? 00:06:47.320 --> 00:06:54.020 I did mention that we have these editors these days that do so much more than they did in 1991. 00:06:54.020 --> 00:06:58.100 And namely, this would be PyCharm and Visual Studio Code, right? 00:06:58.100 --> 00:06:59.620 Those are the two main ones. 00:06:59.620 --> 00:07:00.700 Obviously, there's others. 00:07:00.700 --> 00:07:02.940 But these are the main ones that are like super rich, right? 00:07:02.940 --> 00:07:03.140 Yeah. 00:07:03.140 --> 00:07:13.440 Our friend Miguel Grimberg decided he was going to put together a cool video about setting up Visual Studio Code to work with a full-fledged Flask application. 00:07:13.640 --> 00:07:16.300 So with PyCharm, I think it's pretty straightforward, right? 00:07:16.300 --> 00:07:18.520 PyCharm kind of is what it is. 00:07:18.520 --> 00:07:20.920 You go in and like, all right, here's the project. 00:07:20.920 --> 00:07:21.720 I see that. 00:07:21.720 --> 00:07:23.220 Here's how I run stuff. 00:07:23.220 --> 00:07:26.540 Here's how – like it's sort of really clear what you do. 00:07:26.540 --> 00:07:28.600 There's a lot of stuff going on there and it's really busy. 00:07:28.600 --> 00:07:30.920 But you can look at it and see what you're supposed to do. 00:07:30.920 --> 00:07:32.720 With Visual Studio Code, I don't feel that way. 00:07:32.720 --> 00:07:38.380 I look at it and I go like, all right, I know that this thing can be configured and adapted to do all this amazing stuff. 00:07:38.580 --> 00:07:44.060 And it gives me no breadcrumbs or hints on how to even like take that first step. 00:07:44.060 --> 00:07:46.420 I'm like, man, I know this thing's cool, probably. 00:07:46.420 --> 00:07:49.620 But I'm just going to edit this file and go on, right? 00:07:49.620 --> 00:07:56.880 But this is a video that also has a blog post version from Miguel. 00:07:57.120 --> 00:08:00.720 And it's actually a follow-up to doing the same thing in PyCharm about a year ago. 00:08:00.720 --> 00:08:09.640 And I think the reason he did it in PyCharm, even though I just told you how easy it was, is he's doing it in PyCharm Community, which is not officially able to support web development. 00:08:09.640 --> 00:08:10.740 It's the free version. 00:08:10.740 --> 00:08:17.520 So he's like, how do you set up a web development project in a thing that's not meant for that or officially configured for that or whatever? 00:08:17.520 --> 00:08:20.500 Anyway, so it goes through and it sort of walks you through all the steps. 00:08:20.500 --> 00:08:21.480 And you know what? 00:08:21.480 --> 00:08:22.340 It's really nice. 00:08:22.340 --> 00:08:24.980 And I think the grand finale, you will appreciate it, Brian. 00:08:25.300 --> 00:08:28.440 So as I think a lot of people do, so here's what we're going to do. 00:08:28.440 --> 00:08:29.260 We're going to go set up. 00:08:29.260 --> 00:08:30.540 We're going to clone the repo. 00:08:30.540 --> 00:08:31.640 We're going to create a virtual environment. 00:08:31.640 --> 00:08:48.460 We're going to install the requirements and sort of configure environment variables, maybe run some custom flask commands like flask deploy, which initializes the database or does database migrations and all that kind of stuff in the terminal before we actually get to the editor. 00:08:48.460 --> 00:08:49.720 And this is how I work as well. 00:08:49.720 --> 00:08:50.440 How about you? 00:08:50.440 --> 00:08:53.480 Do you like start from within PyCharm or do you kind of get to it eventually? 00:08:53.480 --> 00:08:55.440 Oh, no, same thing. 00:08:55.440 --> 00:08:56.780 I'm setting up. 00:08:56.780 --> 00:09:04.460 Well, I've got little extra little hooks to create an environment and activate an environment because I'm doing that on the command lane all the time anyway. 00:09:04.460 --> 00:09:08.360 Like if I'm going to clone a repo and stuff, I'm just going to do that. 00:09:08.360 --> 00:09:08.880 Same. 00:09:08.880 --> 00:09:14.520 And I have all these aliases and stuff that will like do multiple steps at once and make it a little bit nicer and so on. 00:09:14.520 --> 00:09:15.000 All right. 00:09:15.000 --> 00:09:16.780 So all that is in terminal. 00:09:16.780 --> 00:09:18.500 But then he says, all right, here's what we're going to do in VS Code. 00:09:18.500 --> 00:09:24.700 You're going to open the folder, which is a thing you could do in VS Code, and it will automatically find the virtual environment. 00:09:24.700 --> 00:09:31.220 But in order for all that stuff to happen, you have to encourage Visual Studio Code to go into Python mode. 00:09:31.220 --> 00:09:32.980 So just open any Python file. 00:09:33.140 --> 00:09:41.900 And that like activates all the little subsystems that fire up like the environment variable detection and all that kind of stuff, the virtual environment detection and so on. 00:09:41.900 --> 00:09:45.760 And then he says, all right, now what we want to do is how do you run the thing? 00:09:45.760 --> 00:09:50.120 So he talks about how to set up a run configuration in the debugger. 00:09:50.120 --> 00:09:54.840 So you open the debugger tab, add a configuration, and you can actually pick Flask. 00:09:55.040 --> 00:09:56.280 And it knows all about Flask. 00:09:56.280 --> 00:09:59.260 It asks you a couple of questions like, well, what's the app PY called? 00:09:59.260 --> 00:10:01.220 And things like that. 00:10:01.220 --> 00:10:02.620 So then it'll set it all up. 00:10:02.620 --> 00:10:06.160 And then you can run it in the debugger or run it without, and that's pretty nice. 00:10:06.160 --> 00:10:12.820 And then it says, finally, there's another thing about this UI that, like I said, it's kind of like water, right? 00:10:12.820 --> 00:10:20.820 It can be whatever you want, but you don't look at water and go, I bet that could be like a sculpture of a seal if I froze it and carved it down, right? 00:10:21.420 --> 00:10:23.700 So it's our example, but yeah, sure, go on. 00:10:23.700 --> 00:10:24.080 Yeah, right. 00:10:24.080 --> 00:10:25.220 Like, okay, ice sculptures. 