MONGO-DB

THIS REPOSITORY ABOUT BASICS OF MONGODB AND SMALL PROJECTS.

• MONGO-DB PRACTICE FILES :

• MONGODB ALL PRACTICE CODE

BASIC COMMANDS TO START MONGODB

• mongosh : To Start Mongo-Shell

• show dbs : To View All Data-Bases

• use shop: Creating Shop Data-Base

• show collections : To see all the collections inside shop data-base

• db.createCollection('<collection-name>’) : To Create Collections

• db.<collection-name>.drop(): To Delete Collections

• db.dropDatabase(): To Drop Data-Base

CREATION AND INSERTION OF DATA INSIDE MONGODB

students> use student switched to db student

• For Creating Collections: student> db.createCollection('data') { ok: 1 }

• For Inserting One Data: student> db.data.insertOne({'name':'Kapil',age:32}) { acknowledged: true, insertedId: ObjectId('66441110832f83448e46b799') }

• For Inserting Many Data student> db.data.insertMany([{'name':'Kapil',age:32},{'name':'John',age:44},{'name':'Sachin',age:50}]) { acknowledged: true, insertedIds: { '0': ObjectId('66441286832f83448e46b79a'), '1': ObjectId('66441286832f83448e46b79b'), '2': ObjectId('66441286832f83448e46b79c') } }

• For Find All Data: db.data.find() [ { _id: ObjectId('66441110832f83448e46b799'), name: 'Kapil', age: 32 }, { _id: ObjectId('66441286832f83448e46b79a'), name: 'Kapil', age: 32 }, { _id: ObjectId('66441286832f83448e46b79b'), name: 'John', age: 44 }, { _id: ObjectId('66441286832f83448e46b79c'), name: 'Sachin', age: 50 } ]

• For Inserting One Data: student> db.data.insertOne({'name':'Kapil',age:32,'course-name':'ComputerScience'}) { acknowledged: true, insertedId: ObjectId('6644144f832f83448e46b79d') }

• For Finding All Data: student> db.data.find() [ { _id: ObjectId('66441110832f83448e46b799'), name: 'Kapil', age: 32 }, { _id: ObjectId('66441286832f83448e46b79a'), name: 'Kapil', age: 32 }, { _id: ObjectId('66441286832f83448e46b79b'), name: 'John', age: 44 }, { _id: ObjectId('66441286832f83448e46b79c'), name: 'Sachin', age: 50 }, { _id: ObjectId('6644144f832f83448e46b79d'), name: 'Kapil', age: 32, 'course-name': 'ComputerScience' } ]

• For Finding Data with specific field: student> db.data.find({'name':'Kapil'}) [ { _id: ObjectId('66441110832f83448e46b799'), name: 'Kapil', age: 32 }, { _id: ObjectId('66441286832f83448e46b79a'), name: 'Kapil', age: 32 }, { _id: ObjectId('6644144f832f83448e46b79d'), name: 'Kapil', age: 32, 'course-name': 'ComputerScience' } ]

• For Find One Sing Data with the specific Field: student> db.data.findOne({'name':'Kapil'}) { _id: ObjectId('66441110832f83448e46b799'), name: 'Kapil', age: 32 }

IMPORTING JSON DATA IN MONGODB

• Command

mongoimport jsonfile.json –d database_name –c collection_name

• Example

mongoimport D:\mongoimport\products.json -d shop -c products

• Output

C:\Users\kapil>mongoimport D:\mongoimport\products.json -d shop -c products 2024-05-16T07:22:02.126+0530 connected to: mongodb://localhost/ 2024-05-16T07:22:02.351+0530 10355 document(s) imported successfully. 0 document(s) failed to import.

IMPORT ARRAY OF OBJECTS OR JSON ARRAY

• Command

mongoimport products.json -d shop -c products --jsonArray

• Example

mongoimport D:\mongoimport\sales.json -d shop -c sales --jsonArray

Here, --jsonArray accepts the import of data expressed with multiple MongoDB documents within a single JSON array.

• Output

C:\Users\kapil>mongoimport D:\mongoimport\sales.json -d shop -c sales --jsonArray 2024-05-16T07:30:08.202+0530 connected to: mongodb://localhost/ 2024-05-16T07:30:08.228+0530 5 document(s) imported successfully. 0 document(s) failed to import.

EXPORTING JSON DATA IN MONGODB

• Command

mongoexport –d database_name –c collection_name -o outpfile.json

• Example

mongoexport -d shop -c sales -o D:\mongoimport\salestest.json

• Output

C:\Users\kapil>mongoexport -d shop -c sales -o D:\mongoimport\salestest.json 2024-05-16T07:42:49.944+0530 connected to: mongodb://localhost/ 2024-05-16T07:42:49.974+0530 exported 5 records

COMPARISON OPERATORS USED IN MONGODB

• $eq - equalto

• $ne - notequalto

• $gt - greaterthan

• $gte - greaterthaneualto

• $lt - lessthan

• $lte - lessthanequalto

• $in - inoperator

• $nin - notinoperator

DESCRIPTION

• $eq : equal-to

Example: db.products.find({'price':{$eg:699} })

Output: [ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c236a2e32f4a51b19b9280'), name: 'Elegant Dining Table', company: '64c23350e32f4a51b19b9239', price: 699, colors: [ '#ffffff', '#ffcc00', '#cc9900' ], image: '/images/product-dining-table.png', category: '64c2342de32f4a51b19b925a', isFeatured: true },

]

• $ne: not-equal-tp

Example : db.products.find({'price':{$ne:99} })

Output : [ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, ]

• $gt` : greater-than

Example : db.products.find({'price':{$gt:999} })

Output : [ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c236a2e32f4a51b19b9282'), name: 'Outdoor Patio Set', company: '64c23350e32f4a51b19b923b', price: 1199, colors: [ '#333333', '#ff9900', '#660000' ], image: '/images/product-outdoor-patio.png', category: '64c2342de32f4a51b19b925a', isFeatured: true } ]

