Python Jumpstart by Building 10 Apps Transcripts
Chapter: App 8: File Searcher App
Lecture: Generators save the day
0:00 We've seen this concept of generator methods
0:03 let's go apply it to everything we have going on here
0:05 and I'll even show you another keyword we haven't had a chance to talk about yet.
0:08 So let's start at the bottom.
0:09 Here is a method that is a traditional method it puts all the stuff
0:12 into the list and then once that computation is done
0:15 everything is computed it returns that whole list and then
0:18 we just have another list above that we had even more to
0:21 and keep extending as we have more and more files.
0:24 We can do better.
0:26 So, instead of doing matches here, let's get rid of this,
0:29 and instead of doing append m we will say yield m
0:32 and we won't have to return the matches
0:35 maybe I'll even comment it out for you, like so,
0:40 so no more lists, we are just doing yield, now this would already work,
0:44 what we are doing is calling up here search file and here we get our generator,
0:50 but we can take any collection generators or iterable collections and extend this list,
0:55 but we can actually do better still
1:00 so this is a generator method, this is a regular one,
1:03 but we can also apply the exactly same idea here and the same idea here,
1:07 now this gets a little tricky because I have to say for m in matches yield m,
1:14 now, that's not the most fun thing to write,
1:16 it would work but I'll show you something better,
1:17 same thing down here for all the matches there we want to do that,
1:21 and then we no longer have our return
1:23 so here is the generator method and this is going to come through
1:26 and each time that we sort go pull something out of this collection,
1:30 it's going to go until it hits one of these,
1:33 which the generator and it's going to hand one back
1:36 so if we only wanted the first 4 matches
1:39 we could compute that extremely quickly,
1:40 however, this line 65, 66 this is not the coolest thing,
1:44 it turns out that python 3.3 added basically a keyword that will do the same thing
1:51 like take a whole set and sort of hand them back one at a time,
1:54 and so we can simplify this and just say yield from matches,
1:57 and if we really wanted to simplify this we could actually come down here
2:00 and just write it as one line, we could just say yield from that,
2:05 never even store matches here, similarly yield from that;
2:10 so down below, we have search files,
2:12 that individual searching of a single file is a generator
2:15 and we only ever have a single line in memory at a time.
2:20 Now up here, as we work through all the files in our directory
2:23 or even recurse into a tree of directories and their files,
2:27 we are only pulling back one item from either here or here at a given time,
2:33 and that means we only have 1 line in memory at a time,
2:37 really one search result and then we can go up here and we are printing out
2:41 now, let me just show you that this is still working,
2:44 let's bring this back and then let me search the simple files again,
2:50 just to show that we are actually still searching just like before.
2:53 So let's search the small set of books for Holmes, there you go,
2:57 you can see 468 matches, and we are searching the Ulysses
3:02 or searching The Adventures of Sherlock Holmes, perfect,
3:06 it works exactly the same but from a performance perspective it's not the same,
3:12 let's run it again, and this time we are going to search the large set of files,
3:17 and again, we are going to search for how many question marks are there,
3:22 there were something like 2.78 million question marks and remember,
3:25 we had to use like almost 400 MB of memory to answer that question,
3:28 remember, 400 MB, what's it going to do this time, can we do better?
3:35 Oh, here, hold on, let me stop this really quick,
3:37 remember, we didn't do the output, it was too much going really,
3:41 we just did a little count, so let's rerun it this way.
3:45 Ok, here we go again,
3:47 it's going, 3.8 MB remember, it should have jumped up to 300 MB, a gigabyte,
3:52 what is going on, this is so absolutely amazing,
3:57 look at this you guys, we are processing gigabytes and gigabytes of code
4:00 with almost identical algorithms
4:03 and yet the memory usage is the same
4:05 as if we are processing like a single line in memory,
4:08 because that's all we are ever holding,
4:10 is a single line in memory, ok,
4:13 great we do have the file stream open to some huge file at some point,
4:16 but we are seeking over, we are streaming across it.
4:20 Let's just let it run and see where it goes.
4:28 It's done, look at that,
4:29 look at the memory usage, look at the CPU,
4:34 look at the performance, it is so much better than it was before,
4:39 in fact, I kept the previous one around,
4:42 let's have a look at it, it's not really fair to put them side by side,
4:46 because the scale of the graph is not the same,
4:49 but I think we'll get the sense anyway.
4:52 So on the left is the old bad sort of standard procedural code style
4:56 and now look at the memory,
4:58 it goes from 3 MB when I was starting out to 394 MB;
5:05 ours went from 3 MB to 4 MB.
5:09 And that was it.
5:11 If you look at the size of the CPU graph or sort of the length of any of these graphs,
5:15 you'll see they are basically identical in computational time,
5:18 it looks actually lower on CPU usage,
5:22 presumably it's doing less garbage collection less allocation,
5:26 doubling of lists and copying them and things like this,
5:29 and all we have done to change that algorithm is use the yield
5:33 and yield from keyword instead of making lists appending and extending them.
5:37 The code we wrote actually got a couple of lines shorter,
5:40 so this is the power of generator methods
5:43 and any time you are processing like a pipeline of lots of data
5:47 you saw that you can chain them together
5:49 to create these pipelines basically effortlessly,
5:52 we'll see that there is even a simpler way to create
5:55 this type of structure something called a generator expression,
5:58 right, but we'll save that for the next app.