00:10:25.220 --> 00:10:28.600 So there's another command you can run in VS Code. 00:10:28.600 --> 00:10:31.920 And this I didn't know about is you can ask it to discover Python tests. 00:10:31.920 --> 00:10:32.760 That's nice. 00:10:32.760 --> 00:10:33.020 Yeah. 00:10:33.020 --> 00:10:37.780 So you can say discover Python tests, and it'll hunt through and find all the tests in your project. 00:10:37.780 --> 00:10:40.240 And it'll even offer what test frameworks you want to run. 00:10:40.240 --> 00:10:43.040 You want to run unit tests or PyCharm or whatever. 00:10:43.040 --> 00:10:49.280 And then once you do that, like a new UI element sort of pops up, and now you can run your tests in a pretty cool runner. 00:10:49.280 --> 00:10:51.240 So it's about a half hour video. 00:10:51.640 --> 00:10:52.640 It's good, I think. 00:10:52.640 --> 00:10:55.680 And there's something really nice about seeing it in action. 00:10:55.680 --> 00:11:01.260 I'm a big fan of learning through, you know, video stuff, as people might imagine, since I put some time and energy into it. 00:11:01.260 --> 00:11:02.880 But it's one thing to read it. 00:11:02.880 --> 00:11:07.860 It's another to, like, see just that sort of process gone through and explain step by step. 00:11:07.860 --> 00:11:10.520 And Miguel does a good job, and I like it. 00:11:10.520 --> 00:11:18.680 At the end, he also talks about a limitation of handling crashing Flask applications with a debugger. 00:11:18.680 --> 00:11:21.800 And he says it's a Flask thing, not a VS Code thing. 00:11:21.800 --> 00:11:24.360 So you have to do it in both PyCharm and VS Code. 00:11:24.360 --> 00:11:26.480 But he shows you the little workaround. 00:11:26.480 --> 00:11:38.020 Yeah, basically you have to stop going through the Flask run option and go to the Flask.py or app.py, run it, and then override some settings in the run there. 00:11:38.020 --> 00:11:38.840 So yeah, it's pretty straightforward. 00:11:38.840 --> 00:11:40.220 But that's definitely a nice touch as well. 00:11:40.300 --> 00:11:47.460 Yeah, and then the other thing I wanted to touch on is when he's showing how to run tests in the video, they're just sort of magically running in the background. 00:11:47.460 --> 00:11:49.980 And you don't see what they're doing. 00:11:49.980 --> 00:11:59.120 And he doesn't cover this, but at the bottom of the screen or at the bottom of your VS Code window, there's some icons that show you the status of the tests. 00:11:59.120 --> 00:12:03.920 And if you click on that, that's where you go look at the output and look at the failures and whatever. 00:12:03.920 --> 00:12:04.740 Yeah, very cool. 00:12:04.740 --> 00:12:05.180 Nice. 00:12:05.180 --> 00:12:06.480 So that's a good one. 00:12:06.480 --> 00:12:09.720 Another thing that I'm a big fan of is parallel programming. 00:12:09.720 --> 00:12:12.580 And you've got a few things on that one for us, huh? 00:12:12.580 --> 00:12:19.580 There's an article called Multiprocessing vs. Threading in Python, What Every Data Scientist Needs to Know. 00:12:19.580 --> 00:12:22.880 It talked about multiprocessing and threading. 00:12:22.880 --> 00:12:24.620 It did not talk about async. 00:12:24.620 --> 00:12:32.860 And I don't know if that's appropriate or not, if async is even something that would be useful for data science or not. 00:12:32.860 --> 00:12:33.260 Sometimes. 00:12:33.260 --> 00:12:34.820 Not computationally, though. 00:12:34.820 --> 00:12:36.300 In any case, I liked it. 00:12:36.300 --> 00:12:39.260 Because a lot of people from data science are coming into programming. 00:12:39.260 --> 00:12:41.920 Like we know, they're coming in not as programmers. 00:12:41.920 --> 00:12:43.440 They're coming in from other fields. 00:12:43.440 --> 00:12:48.840 So there's a lot of background computer science knowledge that they just don't have. 00:12:48.840 --> 00:12:50.900 Or, you know, there might be gaps. 00:12:50.900 --> 00:12:53.440 So that's one of the reasons why I picked this, because I like it. 00:12:53.820 --> 00:13:00.500 I like that it talked about some of the basic concepts of parallelism, parallel computing, how to think about it. 00:13:00.500 --> 00:13:01.560 It has some diagrams. 00:13:01.560 --> 00:13:07.020 And then what the difference between multiprocessing and threading is in general. 00:13:07.020 --> 00:13:11.240 Specifically, threading is within one process. 00:13:11.660 --> 00:13:13.540 You've got a bunch of stuff going on. 00:13:13.540 --> 00:13:16.460 And multiprocessing is you get a bunch of processes. 00:13:16.460 --> 00:13:17.480 But there's tradeoffs. 00:13:18.120 --> 00:13:23.560 And then it also talks about specifically that Python has a GIL, so it's a little different. 00:13:23.560 --> 00:13:27.920 But because of the GIL, so, you know, it talks about that threads wait on. 00:13:27.920 --> 00:13:29.540 You can use either one. 00:13:29.540 --> 00:13:33.420 But in general, the general rule of thumb is CPU intensive work. 00:13:33.420 --> 00:13:35.640 You need multiprocessing. 00:13:35.800 --> 00:13:41.760 If you're IO bound or waiting on users, then threads are fine for that. 00:13:41.760 --> 00:13:47.380 So the surprising bit to me was the charts and some of the graphs that he has. 00:13:47.380 --> 00:13:57.620 Because he sort of does some benchmarks of code, running something on, like, both CPU intensive and IO intensive work. 00:13:57.620 --> 00:14:01.800 And how it speeds up with multiprocessing and multithreading. 00:14:02.240 --> 00:14:07.240 Obviously, throwing more processors at it helps. 00:14:07.240 --> 00:14:08.800 Or more threads. 00:14:08.800 --> 00:14:13.340 But what surprised me is that the difference between the two wasn't really that great. 00:14:13.340 --> 00:14:15.540 I thought it would be more pronounced. 