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<v Narrator>Welcome back.</v>

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In this video and the next one

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I want to introduce you to the MongoDB

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aggregation pipeline which is an extremely powerful

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and extremely useful MongoDB framework

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for data aggregation.

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And the idea is that we basically

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define a pipeline that all documents

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from a certain collection go through

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where they are processed step by step

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in order to transform them into aggregated results.

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For example, we can use the aggregation pipeline

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in order to calculate averages

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or calculating minimum and maximum values

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or we can calculate distances even,

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and we can really do all kinds of stuff.

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It's really amazing how powerful

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this aggregation pipeline is.

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All right, but enough talk.

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Let's now actually start using the aggregation pipeline

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and I'm gonna create a new header function here

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because later on we will then define a new route

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and then use that function for that.

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But that new route, I'm gonna create it later.

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So I want to create a function here that's gonna

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calculate a couple of statistics about our tours.

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So I'm gonna call this one getTourStats.

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Okay, so Stats for statistics,

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and it's gonna be an async function

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and so a vs code now automatically prefilled off this.

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All right, let's create or try catch block here.

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Actually, I'm gonna copy this from right up here,

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and now in here I will create

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a variable called stats.

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Now the aggregation pipeline really is a MongoDB feature.

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But Mongoose, of course, gives us access to it,

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so that we can use it in the Mongoose driver, right?

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So using our tour model in order to access

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the tour collection, we say

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tour.aggregate.

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And so the aggregation pipeline is a bit

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like a regular query and so using the aggregation pipeline

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it's a just a bit like doing a regular query.

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The difference here is that in aggregations,

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as I already mentioned, we can manipulate the data

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in a couple of different steps

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and so let's now actually define these steps.

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And for that, we pass in an array of so-called stages.

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So we pass in an array, and then here we will

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then have a lot of stages.

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And again the documents then pass through these stages

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one by one, step by step in the define sequence

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as we define it here.

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So each of the elements in this array

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will be one of the stages.

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And there are a ton of different stages

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that we can choose from, but I will just tell you

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the most common ones in this lecture

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and also in the next one.

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But anyway, I now want to take a minute

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and take a quick look at the MongoDB documentation

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because I think we didn't do that yet.

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So MongoDB and we already know this website.

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So on our MongoDB website we go here to learn,

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then documentation and then in here we select

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the MongoDB Manual and this documentation is huge.

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It really, it has a lot of stuff in here

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and if you were gonna go through it all,

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well, then you'd become really a MongoDB master.

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So it gives you a nice introduction to MongoDB,

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you have the installation here.

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You have the CRUD Operations, so a nice overview

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about a lot of the stuff

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that we already learned before, right?

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Then you have stuff about the aggregation

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and in particular, the aggregation pipeline

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which we're gonna talk about now,

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but what I want to really show you here

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is actually this reference that we have at the bottom

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because in the reference we have here for example

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the operators and that's where we have

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a lot of important stuff that we've been using all the time.

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So let's take a look here at the query operators,

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for example and here you see, we actually have

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these operators that we already used.

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So for example, greater than or equal,

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or less than and equal, but if someday

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in your own applications you need some other operator,

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well, now you know where you can find them.

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Now of course, it's also always very helpful

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to just do a quick Google search, probably find

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some stack overflow post about it,

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but believe me, many times it's easier

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and faster to just quickly jump into the documentation

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and quickly search for whatever you're needing.

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You just need to know how the documentation works

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and once you know that, it's then gonna be quite easy

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for you to find what you're looking for, all right?

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Here you also have some logical operators

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for example, the OR operator we already used.

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Again, really a lot of stuff, geospatial operators

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and we're gonna use some actually, later,

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and yeah, really a lot of stuff.

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Then what I wanted to show you after this lecture actually,

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are the aggregation pipeline stages.

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So here you have a lot of them

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and what we're gonna use in this lecture here is match,

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for example and group,

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where is that?

