polymorphic pattern

 let's walk through the implementation of a polymorphic pattern in MongoDB with an example of a content management system where different types of content (e.g., articles, videos, and images) are stored in a single collection.

Step 1: Identify Different Document Types

• Determine the types of documents you want to store in the collection. In our example, we have articles, videos, and images.

Step 2: Design Schema

• Define a schema that accommodates different document types using fields to indicate the type or structure. Include common fields shared by all document types, as well as type-specific fields.
• Example schema:

  json
  

• {
    "type": "article" | "video" | "image",
    "title": <string>,
    "content": <string>,
    "url": <string> // Only for video and image types
    // Additional fields specific to each type
  }
  

Step 3: Insert Documents of Different Types

• Insert documents of different types into the MongoDB collection, ensuring they adhere to the specified schema.
• Example documents:

  json
  

• {
    "type": "article",
    "title": "Introduction to MongoDB Polymorphic Pattern",
    "content": "This article provides an overview of implementing a polymorphic pattern in MongoDB.",
    // Additional fields specific to articles
  }
  {
    "type": "video",
    "title": "MongoDB Tutorial",
    "content": "A tutorial on using MongoDB.",
    "url": "https://example.com/mongodb-tutorial"
    // Additional fields specific to videos
  }
  {
    "type": "image",
    "title": "MongoDB Logo",
    "content": "The official MongoDB logo.",
    "url": "https://example.com/mongodb-logo"
    // Additional fields specific to images
  }
  

Step 4: Query Data by Type

• Use MongoDB queries to retrieve documents based on their type field value.
• Example query to retrieve all articles:

  javascript
  

• db.content.find({ "type": "article" })
  

Step 5: Handle Different Document Types

• Implement conditional logic in queries and application code to handle different document types appropriately. This might involve different processing or rendering logic based on the document type.

By following these steps and adjusting them to fit your specific use case, you can effectively implement a polymorphic pattern in MongoDB to store and query documents of different types within a single collection.

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:

---

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.

---

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.

---

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.

mongodb bucketing pattern

 

Let's walk through the implementation of a bucketing pattern in MongoDB with an example of time-series data. In this scenario, we'll create buckets representing different time intervals (e.g., days) for storing sensor data.

Step 1: Identify Data to Bucket

• We have sensor data that records temperature readings every minute.

Step 2: Define Bucketing Criteria

• We'll bucket the sensor data by day, meaning each bucket will represent a single day's worth of temperature readings.

Step 3: Design Schema

• Our schema will include fields for the temperature reading, the timestamp, and a bucketing field to represent the day.
• Example schema:

  json
  

• {
    "temperature": <value>,
    "timestamp": <timestamp>,
    "day_bucket": <date>
  }
  

Step 4: Insert Documents with Bucketing Field

• Insert documents into the MongoDB collection, ensuring each document includes the day_bucket field representing the day it belongs to.
• Example document:

  json
  

• {
    "temperature": 25.5,
    "timestamp": ISODate("2024-05-20T12:30:00Z"),
    "day_bucket": ISODate("2024-05-20")
  }
  

Step 5: Query Data by Bucket

• Use MongoDB's query capabilities to retrieve data based on the bucketing criteria.
• Example query to retrieve temperature readings for May 20, 2024:

  javascript
  

• db.sensor_data.find({ "day_bucket": ISODate("2024-05-20") })
  

Step 6: Aggregate Data Across Buckets

• Utilize MongoDB's aggregation framework to perform calculations across multiple buckets.
• Example aggregation pipeline to calculate the average temperature for each day:

  javascript
  

• db.sensor_data.aggregate([
    {
      $group: {
        _id: "$day_bucket",
        average_temperature: { $avg: "$temperature" }
      }
    }
  ])
  

Step 7: Optimize Performance

• Monitor data distribution across buckets and create indexes on the day_bucket field to optimize query performance.

Step 8: Handle Bucket Growth

• Implement strategies to manage bucket growth, such as archiving or partitioning buckets further, as needed.

By following these steps and adjusting them to fit your specific use case, you can effectively implement a bucketing pattern in MongoDB to organize and query time-series data.

MongoDB modeling techniques

 

Expanding the list to the top 10 modeling techniques in MongoDB provides a broader perspective on the various strategies available for data modeling:

1. Embedded Data Models: Store related data within a single document using nested or embedded structures. Suitable for one-to-one and one-to-many relationships where the embedded data logically belongs to the parent document.

2. Normalized Data Models: Organize related data across multiple collections and establish relationships using references or foreign keys. Ideal for many-to-many relationships or scenarios requiring data integrity and consistency.

3. Array of Objects: Utilize arrays within documents to store related data as a collection of objects. Suitable for scenarios with one-to-many relationships and small, relatively static arrays.

4. Bucketing or Bucketing Patterns: Group related data into "buckets" or categories within a single collection, often used for partitioning data such as time-series or event-based data.

5. Polymorphic Patterns: Accommodate diverse data types within a single collection by using a field to indicate document types or by storing documents with varying structures but similar attributes. Offers flexibility for evolving schemas or heterogeneous data.

6. Tree Structures: Model hierarchical relationships such as organizational charts or category hierarchies using tree structures like parent references or materialized path patterns.

7. Schema Versioning: Implement techniques to manage schema evolution over time, such as versioning documents or using flexible schema designs like the "attribute pattern" or "schemaless" modeling.

8. Sharding and Data Partitioning: Scale out MongoDB deployments by distributing data across multiple shards based on a shard key, partitioning data to improve performance and scalability.

9. Materialized Views: Precompute and store aggregated or derived data in separate collections to improve query performance for frequently accessed data or complex aggregations.

10. Document Versioning: Implement versioning within documents to track changes over time, allowing for historical analysis or data rollback capabilities.

Each modeling technique offers specific advantages and trade-offs, and the selection depends on factors such as data access patterns, query requirements, scalability needs, and data consistency requirements. It's essential to evaluate the characteristics of your data and application to choose the most appropriate modeling approach.