00:14:15.540 --> 00:14:19.340 Basically, if you're not sure which one to use, pick one. 00:14:19.340 --> 00:14:20.780 And it'll speed up your code. 00:14:20.780 --> 00:14:22.700 Interesting, yeah. 00:14:22.700 --> 00:14:28.120 I kind of thought it would be, even with CPU intensive stuff, at least with stuff he was showing, 00:14:28.120 --> 00:14:31.540 that even multithreading helped speed things up. 00:14:31.540 --> 00:14:33.060 So I think this is good. 00:14:33.060 --> 00:14:36.860 And then he goes through a couple of data, specifically data science examples, 00:14:36.860 --> 00:14:42.720 and shows the code and how to throw multiprocessing and multithreading at data science problems. 00:14:42.720 --> 00:14:43.860 That sounds super useful. 00:14:43.860 --> 00:14:45.560 And the comparisons are interesting. 00:14:45.560 --> 00:14:51.260 These benchmarks are always so full of landmines and special cases. 00:14:51.260 --> 00:14:52.720 And I didn't use it that way. 00:14:52.720 --> 00:14:54.780 So I didn't get the right results that you said. 00:14:54.780 --> 00:14:56.700 You know, like, they're just so tricky to get them right. 00:14:56.700 --> 00:14:58.720 But it is cool to have them here. 00:14:58.720 --> 00:14:59.660 I like that a lot. 00:14:59.660 --> 00:15:03.260 One thing I would like to throw out there is, you know, a lot of times you have these sort of, 00:15:03.260 --> 00:15:05.780 I could do it this way or I could do it that way. 00:15:05.780 --> 00:15:06.860 And we'll see what we get. 00:15:06.860 --> 00:15:09.140 And then sometimes it's this, sometimes it's that. 00:15:09.140 --> 00:15:11.460 So now you've got to know two APIs and how you combine them. 00:15:11.820 --> 00:15:26.240 And I'm a big fan of the unsync, U-N-S-Y-N-C library, which takes the async programming model and applies it to multiprocessing to threads and async methods and makes it all nice and clean. 00:15:26.240 --> 00:15:28.680 Just a couple of decorators and they're all the same. 00:15:28.680 --> 00:15:29.880 So do you still have to pick? 00:15:29.880 --> 00:15:32.560 You have to pick at the implementation level. 00:15:32.560 --> 00:15:34.100 So imagine you have three functions. 00:15:34.440 --> 00:15:38.600 One of them is async because it actually implements async await it uses them. 00:15:38.600 --> 00:15:41.720 One is just a regular function you'd like to run on a thread. 00:15:41.720 --> 00:15:43.000 One is a regular function. 00:15:43.000 --> 00:15:46.860 Sorry, one is a function that does computational stuff and one does a weighting. 00:15:46.860 --> 00:15:48.460 So you just put a decorator. 00:15:48.460 --> 00:15:51.500 You say at unsync on the regular async one. 00:15:51.500 --> 00:15:52.600 That will run on asyncio. 00:15:52.600 --> 00:16:00.540 On the one that's doing weighting stuff that would work for threads, you just say at unsync and it automatically runs on threads if it's not an async method. 00:16:00.540 --> 00:16:05.200 In the last one, you would say at unsync CPU bound equals true. 00:16:05.200 --> 00:16:13.180 But then once you consume those, the way you program against it, they're all the same regardless of which style it is. 00:16:13.180 --> 00:16:15.740 So it's like when you define the function, like, oh, this is a CPU bound one. 00:16:15.740 --> 00:16:17.340 Oh, this one is actually async. 00:16:17.340 --> 00:16:19.900 So it just is async and it just adapts. 00:16:19.900 --> 00:16:21.900 It's a pretty cool library. 00:16:22.020 --> 00:16:25.020 It's 126 lines of Python in one file. 00:16:25.020 --> 00:16:27.580 And it does all that to unify all these APIs. 00:16:27.580 --> 00:16:27.980 It's great. 00:16:27.980 --> 00:16:28.640 Wow, that's cool. 00:16:28.640 --> 00:16:29.400 Yeah, so pretty cool. 00:16:29.400 --> 00:16:33.720 Anyway, yeah, this is really nice and certainly something people want to think about. 00:16:33.720 --> 00:16:35.060 It's a little bit tricky. 00:16:35.060 --> 00:16:38.820 We'll see if this is still a discussion in a couple of years, right? 00:16:38.820 --> 00:16:47.180 In Python 3.9, there's talk of maybe using subinterpreters to remove the limitation of the GIL inside of single processes and all sorts of stuff. 00:16:47.180 --> 00:16:48.840 Eric Snow is working on that. 00:16:48.960 --> 00:16:57.980 So if they actually got that working, then you'd probably be better because you can share data better, more richly and faster within a single process. 00:16:57.980 --> 00:17:01.460 And it's about to get even more crazy. 00:17:01.460 --> 00:17:03.000 That's a long discussion. 00:17:03.000 --> 00:17:03.620 Yeah. 00:17:03.620 --> 00:17:08.360 How much more do you have to care about, like, blocking and stuff like that? 00:17:08.360 --> 00:17:08.560 Yeah. 00:17:08.560 --> 00:17:12.080 Yeah, it brings all that stuff back in because you don't have the GIL anymore. 00:17:12.080 --> 00:17:18.220 Actually, with the subinterpreters, they're talking about a mechanism to explicitly share data in a safe way between them. 00:17:18.220 --> 00:17:20.260 So still, it's faster, though. 00:17:20.260 --> 00:17:20.500 Okay. 00:17:20.500 --> 00:17:20.760 Cool. 00:17:20.760 --> 00:17:34.200 Well, speaking of making things faster, you know, if you're looking at your app and you wonder what's going on, it would be nice to see everything that's going on across all the layers, across the database, across the web tier, things like that. 00:17:34.820 --> 00:17:36.180 So you should check out Datadog. 00:17:36.180 --> 00:17:37.160 They're sponsoring this episode. 00:17:37.160 --> 00:17:44.760 It's a modern cloud monitoring and cloud scale monitoring platform that brings together metrics and logs and distributed traces all in one place. 00:17:44.760 --> 00:17:52.340 So it auto instruments things like Django and Flask and Postgres means you get to see everything across all those boundaries. 00:17:52.680 --> 00:17:55.640 And it helps you optimize your Python apps in just a few minutes. 00:17:55.640 --> 00:17:59.360 Start monitoring your environment for free and get a sweet Datadog t-shirt. 00:17:59.360 --> 00:18:02.220 Just visit pythonbytes.fm/Datadog to get started. 00:18:02.220 --> 00:18:02.680 Nice. 00:18:02.680 --> 00:18:06.500 Well, not to be outdone by your async stuff. 