MongoDB with Async Python Transcripts
Chapter: Document Databases
Lecture: Queries: Projections
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The final thing I want to talk about in this native query chapter, we're almost done with it, is projections.
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Now, projections are super, super important.
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Maybe you've done SQL before and people said, ""Never, never select star. Always specify the columns you want back.
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Well, you have a similar bit of advice in MongoDB and document databases, but it's even more important.
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Why might people say, ""Don't select all the columns or parts of your data back""? because you might only need two things.
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What if you only need the ISBN and the title? You don't care about when it was created, you don't care about the ratings,
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well you're still pulling all that data back across the network and off of disk, potentially on the database server,
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to just ignore it when you get to it. So a good practice is to just ask for what you're going to use. We get to Python, we'll talk about
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when you don't want to do projections or when you do and how to do so in Beanie.
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But in the native query syntax, the way you do that is there's a second parameter you can pass to find.
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So in this case, we only care about the ISBN and title. So we say find whatever the query is, and then we project back ISBN title, ISBN title.
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So we get just those back. Notice we get one other thing. We get the underscore ID, the primary key. By default, that's always going to come back.
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However, all the other fields you have to opt in by passing something truthy. This case one will work. This is basically JavaScript.
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If you want to suppress the ID coming back, you could say _id colon zero and then you
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would literally just get back those two other fields, the ISBN and the title.
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So this is really important because maybe we have a bunch of ratings and that's going
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to slow down the response and we're not going to pay attention to them here. So forget them.
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So projecting back a small subset of the document, it really helps performance.
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I gave you some numbers earlier about talk by fun training, we have this number of queries and we have this amount of performance.
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Very very carefully structuring the amount of data that comes back through projections on the server makes a huge difference there.
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Let me just tell you a quick story about our apps. So our apps on the client side, these are native applications for iOS and Android.
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On the server side, though, it's all Python, APIs, and MongoDB. Some of our APIs this app was talking to were taking as long as a second to respond.
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Wow, what is going on with this? These used to be really fast. And I just don't understand why this part of our API started to slow down slower and
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lower. It turns out that we were returning just the entire document for really, really
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small bits of information, but we needed many of them. And so a slight change in how we
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ran our query through a projection, we said, actually, we only need these two pieces of
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data for this API response that we're querying here, not everything in the record. And those
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went from taking like 900 milliseconds to respond down to 10 or 20 milliseconds. Incredible.
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So projections, super, super important, more important than in relational databases, much
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more important sometimes depending on your data model than relational databases.
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Because if you go crazy and select star in relational databases, you'll get all the columns.
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If you go crazy and select star in a document database, you might get whole chunks of other tables and relationships, right?
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Remember, embedded objects, embedded records are kind of like a pre computed join.
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If you don't want that part, project it out, and it'll be way, way faster. So that's it for our quick overview of the native view of MongoDB.
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This is a kind of a for your information for your reference as you need it.
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Chapter of information, you're not necessarily going to have to go write these projections by hand.
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necessarily going to have to use those logical operators by hand, but if you need them, you can come back and find them.
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Mostly, from now on, we're going to focus on Python code for talking to MongoDB.