🔥Mongodb Aggregation Tutorial | Aggregation Functions in Mongodb | Mongodb Tutorial | Simplicode

Summary

This tutorial by Simply Code delves into MongoDB's aggregation framework, essential for handling complex queries. It explains the fundamentals of aggregate queries, introduces key stages of the aggregation pipeline, and illustrates their application with examples. The video guides viewers on using match, group, sort, and project stages to process and analyze data, emphasizing the power of aggregation for pattern recognition and data insights in both SQL and NoSQL environments.

Takeaways

• 📚 MongoDB Aggregation is used to perform complex operations on data, processing multiple documents and returning a single result.
• 🔍 The 'match' stage in MongoDB's aggregation pipeline is similar to the 'WHERE' clause in SQL, filtering documents based on specified conditions.
• 👥 The 'group' stage groups documents with similar characteristics, akin to the 'GROUP BY' clause in SQL, allowing for operations like counting or summing values.
• 📈 The 'sort' stage orders the documents in the output, either in ascending or descending order, based on specified fields.
• 📊 The 'project' stage is used to include or exclude certain fields in the final output, shaping the result set to include only necessary data.
• 🔢 Aggregation operations in MongoDB include sum, average, min, max, and push, which can be used to perform calculations and manipulate array data.
• 📝 The script provided a practical example of using the aggregation pipeline with stages like match, group, and sort to perform operations on a collection of documents.
• 📉 The 'count' operation within the aggregation pipeline can be used to determine the number of documents that meet certain criteria, such as belonging to a specific department.
• 🛠️ Aggregation is essential for analyzing unstructured data in MongoDB, as the data model can change over time with new entries.
• 📈 The tutorial demonstrated the use of MongoDB shell for executing aggregation operations and analyzing the results.
• 👨‍🏫 The video tutorial aimed to educate viewers on the fundamentals of creating aggregate queries and using various expressions in MongoDB's aggregation framework.

Q & A

What is the primary purpose of MongoDB aggregation?

The primary purpose of MongoDB aggregation is to process data records in the database during an aggregation operation and return a single computed result. It groups multiple documents, applies an aggregation operation, and provides a single result.

How does MongoDB aggregation compare to SQL aggregation?

MongoDB aggregation is similar to SQL aggregation in that it allows for filtering, grouping, sorting, and reorganizing data to return documents in a collection, just like using the GROUP BY clause in SQL with operations like COUNT, AVG, MIN, and MAX for complex aggregations.

What are the main reasons for using aggregation in MongoDB?

Aggregation is used to gather data from different sources into a single outcome, process and analyze large amounts of nested data, perform complex operations on the data, and filter and sort documents to analyze data changes, especially important for unstructured data in MongoDB.

Can you explain the MongoDB aggregation pipeline stages?

The MongoDB aggregation pipeline stages include match, group, sort, and project. Match filters documents, group consolidates documents into groups, sort orders the documents, and project specifies the shape of the resulting documents.

How does the 'match' stage in the aggregation pipeline work?

The 'match' stage in the aggregation pipeline filters the input documents to pass only those documents that match the given condition to the next pipeline stage.

What is the 'group' stage in MongoDB aggregation used for?

The 'group' stage in MongoDB aggregation is used to group documents by the specified '_id' expression and can calculate aggregated values like sum, average, min, or max for each group.

What does the 'sort' stage in the aggregation pipeline do?

The 'sort' stage in the aggregation pipeline orders the input documents in ascending or descending order based on the specified fields.

What is the 'project' stage in MongoDB aggregation responsible for?

The 'project' stage in MongoDB aggregation is used to include, exclude, or add new fields to the documents and to reshape each document in the stream.

How can you perform a count operation using MongoDB aggregation to find the total number of employees in a department?

You can use the 'match' stage to filter employees by department ID and then use the 'count' function in the aggregation pipeline to get the total number of employees in that department.

What are some of the main aggregation operations in MongoDB similar to SQL?

Some of the main aggregation operations in MongoDB similar to SQL include SUM, AVG (average), MIN, MAX, and an additional operation, PUSH, which is used to add values to an array in the associated document.