00:18:06.500 --> 00:18:09.500 I also chose some async stuff here. 00:18:09.500 --> 00:18:13.620 So remember, we talked about Starlette a little while ago. 00:18:13.620 --> 00:18:18.560 And Starlette comes from this GitHub organization called Encode, E-N-C-O-D. 00:18:18.560 --> 00:18:21.720 And that place is full of magic. 00:18:21.840 --> 00:18:25.640 So they have UVicorn, which is the ASGI server. 00:18:25.640 --> 00:18:27.180 That's pretty awesome, like G-Unicorn. 00:18:27.180 --> 00:18:33.700 But for async based on the uv event loop, uv loop, event loop, and so on. 00:18:33.700 --> 00:18:34.780 And there's Starlet. 00:18:34.780 --> 00:18:35.960 There's also a Django REST framework. 00:18:35.960 --> 00:18:38.840 But there's HTTPX, which we talked about last time. 00:18:38.840 --> 00:18:42.200 And the last thing I want to just cover is a few more things in here. 00:18:42.200 --> 00:18:43.960 Because like I said, there's a lot of great stuff. 00:18:43.960 --> 00:18:47.520 There's a project just simply called ORM. 00:18:47.520 --> 00:18:50.120 We've got SQLAlchemy and Django ORM. 00:18:51.000 --> 00:18:52.620 And these guys just said, you know what? 00:18:52.620 --> 00:18:55.440 Well, just the term ORM is just free in Python. 00:18:55.440 --> 00:18:56.400 So let's just do that. 00:18:56.400 --> 00:18:59.780 Which is an async ORM. 00:18:59.780 --> 00:19:07.100 And they also have a thing called databases, which adds async support for talking to all these different databases. 00:19:07.520 --> 00:19:09.080 Postgres and whatnot. 00:19:09.080 --> 00:19:13.220 So this is a really cool project, especially this ORM one. 00:19:13.220 --> 00:19:16.460 Because it's kind of like SQLAlchemy. 00:19:16.460 --> 00:19:20.300 And it's actually based on the SQLAlchemy core for building queries. 00:19:21.140 --> 00:19:23.100 And that gives you a bunch of benefits, right? 00:19:23.100 --> 00:19:27.460 That means if you already have some stuff that works with SQLAlchemy, to some degree it will be similar. 00:19:27.460 --> 00:19:36.260 It means that Alembic, which is the tool to do database migrations on SQLAlchemy, also works with this ORM. 00:19:36.260 --> 00:19:38.800 So you can automatically just apply Alembic to it. 00:19:38.800 --> 00:19:39.420 And that's pretty cool. 00:19:39.420 --> 00:19:39.840 Wow. 00:19:39.980 --> 00:19:45.420 Yeah, it uses this database project that I talked about for cross-database async support. 00:19:45.420 --> 00:19:49.840 And it also has this thing called type system for data validation, which is pretty cool. 00:19:49.840 --> 00:19:50.660 I hadn't heard of that either. 00:19:50.660 --> 00:19:59.000 But yeah, it's a really sweet async API for working with databases and ORMs. 00:19:59.000 --> 00:20:01.880 So the way you create the models, it's very similar to SQLAlchemy. 00:20:01.880 --> 00:20:03.420 It's not identical, but it's similar. 00:20:04.060 --> 00:20:10.680 And then from there on, you just work with it kind of like you would do normal ORM stuff, right? 00:20:10.680 --> 00:20:16.340 Like I would say, if I'm working on an album, I might say album.objects.create. 00:20:16.340 --> 00:20:19.300 Or maybe I would do some kind of filter. 00:20:19.300 --> 00:20:22.740 So I'd say track.objects.filter, and I would do something. 00:20:22.740 --> 00:20:25.260 But every one of these operations is async. 00:20:25.260 --> 00:20:26.940 So you just put a weight in front of it. 00:20:26.940 --> 00:20:33.480 And if you have something you've got to scale, a whole lot of concurrent data traffic, like say a website, 00:20:33.480 --> 00:20:35.960 well, this is a pretty good combo. 00:20:35.960 --> 00:20:41.260 So like in the future, will we just have a weight in front of every other word? 00:20:41.260 --> 00:20:42.120 Everything. 00:20:42.120 --> 00:20:42.980 Exactly. 00:20:42.980 --> 00:20:47.560 So I was going to point out that you've got to be pretty async and await savvy to be doing it. 00:20:47.560 --> 00:20:50.700 Like there's a lot of awaiting, isn't there? 00:20:50.700 --> 00:20:52.240 Yeah. 00:20:52.240 --> 00:20:56.560 I think if you want to work with this library, you just have to say, we're just going all in on async. 00:20:56.560 --> 00:20:58.260 And that's the way it goes, right? 00:20:58.260 --> 00:20:59.020 No, it's good. 00:20:59.020 --> 00:21:01.400 If you're already working with async, that's when you would think, 00:21:01.560 --> 00:21:04.540 hey, I wonder if there's an async ORM that I can use. 00:21:04.540 --> 00:21:05.240 Yeah. 00:21:05.240 --> 00:21:06.240 Yeah, it looks good. 00:21:06.240 --> 00:21:08.200 And I like that it's based on SQLAlchemy Core. 00:21:08.200 --> 00:21:16.940 That means a big chunk of like the database conversation and the, say, the table creation and the migrations, 00:21:16.940 --> 00:21:20.360 all that stuff is already known and proven and working really well. 00:21:20.360 --> 00:21:27.560 It's just this API kind of site around the side of the traditional SQLAlchemy conversation, 00:21:27.560 --> 00:21:28.920 like directly with the database. 00:21:29.240 --> 00:21:32.320 I do wish that SQLAlchemy would take this approach. 00:21:32.320 --> 00:21:34.720 I interviewed Mike Bayer about it a long time ago. 00:21:34.720 --> 00:21:39.280 And like four years ago, he said, I don't really think it's going to make that big of a difference. 00:21:39.280 --> 00:21:42.660 But I think it actually would make a huge difference. 00:21:42.660 --> 00:21:45.500 You just got to think about, you know, what is your goal, right? 00:21:45.500 --> 00:21:48.040 If your goal is performance, it probably won't make a big difference. 00:21:48.040 --> 00:21:52.900 If your goal is scalability, it can make a tremendous difference, right? 00:21:52.900 --> 00:21:56.720 Are you trying to make an individual user's experience a little bit faster? 00:21:56.720 --> 00:22:02.700 Or are you trying to make the website not take 10 concurrent users, but 10,000, right? 