Effective PyCharm Transcripts
Chapter: Performance and profiling
Lecture: Profiling the slow app
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Now that you're familiar with this program, it's time to profile it. But one quick hang up here that I just got
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to tell you about. There was a bug in one part of the profile visualization that was in the latest version of PyCharm.
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Not the EAP but the actual shipping one. So I had to come back here and roll back to two 2021.1.3
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. I'm sure by the time you get this course it'll be fixed, it's already been addressed. The only reason I mentioned this is for whatever reason to
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lay out of my toolbar changed. So beware, it probably will be back over here for you. Now. What we want to do is we want to profile this.
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So you're familiar with running the program and you're familiar with debugging it. But if we go over here we can click this and say profile.
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So long as you have a run configuration, you can just press this button and it will run and use Python's c profile to
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figure out where we're spending our time. Let's go. So they're ran like usual. But now we got this cool report, let's check it out over here.
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It shows you where you're spending your time and it uses this own time by default
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And what that means is if a function is being called by another function, how much time are you spending at that level of the call stack?
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It doesn't tell you how much time that function takes to run because if it calls
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other functions itself, it ignores the time that is down calling those other functions. I find this to be not a very helpful terms.
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So what I want to know is how long did the function take to run, including things that might have had to do while it was running.
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So that's this one here. So we're going to sort it by the top and now over here we've got the interesting things.
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We've got program, obviously this one took the longest because that's the start up.
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We've got main and we've got to go and then we started getting into the interesting
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things. So the pieces that we worked on is we have compute analytics. Remember up here we have search get records,
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we have compute analytics. So here's our computer analytics. That one is the slowest. We spent 3.3, seconds there And 68% of the time.
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We have our search where we spent 353 milliseconds, only 7% of the time and get records the database.
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One was the third where we spent 1.2 seconds or 23% of the time. So this is helpful. And you can even do cool things like let's go over
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to this function if I double click it, it takes us right there, which is fantastic.
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And if you right click you can navigate the source or show on call graph. Let's check out the call graph.
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This is actually probably the most useful as your first trying to understand what performance means See how useful and meaningful.
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No, wait, this is not very helpful. We gotta zoom in. Okay, let's zoom in and we'll go over here and I got to find your way to
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the bottom. So notice there's all this stuff around. Find and load and frames and what not that's not really relevant to us.
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We can't control that. That's just Python starting up. So we want to start with when is our script running here?
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They wouldn't come over and see that We're going to run program and notice it's 4.9 seconds or the total time, but 0% of its own time.
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Same for main and same for go. This is like basically just orchestrating these three functions.
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Right? And again, they don't take much of their own time either. They're just calling a bunch of things that themselves take time.
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Notice the colors, red means really slow, relatively speaking. Green means pretty fast, relatively speaking and we're still spending a second here,
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but it's still better than the five. And again, green. So what we can do is we can quickly get a look here and say, all right,
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well, where are we spending our time? What is slow? We should probably start right there. That one is the slowest. So if we could optimize that,
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it's going to have the biggest impact. For example, if we could make a search instant if somehow we could make ping time on the internet go away,
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if we could make the talk Python server was turn instantly to us, what benefit would we have working on this function?
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353 milliseconds out of 4800. So we can go from 4.8 Down to 4.5 seconds So no matter how much we work on this function,
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it's not going to get faster than, you know, 7% faster. Right? So you really want to think about,
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you know, where can I spend my time and where is it meaningful to try to improve this? A lot of times people work on code and they say,
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well this could be faster if we do some crazy things do really optimize it. That makes the code harder to maintain and understand,
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but it will make it faster. Just remember You're only going to ever get 7.2% improvement here. So also think about maintenance, readability,
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all those kind of things. All right. So use this to kind of initially guide where you want to go and again we
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can click over here and navigate the source. Boom, that's probably what we should be working on first. Another thing to notice is, here's our result,
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this course 6 P stat that means it's the sixth time I ran it because I was fiddling around with the previous version when it had the bug,
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I'm gonna put that one on the left and when we run it again we've made no changes yet. But we will and when we run it and we make those
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changes we're going to get another one. So we'll be able to do things like come over here and say well let's sort
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this like that and sort this one like that and then we could just cycle between them. Right? That's that's one way we can kind of get a sense
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for how are we making improvements? How was it before? And how was it after? I'm going to make a point to leave this course 6p stat over here so that
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I can have it as a reference as we improve throughout this chapter.