Python Memory Management and Tips Transcripts
Chapter: Memory and classes
Lecture: Where do classes store memory?

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0:00 Let's do another little exploration here. To understand why classes take as much memory as they
0:07 do, we need to figure out where they store their data. and it's also setting the stage for a very big improvement that we're gonna make in a minute,
0:16 but you need to understand this first. So let's go and say "app_storage". Set that that to run. Do our fmain magic, and
0:27 up here, I'm actually gonna paste some code that's not worth watching me type out. But, we're gonna do a little trick so
0:32 we can import size utility. I'm gonna define a class called "thing". Now, thing contains thing1 and thing2, and it has a way to print out what things
0:42 it has. So a thing object with the things in here, and also like this. So we're just going to use that. There's not a whole lot to it,
0:50 just think of this as a standard class that has two fields, and it just knows how to talk about itself,
0:55 Okay? That's all there is that's happening here. So let's go and create some things. We'll have a thing, which is a hat, and a
1:06 mat, you know, thing1 and thing2, we could do this Dr.Seuss style, I suppose. Hat and mat, dog and cat, bat and bird, car and bike.
1:22 And let's just print out the things, we'll make a new line. There we have thing object at some address with hat and
1:30 mat. Notice 20, and 4, different location, with dog and cat and so on. These are our things. Pretty cool,
1:37 right? Nothing special there. Standard class stuff. If you look at any of these things, any class at all, it will have a "__dict__"
1:47 and it's, this is where Python fields, when you say "self.something" here, what that really does is it creates an entry. This is equivalent to saying
1:58 "self.dictionary of thing1 equals t1", right? Those, the lines above these two are the same. Put that down here so I can
2:12 keep it. Those two things mean the same thing. Alright, so whenever you have an instance of a class,
2:19 you have the instance of a dictionary on top of all the other stuff that might have to be tracked and whatnot.
2:25 So the cost in terms of memory of a class is a dictionary. Now you might think like many languages, the cost is that string and that string,
2:37 but there's actually two sides to that coin. The cost is this string right here, thing, and that. The entry that goes there,
2:46 these are actually reused. So there's only one, I guess there's not too much,
2:51 but there is a pointer, got a 64 bit system, so that's 8 bytes plus this thing for every field. So that's pretty interesting.
2:59 And down here, this is gonna be one of those. So let's actually look at all of them. So we'll say "obj.__dict__, for obj in things".
3:14 And then let's just print out the dictionaries. And look at that. Here we have a thing with hat and mat and here we have a dictionary, thing1
3:24 is hat, thing2 is mat. thing1 is dog, thing2 is cat, right? There it is. Those are the fields self.thing1 self.thing2.
3:36 So you might wonder, well how much size does this use? So we could print out the size, we'll just get the first one of the dictionary.
3:46 How much size does that use? And let's also do class, things zero. Class itself. Perfect. So we run those. You can see the size of the dictionary.
4:05 Ah, we're using the wrong one. Let's go and use our size util, get full size of the object. That's right. It's not traversing, is it? There we go.
4:16 That makes more sense. So the dictionary is 318 and the class is 366. So it's another 48 Bytes added on here to
4:25 have a class, but really the understanding that the dictionary is the thing that really holds most of the stuff to do with the class.
4:33 That's pretty interesting, right? So this is where it's stored. Now, question is, are these different dictionaries? Are they copies? What's the story?
4:42 So what we can actually do is we can go and look at the locations of the dictionaries. We could say "this is going to be the id of d, for d in dicts".
4:52 And we could just print those, print the locations. And If we look at that, what this is telling us is these are the memory
4:59 locations of the dictionaries of the classes. And so the same class, over and over and over again,
5:05 right? Those are different. So what that's telling you is there's every time you say
5:09 a new class, you get a new dictionary and that allocates some bits that are
5:13 required to be tracked and managed in memory for every single one of these. In particular, as I said at the beginning,
5:21 that you've got to keep track of the entries, right? You can see thing1, thing1, thing1, now the value of that string
5:29 is actually reused, but, the fact that it's appearing over and over, you still have to keep track of that in the dictionary,
5:35 right? And that takes a lot of space, relatively. Over here, we've got new dictionaries. You might wonder,
5:41 why is that required? So let's go over here and say things, go to the last thing, I don't know which one it is, but we're going to say that,
5:51 um, it was thing1 and thing2, those are its two fields. Let's say we want to give it a thing3. Python is a dynamic language,
5:59 and will let you just dynamically apply these things. Now, PyCharm says "you're asking for a hurt here", but you can tell PyCharm
6:06 "don't bother me, I want to do this" and then we can go and we can print out all the things again. Let's do, I'll just print the dictionaries.
6:19 So check this out. We have different ones, and notice thing1 thing2, thing1 thing2, and the next one, thing1 thing2, but the third one, thing1 thing2
6:30 thing3, right? Now there's a key 3, thing3 in the third one because we did this line 59 here, but the others they were unmodified.
6:39 Because of this dynamic nature, you can just go to an object and go "bam, You have more stuff than you knew about".
6:44 That means you have to have a dictionary that's dedicated to each class that tracks all the things that not just came with it,
6:50 but also were dynamically added. That's really flexible, but it also adds a lot of overhead.
6:55 Keep that in mind. We're going to see if we could trade off this flexibility for much better performance. Hint, we can. The take away from
7:04 this is whenever you have a standard class, the fields actually get stored like this into "__dict__" field name equals field value,
7:13 and when you create a class, each one of them gets a separate,
7:17 dedicated dictionary that is its own thing that was created in memory and managed and allocated and populated and taken care of like that.
7:25 So we, come over here and we look for the locations. They're all different. And the reason that you have a unique one for each class
7:31 is to support this dynamism here, right? Thing3 is not really an aspect of things,
7:37 that's why PyCharm, if I don't tell it to stop, complains unresolved attribute reference
7:42 thing3 for class thing, because you're kind of not treating this right. But it will accept it, and some programs were written this way,
7:49 so Python needs to be flexible for classes like this.

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