Effective PyCharm Transcripts
Chapter: Performance and profiling
Lecture: Optimizing the database access code
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0:00
We've optimized our computer analytics where to next? Well let's look at this search. It's Not very fast. It did take 1.4 seconds.
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You know, I think the internet is just being slow right now. Let's try to run it again and see if what the time is going to be
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like. If we look into it over here, you'll notice there's not a lot we can do we call get on requests and then you know, that's it.
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We wait let's just go see if there's any sort of difference over here now. It's 448 milliseconds. That's a huge difference. But what are we gonna do?
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There's not a whole lot else we can do. I'll go and keep this as our baseline now.
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But we've got search not a lot we can do compute analytics which we've made about
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10 times faster. And now we've got get records And over here and get records
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We have create connection being called once that's 12% and there's run query which is
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really running read row. So Send query might be the place to focus on but notice 889888. So read row is actually really the challenge.
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Let's go and focus on this section. They're down here. Again, we're simulating.
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We're not creating a real database with huge amounts of data that will make a slow
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query. But imagine we're doing a query against a database here and then because it
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has a lot of data, it's somewhat slow when we're working with databases and we have a lot of data. The thing you can do more than almost any other
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thing to make it faster is to add an index. So let's imagine there was and this one we had no index and we decided,
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you know what, we're going to go and look at the query performance stuff over in the database and realize, oh,
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there's no data, there's no index on this. That's a problem adding an index when there's tons of data can easily make a query
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1000 times faster. Let's just imagine this one makes it 100 times faster. So put a couple zeros there again,
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somewhat contrived. But this is real world type of stuff. You see a database query that's slow and you go and you optimize it by optimize
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it in the database first. The other thing that's often done in this case is if you want to run a query we might use something like Redis or an in
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memory cache where we cash the results. So we don't even go to the database again.
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But in my mind it's better to make the database faster if that's at all possible
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because adding a caching layer, all of a sudden you end up with cache invalidation and staleness and all kinds of challenges.
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So start by making the database faster. That's what we're simulating by adding index that's running again, we did it. Oh look at that.
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This thing is starting to the zip. So we can't really speed up the queries or the web request.
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I'll make a quick comment before we wrap up this video on what we could do some of the time. But in this situation we can't make it any faster.
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The database seemed faster. And we already saw that this is much faster. Let's profile it and get a real answer up over the call,
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graph those side by side. See how we're doing. Our program is still red because well, that's the slowest thing here. It's always going to be true.
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But look at how much faster we got on our get records Now, it's 20% of the time, whereas before it was 55% goes from 1.1 second down
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to just 0.26 or 265 milliseconds. That is a huge improvement. All right, well, I feel like we've massively optimized this app.
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We've gone what was the very original one? It was something like six seconds. We've gone from six seconds. Stand at 1.2 and we've done that.
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Being guided by what we got in this picture right here. Make a small change. Run it in the profiler. See if it got better if it got worse.
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Don't don't keep it roll that back. Make another change. Figure out what's slow now in this new context. Optimize that and keep going and keep going.
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My final comment on this search here. What we're doing is we're going over and passing a search text profile.
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profiling over to talk Python and asking for the answer and it's got to go through
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the internet all the ping Times and what not to the east coast from the west coast of the U. S. That is at least 100 milliseconds.
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Ping time plus the server as to respond. And we have to process and print out the results. Okay, So we can't really do anything about that.
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I mean we could not print out the results but we kind of want to see them. We could not do the query but we need the answer.
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Right? So on one hand it feels like, well we can't do a whole lot but we could say if we're gonna if this
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was more interactive, if it was longer running, we could actually go up here and say at Funk tools and add an 'lru_cache( )' to this as well.
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At least that way, if we ever search for the same text twice it's going to be twice as fast. The second time we ask for the search,
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we're going to assume that the data set over there hasn't changed does sometimes,
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but it's not that frequent when assuming hasn't changed for the lifetime of this app running and we're just going to give back the same answer,
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we can add this, it'll make zero difference in terms of performance because we're calling
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it only once but if we were calling it multiple times with potentially the same input we have a much better response there.
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Alright, final time, let's go and run our app. See how it works. Boom still works much, much faster. Pretty awesome, right?