• $gte : greater-than-equal-to

Example : db.products.find({'price':{$gte:1999} })

Output : [ { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

• $lt: less-than

Example : db.products.find({'price':{$lt:2} })

Output: [ { _id: ObjectId('64c23e543e180eb8a268302f'), name: 'Small Fresh Ball', company: ObjectId('64c23350e32f4a51b19b924c'), price: 1, colors: [ '#176e6d' ], image: '/images/product-Oriental.png', category: ObjectId('64c2342de32f4a51b19b9252'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f856019ba'), name: 'Tasty Cotton Shoes', company: ObjectId('64c23350e32f4a51b19b924b'), price: 1, colors: [ '#7d4625' ], image: '/images/product-Luxurious.png', category: ObjectId('64c2342de32f4a51b19b925e'), isFeatured: false, __v: 0 } ]

• $lte: less-than-eual-to

Example : db.products.find({'price':{$lte:9} })

Output : [ { _id: ObjectId('64c23e543e180eb8a2683025'), name: 'Tasty Concrete Computer', company: ObjectId('64c23350e32f4a51b19b9249'), price: 9, colors: [ '#243172' ], image: '/images/product-Licensed.png', category: ObjectId('64c2342de32f4a51b19b9258'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23e543e180eb8a268300e'), name: 'Recycled Plastic Bacon', company: ObjectId('64c23350e32f4a51b19b9240'), price: 6, colors: [ '#746c54' ], image: '/images/product-Sleek.png', category: ObjectId('64c2342de32f4a51b19b924e'), isFeatured: false, __v: 0 } ]

• $in: in-operator

Example : db.products.find({'price': {$in: [699,129,39]}})

It will show the price only-in 699,129,29

Output :

[ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9266'), name: 'Fashionable Sunglasses', company: '64c23350e32f4a51b19b9234', price: 129, colors: [ '#000000', '#666666', '#ffcc00' ], image: '/images/product-sunglasses.png', category: '64c2342de32f4a51b19b9250', isFeatured: false }, { _id: ObjectId('64c2363be32f4a51b19b9271'), name: 'Pet Grooming Kit', company: '64c23350e32f4a51b19b923e', price: 39, colors: [ '#ffffff', '#ffcc00', '#99cc00' ], image: '/images/product-pet-grooming.png', category: '64c2342de32f4a51b19b9257', isFeatured: false } ]

• $nin: not-in-operator

Example : db.products.find({'price': {$nin: [699,129,39]}})

It will show the excluding : 699,12,39

Output:

[ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, { _id: ObjectId('64c23601e32f4a51b19b9264'), name: 'Smart Watch', company: '64c23350e32f4a51b19b9232', price: 249, colors: [ '#000000', '#ff0000', '#00ff00' ], image: '/images/product-smartwatch.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9265'), name: 'Kitchen Blender', company: '64c23350e32f4a51b19b9233', price: 89, colors: [ '#ffffff', '#ff9900', '#990000' ], image: '/images/product-blender.png', category: '64c2342de32f4a51b19b924f', isFeatured: true } ]

CURSOR IN MONGODB

CURSOR-METHODS :

1. count()

2. limit()

3. skip()

4. sort()

DESCRIPTION :

• count() : To see the number of count inside the data

Example:

db.products.find({'price':{$gt:259}}).count()- It displays the number of price fiels avilabe which are greater the 259

Output: 7570

• limit() : To set the limits in viewing the data

Example:

db.products.find({'price':{$gt:259}}).limit(1): - It limits the data to 1

Output :

[ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false } ]

• skip(): Along with the limit it also skips the data

Example :

db.products.find({'price':{$gt:259}}).limit(2).skip(1)

Output:

[ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, { _id: ObjectId('64c23601e32f4a51b19b9268'), name: 'Outdoor Camping Tent', company: '64c23350e32f4a51b19b9236', price: 299, colors: [ '#333333', '#99cc00', '#ffcc00' ], image: '/images/product-tent.png', category: '64c2342de32f4a51b19b9252', isFeatured: false } ]

• sort() : It sort the data according to ascending and descending order

Command Example: `db.products.find({ price: { $gt: 1250 } }'.limit(3).sort({ price: 1 }));

(1) for ascending and (-1) for descending`

For ascending order :

Example : db.products.find({'price' : {$gt:1250}}).limit(3).sort({'price':1})

Output : It is in ascending order

[ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

For Descending Order:

Example : db.products.find({'price': {$gt:1250}}).limit(3).sort({'price':-1})

Output : It is in descending order

[ { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true } ]

LOGICAL OPERATORS IN MONGODB

• $and

• $or

• $nor

• $not

DESCRIPTION

• $and : Performs a Logical AND Operation on an array of expressions, where all expressions must be true for the document to match

Command : { $and: [ { condition1 }, { condition2 }, ... ]}

Example : Find Products with price greater than 100 and name equal to 'Diamond Ring'

db.products.find({$and:[{'price':{$gt:100}},{'name':'Diamond Ring'}]})

Output :

[ { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

• $or : Performs a Logical OR operation on an array of expressions, where at lest one expression must be true for the document to match

Command : { $or: [ { condition1 }, { condition2 }, ... ]}

Example : Find Products with price greater than 7000 and name equal to 'Diamond Ring'

db.products.find({$or:[{'price':{$gt:7000}},{'name':'Diamond Ring'}]})

Output :

[ { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

• nor: Performs a Logical NOR operation on an array of expressions , where none of the expressions must be true for the document to match.

