Cara menggunakan mongodb group by having

Available in Non-Essentials editions of Navicat for MySQL, PostgreSQL, SQLite, MariaDB, and Navicat Premium, the Query Builder allows anyone to create and edit queries with only a cursory knowledge of SQL. In today's blog, we'll use it to write a query to fetch a list of actors that appeared in movies released during a given year.

Table of Contents Show

Adding WHERE Criteria

The query that we'll be building will run against the . A former member of the MySQL AB documentation team named Mike Hillyer created the Sakila database specifically for the purpose of providing a standard schema for use in books, tutorials, and articles just like the one you're reading.

The database contains a number of tables themed around the film industry that cover everything from actors and film studios to video rental stores. Please refer to the tutorial for instructions on downloading and installing the Sakila database.

You can think of the Query Builder as a tool for building queries visually. It's accessible from the Query Designer screen. Let's bring it up by opening a new query:

Click the Query icon on the main toolbar, followed by the New Query button from the Object toolbar:

In the Query Designer, click the Query Builder button to open the visual SQL Builder.
The database objects are displayed in left pane, whereas the right pane is divided into two portions: the upper Diagram Design pane, and the lower Syntax pane:

It's a good idea to select the tables first, so that the Query Builder knows which fields to present for the field list:

Drag a table/view from the left pane to the Diagram Design pane or double-click it to add it to query. We'll be needing the actor, film_actor, and film tables.
You can assign table aliases by clicking on "" beside each table. To add the table alias, simply double-click the table name and enter the alias in the Diagram Design pane.

Note how the Query Builder already knows the table relationships. That's because foreign key constraints have already been declared on Table objects:

To include a field in the query, check the left of the field name in the Diagram Design pane. To include all the fields, click at the left of the object caption. Select the actor first and last names as well as the film title.

Adding WHERE Criteria

Clicking on "" beside the WHERE keyword adds a default WHERE condition of "<--> = <-->".

Click on the left-hand "<--> = <-->" to select a field. That opens a popup dialog that contains a List of fields as well as an Edit tab.
Click the List tab and choose the f.release_year field.
Click OK to close the dialog.
Next, click on the right-hand "<--> = <-->" to set the release year. This time enter a value of "2006" in the Edit tab. Click OK to close the dialog.
Click OK to close the Query Builder. You should now see the generated SELECT statement in the Query Editor:

SELECT
a.first_name,
a.last_name,
f.title
FROM
actor AS a
INNER JOIN film_actor AS fa ON fa.actor_id = a.actor_id
INNER JOIN film AS f ON fa.film_id = f.film_id
WHERE
f.release_year = 2006

Click the Run button to execute the query. The results will be sorted by Film title:

Whether you're a novice or experience DBA, Navicat's Query Builder makes writing SELECT queries easier than ever before. In an upcoming blog, we'll get into some of its more advanced features.

When you start working with MongoDB, you will typically use the

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

4 command for a wide range of queries. However, as soon as your queries get more advanced, you will need to know more about MongoDB aggregation.

In this article, I will explain the main principles of building aggregate queries in MongoDB and how to take advantage of indexes for speeding them up.

Furthermore, I will introduce the most important stages of the aggregation pipeline with short examples using each one, and how to apply them to the pipeline.

Aggregation is a way of processing a large number of documents in a collection by means of passing them through different stages. The stages make up what is known as a pipeline. The stages in a pipeline can filter, sort, group, reshape and modify documents that pass through the pipeline.

One of the most common use cases of Aggregation is to calculate aggregate values for groups of documents. This is similar to the basic aggregation available in SQL with the GROUP BY clause and COUNT, SUM and AVG functions. MongoDB Aggregation goes further though and can also perform relational-like joins, reshape documents, create new and update existing collections, and so on.

While there are other methods of obtaining aggregate data in MongoDB, the aggregation framework is the recommended approach for most work.

There are what are called single purpose methods like

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

6, and

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

7 which are appended to a

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

4 query making them quick to use but limited in scope.

The map-reduce framework on MongoDB is a predecessor of the aggregation framework and much more complex to use. MongoDB have deprecated

Here is a diagram to illustrate a typical MongoDB aggregation pipeline.