Outlines

00:00

📚 Introduction to MongoDB Aggregation

This paragraph introduces the concept of MongoDB aggregation, explaining its necessity as queries become more complex. It emphasizes the aggregation pipeline's ability to perform complex operations on data, grouping documents, and returning a single computed result. The tutorial aims to cover the fundamentals of creating aggregate queries, using various expressions, and the stages of the aggregation pipeline with examples. It also encourages new viewers to subscribe to the channel for the latest tech content.

05:00

🔍 Understanding Aggregation in MongoDB

The paragraph delves into the definition of aggregation in MongoDB, comparing it to SQL's 'GROUP BY' clause and explaining how it processes data records to return a single result. It discusses the use of aggregation for filtering, grouping, and sorting data, and highlights the importance of aggregation in analyzing data to find patterns or insights. The paragraph also touches on MongoDB's flexibility with data models and the need for aggregation to handle unstructured data effectively.

10:03

🛠️ Aggregation Pipeline Stages in MongoDB

This section outlines the four main stages of the MongoDB aggregation pipeline: match, group, sort, and project. It describes how these stages work together to perform aggregation operations. The match stage is used to filter documents, group to collect related data, sort to order the data, and project to shape the output. An example using a collection named 'orders' illustrates how these stages can be applied to perform an aggregation operation, including grouping by customer ID and summing amounts.

15:06

📈 Aggregation Operations and MongoDB Shell Execution

The final paragraph discusses the main aggregation operations in MongoDB, such as sum, average, min, max, and push, which are analogous to SQL operations. It then transitions into a practical demonstration using the MongoDB shell, showing how to execute aggregation operations on a collection named 'employee'. The paragraph includes a step-by-step guide on writing queries for match and group stages, and how to count the total number of employees in specific departments, providing a hands-on understanding of MongoDB aggregation.

Keywords

💡Aggregation

Aggregation in the context of databases refers to the process of combining data from multiple records into a single result. In the video, it is a key concept for understanding MongoDB's approach to handling complex queries. It is used to perform operations such as filtering, grouping, and reorganizing data. The script provides examples of how aggregation is used to simplify complex queries and retrieve summarized data from a collection of documents.

💡MongoDB

MongoDB is a NoSQL database that is known for its document-oriented structure. The script discusses MongoDB's aggregation framework, which is used for processing and grouping data records to return a computed result. MongoDB's aggregation is highlighted as a powerful tool for data analysis, especially when dealing with unstructured data.

💡Aggregation Pipeline

The Aggregation Pipeline is a concept in MongoDB that allows for a multi-stage processing of data. The script explains that it consists of stages like 'match', 'group', 'sort', and 'project', which are used to filter, group, order, and transform data, respectively. The pipeline is central to the video's tutorial on how to perform complex data operations in MongoDB.

💡Match

In the context of the video, 'match' is an aggregation pipeline stage used to filter documents to pass into the next stage of the pipeline. It is compared to the 'WHERE' clause in SQL and is used to narrow down the dataset for further operations. The script demonstrates using 'match' to select documents with a specific status or department ID.

💡Group

The 'group' stage in MongoDB's aggregation pipeline is used to group documents by specified fields, similar to the 'GROUP BY' clause in SQL. The video script illustrates how 'group' can be used to consolidate data based on common attributes, such as grouping employees by department ID and then performing calculations on the grouped data.

💡Sort

'Sort' is an aggregation pipeline stage that orders the output documents. The script mentions that sorting can be done in ascending or descending order based on the field values. It is used to organize the data in a specific sequence, which is crucial for analysis and presentation.

💡Project

The 'project' stage in MongoDB's aggregation pipeline is used to include or exclude certain fields from the output documents. The script explains that 'project' can be used to shape the result by removing unnecessary fields or adding computed fields, thus customizing the final output for specific needs.

💡Sum

'Sum' is an aggregation operation that adds up values from documents in a collection. The video script uses 'sum' as an example of an operation that can be performed within the aggregation pipeline, such as calculating the total amount for orders or the total salary for employees in a department.

💡Average

'Average' is another aggregation operation that computes the mean value of a set of numbers. In the script, 'average' is mentioned as a way to find the mean salary or other numerical values across a group of documents, providing an insight into central tendencies within the data.

💡Minimum and Maximum

The 'minimum' and 'maximum' are aggregation operations that return the smallest and largest values from a collection, respectively. The script discusses these operations as part of the set of standard aggregation functions in MongoDB, which can be used to find extremes in data sets, such as the lowest or highest salaries.