Command : { $nor: [ { condition1 }, { condition2 }, ... ]}

Example : Find Products with price not equal to 20 and name equal to 'Notebook Collection '

db.products.find({$nor:[{'price':{$gt:20}},{'name':'Notebook Collection'}]})

Output :

shop> db.products.find({$nor:[{'price':{$gt:20}},{'name':'Notebook Collection'}]}) [ { _id: ObjectId('64c23601e32f4a51b19b9270'), name: 'Healthy Snack Pack', company: '64c23350e32f4a51b19b923e', price: 19, colors: [ '#ffffff', '#ffcc00', '#99cc00' ], image: '/images/product-snack-pack.png', category: '64c2342de32f4a51b19b9256', isFeatured: true }, { _id: ObjectId('64c236a2e32f4a51b19b9285'), name: 'Healthy Snack Pack', company: '64c23350e32f4a51b19b923e', price: 19, colors: [ '#ffffff', '#ffcc00', '#99cc00' ], image: '/images/product-snack-pack.png', category: '64c2342de32f4a51b19b9256', isFeatured: true } ]

• $not: Performs a Logical NOT Operation on the specified expression, inverting the result

Command : { field: { $not: { operator: value } } }

Example : Find Products with price not equal to 100

db.products.find({'price': {$ne:100}})

Output :

[ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true } ]

COMPLEX EXPRESSIONS IN MONGODB

The $expr operator allows using aggregation expressions within a query. Useful when you need to compare fields from the same document in a more complex manner.

Command : { $expr: { operator: [field, value] } }

Example: Find Products which has price greater than 1340 using $expr

db.products.find({$expr:{$gt:['$price',1340]}})

Output:

[ { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

• ADVANTAGES OF USING $expr

Example -

Find Sales where(quantity *price ) is greater than target price ?

• Sales Json Data : [ { _id: 1, quantity: 10, price: 15, targetPrice: 120 }, { _id: 3, quantity: 6, price: 35, targetPrice: 100 }, { _id: 4, quantity: 5, price: 55, targetPrice: 150 }, { _id: 5, quantity: 5, price: 55, targetPrice: 150 }, { _id: 2, quantity: 5, price: 25, targetPrice: 100 } ]

Command - db.sales.find({$expr:{$gt:[{$multiply:['$quantity','$price']},'$targetPrice']}})

Output - [ { _id: 1, quantity: 10, price: 15, targetPrice: 120 }, { _id: 3, quantity: 6, price: 35, targetPrice: 100 }, { _id: 4, quantity: 5, price: 55, targetPrice: 150 }, { _id: 5, quantity: 5, price: 55, targetPrice: 150 }, { _id: 2, quantity: 5, price: 25, targetPrice: 100 } ]

ELEMENTS OPERATOR IN MONGODB

• $exists : Matches Documents that have a specific value,regardless of its value

  Command : db.products.find({price:{$exists:true}}).count()

  Example : Find documents with the "price" field present,and if present then check the value greater than 1200?

  Command : db.products.find({price:{$exists:true},price:{$gt:1200}})

  Output : [ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, { _id: ObjectId('64c236a2e32f4a51b19b9281'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false }, { _id: ObjectId('64c23707e32f4a51b19b9296'), name: 'Diamond Ring', company: '64c23350e32f4a51b19b923a', price: 1999, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-diamond-ring.png', category: '64c2342de32f4a51b19b9259', isFeatured: false } ]

• $type : The $type operator filters documents based on the BSON data type of a field.Basically we need to search or find the fields based on types(BSON type)

  Examples: - db.products.find({price:{$type:'number'}}) - db.products.find({price:{$type:'string'}})

• $size : The $size operator matches documents where the size of an array field matches a specified field

  Command : db.comments.find({'comments':{$size:4}})

  Output : [ { _id: 1, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'John Doe', comments: [ { user: 'Alice', text: 'Great article!' }, { user: 'Bob', text: 'Thanks for sharing.' }, { user: 'Eva', text: 'Its beatifull!' }, { user: 'jessy' } ], metadata: { views: 1000, likes: 50 } } ]

PROJECTION IN MONGODB

Command : db.collection.find({}, { field1: 1, field2: 1 })

Description :

• To include specific fields, use projection with a value of 1 for the fields you want.

• To exclude fields, use projection with a value of 0 for the fields you want to exclude.

• You cannot include and exclude fields simultaneously in the same query projection.

Example-1 :

Command : db.comments.find({'comments':{$size:2}},{comments:1})

Output: [ { _id: 2, comments: [ { user: 'Charlie', text: 'Very informative!' }, { user: 'David', text: 'Well explained.' } ] }, { _id: 5, comments: [ { user: 'Ivy', text: 'Very practical advice!' }, { user: 'Jack', text: 'Helped me a lot.' } ] }, { _id: 4, comments: [ { user: 'Grace', text: 'Mind-blowing content!' }, { user: 'Henry', text: 'Impressive examples.' } ] }, { _id: 6, comments: [ { user: 'Kevin', text: 'Invaluable insights!' }, { user: 'Lily', text: 'Well-structured explanations.' } ] }, { _id: 7, comments: [ { user: 'Alice', text: 'Awesome article!' }, { user: 'Vinod', text: 'Thanks for sharing.' } ] }, { _id: 3, comments: [ { user: 'Eva', text: 'Very helpful!' }, { user: 'Frank', text: 'Clear explanations.' } ] } ]

Example-2 :

Command : db.comments.find({'comments':{$size:2}},{title:1})

Output : [ { _id: 2, title: 'Deep Dive into Aggregation Framework' }, { _id: 5, title: 'Optimizing MongoDB Performance' }, { _id: 4, title: 'Advanced Queries in MongoDB' }, { _id: 6, title: 'Schema Design Best Practices' }, { _id: 7, title: 'Introduction to MongoDB' }, { _id: 3, title: 'Getting Started with NoSQL Databases' } ]

MANAGING EMBEDDED DOCUMENTS IN MONGODB

Dealing with Arrays and Objects

Query documents inside embedded documents using dot notation.

