MongoDB

MongoDB = Document-oriented database

• MongoDB stores data in flexible, JSON-like documents. It internally uses BSON (Binary JSON)

• MongoDB is an open source NOSQL dataset in C++ -- offers flexible data models, indexing, replication and modern applications for scalable architecture.

Why MongoDB?

• Schema-less – Documents can have varying structures. Great for fast development.

• Horizontal scaling – Supports sharding (splitting data across servers).

• High performance – Optimized for read/write-heavy workloads.

• Built-in replication – Supports replica sets for fault tolerance.

• Rich query language – Can filter, sort, aggregate, and update nested documents.

Data Model

Database

• Like a “container” for collections.

• Example: myAppDB.

Collection

• Like a table in SQL.

• Stores multiple documents.

• Example: users, products.

Document

• Like a row in SQL but flexible.

• Stored in JSON/BSON format.

• Can have different fields across documents in the same collection.

Example:

{

  "_id": ObjectId("6567e9f1c2f5a3b8e9a0b1c2"),

  "name": "Alice",

  "age": 28,

  "email": "alice@example.com",

  "hobbies": ["reading", "cycling"]

}

• _id is the unique identifier automatically generated if not provided.

Basic Operations

Inserting a document

db.users.insertOne({

  name: "Alice",

  age: 28,

  email: "alice@example.com"

})

Querying

// Find all users

db.users.find({})

// Find users older than 25

db.users.find({ age: { $gt: 25 } })

Updating

db.users.updateOne(

  { name: "Alice" },

  { $set: { age: 29 } }

)

Deleting

db.users.deleteOne({ name: "Alice" })

Aggregation Framework

• Complex queries like SQL GROUP BY.

• Example: Count users by age:

db.users.aggregate([

  { $group: { _id: "$age", count: { $sum: 1 } } }

])

Indexing

• Improve query performance.

Example: db.users.createIndex({ email: 1 })