💡Push

'Push' is a MongoDB aggregation operation used to append values to an array. The script points out that since MongoDB is a NoSQL database, it often deals with arrays of elements, and 'push' is a way to add items to these arrays within the aggregation pipeline, which is a unique operation not typically found in SQL databases.

Highlights

Introduction to MongoDB aggregation concepts for complex queries.

Explanation of how MongoDB processes data records during an aggregation operation.

MongoDB's wide range of aggregation operations compared to relational databases.

The use of aggregation in MongoDB for filtering, grouping, sorting, and reorganizing data.

The SQL comparison with MongoDB's aggregation method, including group by clause and operations like count, average, min, and max.

Reasons for using aggregation: processing data from different sources to a single outcome.

Aggregation's role in handling nested data and performing complex operations.

The necessity of aggregation for analyzing data and finding patterns or information.

Aggregation's utility in dealing with the changing data model in MongoDB and analyzing unstructured data.

MongoDB's aggregation pipeline stages: match, group, sort, and project.

Detailed explanation of the match method in the aggregation pipeline.

The group method's functionality similar to SQL's group by clause.

How to use the sort method for ordering data in the aggregation pipeline.

The project method's role in removing unnecessary fields from the result.

An example of how aggregation works in MongoDB with a collection named 'orders'.

MongoDB shell demonstration of executing aggregation pipeline stages.

Using match, group, and count in an aggregation pipeline to find total employees in a department.

MongoDB aggregation operations: sum, average, min, max, and push.

End of session summary and invitation for further queries in the comments section.

Transcripts

foreign

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I hope you guys are doing and staying