Command : db.collection.find({ “parent.child”: value })

Example-1 : Find Posts with comments by a specific user (Array)?

db.comments.find({'comments.user':'Lily'})

Output:

[ { _id: 6, title: 'Schema Design Best Practices', content: 'Designing schemas for optimal performance...', author: 'Emily Brown', comments: [ { user: 'Kevin', text: 'Invaluable insights!' }, { user: 'Lily', text: 'Well-structured explanations.' } ], metadata: { views: 700, likes: 65 } } ]

Example-2 : Find the documents where the views in metadata field > 1200

db.comments.find({'metadata.views':{$gt:1200}})

Output:

[ { _id: 4, title: 'Advanced Queries in MongoDB', content: 'Learn how to write complex queries...', author: 'Michael Johnson', comments: [ { user: 'Grace', text: 'Mind-blowing content!' }, { user: 'Henry', text: 'Impressive examples.' } ], metadata: { views: 1500, likes: 60 } } ]

Example-3 : We need to find out the document where the user in comments = Henry and also in the metadata likes value > 50.

db.comments.find({'comments.user':'Henry','metadata.likes':{$gt:50}})

output:

[ { _id: 4, title: 'Advanced Queries in MongoDB', content: 'Learn how to write complex queries...', author: 'Michael Johnson', comments: [ { user: 'Grace', text: 'Mind-blowing content!' }, { user: 'Henry', text: 'Impressive examples.' } ], metadata: { views: 1500, likes: 60 } } ]

Example-3 : We need to return an comments array which must have this user alice and vinod eleemnts only in it?

db.comments.find({'comments.user':'Alice','comments.user':'Vinod'})

Output:

[ { _id: 7, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'Vinod Thapa', comments: [ { user: 'Alice', text: 'Awesome article!' }, { user: 'Vinod', text: 'Thanks for sharing.' } ], metadata: { views: 1000, likes: 70 } } ]

COMPARING "$all" and "$elemMatch" Operators

• The $all operator selects the documents where the value of a field is an array that contains all the specified elements. { : { $all: [ , ... ] } }

• The $elemMatch operator matches documents that contain an array field with at least one element that matches all the specified query criteria. { : { $elemMatch: { , , ... } } }

Example of $all : We need to return an comments array which must have this user alice and vinod eleemnts only in it?

db.comments.find({'comments.user': {$all:['Alice','Vinod']}})

Output:

[ { _id: 7, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'Vinod Thapa', comments: [ { user: 'Alice', text: 'Awesome article!' }, { user: 'Vinod', text: 'Thanks for sharing.' } ], metadata: { views: 1000, likes: 70 } } ]

Example of $elemMatch : We need to return an comments array which must have this user must be 'Vinod' and text must be 'Thanks for sharing.' ?

db.comments.find({'comments':{$elemMatch:{'user':'Vinod','text':'Thanks for sharing.'}}})

Output:

[ { _id: 7, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'Vinod Thapa', comments: [ { user: 'Alice', text: 'Awesome article!' }, { user: 'Vinod', text: 'Thanks for sharing.' } ], metadata: { views: 1000, likes: 70 } } ]

UPDATE OPERATION IN MONGODB

• updateOne() and updateMany()

updateOne() :

Example-1 : Update the price value = 45 in a products collections, where the _id = ObjectId("64c2363be32f4a51b19b9271")

Sample-Command : db.products.find({_id:ObjectId("64c2363be32f4a51b19b9271")})

Sample-Output :

[ { _id: ObjectId('64c2363be32f4a51b19b9271'), name: 'Pet Grooming Kit', company: '64c23350e32f4a51b19b923e', price: 39, colors: [ '#ffffff', '#ffcc00', '#99cc00' ], image: '/images/product-pet-grooming.png', category: '64c2342de32f4a51b19b9257', isFeatured: false } ]

Example-Command : db.products.updateOne({_id:ObjectId("64c2363be32f4a51b19b9271")},{$set:{'price':45}})

Output: : { acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Output :

[ { _id: ObjectId('64c2363be32f4a51b19b9271'), name: 'Pet Grooming Kit', company: '64c23350e32f4a51b19b923e', price: 45, colors: [ '#ffffff', '#ffcc00', '#99cc00' ], image: '/images/product-pet-grooming.png', category: '64c2342de32f4a51b19b9257', isFeatured: false } ]

Example-2 : Update the isFeature value = false in a products collections,where the name = Designer Handbag

Command : db.products.updateOne({'name':'Designer Handbag'},{$set:{'isFeatured':false}})

Output:

{ acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Output:

[ { _id: ObjectId('64c23601e32f4a51b19b926c'), name: 'Designer Handbag', company: '64c23350e32f4a51b19b923a', price: 349, colors: [ '#000000', '#cc6600', '#663300' ], image: '/images/product-handbag.png', category: '64c2342de32f4a51b19b9250', isFeatured: false } ]

updateMany() :

Example-1 : Update all the isFeatured = true in the products,collections where the price = 120

Command : db.products.updateMany({'price':120},{$set:{'isFeatured':true}})

Output: { acknowledged: true, insertedId: null, matchedCount: 5, modifiedCount: 5, upsertedCount: 0 }

Updated-Output:

[ { _id: ObjectId('64c23edf9dc1fb2f85602168'), name: 'Ergonomic Granite Sausages', company: ObjectId('64c23350e32f4a51b19b9240'), price: 120, colors: [ '#641e3b' ], image: '/images/product-Luxurious.png', category: ObjectId('64c2342de32f4a51b19b925f'), isFeatured: true, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f85602c38'), name: 'Unbranded Frozen Chicken', company: ObjectId('64c23350e32f4a51b19b9234'), price: 120, colors: [ '#146d77' ], image: '/images/product-Tasty.png', category: ObjectId('64c2342de32f4a51b19b9261'), isFeatured: true, __v: 0 }, { _id: ObjectId('64c23f13f8ffc4957dc4f16e'), name: 'Electronic Concrete Bacon', company: ObjectId('64c23350e32f4a51b19b923f'), price: 120, colors: [ '#383d3d' ], image: '/images/product-Fantastic.png', category: ObjectId('64c2342de32f4a51b19b9258'), isFeatured: true, __v: 0 }, { _id: ObjectId('64c23f13f8ffc4957dc4fe9e'), name: 'Practical Concrete Ball', company: ObjectId('64c23350e32f4a51b19b9230'), price: 120, colors: [ '#34221e' ], image: '/images/product-Sleek.png', category: ObjectId('64c2342de32f4a51b19b9250'), isFeatured: true, __v: 0 }, { _id: ObjectId('64c23f13f8ffc4957dc4ff92'), name: 'Incredible Plastic Sausages', company: ObjectId('64c23350e32f4a51b19b9242'), price: 120, colors: [ '#332648' ], image: '/images/product-Ergonomic.png', category: ObjectId('64c2342de32f4a51b19b9256'), isFeatured: true, __v: 0 } ]

REMOVING AND RENAMING THE FIELDS

Example-1 : Renaming the products collection isFeatured field to isFeature,where the price = 123 ?

