🔥Mongodb Aggregation Tutorial | Aggregation Functions in Mongodb | Mongodb Tutorial | Simplicode
Summary
TLDRThis 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
📚 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.
🔍 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.
🛠️ 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.
📈 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.
Mindmap
Keywords
💡Aggregation
💡MongoDB
💡Aggregation Pipeline
💡Match
💡Group
💡Sort
💡Project
💡Sum
💡Average
💡Minimum and Maximum
💡Push
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
[Music]
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
[Music]
関連動画をさらに表示
SQL vs NoSQL in 2024 Make the Right Choice (Difference Explained)
Spring Data JPA Native Query Examples
SQL Basics for Beginners | Learn SQL | SQL Tutorial for Beginners | Edureka
ETL - Extract Transform Load | Summary of all the key concepts in building ETL Pipeline
Power BI Project For Beginners | Sales Insights Data Analysis Project - 3 - Data Analysis Using SQL
What Is MongoDB? | What Is MongoDB And How It Works | MongoDB Tutorial For Beginners | Simplilearn
5.0 / 5 (0 votes)