safe welcome to yet another tutorial by

simply code in today's session we'll be

discussing about mongodb aggregation

Concepts now when you first start

working with mongodb you will most

likely use the fine method or command

for a variety of queries now however as

your queries become more complex you

will need to learn more about

aggregation in mogodb which is used to

perform various complex operations so in

this tutorial we'll be covering the

fundamentals of creating aggregate

queries in mongodb and how to use

various expressions in mongodb

aggregation and furthermore we'll

introduce the most important stages of

aggregation pipeline along with examples

of how to apply each one to this

pipeline stages but before we begin if

you're new to the channel and haven't

subscribed already consider getting

subscribed to our channel it's simply

code to stay updated with all the latest

tech content and hit that Bell icon to

never miss an update from us so without

any further Ado let's get started with

today's topic

all right firstly let us understand what

is aggregation and mongodb mongodb

process the data records in the database

during an aggregation operation and

returns a single computed result it

actually groups multiple documents

present in a collection applies an

aggregation operation to it and then

provides the user with a single result

now mongodb offers a wide range of

aggregation operations just like many

other database systems in relational

databases as well mongodb being a no SQL

database also provides with the same

functionality this enables you to filter

data as you might with a query as well

as grouping data sorting data into a

particular order or reorganizing for

returning the documents in the

collection now this is just similar to

the SQL wherein we use the group by

clause in our aggregation function with

the help of certain operations like

count average some minimum and value to

perform complex aggregation operations

in the similar way mongodb aggregation

is also similar to that of the SQL

aggregation method that we follow

so I hope you understood what is

aggregation in mongodb Let Us Now

understand why we use aggregation now

data from different sources is gathered

by an aggregation method which are then

processed to a single outcome now to

perform an aggregate function in a

relational database the database

management system typically extracts

data from multiple rows of the same

table but in a document oriented

database like mongodb the database will

gather information from various

documents in the same collection so it

basically group values from multiple

documents together to a single resultant

value now the next one is it fetches a

lot of nested data to perform complex

operations Now when using a database

management system you must run an

operation known as a query right each

time you want to retrieve data from

database however queries only return the

information that is already present in

the database you will frequently need to

carry out another type of operation

which is known as aggregation in order

to analyze your data in order to find

patterns or other information about the

data so if there are if there is a

complex operation that is needed for the

data that is present in the documents

you need aggregation and finally it

filters and sort documents and analyze

data changes now uh mongodb being an SQL

nosql database the data model will

frequently change as the data that is

being you know inserting into the

database might change over the time so

in order to analyze this unstructured

data it becomes quite important that you

uh make a proper you know filtration and

sorting of these documents to get a

proper insights for your businesses and

as well as your you know requirements so

in that case aggregation can be quite

useful which simplifies you know the

complexity of the query that you have

written and fetches the data in no time

so these are some of the main reasons on

why we use aggregation

now aggregation uh in mongodb is

performed using uh aggregation pipeline

stages with consists of four different

you know methods the first one is match

next we have group Next we have sort and

finally we have project these four

combinedly are known as aggregation

pipeline stages which perform

aggregation as per the requirement now

firstly when you take a collection and

the first thing you need to is if you

want to match only certain Fields you

know within your documents to perform

aggregation then you can use the match

method next we have the group now just

like the group by clause in SQL you can

use the group uh you know function here

as well in order to group all this

similar or the related uh type of data

in your

you know uh present in your collections

that is that the data that is present in

your documents to in order to retrieve

the similar kind of data you can use the

group Next sort again sorting can be

done in any other way like from

ascending order or descending order next

we have the project as we discussed

earlier in a previous tutorial project

is used to uh you know remove the

unnecessary fields that you want in a

result instead and finally after you

know matching all the four uh pipeline

stages then you get the resultant output

so let us understand this uh with an

example wherein we'll discuss how

aggregation Works in mongodb now as

discussed earlier the firstly the match

condition uh you know specifies only the

related documents that you want to

consider so for example I have a

collection name orders here and I'm

performing uh you know an aggregation

operation here so first I've mentioned

the match for status a so I have like

four documents here if you see any

orders like for the four different

customer IDs we have the amount and

status now for I want to retrieve only

uh those documents whose status is a so

for that I'm using the match command

here so match command only retrieve the

one that you have specified here which

is data say so we have only three

records that have the status as field

and their value is a so we have sorted

initially with the match command next we

want to group the values in the order of

their let's say IDs and then so if you

look at here we have two similar IDs

which is A123 and b212 so what group

does is it will match all the similar

related fields together and produce a

result so you can see the A123 and B21

b212 are separated into a different

groups and finally what I'm doing is I'm

sorting I'm sorting and I'm applying an

operation here with sum so what it does

is it will basically calculate the total

amount here so if you look at the first

ID A123 the amount is 500 and 250. so

the resultant output would be 750 and

finally we have the customer ID of p212

the amount is just 200. so in this way

the aggregation pipeline Works in a

methodology of wherein it matches the

values that is the similar values from

the documents then you can even group

the data that you want to show in a

resultant output and you can perform

various operation uh using the aggregate

functions like some minimum average with

the help of sorting as well so this is

basically how aggregation Works in mover

DB so and finally as discussed earlier

just like similar to SQL we have the

same five or the main five aggregation

operations in SQL which is the first one

is sum it basically adds up the values

of every documents of a collection next

we have the average which computes the

average value of every document of a

collection next we have minimum or Min

Returns the minimum of all values from

the collection and next we have Max

which is opposite to that of minimum

minimum which Returns the maximum of all

values from within the collection and

finally we have an additional uh you

know operation which is a push operation