$rename:

Command:db.products.updateMany({price:123},{$rename:{'isFeatured':'isFeature'}})

Output: { acknowledged: true, insertedId: null, matchedCount: 7, modifiedCount: 7, upsertedCount: 0 }

Updated-Output:

[ { _id: ObjectId('64c23e543e180eb8a2682ff4'), name: 'Rustic Frozen Towels', company: ObjectId('64c23350e32f4a51b19b9235'), price: 123, colors: [ '#50215b' ], image: '/images/product-Rustic.png', category: ObjectId('64c2342de32f4a51b19b9255'), __v: 0, isFeature: false } ]

ADDITION AND DELETION IN MONGODB

Sample-Data : [ { _id: 2, title: 'Deep Dive into Aggregation Framework', content: 'The aggregation framework in MongoDB...', author: 'Jane Smith', comments: [ { user: 'Charlie', text: 'Very informative!' }, { user: 'David', text: 'Well explained.' } ], metadata: { views: 800, likes: 70 } } ]

Example-1: Add a new user in the above data

Command : db.comments.updateOne({_id:2},{$set:{'user':'Hrithik'}})

Output : { acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Output :

[ { _id: 2, title: 'Deep Dive into Aggregation Framework', content: 'The aggregation framework in MongoDB...', author: 'Jane Smith', comments: [ { user: 'Charlie', text: 'Very informative!' }, { user: 'David', text: 'Well explained.' } ], metadata: { views: 800, likes: 70 }, user: 'Hrithik' } ]

Example-2: Delete the data with _id:2

Command : db.comments.updateOne({_id:2},{$unset:{'user':1}})

Output:

{ acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Output:

[ { _id: 2, title: 'Deep Dive into Aggregation Framework', content: 'The aggregation framework in MongoDB...', author: 'Jane Smith', comments: [ { user: 'Charlie', text: 'Very informative!' }, { user: 'David', text: 'Well explained.' } ], metadata: { views: 800, likes: 70 } } ]

UPDATE EMBEDDED DOCUMENTS IN MONGODB

Example : How do you add a new element to an array using the $push operator ?

Sample-Command : db.comments.find({_id:5})

Sample-Data : [ { _id: 5, title: 'Optimizing MongoDB Performance', content: 'Tips and tricks to improve MongoDB performance...', author: 'Alex Turner', comments: [ { user: 'Ivy', text: 'Very practical advice!' }, { user: 'Jack', text: 'Helped me a lot.' } ], metadata: { views: 900, likes: 55 } } ]

Now we are going to add a new user using $push inside the array

Command : db.comments.updateOne({_id:5},{$push:{comments:{user:'John',text:'Never Give Up'}}})

Output : { acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Data : db.comments.find({_id:5}) [ { _id: 5, title: 'Optimizing MongoDB Performance', content: 'Tips and tricks to improve MongoDB performance...', author: 'Alex Turner', comments: [ { user: 'Ivy', text: 'Very practical advice!' }, { user: 'Jack', text: 'Helped me a lot.' }, { user: 'John', text: 'Never Give Up' } ], metadata: { views: 900, likes: 55 } } ]

Updating a field within an embedded document :

Example : Update the text value within the comments array = "Awesome Kapil !,where the id = 7 and username = Alice"

Sample-Command : db.comments.find({_id:7,'comments.user':'Alice'})

Sample-Data: [ { _id: 7, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'Vinod Thapa', comments: [ { user: 'Alice', text: 'Awesome article!' }, { user: 'Vinod', text: 'Thanks for sharing.' } ], metadata: { views: 1000, likes: 70 } } ]

Command : db.comments.updateOne({_id:7,'comments.user':'Alice'},{$set:{'comments.$.text':'Awesome Kapil'}})

Output : { acknowledged: true, insertedId: null, matchedCount: 1, modifiedCount: 1, upsertedCount: 0 }

Updated-Output : [ { _id: 7, title: 'Introduction to MongoDB', content: 'MongoDB is a popular NoSQL database...', author: 'Vinod Thapa', comments: [ { user: 'Alice', text: 'Awesome Kapil' }, { user: 'Vinod', text: 'Thanks for sharing.' } ], metadata: { views: 1000, likes: 70 } } ]

DELETION :

In MONGODB the DELETE Operations are used to remove documents from a collection. There are two main methods to perform deletion : deleteOne() and deleteMany()

Delete a Single Document :

Command : db.comments.deleteOne({_id:1})

Output: { acknowledged: true, deletedCount: 1 }

Delete a Multiple Document :

Example : Delete multiple documents in the products field where the price:55

Sample - Command : db.products.find({'price':55})