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Yeah group, that is a very important one,

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and so as I mentioned, I'm gonna show you

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the most important ones in this lecture and the next one.

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But again, if you in some case need something else,

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then just go ahead, come here to this documentation

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and you will find what you're looking for.

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All right?

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Anyway, let's now learn how we can actually define

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one of these stages, all right?

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And I'm gonna start with match.

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And match is basically to select

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or to filter certain documents.

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And so it's very simple.

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It's really just like a filter object in MongoDB,

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such like we've been using so many times.

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So each of the stages is an object

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and then here comes the name of the stage.

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So this one is the match stage, all right?

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And as I mentioned, it's really just a query.

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And so let's say that for starters,

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we only want to select documents

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which have a ratings average greater or equal than 4.5.

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So can you do that?

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I'm just gonna give you a second to do that here.

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So I hope you did it.

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So ratings average, should be

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greater or equal than 4.5.

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And so that's it, that's the first stage.

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And usually this match stage is just a preliminary stage

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to then prepare for the next stages which come ahead.

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So the next one is now the group stage.

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So group and then in there

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we need to always pass just another object.

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So it looks kinda weird with all these objects,

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but, yeah, you've seen that before

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and MongoDB just works this way.

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It's always objects, inside of objects, inside of objects.

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And this group here is where the real magic happens

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because as the name says,

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it allows us to group documents together,

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basically using accumulators.

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And an accumulator is for example,

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even calculating an average.

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So if we have five tours, each of them has a rating,

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we can then calculate the average rating using group.

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And so let's do exactly that right here.

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Now the first thing, is we always need to specify

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is the id because this is where we're gonna specify

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what we want to group by.

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For now, we say null here because we want

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to have everything in one group so that we can calculate

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the statistics for all of the tours together

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and not separate it by groups.

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We will, a bit later then also group by different stuff,

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for example we can group by the difficulty

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and we can then, for example calculate the average

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for the easy tours, the average for the medium tours

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and the average for the difficult tours.

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So again, we can group by one of our fields

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and that field, we are gonna specify in here,

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but for now, as I said, we want to calculate these averages

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for all the tours together in one big group.

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So in that case we say _id and set it to null.

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Now let's actually calculate the average rating.

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In order to do that, we simply specify a new field,

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so let's simply call it the average rating,

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so like this, and this will be

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well, the average,

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which is yet another MongoDB operator, so this one here.

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You will find it in the reference if you look it up.

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So this is a mathematical operator calculating

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the average and now the name of the field.

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And again, I know that this is gonna look very weird,

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but in order to specify the field which we want

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to calculate the average from, we need to use

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the dollar sign, but in quotes here

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and then the name of the field.

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So ratings average in this case.

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And let's also calculate the average price.

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So average price, and again, did it wrong here,

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and so

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the average and then again,

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the name of the field and in quotes

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and with this dollar sign.

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Right, so it looks kind of weird again,

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but this is just the way

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that the aggregation pipeline works.

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So we have the average price.

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Let's actually also calculate the minimum price,

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so the smallest price, and the largest price.

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So minPrice

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and from now on, it's gonna all look kind of similar,

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so now we use the min operator

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and then again the field name like this.

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And a comma here and let's now just duplicate this one

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in order to calculate the maximum price.

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And now, let's actually take a look

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at what we already got at this point.

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So we now need to actually send out this response,

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and so again, I'm just gonna go head

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and grab some of these already done responses here.

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So the data that we want to send out is called stats.

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And now all we need to do is actually add a new route

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here in our tour routes and again,

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I'm gonna edit here, right at the top.

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So router.route

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/gettour, or actually just tour-stats, just like this

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because the get is already defined in the HTTP method.

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So no need to repeat it in the name.

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So tourController.getTourStats.

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Give it a safe and let's actually test it out now.

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So I'm really exciting to test it out now, actually

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to see if it works because again it's really an amazing

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and really powerful tool

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which allows you to do so much stuff.