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 stage – filters those documents we need to work with, those that fit our needs

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

0 stage – does the aggregation job

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 stage – sorts the resulting documents the way we require (ascending or descending)

The input of the pipeline can be a single collection, where others can be merged later down the pipeline.

The pipeline then performs successive transformations on the data until our goal is achieved.

This way, we can break down a complex query into easier stages, in each of which we complete a different operation on the data. So, by the end of the query pipeline, we will have achieved all that we wanted.

This approach allows us to check whether our query is functioning properly at every stage by examining both its input and the output. The output of each stage will be the input of the next.

Tools like Studio 3T let you as you build your aggregation query.

Download Studio 3T Free for Mac, Windows, or Linux!

There is no limit to the number of stages used in the query, or how we combine them.

To achieve optimum query performance there are a number of best practices to take into account. We will come to those later in the article.

This is an example of how to build an aggregation query:

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

where collectionName – is the name of a collection,
pipeline – is an array that contains the aggregation stages,
options – optional parameters for the aggregation

This is an example of the aggregation pipeline syntax:

pipeline = [
        { $match : { … } },
        { $group : { … } },
        { $sort : { … } }
       ]

Aggregation works in memory. Each stage can use up to 100 MB of RAM. You will get an error from the database if you exceed this limit.

If it becomes an unavoidable problem you can opt to page to disk, with the only disadvantage that you will wait a little longer because it is slower to work on the disk rather than in memory. To choose the page to disk method, you just need to set the option

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

3 to true like this:

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

Note that this option is not always available shared services. For example Atlas M0, M2 and M5 clusters disable this option.

The documents returned by the aggregation query, either as a cursor or stored via

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

4 in another collection, are limited to 16MB. That is, they can’t be bigger than the maximum size of a MongoDB document.

If you are likely to exceed this limit, then you should specify that the output of the aggregation query will be as a cursor and not as a document.

I will be showing MongoDB aggregate examples for the most important pipeline stages.

To illustrate the examples, I am going to use two collections. The first is called

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

5 and is made up of these documents (the data is not real):

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

If you would like to test these examples on your own installation, you can insert them with the bulk command below, or :

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

The second and last collection is called

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

6 and looks like this:

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

Again, you can insert them in the same way, using the following code or by importing as a JSON file:

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

where you’ll find the JSON files available for download.

The

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 stage allows us to choose just those documents from a collection that we want to work with. It does this by filtering out those that do not follow our requirements.

In the following example, we only want to work with those documents which specify that

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

8 is the value of the field

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

9, and

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

0 is the value of the field

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

In order to get a readable output, I am going to add

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

2 at the end of all the commands.

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

The output is…

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

It is rare that you ever need to retrieve all the fields in your documents. It is good practice to return only those fields you need so as to avoid processing more data than is necessary.

The

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

3 stage is used to do this and to add any calculated fields that you need.

In this example, we only need the fields

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

1 and

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

In the code that follows, please note that:

We must explicitly write

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

7 when this field is not required

Apart from the

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

8 field, it is sufficient to specify only those fields we need to obtain as a result of the query

This stage …

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

..will give the result …

{ "country" : "Spain", "city" : "Salamanca", "name" : "USAL" }
{ "country" : "Spain", "city" : "Salamanca", "name" : "UPSA" }

Here’s another example of MongoDB $project.

With the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

0 stage, we can perform all the aggregation or summary queries that we need, such as finding counts, totals, averages or maximums.

In this example, we want to know the number of documents per university in our ‘

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

0’ collection:

The query …

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

..will produce this result …

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

The $group stage supports certain expressions (operators) allowing users to perform arithmetic, array, boolean and other operations as part of the aggregation pipeline.

OperatorMeaning$countCalculates the quantity of documents in the given group.$maxDisplays the maximum value of a document’s field in the collection.$minDisplays the minimum value of a document’s field in the collection.$avgDisplays the average value of a document’s field in the collection.$sumSums up the specified values of all documents in the collection.$pushAdds extra values into the array of the resulting document.

Check out to see other MongoDB operators and learn more on this topic.

