Duration: 6 hours (Lecture: 6 hours, Lab: 8 hours)

Syllabus Topics:

• Lecture:
  • Introduction to MongoDB, Features of MongoDB
  • MongoDB Command Interface and MongoDB Compass
  • MongoDB Documents & Collections
  • RDBMS & MongoDB Analogies: Relations/Tables => Collections; Tuples/Records => Documents
  • JSON and BSON Documents
  • Performing CRUD (CREATE, READ, UPDATE, DELETE) Operations, UPSERT
  • MongoDB – Operators, Sorting, Indexing
• Lab (8 hours):
  • Using MongoDB Shell and Compass
  • Creating Database, Connecting to a Database, Creating Collections
  • Performing CRUD Operations
  • MongoDB: Complex Read Using Operators, Sorting Operations, Creating Indexes

Lecture Notes (In-Depth)

1. Introduction to MongoDB, Features of MongoDB

• Introduction to MongoDB:
  • MongoDB is an open-source, document-oriented NoSQL database designed for scalability, flexibility, and performance.
  • Uses a document model, storing data in JSON-like BSON (Binary JSON) documents within collections.
  • Ideal for applications requiring dynamic schemas, large-scale data, and high availability (e.g., e-commerce, social media, IoT).
• Features of MongoDB:
  • Document-Based: Stores data as documents (BSON), allowing nested structures and varied fields.
    • Example: { "name": "Alice", "skills": ["Java", "Python"] }.
  • Schema-Less: No fixed schema; documents in a collection can have different structures.
  • Horizontal Scaling: Supports sharding (data partitioning across servers) and replication (data copies for redundancy).
  • High Availability: Replica sets ensure failover and data redundancy.
  • Rich Query Language: Supports complex queries, aggregations, and indexing.
  • Indexing: Supports various index types (e.g., single, compound, text) for performance.
  • Aggregation Framework: Pipeline-based processing for data transformation (e.g., group, filter, sort).
  • Ad Hoc Queries: Real-time querying without predefined schemas.
  • Geospatial Queries: Supports location-based queries (e.g., find nearby users).
  • CAP Theorem: MongoDB is CP (Consistency, Partition Tolerance) by default but can be tuned for AP (Availability, Partition Tolerance).
• Key Notes:
  • MongoDB is widely used in web applications, big data, and real-time analytics.
  • Comparison to MySQL: MongoDB sacrifices strict ACID compliance for scalability and flexibility.

2. MongoDB Command Interface and MongoDB Compass

• MongoDB Command Interface (mongosh):
  • mongosh is the MongoDB Shell, a JavaScript-based CLI for interacting with MongoDB.
  • Commands:
    • Connect: mongosh "mongodb://localhost:27017".
    • Show databases: show dbs.
    • Switch database: use mydatabase.
    • Show collections: show collections.
  • Used for executing CRUD operations, managing indexes, and running administrative tasks.
• MongoDB Compass:
  • A GUI tool for MongoDB, providing a visual interface to explore, query, and manage data.
  • Features:
    • Browse databases and collections.
    • Create and execute queries with a visual query builder.
    • Analyze schema and performance (e.g., index usage).
    • Manage indexes and run aggregations.
  • Example: Use Compass to insert a document or view query execution plans.
• Key Notes:
  • mongosh is ideal for scripting and automation; Compass is better for visual exploration.
  • Both tools support authentication and connect to local or cloud-hosted MongoDB instances.

3. MongoDB Documents & Collections

• Documents:

  • Basic unit of data in MongoDB, stored as BSON (JSON-like key-value pairs).

  • Example:

    {
      "_id": ObjectId("507f1f77bcf86cd799439011"),
      "name": "Alice",
      "salary": 60000,
      "dept": "IT"
    }
    
    

  • _id: A unique identifier (auto-generated as ObjectId unless specified).

• Collections:
  • Groups of documents, analogous to tables in RDBMS.
  • No predefined schema; documents in a collection can have different fields.
  • Example: A employees collection may contain documents with varying attributes.
• Key Notes:
  • Collections are created implicitly when inserting the first document.
  • Documents are flexible but should follow a logical structure for application consistency.

4. RDBMS & MongoDB Analogies