DBMS Notes

MongoDB — Introduction

MongoDB is the most popular document-oriented NoSQL database. It stores data as BSON (Binary JSON) documents in collections (analogous to tables), with no...

What is MongoDB?

MongoDB is the most popular document-oriented NoSQL database. It stores data as BSON (Binary JSON) documents in collections (analogous to tables), with no fixed schema required.

Developed by MongoDB Inc. (2007), used by companies like Adobe, eBay, Forbes, and thousands of startups.

Document Structure (BSON/JSON)

Example

{ "_id": ObjectId("64a1f2c3b8e4a12b3c4d5e6f"), "emp_id": "E001", "name": "Alice Johnson", "age": 32, "salary": 75000, "department": { "dept_id": "D01", "dept_name": "Computer Science" }, "skills": ["Java", "Python", "MongoDB", "SQL"], "address": { "street": "12 MG Road", "city": "Delhi", "pin": "110001" }, "active": true, "hire_date": ISODate("2020-06-15") }

Key points:

Documents can have nested objects and arrays
Each document can have different fields (schema-flexible)
_id is the primary key — auto-generated as ObjectId if not specified

Basic MongoDB Operations (CRUD)

Create (Insert)

Example

// Insert one document db.employees.insertOne({ emp_id: "E002", name: "Bob Smith", salary: 62000, department: "Mathematics", skills: ["R", "Statistics"] }); // Insert many documents db.employees.insertMany([ { emp_id: "E003", name: "Carol", salary: 88000, department: "CS" }, { emp_id: "E004", name: "David", salary: 55000, department: "Physics" } ]);

Read (Query)

javascript example

// Find all documents
db.employees.find();

// Find with condition (WHERE salary > 70000)
db.employees.find({ salary: { $gt: 70000 } });

// Find with multiple conditions (AND)
db.employees.find({ salary: { $gt: 60000 }, department: "CS" });

// Find specific fields (Projection — like SELECT columns)
db.employees.find(
  { department: "CS" },
  { name: 1, salary: 1, _id: 0 }   // 1 = include, 0 = exclude
);

// Find one document
db.employees.findOne({ emp_id: "E001" });

// Sort, limit, skip
db.employees.find().sort({ salary: -1 }).limit(5);

// Count
db.employees.countDocuments({ department: "CS" });

Update

javascript example

// Update one document
db.employees.updateOne(
  { emp_id: "E001" },                       // filter
  { $set: { salary: 80000 } }               // update operation
);

// Update many documents
db.employees.updateMany(
  { department: "CS" },
  { $inc: { salary: 5000 } }                // increment salary by 5000
);

// Replace entire document
db.employees.replaceOne(
  { emp_id: "E001" },
  { emp_id: "E001", name: "Alice Johnson", salary: 80000 }
);

Delete

javascript example

// Delete one document
db.employees.deleteOne({ emp_id: "E004" });

// Delete many documents
db.employees.deleteMany({ salary: { $lt: 50000 } });

// Delete all documents in collection
db.employees.deleteMany({});

Query Operators

Example

// Comparison $eq, $ne, $gt, $gte, $lt, $lte // Array db.employees.find({ skills: "Python" }); // array contains db.employees.find({ skills: { $in: ["Java","Python"] } }); // Logical db.employees.find({ $or: [ {dept:"CS"}, {salary:{$gt:80000}} ] }); db.employees.find({ $and: [ {dept:"CS"}, {salary:{$gt:70000}} ] }); db.employees.find({ salary: { $not: { $lt: 60000 } } }); // Field existence db.employees.find({ phone: { $exists: true } }); // Regular expression db.employees.find({ name: { $regex: /^Alice/i } });

Aggregation Pipeline

MongoDB's aggregation pipeline is equivalent to SQL's GROUP BY, JOIN, HAVING:

Example

// Average salary by department (like SQL GROUP BY) db.employees.aggregate([ { $group: { _id: "$department", avg_salary: { $avg: "$salary" }, count: { $sum: 1 } }}, { $sort: { avg_salary: -1 } }, { $match: { avg_salary: { $gt: 60000 } } } ]); // Pipeline stages: // $match → WHERE / HAVING // $group → GROUP BY + aggregate functions // $project → SELECT columns // $sort → ORDER BY // $limit → LIMIT // $skip → OFFSET // $lookup → LEFT JOIN // $unwind → flatten arrays

Indexes in MongoDB

javascript example

// Single field index
db.employees.createIndex({ salary: 1 });      // 1=ascending, -1=descending

// Compound index
db.employees.createIndex({ department: 1, salary: -1 });

// Unique index
db.employees.createIndex({ emp_id: 1 }, { unique: true });

// Text index (full-text search)
db.employees.createIndex({ name: "text", skills: "text" });
db.employees.find({ $text: { $search: "Java developer" } });

// View query plan
db.employees.find({ salary: { $gt: 70000 } }).explain("executionStats");

Schema Design — Embedding vs Referencing

Embedding (Denormalized)

Store related data inside the same document:

Example

{ "order_id": "O001", "customer": { "id": "C1", "name": "Alice" }, "items": [ { "product": "Laptop", "qty": 1, "price": 50000 }, { "product": "Mouse", "qty": 2, "price": 500 } ] }

Use embedding when: data is always accessed together; 1:1 or 1:few relationships.