Sample-Output : [ { _id: ObjectId('64c23edf9dc1fb2f85601b55'), name: 'Unbranded Metal Soap', company: ObjectId('64c23350e32f4a51b19b9233'), price: 55, colors: [ '#3f321d' ], image: '/images/product-Awesome.png', category: ObjectId('64c2342de32f4a51b19b924f'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f856025ca'), name: 'Gorgeous Fresh Salad', company: ObjectId('64c23350e32f4a51b19b924a'), price: 55, colors: [ '#746377' ], image: '/images/product-Bespoke.png', category: ObjectId('64c2342de32f4a51b19b9250'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f85602628'), name: 'Electronic Steel Pants', company: ObjectId('64c23350e32f4a51b19b924c'), price: 55, colors: [ '#377f44' ], image: '/images/product-Small.png', category: ObjectId('64c2342de32f4a51b19b925c'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f85602796'), name: 'Practical Rubber Keyboard', company: ObjectId('64c23350e32f4a51b19b924b'), price: 55, colors: [ '#0c3c05' ], image: '/images/product-Licensed.png', category: ObjectId('64c2342de32f4a51b19b9250'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23f13f8ffc4957dc4ef39'), name: 'Sleek Steel Fish', company: ObjectId('64c23350e32f4a51b19b9249'), price: 55, colors: [ '#057725' ], image: '/images/product-Awesome.png', category: ObjectId('64c2342de32f4a51b19b924f'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23f13f8ffc4957dc4f308'), name: 'Ergonomic Wooden Fish', company: ObjectId('64c23350e32f4a51b19b923f'), price: 55, colors: [ '#30030f' ], image: '/images/product-Handcrafted.png', category: ObjectId('64c2342de32f4a51b19b925f'), isFeatured: false, __v: 0 } ]

Command : db.products.deleteMany({price:55})

Output : { acknowledged: true, deletedCount: 6 }

INDEXES IN MONGODB

Indexes are specialized data structures that optimize data retrieval speed in MongoDB.

• Indexes store a fraction of data in a more searchable format.

• They enable MongoDB to locate data faster during queries.

• Indexes are separate from collections and multiple indexes can exist per collection.

Creating Indexes :

• Indexes can be created using the createIndex() method.

• Command : db.collectionName.createIndex({fieldName:1})

• In this case, 1 represents ascending order,and -1 would be descending order.

Getting Indexes :

• Command : db.products.getIndexes()

• _id is automatically added by mongodb and it's a default unique index.

Example-1 : Creating a Index for name.

• Command : db.products.createIndex({'name':1})

• Output : name_1

Example-2 : Drooping a Index for name.

• Command : db.products.dropIndex({name:1})

• Output : { nIndexesWas: 2, ok: 1 }

Example-3 : Creating a Unique Index.

• Command : db.users.createIndex({email:1},{unique:true})

When not to Create a Index :

• Indexes on Rarely Used Fields : Indexing fields that are seldom used in queries can consume unnecessary space and resources.

• Balancing Act : Indexing requires disk space and memory. Over indexing can lead to resource strain and impact overall performance.

• Indexing Small Collections : In smaller collections, the cost of index maintenance might outweigh the benefits gained from querying

AGGREGATION FRAMEWORK IN MONGODB

• Definition : Aggregation is the process of performing transformations on documents and combining them to produce computed results.

• Pipeline Stages : Aggregations consist of multiple pipeline stages, each performing a specific operation on the input data.

• Benefits :

1. Aggregating Data: Complex calculations and operations are possible.

2. Advanced Transformations: Data can be combined, reshaped, and computed for insights.

3. Efficient Processing: Aggregation handles large datasets efficiently.

$match :

• The $match stage is similar to the query used as the first argument in .find().

• It filters documents based on specified conditions.

Example - 1 : Retrieve all products with a name = Sleek Wooden Tuna ?

Command : db.products.aggregate([{$match:{'name':'Sleek Wooden Tuna'}}])

Output : [ { _id: ObjectId('64c23edf9dc1fb2f85601ae5'), name: 'Sleek Wooden Tuna', company: ObjectId('64c23350e32f4a51b19b9235'), price: 101, colors: [ '#29505b' ], image: '/images/product-Recycled.png', category: ObjectId('64c2342de32f4a51b19b9255'), isFeatured: false, __v: 0 }, { _id: ObjectId('64c23edf9dc1fb2f856027db'), name: 'Sleek Wooden Tuna', company: ObjectId('64c23350e32f4a51b19b9241'), price: 363, colors: [ '#464438' ], image: '/images/product-Sleek.png', category: ObjectId('64c2342de32f4a51b19b9256'), isFeatured: false, __v: 0 } ]

Example - 2 : Retrieve all the products where price is greater than 50

Command : db.products.aggregate([{$match: {price:{$gt:50}}}])

Output : [ { _id: ObjectId('64c23601e32f4a51b19b9262'), name: 'Smartphone Model X', company: '64c23350e32f4a51b19b9230', price: 699, colors: [ '#000000', '#ffffff', '#ff0000' ], image: '/images/product-smartphone.png', category: '64c2342de32f4a51b19b924e', isFeatured: false }, { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true } ]

$group :

• The $group stage groups documents by specified fields and performs aggregate operations on grouped data.

• It is like the reduce() method in Java Script.

• When dealing with the $group stage we need to pass $sign for our existing field not the one we are going to create.

Command : db.products.aggregate([{$group:{_id:"$company",totalProducts:{$sum:1}}}])

Output: [ { _id: ObjectId('64c23350e32f4a51b19b9233'), totalProducts: 320 }, { _id: '64c23350e32f4a51b19b9254', totalProducts: 1 }, { _id: '64c23350e32f4a51b19b9250', totalProducts: 2 }, { _id: ObjectId('64c23350e32f4a51b19b9243'), totalProducts: 351 }, { _id: ObjectId('64c23350e32f4a51b19b923e'), totalProducts: 381 }, { _id: ObjectId('64c23350e32f4a51b19b923d'), totalProducts: 337 }, { _id: '64c23350e32f4a51b19b9252', totalProducts: 2 }, { _id: '64c23350e32f4a51b19b924b', totalProducts: 2 }, { _id: '64c23350e32f4a51b19b9256', totalProducts: 1 }, { _id: '64c23350e32f4a51b19b923f', totalProducts: 5 }, { _id: '64c23350e32f4a51b19b9245', totalProducts: 2 }, { _id: ObjectId('64c23350e32f4a51b19b9248'), totalProducts: 374 }, { _id: ObjectId('64c23350e32f4a51b19b9230'), totalProducts: 319 }, { _id: '64c23350e32f4a51b19b9249', totalProducts: 2 }, { _id: '64c23350e32f4a51b19b9231', totalProducts: 6 }, { _id: '64c23350e32f4a51b19b9253', totalProducts: 1 }, { _id: ObjectId('64c23350e32f4a51b19b9245'), totalProducts: 313 }, { _id: ObjectId('64c23350e32f4a51b19b924d'), totalProducts: 342 }, { _id: '64c23350e32f4a51b19b9233', totalProducts: 6 }, { _id: ObjectId('64c23350e32f4a51b19b9246'), totalProducts: 305 } ]