This is an unusual type of stage because it allows you to carry the results of your aggregation over into a new collection, or into an existing one after dropping it, or even adding them to the existing documents (new in 4.1.2 version).

The

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

4 stage must be the last stage in the pipeline.

For the first time, we are using an aggregation with more than one stage. We now have two, a

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

0 and an

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

Now, we check the content of the new ‘

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

4’ collection:

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

Here’s how we used the $out stage in this three-part example.

Now we’ve produced a multi-stage aggregation, we can go on to build up a pipeline.

The

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

5 stage in MongoDB is commonly found in a pipeline because it is a means to an end.

You cannot work directly on the elements of an array within a document with stages such as

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

0. The

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

5 stage enables us to work with the values of the fields within an array.

Where there is an array field within the input documents, you will sometimes need to output the document several times, once for every element of that array.

Each copy of the document has the array field replaced with the successive element.

In the next example, I am going to apply the stage only to the document whose field

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

6 contains the value

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

This is the document:

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

Now, we apply the

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

5 stage, over the student’s array, and check that we get a document per each element of the array.

The first document is made up of the fields in the first element of the array and the rest of the common fields.

The second document is made up of the fields in the second element of the array and the rest of the common fields, and so on.

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

You need the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 stage to sort your results by the value of a specific field.

For example, let’s sort the documents obtained as a result of the

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

5 stage by the number of students in descending order.

In order to get a lesser output, I am going to project only the year and the number of students.

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

This gives the result …

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

The

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 stage can be used with other stages to reduce data in a MongoDB collection to just exactly what you need.

What if you are only interested in the first two results of your query? It is as simple as:

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

Notice that when you need to limit the number of sorted documents, you must use the

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

4 stage just after the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

Now we have a full pipeline.

We can paste this whole MongoDB aggregate query and all its stages straight into the .

It is pasted in by copying it and clicking on the code paste button as shown.

Read more about the Aggregation Editor, Studio 3T’s stage-by-stage MongoDB aggregation query builder.

Just the part shown below is copied and pasted in

db.collectionName.aggregate(pipeline, { allowDiskUse : true })

In the next screenshot, we can see the full pipeline in Studio 3T and its output.

Removing stages in Studio 3T is a simple matter of using the button shown in the next screenshot.

Studio 3T’s Aggregation Editor supports these MongoDB aggregation operators and stages.

It is possible that you need to make some changes to your output in the way of new fields. In the next example, we want to add the year of the foundation of the university.

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

This gives the result …

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

The

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

6 stage provides an easy way to check the number of documents obtained in the output of the previous stages of the pipeline.

Let’s see it in action:

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

This provides the total of those years for which we know the number of students at the University.

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

Because MongoDB is document-based, we can shape our documents the way we need. However, there is often a requirement to use information from more than one collection.

Using the

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

7, here is an aggregate query that merges fields from two collections.

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

Need another

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

7 example? Here’s one.

If you want this query to run fast, you are going to need to index the

db.courses.insert([
{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
},
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
},
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}
])

6 field in the

db.universities.aggregate([
  { $match : { country : 'Spain', city : 'Salamanca' } }
]).pretty()

0 collection and the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

1 field in the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

2 collection.

In other words, do not forget to index the fields involved in the

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

Here’s the quickest way to .

This stage is a shortcut for grouping, counting and then sorting in descending order the number of different values in a field.

Suppose you want to know the number of courses per level, sorted in descending order. The following is the query you would need to build:

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

This is the output:

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

Sometimes when creating a report on data, you find that you need to do the same preliminary processing for a number of reports, and you are faced with having to create and maintain an intermediate collection.

You may, for example, do a weekly summary of trading that is used by all subsequent reports. You might have wished it were possible to run more than one pipeline simultaneously over the output of a single aggregation pipeline.

We can now do it within a single pipeline thanks to the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

4 stage.

Take a look at this example:

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

What we have done is to create two reports from our database of university courses. CountingLevels and YearWithLessStudents.