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So tour-stats, send it

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and that's not really the result

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that we were waiting for, so that's just the pipeline

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that we defined, but I already know why that is.

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So we did not await the result.

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So this, basically, just like a normal query

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is gonna return an aggregate object.

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So .find is gonna return a query, and .aggregate

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is gonna return an aggregate object.

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And then only when we await it,

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it actually comes back with the result.

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So that's also why we defined this function here

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as an async function, so that we can then use await

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in there and so this is the right place.

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Let's try it again.

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And here we go!

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So great!

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We have the average rating of all our tours,

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we have the average price which you see is

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1563 and then the minimum and the maximum, right?

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And from our data, we can actually confirm

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that this is true.

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So great!

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Really, really great.

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Now, one more thing that I want to do here

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is to actually calculate the total number

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of ratings that we have and also the total number of tours.

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So we have the average rating here

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and let's actually do it before,

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so numRatings,

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like this

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and you can probably guess

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that this one is called sum and then the ratingsAverage,

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or actually not ratingsAverage, but ratingsQuantity, right?

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So that's where the number of ratings is stored

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and so the number of ratings, the total,

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will be the sum of all of these together.

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And now, the last one, is the number of tours

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and that one is a bit trickier,

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and so that's a nice one to show you here.

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So we still want to sum, so to add everything together,

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basically, so we still use sum,

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but what are we gonna add together?

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Well, we basically add one for each document,

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and so we say 1, and that's it.

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So basically for each of the document

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that's gonna go through this pipeline,

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one will be added to this num counter.

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Let's call it numTours.

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And so, yeah, let's test it out now, again.

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And indeed we get our nine tours

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and we already know that we have nine.

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So these are all the statistics for all the tours together,

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but let's now take it to the next level.

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As I said before, we can now group our results

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for different fields.

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And let's actually start out with the difficulty.

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And so that's quite similar to specifying the fields

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down there which is simply the dollar sign,

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and then the name of the field.

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So $difficulty, give it a safe and that's it.

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Send it here, and now we have the statistics

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all defined for each of these three difficulties.

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So that's really, really amazing.

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So this is the part that blows my mind.

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It's absolutely fantastic.

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I mean, you can start to see all the kinds of stuff,

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all the kinds of data manipulation

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that you can do using this pipeline, right?

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So we really can now start to analyze stuff,

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for example, we see that the easiest tours

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are the ones who get the poorest ratings

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with a 4.6, while the medium tours

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are the ones who get the highest ratings.

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We can also see that the difficult tours

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are the most expensive ones with an average price

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of 1997, while the easiest ones are also the cheapest ones

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with a price of 1272, right?

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We also see that the easiest tours

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are the ones which we have the most,

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so four easy ones and only two difficult ones, right?

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So we can use this, of course,

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to get all kinds of insights into our data.

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Let's comment this one out here

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and let's just try another one,

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for example, by ratings, why not,

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let's just try out, see what we get.

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So for example, we have two tours,

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so basically with a rating of 4.5,

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we have two tours with 4.7, one tour with 4.8,

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and three tours with 4.9.

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Oh, and also down here, one tour with 4.6,

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and so for example, one insight that we can get here

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is that the very expensive tours with this average price

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of 1997 get extremely good ratings.

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So all kinds of stuff, really that we can do here.

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Let's get rid of this one, let's add this one back,

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and we can now actually even,

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do some operations with this one.

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So just for fun, let's put this difficulty to uppercase.

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So this of course, is gonna be another object here

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and then here in front is the operator.

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And so the operator in this case, is called $toUpper.

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So just, yet another MongoDB operator.

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So check it out, and now it's spelled uppercase.

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Great!

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So we have the group stage here which is quite complete.

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So let's try another one which is a sort stage.

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So another object in our array for yet another stage

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and this one is called sort.