00:22:02.700 --> 00:22:09.540 Like it probably might even make it a tiny bit slower for that one person, but it might make that 10 to 10,000 like no big deal. 00:22:09.540 --> 00:22:11.500 So it depends on what you're after, right? 00:22:11.500 --> 00:22:12.280 Yeah, definitely. 00:22:12.460 --> 00:22:14.200 Speaking of what you're after, what's next for us? 00:22:14.200 --> 00:22:19.340 One of the things you might be after is some data on somebody else's website, like through an API. 00:22:19.340 --> 00:22:19.900 Yes. 00:22:19.900 --> 00:22:20.840 There's more and more people. 00:22:20.840 --> 00:22:28.340 And I think it's great kind of doing the data science stuff of people coming into Python and programming from just trying to get their work done. 00:22:29.100 --> 00:22:34.900 And this is a dataquest.io blog post called Getting Started with APIs. 00:22:34.900 --> 00:22:37.880 And it's not getting started writing APIs. 00:22:37.880 --> 00:22:41.240 It's getting started consuming them with Python. 00:22:41.240 --> 00:22:50.100 If you kind of know what all this stuff is, but you haven't really thought about the basics, that's why I picked up this post is because it's really good with the basics. 00:22:50.100 --> 00:23:20.080 It's really good with the basics, right? 00:23:20.080 --> 00:23:21.440 If you want to specify it. 00:23:21.440 --> 00:23:28.740 So with APIs, you can have parameters to your queries to say, I only want the data for this user. 00:23:28.740 --> 00:23:33.260 Or they gave an example of Spotify music or something. 00:23:33.260 --> 00:23:41.660 You don't want to have all the data for all the songs that Spotify knows about, but maybe just the songs from a particular artist or something. 00:23:41.660 --> 00:23:44.200 So things like that are good. 00:23:44.200 --> 00:23:48.220 But this is actually the first time I've seen this, and they're probably all over the place. 00:23:48.220 --> 00:23:55.280 But talked about status codes, especially git status codes, because that's what we're doing here is retrieving things. 00:23:55.280 --> 00:24:06.900 And had a nice list of all the descriptions and things that you might run into for error codes, including like the 301, which isn't necessarily a problem, but you're getting redirected. 00:24:07.060 --> 00:24:08.800 So maybe you want to know about that. 00:24:08.800 --> 00:24:14.020 And then the 400 is something's not wrong on their end. 00:24:14.020 --> 00:24:15.200 It's wrong on your end. 00:24:15.200 --> 00:24:18.320 It's the server thinks you made a bad request. 00:24:18.800 --> 00:24:24.760 So that might be an endpoint that expects data or parameters, but you didn't send any parameters with it. 00:24:24.760 --> 00:24:27.480 Or you sent an end when it expected a string or whatever. 00:24:27.480 --> 00:24:34.900 And then, you know, it talks about endpoints and endpoints that take query parameters, endpoints being the specific API. 00:24:35.180 --> 00:24:40.040 So we think of a service providing an API, but it's usually not just one API. 00:24:40.040 --> 00:24:49.840 It's usually a whole bunch of related different bits of data that you can query together or query separately for different aspects of it. 00:24:49.840 --> 00:24:53.800 And then, of course, what APIs usually return is JSON data. 00:24:53.920 --> 00:24:57.200 So it has a little bit of an explanation for what JSON looks like. 00:24:57.200 --> 00:25:04.340 And then using the JSON module to convert back and forth between native Python stuff and JSON. 00:25:04.340 --> 00:25:08.280 And it also talks about requests and a bunch of examples for how to pull this. 00:25:08.280 --> 00:25:15.100 So if you're getting started trying to pull some data from an API somewhere, this is a good way to get started. 00:25:15.100 --> 00:25:18.200 It's a nice blend of theory and steps, right? 00:25:18.200 --> 00:25:20.500 It doesn't just say, well, you open up requests and you do this. 00:25:20.500 --> 00:25:22.860 It's like, here's what an API is. 00:25:22.920 --> 00:25:24.620 Here's what the HTTP verbs mean. 00:25:24.620 --> 00:25:27.120 Here's what the status codes are. 00:25:27.120 --> 00:25:28.180 Here's how you get to that. 00:25:28.180 --> 00:25:31.260 And, you know, how do you, like, manifest that in Python and stuff? 00:25:31.260 --> 00:25:31.760 Yeah, it's nice. 00:25:31.760 --> 00:25:35.360 Yeah, but it's not at the level of, like, a college course lecture. 00:25:35.360 --> 00:25:38.500 It's just enough to get the concepts right. 00:25:38.500 --> 00:25:38.960 Exactly. 00:25:38.960 --> 00:25:44.040 It's not trying to make you read the restful dissertations and things like that. 00:25:44.040 --> 00:25:47.220 Yeah, I don't even know if it mentions rest, even though that's what we're talking about. 00:25:47.220 --> 00:25:47.480 Cool. 00:25:47.480 --> 00:25:48.680 That's probably a good thing. 00:25:48.680 --> 00:25:50.320 That was overdone for a while. 00:25:50.700 --> 00:25:53.680 Now, last thing I want to cover is memory management in Python. 00:25:53.680 --> 00:25:56.440 This is an article entitled Memory Management in Python. 00:25:56.440 --> 00:26:02.100 But what it really is, is it's a narrow slice, but a common slice of memory management in Python. 00:26:02.100 --> 00:26:05.080 So you probably don't think about memory very much in Python, huh, Brian? 00:26:05.080 --> 00:26:06.320 No, I usually forget about it. 00:26:06.320 --> 00:26:07.640 Yeah, just forget about it. 00:26:07.640 --> 00:26:08.320 That's right. 00:26:09.260 --> 00:26:14.300 So you don't use malloc or free or new or any of these things. 00:26:14.300 --> 00:26:15.680 Definitely not delete. 00:26:15.680 --> 00:26:19.520 If you use delete, it means something else, sort of, and things like that, right? 00:26:19.520 --> 00:26:19.940 Yeah. 00:26:20.040 --> 00:26:30.780 So I think it's actually pretty interesting that the story of understanding how the runtime experience is in CPython is kind of opaque a little bit, right? 00:26:30.780 --> 00:26:37.120 There's not a lot written about memory management, which is why I decided to pick this thing and talk a little bit about what it covers. 