4. Overview of MongoDB

MongoDB is a leading NoSQL database known for its flexibility, scalability, and developer-friendly features. It uses a document-oriented model to store data, making it ideal for modern applications.

Key Features of MongoDB

1. Document-Oriented Storage

• Data is stored in JSON-like documents (Binary JSON or BSON).
• Documents represent entities and their attributes.
• Documents are flexible, allowing varying structures within the same collection.
• Example Document:

  {
    "_id": "12345",
    "name": "John Doe",
    "email": "john.doe@example.com",
    "orders": [
      { "orderId": "001", "total": 150.0 },
      { "orderId": "002", "total": 200.0 }
    ]
  }

2. Flexible Schema

• MongoDB does not enforce a fixed schema.
• Collections (similar to tables in SQL) can store documents with different fields.
• Suitable for rapidly changing or diverse data. – Example: Different Document Structures in the Same Collection

  // Document 1
  {
    "_id": "123",
    "name": "Laptop",
    "price": 1200,
    "brand": "Dell"
  }
  
  // Document 2
  {
    "_id": "124",
    "name": "Smartphone",
    "price": 800,
    "specifications": {
      "RAM": "8GB",
      "storage": "128GB"
    }
  }

Why Choose MongoDB?

1. Scalability

• Built for horizontal scaling.
• Uses sharding to distribute data across multiple servers.
• Ideal for applications with growing data or high read/write loads.

2. Developer-Friendly

• Built-in support for JSON-like structures makes it intuitive for developers.
• Supports a wide range of programming languages via drivers (e.g., Node.js, Python).

3. High Performance

• Optimized for fast reads and writes, especially with large amounts of data.
• Indexing improves query speed significantly.

4. Rich Query Language

• Supports powerful queries, including:
  • Filtering (e.g., find documents where a field matches a value).
  • Aggregations (e.g., calculate averages, sums, or group data).
  • Geospatial queries for location-based data.

5. Flexible Data Model

• Easily accommodates semi-structured or unstructured data.
• Supports nested arrays and documents for complex relationships.

Architecture Overview

1. Collections and Documents

• Collection = Table (SQL equivalent)
• Document = Row (SQL equivalent)

2. Indexes

• Used to improve query performance.
• Support for single-field, compound, text, and geospatial indexes.

3. Replica Sets

• Provides high availability through automatic failover.
• Data is replicated across multiple servers.

4. Sharding

• Enables horizontal scaling by distributing data across shards.
• Each shard acts as a subset of the database.

When to Use MongoDB

• MongoDB is well-suited for:
  • Applications requiring flexibility in data structure.
  • Use cases with high data volume and frequent schema changes.
  • Scenarios demanding high performance for reads and writes.
  • Cloud-native applications and distributed systems.
• Examples:
  • Real-time analytics
  • Content management systems
  • IoT applications
  • Social media platforms

Strengths and Limitations

Strengths:

1. Schema flexibility allows rapid development.
2. High availability through replication.
3. Scalable architecture with sharding.
4. Rich query capabilities and support for complex data types.

Limitations:

1. Lacks the strong consistency guarantees of SQL databases by default.
2. May require careful design to avoid redundancy and inefficiency.
3. Complex relationships (e.g., many-to-many) are less intuitive compared to relational models.