This course will get you up and running with MongoDB quickly, and teach you how to leverage its power for data analytics. We'll start by mastering the fundamentals of MongoDB, including MongoDB’s Document data model, importing data into a cluster, working with our CRUD API and Aggregation Framework. These topics will be taught through a demo application which will give you a great first encounter of how simple and practical it can be to build applications with MongoDB. In addition to these essential topics, you will also learn and work with useful MongoDB tools and services. You will work with Atlas, MongoDB's database as a service, MongoDB Compass, a schema visualization tool, as well as many other useful command-line utilities.
Sort, Skip, and Limit
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Из курса от партнера MongoDB Inc.
Introduction to MongoDB
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Из урока
Week 2
Познакомьтесь с преподавателями
Norberto LeiteLead Engineer
Engineering
Kirby KohlmorgenSenior Curriculum Engineer
Education
Shannon BradshawVice President
Education
In this lesson, I want to talk about how mflix queries MongoDB
to generate its homepage.
By examining how the mflix page is generated, we'll actually get to
learn about the following cursor methods, sort, skip, and limits.
So let's take a closer look at this page.
I want to point out a few interesting characteristics.
First, you'll notice that there are 46,000 movies in mflix,
but this homepage is only displaying the first 20.
Moreover, you'll notice that the 20 that we're displaying
are all very well known films here in the US.
And the last thing I want to point out, is if we go ahead and scroll down and
click Next Page, you'll see that the next page displays the next 20 movies,
movies 21 through 40.
So let's go ahead and look at the code of mflix to see a little bit how this works.
So let's go ahead and take a look at mflix.py,
this is where our function exist to generate the homepage.
And this is the function,
and we know this is the right function because of app.route being /.
So whenever you go to the route, part of the website,
the homepage, this is the method that will get called.
In the first 25 lines or so
here are just about making sure that we understand what page we're on.
Seeing if a certain genre was selected or
seeing if a query was made about a text search.
But we're going to ignore all of that for the most part.
Really all we care about here is this call to get movies.
And this is where we actually make the call to the database.
So as you can see here,
we pass in three parameters, pass in our filters as we set them up.
We pass in the page that we're currently on, and
we pass how many movies we want on each page.
And that's going to return to us a list of movies,
as well as the total number of all the movies in the collection.
So let's go ahead and look at get movies.
This is in db.py, and here you can see the get movies function.
Now let's go ahead and copy this and put it in our Jupyter Notebook so
you can actually play around with the function a little bit.
So the first things I'm going to do here is define the parameters that we go ahead
and pass in to get movies.
So this is a very simple example, we're not making a text search or
searching by genre.
So our filter's just going to be an empty dictionary.
And, we want this query to be for the homepage.
So we're going to say page is = to 0.
And just like on the homepage, we're going to render 20 movies per page.
Now I've gone ahead and simplified the get movies function a little bit, by removing
the function and kind of splitting up the code across multiple cells,
so that we can more easily see what's happening.
And in this first cell, this is where we ensure that the homepage is
filled with movies familiar to our users.
So by default, when we query MongoDB,
the documents are returned without any particular order.
But to make sure that these movies are relevant to our users,
here we're going to sort them by the number of reviews they have.
Now obviously, there are many different ways that we could implement this but
this naive approach works pretty well for this example application.
And as you can see, our sort key uses dot notation.
And that's because we're sorting on a value that is
nested through a series of nested documents.
And this is the exact same dot notation that we use when we're filtering for
embedded documents.
In this case, we're now using it for sorting.
So very simply, we just pass in our filters to find, and
then we append the sort method, passing our sort key.
And then, of course, similar to how we do sorting in aggregation,
we also have to pass a direction.
So we want to sort things so that the most reviews are at the front, so
we're going to sort this descending.
And then second thing we want to do is go ahead and get the total movie count.
So this query is going to currently return all of the movies, and so
when we run count you can see that, indeed, it does return all of them.
And so the first thing this query does is go ahead and
counts how many documents are on this cursor.
And we go ahead and store that in this variable called total num movies.
And this is one of the return values of the get movies function.
Now, would it be very cool to send all 46,000 movies to our user?
They certainly don't have time to look at all of them, but moreover, it would take
a long time to send them over the network and to render them on the page.
And that's why we limit the number of movies that we're sending.
Or specifically we're limiting our results to 20 per page.
When users aren't on the homepage, we're going to need to go ahead and
skip over the entries that precede the pages that they're currently on,
and that's what skip will let us achieve.
In this case, since page is 0, moves page are still 20, this is going to evaluate to
0, so we're not going to be skipping any for the homepage.
And then when we go ahead and turn this cursor into a list, and
look at its length, you see that we're getting 20 movies.
And if we look at the first movie, if we look at tomatoes and look at viewer,
and look at the number of reviews, you can see that there are quite a few reviews.
And that's why Titanic is the first movie that we see on mflix.
Now we can go ahead and simulate going to the next page by setting page equal to 1.
And then running the same query again where have no filters.
We're sorting on the number of reviews, and then this time we're skipping,
hopefully, 20 movies.
And as you can see, we are now returning 20 less movies than what
would have been returned if we returned all of the movies.
Let's recap what we discussed in this lesson.
We looked at these three common cursor methods.
But moreover, we saw how, together, they can be used to generate our movie
homepage, as well as through the use of skip and limits support pagination.
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