since mongodb is non or relational

database we also work on the array of

elements so in order to add the values

to an array in the associated document

within a collection you can use the push

operation as well now this might be a

bit confusing for you guys now so let us

jump into mongodb shell for execution

part where it will perform uh

different operations using the

aggregation pipeline stages and see how

they are getting implemented and also

perform a detailed analysis on the uh

data The Collection that we have and we

will bring out some inferences using the

aggregation operations using various

expressions like some average minimum

and maximum so without any further delay

let us jump into mongodbition so as you

can see we have opened the mongodb shell

command prompt line so let us just uh

use the command show DBS to find out the

database present so we have different

databases like admin config local and

simplified one values is simply code one

as usual so use

write the database name user B code one

now let us see the collections that are

present in the table so we have the

employee collection again so let us

write the command using the DB dot find

DB dot collection name.find which is

employee dot find

so it will retrieve all the values that

are present in this collection so we

have our different fields like employee

ID first name last name email phone

number hiring date job ID salary manager

ID and Department ID so all right let us

now perform the aggregate operations in

a sequential way firstly we look at the

match aggregation you know stage where

it will match the related fields that we

want in a result then set now let's say

if I want to match the department ID

whose all the Embers who belong to the

department added let's say 100 so in

that case the following query would be

DB Dot

employee dot mention the aggregate

keyword here which is a must otherwise

it will throw an error when open the

parenthesis and mention the square

brackets and use the dollar symbol to

write the statement for match

put the column

and open the parenthesis and mention the

department ID

field

which is uh on which we are basically

you know matching the attic watch so

mentioned it again the colon and within

single quotes mentioned the department

ID name so since we have taken as 100

I'm just taking it close the uh flower

brackets

two times and again mention the square

bracket once again and close the

parenthesis all right so this is the uh

basically the query that we have written

here which is DB dot imported Aggregate

and we're using the match aggregation

method here wherein we are matching all

the records of the employees whose

Department ID belongs to 100 so let's

now enter

uh I think there is an error here

so we had a error in the syntax guys uh

so as you can see in the output we can

see only those records of the employees

were in it is showing uh the records

whose Department ID is hundred so we

have the employee id108 109 110 111 and

112 113 and all of them have their

employee Department IDs sorry our

department IDs 100. so I hope you

understood this so let us now uh go

through the next stage of aggregation

Pipeline with valenvale group uh the

values now so for that the following

query would be DB dot employee

dot aggregate

which is the keyword again open the

parenthesis

and mention the double

square brackets open the flower brackets

and use the dollar symbol again write

the group keyword

and open the flower brackets again

and mention the ID here so I'll explain

why we are taking this here again

so firstly let us just write the you

know command and then I'll explain to

you guys that why we are implementing

that so I want to group all the

basically the department IDs right so I

am taking Department ID

so mention the dollar symbol

so the field name which is the

department ID

close the

flower brackets twice mentioned are a

double bracket square bracket as well

so when you enter uh you can see the

list of all the department IDs that are

present in our collection so we have

various I uh Department IDs like 60 10

30 70 40 110 115 20 98 and so on

so now you might have a doubt that why

we are using the ID field layer now we

are using the group stage to group the

input documents by the specified you

know ID expression which returns a

single document containing the all the

cumulative values of all the ER

Department IDs that we have taken here

so in this above example only the group

stage is specified in the pipeline

whereas the group uses the ID field to

calculate at the value for all the input

documents as a whole so the expression

ID column uh the dollar symbol

Department ID creates a distinct group

on the field Department name since you

don't uh we didn't calculate any

accumulated values in our document it

Returns the distinct values of

Department IDs that are present in the

table or in a document so in this way

you can use the group aggregation method

as well

so this is how you can use the match and

group stages wherein you can combine

both these as well so next let us look

at how to create an aggregation

operation using one of the expression

that we have like count average sum or

minimum now let's say if I want to count

the total employees in a particular

Department in that case I'll basically

use the match statement wherein it will

group all the values of the employees

whose Department IDs in a particular you

know value and then it will count the

total number of employees in that group

so for that the following query would be

DB Dot

employee Dot

aggregate

open the

open the brackets and uh

square brackets as well and then mention

the flower back brackets and then use

the dollar match group stage

wherein we are matching uh you know

the department ID let's say Department

ID of 60. so in that case it would be

Department

ID

I have taken the value as 60 mentioned

in the double quotes

and close the brackets

put a comma and then basically we are

counting right so I'll use the count

function here

so put the dollar sign

and check the count keyword

and let's say

the statement would be total

employees

in Department

60.

right so

mention the put these close the uh

brackets as well

square brackets

second

so when you press the enter in a

resultant output it states that total

employees and Department 60 is 5. so

similarly you can check for various

other departments as well so let us just

verify if it's showing correctly or not

I'll just copy paste this and let's say

for 100 now I want the total number of

employees present in the department 100

so in that case I'll just mention it

I'll just change the values and let us

now print enter so as you can see in the

resultant set or result in the output

you can see the total number of

employees and Department hundred is six

wherein we have the total number of

employees in Department 60 is five so in

this way you can use the various

aggregation stages using the match group

and as well as sort as well and perform

various aggregation operations like Sim

uh some minimum maximum as well

so I think we have covered most of the

concepts that we have discussed in our

you know tutorial so that brings us to

the end of today's session guys I hope

you understood uh what aggregation

methods and how they are used in mongodb

thank you for watching the video guys if

you found this tutorial informative and

helpful give it a thumbs up and share it

with your friends and colleagues if you

have any further queries regarding any

of the topics covered in today's session

feel free to let us know in the comment

section below and a team of experts will

be more than happy to help resolve all

your queries at the earliest until next

time stay safe and keep coding

foreign

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