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So sort and then in there, we need yet another object,

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and it kind of auto-completed this one, I don't know why,

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00:17:52,190 --> 00:17:54,860
and so here we can now specify which field we want

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to sort this by and let's actually use the average price.

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And now here in the sorting we actually need to use

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the field names that we specified up here in the group.

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We can no longer use the old names because at this point

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they are already gone.

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They no longer exist.

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So at this point, in the aggregation pipeline,

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we really already have these results.

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So these are now our documents basically.

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So if you want to sort by the average price,

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then this is the field name we gotta use.

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So we can say, the average price

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and then we can say 1 for ascending.

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00:18:37,680 --> 00:18:38,883
So let's try that out.

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00:18:41,550 --> 00:18:43,070
And yeah, indeed.

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00:18:43,070 --> 00:18:46,480
That's the lowest one and that's the highest one.

354
00:18:46,480 --> 00:18:47,820
Great!

355
00:18:47,820 --> 00:18:50,210
So we did a lot of stuff.

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00:18:50,210 --> 00:18:53,480
Let me just show you that we can also repeat stages.

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00:18:53,480 --> 00:18:55,710
So let's actually do another match here

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00:18:58,740 --> 00:19:00,930
and this one is really just to show you

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00:19:00,930 --> 00:19:03,240
that we actually can repeat stages

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00:19:03,240 --> 00:19:07,130
and I also want to show you another operator.

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00:19:07,130 --> 00:19:11,020
So let's now select by id and remember that id

362
00:19:11,020 --> 00:19:12,920
is now the difficulty, right?

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00:19:12,920 --> 00:19:15,023
So we just specified that up here.

364
00:19:16,820 --> 00:19:20,670
So we want the id and now a new operator

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00:19:20,670 --> 00:19:22,500
that we didn't use yet.

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00:19:22,500 --> 00:19:26,913
We want it to be not equal to, and let's say easy.

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00:19:28,210 --> 00:19:29,830
And so, just like this we're gonna select

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00:19:29,830 --> 00:19:32,260
all the documents that are not easy.

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00:19:32,260 --> 00:19:33,880
So in this case, the one that says

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00:19:33,880 --> 00:19:36,340
medium and difficult, right?

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00:19:36,340 --> 00:19:41,007
So basically, excluding the one that says easy, right?

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00:19:42,200 --> 00:19:43,790
So again, it's not really useful here

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00:19:43,790 --> 00:19:47,950
because that will take away a lot of our meaningful data,

374
00:19:47,950 --> 00:19:50,090
but just to show you that we can of course,

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00:19:50,090 --> 00:19:51,980
match multiple times.

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00:19:51,980 --> 00:19:54,750
So in this case, we matched once before we actually

377
00:19:54,750 --> 00:19:58,160
did the group and then we matched once we were ready,

378
00:19:58,160 --> 00:19:59,475
doing the grouping.

379
00:19:59,475 --> 00:20:01,720
Now, let's actually keep this here,

380
00:20:01,720 --> 00:20:03,798
out of our aggregation pipeline,

381
00:20:03,798 --> 00:20:06,200
but I'm gonna leave it here,

382
00:20:06,200 --> 00:20:08,200
just so that you keep it as a reference.

383
00:20:09,580 --> 00:20:12,750
Give it another save, give it another try,

384
00:20:12,750 --> 00:20:14,730
and so these are our results

385
00:20:14,730 --> 00:20:16,590
for getting our tour statistics.

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00:20:16,590 --> 00:20:19,030
So, really nice, really great.

387
00:20:19,030 --> 00:20:21,070
I hope you enjoyed this video

388
00:20:21,070 --> 00:20:23,200
and in the next one, we're actually gonna keep using

389
00:20:23,200 --> 00:20:26,810
the aggregation pipeline to do some more calculations.

390
00:20:26,810 --> 00:20:30,050
And to show you some more different stages.

391
00:20:30,050 --> 00:20:31,973
So keep tuned for that one.