Command : db.products.aggregate([{$match:{company:"64c23350e32f4a51b19b9252"}}])

Output : [ { _id: ObjectId('64c23871e32f4a51b19b92cb'), name: 'Dumbbell Set', company: '64c23350e32f4a51b19b9252', price: 49, colors: [ '#333333', '#ff9900', '#00cc99' ], image: '/images/product-dumbbell-set.png', category: '64c2342de32f4a51b19b925e', isFeatured: false }, { _id: ObjectId('64c239f1e32f4a51b19b92f7'), name: 'Wireless Charging Dock', company: '64c23350e32f4a51b19b9252', price: 49, colors: [ '#000000', '#666666', '#cc9900' ], image: '/images/product-charging-dock.png', category: '64c2342de32f4a51b19b924e', isFeatured: false } ]

Example : Now from the above output add the price field

Command: db.products.aggregate([{$group:{_id:"$company",totalProducts:{$sum:"$price"}}}])

Output: [ { _id: ObjectId('64c23350e32f4a51b19b9231'), totalProducts: 168915 }, { _id: '64c23350e32f4a51b19b924e', totalProducts: 598 }, { _id: ObjectId('64c23350e32f4a51b19b9249'), totalProducts: 155570 }, { _id: '64c23350e32f4a51b19b9241', totalProducts: 1544 }, { _id: '64c23350e32f4a51b19b9236', totalProducts: 1344 }, { _id: '64c23350e32f4a51b19b9248', totalProducts: 228 }, { _id: '64c23350e32f4a51b19b923c', totalProducts: 844 }, { _id: ObjectId('64c23350e32f4a51b19b924c'), totalProducts: 169452 }, { _id: ObjectId('64c23350e32f4a51b19b924b'), totalProducts: 171747 }, { _id: ObjectId('64c23350e32f4a51b19b923f'), totalProducts: 165420 }, { _id: '64c23350e32f4a51b19b9246', totalProducts: 978 }, { _id: '64c23350e32f4a51b19b923e', totalProducts: 309 }, { _id: '64c23350e32f4a51b19b923a', totalProducts: 4884 }, { _id: '64c23350e32f4a51b19b924f', totalProducts: 118 }, { _id: ObjectId('64c23350e32f4a51b19b9238'), totalProducts: 173258 }, { _id: '64c23350e32f4a51b19b9257', totalProducts: 79 }, { _id: ObjectId('64c23350e32f4a51b19b9237'), totalProducts: 166583 }, { _id: '64c23350e32f4a51b19b9239', totalProducts: 1704 }, { _id: ObjectId('64c23350e32f4a51b19b9241'), totalProducts: 168643 }, { _id: ObjectId('64c23350e32f4a51b19b9235'), totalProducts: 168061 } ]

Example : From the products field find price greater than 1200 group by company name and also sum their prices?

Command : db.products.aggregate([{$match:{price:{$gt:1200}}},{$group:{_id:"$company",totalProducts:{$sum:"$price"}}}])

Output: [ { _id: '64c23350e32f4a51b19b923a', totalProducts: 3998 }, { _id: '64c23350e32f4a51b19b9231', totalProducts: 1299 } ]

Example : In the sales collection find the quantity = 5, group them with the same quantity and find the average price ?

Sample - Data: [ { _id: 1, quantity: 10, price: 15, targetPrice: 120 }, { _id: 3, quantity: 6, price: 35, targetPrice: 100 }, { _id: 4, quantity: 5, price: 55, targetPrice: 150 }, { _id: 5, quantity: 5, price: 55, targetPrice: 150 }, { _id: 2, quantity: 5, price: 25, targetPrice: 100 } ]

Command : db.sales.aggregate([{$match:{quantity:5}},{$group:{_id:'$quantity',priceTotal:{$sum:'$price'},priceAvg:{$avg:'$price'}}}])

Output : [ { _id: 5, priceTotal: 135, priceAvg: 45 } ]

$sort :

Example : From the products field find price greater than 1200 group by company name and also sum their prices in descending order using $sort?

Sorting According to descending order:

Command : db.products.aggregate([{$match:{price:{$gt:1200}}},{$group:{_id:"$category",totalPrice:{$sum:"$price"}}},{$sort:{totalPrice:-1}}])

Output : [ { _id: '64c2342de32f4a51b19b9259', totalPrice: 3998 }, { _id: '64c2342de32f4a51b19b924e', totalPrice: 1299 } ]

Sorting According to ascending order:

Command : db.products.aggregate([{$match:{price:{$gt:1200}}},{$group:{_id:"$category",totalPrice:{$sum:"$price"}}},{$sort:{totalPrice:1}}])

Output: [ { _id: '64c2342de32f4a51b19b924e', totalPrice: 1299 }, { _id: '64c2342de32f4a51b19b9259', totalPrice: 3998 } ]

$project :

• The $project stage reshapes documents, includes or excludes fields, and performs operations on fields.

• Command : { $project: { <field1>: <expression1>, ... } }

Example : Using the project operator it shows the value greater than 1200 and the discount price i.e multiplied by 0.8 and the actual price

Command : db.products.aggregate([{$match:{price:{$gt:1200}}},{$project:{price:1,discountPrice:{$multiply:['$price',0.8]}}}])

Output: [ { _id: ObjectId('64c23601e32f4a51b19b9263'), price: 1299, discountPrice: 1039.2 }, { _id: ObjectId('64c236a2e32f4a51b19b9281'), price: 1999, discountPrice: 1599.2 }, { _id: ObjectId('64c23707e32f4a51b19b9296'), price: 1999, discountPrice: 1599.2 } ]

$push :

• The $push stage adds elements to an array field within documents.