Referencing (Normalized)

Store _id of related document:

Example

{ "order_id": "O001", "customer_id": "C1", "items": ["I001", "I002"] }

Use referencing when: data is large or shared; 1:many or many:many relationships.

Exam Focus

Revise definitions, diagrams, examples, and short-answer points for MongoDB — Introduction.

Interview Use

Prepare one clear explanation, one practical example, and one common mistake for this Database Management Systems (DBMS) topic.

Search Terms

dbms, database management systems (dbms), unit, mongodb, introduction, mongodb — introduction

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DBMS Notes

MongoDB — Introduction

MongoDB is the most popular document-oriented NoSQL database. It stores data as BSON (Binary JSON) documents in collections (analogous to tables), with no...

What is MongoDB?

MongoDB is the most popular document-oriented NoSQL database. It stores data as BSON (Binary JSON) documents in collections (analogous to tables), with no fixed schema required.

Developed by MongoDB Inc. (2007), used by companies like Adobe, eBay, Forbes, and thousands of startups.

Document Structure (BSON/JSON)

Example

Key points:

Documents can have nested objects and arrays
Each document can have different fields (schema-flexible)
_id is the primary key — auto-generated as ObjectId if not specified

Basic MongoDB Operations (CRUD)

Create (Insert)

Example

Read (Query)

javascript example

// Find all documents
db.employees.find();

// Find with condition (WHERE salary > 70000)
db.employees.find({ salary: { $gt: 70000 } });

// Find with multiple conditions (AND)
db.employees.find({ salary: { $gt: 60000 }, department: "CS" });

// Find specific fields (Projection — like SELECT columns)
db.employees.find(
  { department: "CS" },
  { name: 1, salary: 1, _id: 0 }   // 1 = include, 0 = exclude
);

// Find one document
db.employees.findOne({ emp_id: "E001" });

// Sort, limit, skip
db.employees.find().sort({ salary: -1 }).limit(5);

// Count
db.employees.countDocuments({ department: "CS" });

Update

javascript example

// Update one document
db.employees.updateOne(
  { emp_id: "E001" },                       // filter
  { $set: { salary: 80000 } }               // update operation
);

// Update many documents
db.employees.updateMany(
  { department: "CS" },
  { $inc: { salary: 5000 } }                // increment salary by 5000
);

// Replace entire document
db.employees.replaceOne(
  { emp_id: "E001" },
  { emp_id: "E001", name: "Alice Johnson", salary: 80000 }
);

Delete

javascript example

// Delete one document
db.employees.deleteOne({ emp_id: "E004" });

// Delete many documents
db.employees.deleteMany({ salary: { $lt: 50000 } });

// Delete all documents in collection
db.employees.deleteMany({});

Query Operators

Example

Aggregation Pipeline

MongoDB's aggregation pipeline is equivalent to SQL's GROUP BY, JOIN, HAVING:

Example

Indexes in MongoDB

javascript example

// Single field index
db.employees.createIndex({ salary: 1 });      // 1=ascending, -1=descending

// Compound index
db.employees.createIndex({ department: 1, salary: -1 });

// Unique index
db.employees.createIndex({ emp_id: 1 }, { unique: true });

// Text index (full-text search)
db.employees.createIndex({ name: "text", skills: "text" });
db.employees.find({ $text: { $search: "Java developer" } });

// View query plan
db.employees.find({ salary: { $gt: 70000 } }).explain("executionStats");

Schema Design — Embedding vs Referencing

Embedding (Denormalized)

Store related data inside the same document:

Example

{ "order_id": "O001", "customer": { "id": "C1", "name": "Alice" }, "items": [ { "product": "Laptop", "qty": 1, "price": 50000 }, { "product": "Mouse", "qty": 2, "price": 500 } ] }

Use embedding when: data is always accessed together; 1:1 or 1:few relationships.

Referencing (Normalized)

Store _id of related document:

Example

{ "order_id": "O001", "customer_id": "C1", "items": ["I001", "I002"] }

Use referencing when: data is large or shared; 1:many or many:many relationships.

Exam Focus

Revise definitions, diagrams, examples, and short-answer points for MongoDB — Introduction.

Interview Use

Prepare one clear explanation, one practical example, and one common mistake for this Database Management Systems (DBMS) topic.

Search Terms

dbms, database management systems (dbms), unit, mongodb, introduction, mongodb — introduction

Related Database Management Systems (DBMS) Topics

Continue learning this concept

PreviousDistributed DBMS Architecture, Fragmentation & Replication NextNoSQL Database