00:26:37.120 --> 00:26:43.000 Because I think it doesn't really matter that you know this in some sense, right? 00:26:43.000 --> 00:26:54.220 Like your Python code will still work, but you more closely understand what your code is doing, how that might map over to like CPU architectures and caches and RAM and all that kind of stuff. 00:26:54.280 --> 00:26:58.000 And, you know, just having a high-level understanding of that's good. 00:26:58.000 --> 00:27:04.400 Yeah, so here's a pretty deep, detailed article, not too long, get to it pretty quick, about memory management in Python. 00:27:04.400 --> 00:27:07.440 But it only covers, like I said, a little bit. 00:27:07.440 --> 00:27:14.440 It's really about how does small object allocation and deallocation happen in Python. 00:27:14.440 --> 00:27:17.860 It doesn't talk about the gill, which is about thread safety and memory allocation. 00:27:17.860 --> 00:27:19.800 It doesn't talk about reference counts. 00:27:19.920 --> 00:27:24.740 It doesn't talk about garbage collection for cycles or much else. 00:27:24.740 --> 00:27:26.500 So it's all about small objects. 00:27:26.500 --> 00:27:29.800 But most things we make in Python are small objects. 00:27:29.800 --> 00:27:33.420 Even when they're big, they're really just a bunch of small things all pointed at each other, right? 00:27:33.420 --> 00:27:39.680 So if I've got like a list of a million items, I don't have each of those items is 10 bytes. 00:27:39.680 --> 00:27:41.340 I don't have 10 million bytes. 00:27:41.340 --> 00:27:44.900 I have this big list with a bunch of things. 00:27:44.900 --> 00:27:49.140 But then each one of those is a pointer out to its actual thing that it is, right? 00:27:49.700 --> 00:27:53.520 Even when you have strings or even numbers, right? 00:27:53.520 --> 00:27:58.280 A lot of languages, numbers are allocated on the stack and treated as value types and stuff. 00:27:58.280 --> 00:28:00.520 But, you know, everything is an object. 00:28:00.520 --> 00:28:03.560 So every little thing that you make has to get allocated and deallocated. 00:28:03.560 --> 00:28:08.440 So understanding how these small objects get allocated, that's pretty interesting. 00:28:08.440 --> 00:28:10.000 So that's what this article talks about. 00:28:10.000 --> 00:28:13.200 So I'll try to like summarize some of the stuff covered there. 00:28:13.200 --> 00:28:18.700 One of the problems you have with memory allocation is that memory can get super fragmented, right? 00:28:18.740 --> 00:28:27.060 If I just allocate a bunch of stuff and then delete it and keep allocating it and just let that grow, you know, just keep adding on the end, wherever the memory is. 00:28:27.060 --> 00:28:28.840 And I want to interact with that. 00:28:28.840 --> 00:28:34.500 That can really mess up like reading from RAM and getting stuff on cache to be high performance and stuff like that, right? 00:28:34.700 --> 00:28:55.380 So what Python does is it actually pre-allocates these little 256K chunks and then it partitions those up and it plucks in the small objects into those spaces and then will potentially take them back out and then reuse those spaces that it had already allocated when it needs to make a new small thing. 00:28:55.880 --> 00:28:55.960 Okay. 00:28:55.960 --> 00:28:56.260 Okay. 00:28:56.260 --> 00:28:56.660 All right. 00:28:56.660 --> 00:29:03.880 So that's supposed to help with memory optimization, the locality stuff, the fragmentation and so on. 00:29:03.880 --> 00:29:10.420 So there's a special memory manager in Python called PyMalloc, general purpose allocator. 00:29:10.420 --> 00:29:14.800 On top of like C malloc, there's a Python allocator, right? 00:29:14.820 --> 00:29:19.120 So there's like this layer, we have RAM, we have the operating systems, virtual memory management. 00:29:19.120 --> 00:29:20.820 We have C's malloc. 00:29:20.820 --> 00:29:23.960 We have this PyMem, PyMalloc thing. 00:29:23.960 --> 00:29:29.360 We have the Python object allocator that then figures out where to place these things and we actually have object memory. 00:29:29.360 --> 00:29:33.940 So there's a lot of stuff going on here and they break it into three levels of organization. 00:29:34.780 --> 00:29:35.260 Okay. 00:29:35.260 --> 00:29:41.600 So for small objects, which are things that are individually smaller than 512 bytes, right? 00:29:41.600 --> 00:29:45.540 Not like maybe a list that has a bunch of stuff, but each, each little bit smaller, right? 00:29:45.540 --> 00:29:48.440 So those are the things we're talking about. 00:29:48.440 --> 00:29:54.760 And what happens is it gets broken into these three things called the block, the pool and the arena. 00:29:54.760 --> 00:30:02.940 So a block is a chunk of memory of a certain size and it only holds Python objects of a certain size. 00:30:03.160 --> 00:30:07.780 So maybe there's a block that holds 16 byte Python things. 00:30:07.780 --> 00:30:08.380 Yeah. 00:30:08.380 --> 00:30:08.720 Okay. 00:30:08.720 --> 00:30:09.560 It's weird. 00:30:09.560 --> 00:30:10.440 Yeah. 00:30:10.440 --> 00:30:17.000 So the reason is, is Python can then, it knows how to exactly fill up and then reuse those blocks. 00:30:17.000 --> 00:30:17.660 Oh yeah. 00:30:17.660 --> 00:30:18.140 Right. 00:30:18.140 --> 00:30:23.360 So if it's like, oh, I'm going to get a bunch of numbers, all the numbers are the same size unless they become utterly huge. 00:30:23.360 --> 00:30:26.100 So we can just like allocate them into the spot. 00:30:26.100 --> 00:30:27.120 Some of those numbers go away. 00:30:27.120 --> 00:30:27.860 We got another block. 00:30:27.860 --> 00:30:32.280 We dropped that new number pointer in right there or the number, which we then point at right there and so on. 00:30:32.600 --> 00:30:34.260 So there's these different blocks. 00:30:34.260 --> 00:30:39.120 Each one is a uniform size between eight and 512 bytes. 00:30:39.120 --> 00:30:45.760 And then the blocks are managed by this thing called a pool, which is usually limited to a memory page size. 00:30:45.760 --> 00:30:47.020 So four kilobytes. 