Example : Find document with price greater than 1200, then group them by price and create an array of colors for each group ?

Command : db.products.aggregate([{$match:{price:{$gt:1200}}},{$group:{_id:'price',allColors:{$push:'$colors'}}}])

Output: [ { _id: 'price', allColors: [ [ '#333333', '#cccccc', '#00ff00' ], [ '#000000', '#cc6600', '#663300' ], [ '#000000', '#cc6600', '#663300' ] ] } ]

• But If want the output in a single array then we have to use $unwind

$unwind :

• The $unwind stage deconstructs an array field and produces multiple documents.

Command : db.products.aggregate([{$unwind:'$colors'},{$match:{price:{$gt:1200}}},{$group:{_id:'price',allColors:{$push:'$colors'}}}])

Output: [ { _id: 'price', allColors: [ '#333333', '#cccccc', '#00ff00', '#000000', '#cc6600', '#663300', '#000000', '#cc6600', '#663300' ] } ]

• But there are similar data in the array so to avoid that we use $addToSet in place of $push

$addToSet :

• The $addToSet stage adds elements to an array field while preventing duplicates

Command : db.products.aggregate([{$unwind:'$colors'},{$match:{price:{$gt:1200}}},{$group:{_id:'price',allColors:{$addToSet:'$colors'}}}])

Output : [ { _id: 'price', allColors: [ '#00ff00', '#333333', '#000000', '#cc6600', '#663300', '#cccccc' ] } ]

$filter :

• The $filter stage filters elements of an array based on specified conditions.

MONGODB ATLAS :

• MongoDB Atlas is MongoDB's fully managed cloud database service.

• It offers an easy way to deploy, manage, and scale MongoDB databases in the cloud.

• Atlas eliminates the need for manual setup and maintenance, allowing developers to focus on their applications.

• It provides automated scaling options to accommodate growing workloads.

• Atlas supports global clusters, enabling databases to be deployed across multiple regions for better data availability and reduced latency.

• Alas server as your database service provider.Data is hosted on Atlas and for data manipulation.

• Two Options are available .Compass offering a graphical interface and mongoshell a command line tool. Both tools facilitate data management and interaction with atlas.

CLUSTERS IN MONGODB :

In MongoDb a Cluster refers to a group of interconnected servers (nodes) that work together to manage data.

Name of Cluster : Cluster0

UserName : ksarkar81

Password: 12345

ConnectionString : mongosh "mongodb+srv://cluster0.fpbpqh8.mongodb.net/" --apiVersion 1 --username ksarkar81

After typing this connection string on cmd we got enter the password the we get the output as :

Output : `Current Mongosh Log ID: 6656933d623f81c8d746b798 Connecting to: mongodb+srv://@cluster0.fpbpqh8.mongodb.net/?appName=mongosh+2.2.5 Using MongoDB: 7.0.8 (API Version 1) Using Mongosh: 2.2.5 mongosh 2.2.6 is available for download: https://www.mongodb.com/try/download/shell

For mongosh info see: https://docs.mongodb.com/mongodb-shell/`

Now it is same like before we were playing with database hosted in our localhost but now we are connected to the live database.Rest Everything is same.

Connection string for MongoDB Compass : mongodb+srv://ksarkar81:<password>@cluster0.fpbpqh8.mongodb.net/

Password: 12345

COMMAND ON MONGO-DB SHELL VS COMPASS

• Command on Shell : db.products.find({'price':{$gt:1200}}).limit(1)

Output On shell [ { _id: ObjectId('64c23601e32f4a51b19b9263'), name: 'Laptop Pro', company: '64c23350e32f4a51b19b9231', price: 1299, colors: [ '#333333', '#cccccc', '#00ff00' ], image: '/images/product-laptop.png', category: '64c2342de32f4a51b19b924e', isFeatured: true }, ]

• Command on Compass : {price:{$gt:1200}} and set the limit to 1 from Options

Output On Compass :

{ "_id": { "$oid": "64c23601e32f4a51b19b9263" }, "name": "Laptop Pro", "company": "64c23350e32f4a51b19b9231", "price": 1299, "colors": [ "#333333", "#cccccc", "#00ff00" ], "image": "/images/product-laptop.png", "category": "64c2342de32f4a51b19b924e", "isFeatured": true }

MONGODB DRIVERS :

• Software libraries that allow applications to interact with MongoDB databases.

• MongoDB offers official and community-supported drivers for various programming languages.

• Drivers provide APIs tailored to specific programming languages.

WORKING WITH MONGOOSE :

• It's an Object Data Modeling (ODM) library for MongoDB and Node.js.

• It makes MongoDB interaction more straightforward and organized.

• It provides a structured, schema-based data modeling approach.

Why Mongoose instead of official driver?

1. Structured Schemas : Mongoose lets you define your data structure using schemas which makes it easier to understand your database structure and work with it.

2. Validation : Mongoose provides built-in validation to ensure validity before saving it to database.

3. Relationships : MongoDB doesn’t provide relations itself. So, Mongoose helps to replicate relations in MongoDB and helps us to relate schemas with each other easily (one-toone, one-to-many, etc.)

4. Middleware : Mongoose offer running custom functions before or after certain operations which can be useful in many cases.

MINI PROJECT ON CONNECTING MONGODB WITH NODEJS IN THE LOCAL SYSTEM :

• FOLDER LINK : LINK

MONGOOSE - NODEJS PROJECT

• THIS IS A BASIC PROJECT

• CREATE,READ, UPDATE, DELETE OPERATION USING MONGOOSE NODE-JS

• THESE OPERATIONS ARE PERFORMED USING ON SHELL , COMPASS AND ATLAS.

• FOLDER LINK : LINK