They both used the output from the first two stages, the

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 and the

{
"_id" : ObjectId("5b7d9d9efbc9884f689cdba9"),
"country" : "Spain","city" : "Salamanca",
"name" : "USAL",
"location" : {
      "type" : "Point",
      "coordinates" : [
            -5.6722512,
             17,
             40.9607792
       ]
},
"students" : [
		{
			"year" : 2014,
			"number" : 24774
		},
		{
			"year" : 2015,
			"number" : 23166
		},
		{
			"year" : 2016,
			"number" : 21913
		},
		{
			"year" : 2017,
			"number" : 21715
		}
	]
}
{
	"_id" : ObjectId("5b7d9d9efbc9884f689cdbaa"),
	"country" : "Spain",
	"city" : "Salamanca",
	"name" : "UPSA",
	"location" : {
		"type" : "Point",
		"coordinates" : [
			-5.6691191,
			17,
			40.9631732
		]
	},
	"students" : [
		{
			"year" : 2014,
			"number" : 4788
		},
		{
			"year" : 2015,
			"number" : 4821
		},
		{
			"year" : 2016,
			"number" : 6550
		},
		{
			"year" : 2017,
			"number" : 6125
		}
	]
}

With a large collection, this can save a great deal of processing time by avoiding repetition, and we no longer need to write an intermediate temporary collection.

{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
}
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}

Read more about other use cases of the $facet stage in MongoDB aggregation.

Now, try to resolve the next exercise by yourself.

How do we get the total number of students that have ever belonged to each one of the universities?

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

The output:

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

Yes, I have combined two stages. But, how do we build a query that sorts the output by the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

7 field in a descending order?

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

Right, we need to apply the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

8 stage at the output of the

db.universities.aggregate([
  { $project : { _id : 0, country : 1, city : 1, name : 1 } }
]).pretty()

I mentioned earlier that it is very easy, and indeed essential, to check that the stages of our query are performing the way we need them to perform.

With Studio 3T, you have two dedicated panels to .

The aggregation pipeline automatically reshapes the query with the aim of improving its performance.

If you have both

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 and

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 stages, it is always better to use the

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 before the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 in order to minimize the number of documents that the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 stage has to deal with.

To take advantage of indexes, you must do it in the first stage of your pipeline. And here, you must use the

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

9 or the

{
  university : 'USAL',
  name : 'Computer Science',
  level : 'Excellent'
}
{
  university : 'USAL',
  name : 'Electronics',
  level : 'Intermediate'
}
{
  university : 'USAL',
  name : 'Communication',
  level : 'Excellent'
}

1 stages.

We can check whether the query is using an index through the

{ "country" : "Spain", "city" : "Salamanca", "name" : "USAL" }
{ "country" : "Spain", "city" : "Salamanca", "name" : "UPSA" }

7 method.

use 3tdb
db.universities.insert([
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'USAL',
  location : {
    type : 'Point',
    coordinates : [ -5.6722512,17, 40.9607792 ]
  },
  students : [
    { year : 2014, number : 24774 },
    { year : 2015, number : 23166 },
    { year : 2016, number : 21913 },
    { year : 2017, number : 21715 }
  ]
},
{
  country : 'Spain',
  city : 'Salamanca',
  name : 'UPSA',
  location : {
    type : 'Point',
    coordinates : [ -5.6691191,17, 40.9631732 ]
  },
  students : [
    { year : 2014, number : 4788 },
    { year : 2015, number : 4821 },
    { year : 2016, number : 6550 },
    { year : 2017, number : 6125 }
  ]
}
])

You can always view the

{ "country" : "Spain", "city" : "Salamanca", "name" : "USAL" }
{ "country" : "Spain", "city" : "Salamanca", "name" : "UPSA" }

7 plan of any aggregation query as a diagram or in JSON by clicking on the Explain tab.

I’ve introduced the MongoDB aggregation pipeline and demonstrated with examples how to use only some stages.

The more that you use MongoDB, the more important the aggregation pipeline becomes in allowing you to do all those reporting, transforming, and advanced querying tasks that are so integral to the work of a database developer.

With the more complex pipeline processes, it becomes increasingly important to check and debug the input and output of every stage.

There is always a point at which one needs to paste the growing aggregation pipeline into an IDE for MongoDB such as Studio 3T, with a built-in Aggregation Editor, so you can debug every stage independently.

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