00:30:47.020 --> 00:30:51.500 And then the pools are managed as these things called arenas. 00:30:51.760 --> 00:30:54.120 And these are the things that are allocated on the heap. 00:30:54.120 --> 00:31:04.320 I believe they are 256K pieces of memory, which hold 64 pools, which hold some number of blocks and things like that. 00:31:04.320 --> 00:31:04.520 Right. 00:31:04.520 --> 00:31:14.860 So there's this really intricate way in which memory is trying to be grouped together and then also trying to be reused without reallocating it from the operating system. 00:31:14.860 --> 00:31:15.160 Okay. 00:31:15.160 --> 00:31:15.440 Right. 00:31:15.440 --> 00:31:23.140 So even though Python might new up a bunch of objects, it actually says, well, but we already have this block that holds those size of things. 00:31:23.140 --> 00:31:24.160 And there's some spots in there. 00:31:24.160 --> 00:31:25.340 So let's fill that bad boy up. 00:31:25.340 --> 00:31:25.920 Oh, all right. 00:31:25.920 --> 00:31:26.160 Yeah. 00:31:26.160 --> 00:31:33.000 Anyway, so it's pretty interesting how all the stuff is working together, but that's the Python small object allocator. 00:31:33.000 --> 00:31:35.940 Never thought about that before, but kind of interesting. 00:31:35.940 --> 00:31:41.420 Also, I'm trying to visualize like a sports arena with 64 swimming pools in it. 00:31:41.420 --> 00:31:43.440 That's not a bad one. 00:31:43.440 --> 00:31:48.900 And then each pool is filled with exactly the same size people or creatures swimming around, something like that. 00:31:48.900 --> 00:31:49.200 Yeah. 00:31:49.200 --> 00:31:49.780 Yeah, there you go. 00:31:49.780 --> 00:31:50.560 That makes a lot of sense. 00:31:50.560 --> 00:31:52.820 The first part of it totally made sense. 00:31:52.820 --> 00:31:54.620 The last bit, maybe not so much. 00:31:54.620 --> 00:31:54.880 All right. 00:31:54.940 --> 00:32:04.020 Well, anyway, what I like about this article is it seems like it has a lot of stuff from like, here's the actual C code that defines what an arena is. 00:32:04.020 --> 00:32:06.800 Here you can see it's like a doubly linked list and how it all fits together. 00:32:06.800 --> 00:32:08.360 And it's just got some good analysis. 00:32:08.360 --> 00:32:11.060 So have a look if you've wondered about this. 00:32:11.060 --> 00:32:11.840 All right. 00:32:11.840 --> 00:32:12.980 Well, that's it for our main items. 00:32:12.980 --> 00:32:18.740 I know, Brian, you have big news for the entire world if they live near Portland. 00:32:18.740 --> 00:32:22.360 If they live in Portland or really close to Portland. 00:32:22.360 --> 00:32:23.580 Or want to come to Portland. 00:32:23.960 --> 00:32:29.020 September 26th, I'll be speaking downtown at the Portland Python user group. 00:32:29.020 --> 00:32:32.240 And I'm still working on my talk, but I'll be there. 00:32:32.240 --> 00:32:33.400 That'll be fun. 00:32:33.400 --> 00:32:35.780 And then I'll probably polish it more. 00:32:35.780 --> 00:32:38.680 And people have to volunteer for this other talk. 00:32:38.680 --> 00:32:46.540 So on October 6th, it's the inaugural first day of meeting the Python PDX West. 00:32:46.540 --> 00:32:50.500 So we've got a new user group for Python in town. 00:32:50.500 --> 00:32:52.660 I'm hosting it along with you. 00:32:52.660 --> 00:32:53.240 Yeah, it'll be fun. 00:32:53.240 --> 00:32:54.380 I'm really looking forward to it. 00:32:54.380 --> 00:32:54.660 Yeah. 00:32:54.660 --> 00:32:55.860 And you'll be speaking there. 00:32:55.860 --> 00:32:56.280 I will. 00:32:56.280 --> 00:32:58.720 And I'm trying to get other people to volunteer to speak. 00:32:58.720 --> 00:33:01.220 And if they don't, then it'll just be you and me speaking. 00:33:01.220 --> 00:33:02.540 But I think it'll be fun. 00:33:02.820 --> 00:33:05.140 So we've got a bunch of people signed up so far. 00:33:05.140 --> 00:33:07.100 So it's filling up fast. 00:33:07.100 --> 00:33:08.300 People should sign up. 00:33:08.300 --> 00:33:08.640 That's cool. 00:33:08.640 --> 00:33:12.660 Maybe we could do a live Python Bytes sometime there as well at the end of the day or something. 00:33:12.660 --> 00:33:13.140 Who knows? 00:33:13.140 --> 00:33:14.040 That's a great idea. 00:33:14.040 --> 00:33:14.500 Yeah. 00:33:14.500 --> 00:33:14.940 We could have. 00:33:15.020 --> 00:33:18.020 Maybe not Tuesday, October 6th, but maybe someday we can make that happen. 00:33:18.020 --> 00:33:18.780 Maybe someday. 00:33:18.780 --> 00:33:19.080 Yeah. 00:33:19.080 --> 00:33:19.320 Yeah. 00:33:19.320 --> 00:33:19.960 That's great news. 00:33:19.960 --> 00:33:23.480 If you happen to be around, definitely drop in. 00:33:23.480 --> 00:33:24.020 That'd be great. 00:33:24.020 --> 00:33:25.360 It's on meetup.com, right? 00:33:25.360 --> 00:33:26.320 People want to sign up there. 00:33:26.320 --> 00:33:26.580 Yep. 00:33:26.580 --> 00:33:27.700 And a link in the show notes. 00:33:27.700 --> 00:33:34.840 Do you have any intention of recording live casting or otherwise spreading this in a farther path? 00:33:34.840 --> 00:33:35.960 It's not a bad idea. 00:33:35.960 --> 00:33:38.100 We don't have anything like that set up right away. 00:33:38.100 --> 00:33:39.800 In the future, maybe we could do that. 00:33:39.800 --> 00:33:42.500 Probably people would be interested in watching these. 00:33:42.880 --> 00:33:47.920 But I also want to make it really accessible to people that are new to presenting as well. 00:33:47.920 --> 00:33:52.440 I'd love to have people come in and do a talk that they're working on. 00:33:52.440 --> 00:33:53.760 It's not quite polished yet. 00:33:53.760 --> 00:34:02.100 I want it to not just be experts talking to everybody else, but I'd like it to be people working out things that they're just interested in. 00:34:02.100 --> 00:34:03.780 So I think it would be good. 00:34:03.780 --> 00:34:03.960 Yeah. 00:34:03.960 --> 00:34:05.540 That sounds like a great philosophy for it. 00:34:05.540 --> 00:34:06.040 How about you? 00:34:06.040 --> 00:34:07.140 Any extras? 