MongoDB Patterns

Certainly! Here's a concise cheat sheet covering various MongoDB data modeling patterns with schema design and a retail domain example for each:

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Embedded Data Pattern

• Description: Store related data within a single document using nested structures.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "order_id": "ORD123",
    "customer": {
      "name": "John Doe",
      "email": "john@example.com",
      "address": {
        "street": "123 Main St",
        "city": "Anytown",
        "country": "USA"
      }
    },
    "products": [
      {
        "name": "Product 1",
        "quantity": 2,
        "price": 50
      },
      {
        "name": "Product 2",
        "quantity": 1,
        "price": 75
      }
    ]
  }
  

• Retail Domain Example: Order document containing customer details and ordered products.

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Normalized Data Pattern

• Description: Organize related data across multiple collections and establish relationships using references.
• Schema Design:

  json
  

• // Customers collection
  {
    "_id": ObjectId("..."),
    "name": "John Doe",
    "email": "john@example.com"
  }
  
  // Orders collection
  {
    "_id": ObjectId("..."),
    "customer_id": ObjectId("..."),
    "order_id": "ORD123",
    // Other order fields...
  }
  
  // Products collection
  {
    "_id": ObjectId("..."),
    "name": "Product 1",
    "price": 50
    // Other product fields...
  }
  

• Retail Domain Example: Separate collections for customers, orders, and products with references between them.

---

Array of Objects Pattern

• Description: Store related data as an array of objects within a document.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "customer": "John Doe",
    "orders": [
      {
        "order_id": "ORD123",
        "products": [
          {
            "name": "Product 1",
            "quantity": 2,
            "price": 50
          },
          {
            "name": "Product 2",
            "quantity": 1,
            "price": 75
          }
        ]
      }
    ]
  }
  

• Retail Domain Example: Customer document with an array of orders, each containing ordered products.

---

Bucketing Pattern

• Description: Group related data into buckets or categories within a single collection.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "timestamp": ISODate("..."),
    "category": "sales",
    "order_id": "ORD123",
    // Other sales-related fields...
  }
  

• Retail Domain Example: Sales data bucketed by categories like orders, returns, discounts, etc.

---

Polymorphic Pattern

• Description: Accommodate different types of data within a single collection.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "entity_type": "customer",
    // Customer fields...
  }
  {
    "_id": ObjectId("..."),
    "entity_type": "product",
    // Product fields...
  }
  {
    "_id": ObjectId("..."),
    "entity_type": "order",
    // Order fields...
  }
  

• Retail Domain Example: Documents representing customers, products, and orders stored in a single collection.

---

Shredding Pattern

• Description: Decompose complex, nested structures into simpler, flatter documents.
• Schema Design:
  • Decompose nested structures into separate collections and establish relationships using references.
• Retail Domain Example: Decompose order documents into separate collections for customers, orders, and products.

---

Document Versioning Pattern

• Description: Track changes to documents over time.
• Schema Design:

  json
  

• {
    "_id": ObjectId("..."),
    "order_id": "ORD123",
    "status": "shipped",
    "__v": 1 // Version number
  }
  

• Retail Domain Example: Order documents with a versioning field to track status changes.

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By utilizing these patterns with appropriate schema designs in a retail domain context, you can effectively model your data in MongoDB to handle various aspects of a retail business, such as orders, customers, products, and sales data.