00:34:07.140 --> 00:34:12.760 I have a couple presenting and speaking PyCon 2020, which is a little earlier this year. 00:34:12.840 --> 00:34:14.460 I believe it's like in April or something. 00:34:14.460 --> 00:34:15.460 The website's up. 00:34:15.460 --> 00:34:16.200 Yeah. 00:34:16.200 --> 00:34:19.020 So April 15th to 23rd. 00:34:19.020 --> 00:34:23.520 So the call for proposals is now open for PyCon 2020. 00:34:23.520 --> 00:34:29.000 So if you would like to be considered, a talk of yours to be considered there, then now is the time. 00:34:29.000 --> 00:34:29.220 Yeah. 00:34:29.220 --> 00:34:33.280 Go ahead and submit those because you know you're only going to spend like a week writing it up anyway. 00:34:33.280 --> 00:34:34.440 So I may as well get that done. 00:34:34.440 --> 00:34:36.320 That's right. 00:34:36.320 --> 00:34:37.420 Do it like a Band-Aid. 00:34:37.420 --> 00:34:38.280 Stop worrying about it. 00:34:38.280 --> 00:34:39.020 Just get it over with. 00:34:39.020 --> 00:34:39.460 Yeah. 00:34:39.460 --> 00:34:40.040 Pull it right off. 00:34:40.180 --> 00:34:40.400 All right. 00:34:40.400 --> 00:34:41.960 Another thing I just... 00:34:41.960 --> 00:34:42.880 Have you heard of Gitbook? 00:34:42.880 --> 00:34:45.140 Yeah, but I haven't really looked into it much. 00:34:45.140 --> 00:34:45.900 I hadn't either. 00:34:45.900 --> 00:34:50.900 I was interviewing the guy, Joe, from Masonite, the Masonite Web Framework. 00:34:50.900 --> 00:34:56.700 And I noticed that Masonite's documentation is written in Gitbook. 00:34:57.180 --> 00:34:59.580 And so I looked at it and Gitbook is pretty interesting. 00:34:59.580 --> 00:35:05.000 You can use it as kind of like almost a base camp project management type thing. 00:35:05.000 --> 00:35:09.180 So stuff, personal notes or things you want to track or stuff like that. 00:35:09.180 --> 00:35:13.540 But you can also use it for documentation and knowledge bases and whatnot. 00:35:13.540 --> 00:35:15.280 So it looked pretty cool. 00:35:15.280 --> 00:35:17.740 And so I thought I'd just, you know, let people know that it's out there. 00:35:17.740 --> 00:35:19.880 It's free for small teams, like with some limitations. 00:35:20.820 --> 00:35:26.560 It's cost a little bit of money for non-trivial small teams, like $7 user. 00:35:26.560 --> 00:35:30.600 But it's also free for open source and nonprofit teams, which is kind of cool. 00:35:30.600 --> 00:35:32.840 So I'm also a big fan of Read the Docs. 00:35:32.840 --> 00:35:35.480 So it's, you know, I'm not saying they shouldn't use that. 00:35:35.480 --> 00:35:38.320 But here's an interesting project that I ran across that I hadn't heard of. 00:35:38.320 --> 00:35:39.160 It looks nice. 00:35:39.160 --> 00:35:43.760 If people, for some reason, are opposed to Read the Docs, I don't know why you would be. 00:35:43.760 --> 00:35:46.760 Or just like this look better is another opportunity. 00:35:46.760 --> 00:35:48.400 So good to have options. 00:35:48.540 --> 00:35:49.180 Good to have options. 00:35:49.180 --> 00:35:50.560 Also good to have laughs. 00:35:50.560 --> 00:35:51.700 Yeah, let's do some jokes. 00:35:51.700 --> 00:35:52.460 All right. 00:35:52.460 --> 00:35:53.860 How about you go first? 00:35:53.860 --> 00:35:54.460 Okay. 00:35:54.460 --> 00:36:00.660 So I pulled these out of a list of dad jokes you had posted somewhere on our Trello, but 00:36:00.660 --> 00:36:01.780 changed it a little bit. 00:36:01.780 --> 00:36:05.400 So what do you call a 3.14 foot long snake? 00:36:05.400 --> 00:36:05.980 I don't know. 00:36:05.980 --> 00:36:07.840 Well, that would be a python, of course. 00:36:07.840 --> 00:36:10.160 With the Greek symbol thon, yeah? 00:36:10.160 --> 00:36:10.500 Python? 00:36:10.500 --> 00:36:10.860 Yeah. 00:36:10.860 --> 00:36:15.140 So if it's not feet, but 3.14 inches, then what is it? 00:36:15.140 --> 00:36:16.360 It's a micro python. 00:36:16.360 --> 00:36:18.720 It's a micro python, a mu python. 00:36:18.720 --> 00:36:20.080 Yeah. 00:36:20.080 --> 00:36:21.900 I feel like we're back in calculus or physics. 00:36:21.900 --> 00:36:22.360 Yeah. 00:36:22.360 --> 00:36:23.940 So do you want to do some of these? 00:36:23.940 --> 00:36:24.480 Sure. 00:36:24.480 --> 00:36:28.020 So why doesn't Hollywood make more big data movies? 00:36:28.020 --> 00:36:28.660 I don't know. 00:36:28.660 --> 00:36:28.900 Why? 00:36:28.900 --> 00:36:29.580 No sequel. 00:36:29.580 --> 00:36:32.920 This last one, it's a little bit crass. 00:36:32.920 --> 00:36:35.920 It's, I don't know, it's a little low level, but I'll see what I can do here. 00:36:35.920 --> 00:36:40.060 So why didn't the angle bracket div get invited to the dinner party? 00:36:40.060 --> 00:36:40.620 I don't know. 00:36:40.620 --> 00:36:40.920 Why? 00:36:41.240 --> 00:36:42.360 It had no class. 00:36:42.360 --> 00:36:45.040 Oh, yeah. 00:36:45.040 --> 00:36:45.640 That's a good one. 00:36:45.640 --> 00:36:45.980 All right. 00:36:45.980 --> 00:36:47.480 Well, thanks for throwing those in there. 00:36:47.480 --> 00:36:47.940 These are fun. 00:36:47.940 --> 00:36:48.140 Yeah. 00:36:48.140 --> 00:36:51.940 Thank you once again for talking with me on a nice Wednesday. 00:36:51.940 --> 00:36:52.500 Absolutely. 00:36:52.500 --> 00:36:53.180 See you later. 00:36:53.180 --> 00:36:53.420 Bye. 00:36:53.800 --> 00:36:55.380 Thank you for listening to Python Bytes. 00:36:55.380 --> 00:36:57.920 Follow the show on Twitter via at Python Bytes. 00:36:57.920 --> 00:37:00.780 That's Python Bytes as in B-Y-T-E-S. 00:37:00.780 --> 00:37:04.020 And get the full show notes at pythonbytes.fm. 00:37:04.020 --> 00:37:08.220 If you have a news item you want featured, just visit pythonbytes.fm and send it our way. 00:37:08.220 --> 00:37:10.920 We're always on the lookout for sharing something cool. 00:37:10.920 --> 00:37:14.020 On behalf of myself and Brian Okken, this is Michael Kennedy. 00:37:14.020 --> 00:37:17.460 Thank you for listening and sharing this podcast with your friends and colleagues.