Schema Design Patterns For MongoDB

1. Polymorphic Pattern:

• Concept: Stores documents with different structures within the same collection. A common field identifies the document type.
• Use Case: When you have related data that shares some common fields but also has distinct fields based on the type. For example, storing different types of products (books, electronics, clothing) in a single "products" collection.
• Example:

{ "_id": 1, "type": "book", "title": "The Book", "author": "John Doe" }
{ "_id": 2, "type": "electronics", "name": "Laptop", "brand": "XYZ", "processor": "Intel" }

2. Attribute Pattern:

• Concept: Uses key-value pairs to represent attributes that may vary significantly between documents.
• Use Case: When you have a large number of optional or dynamic attributes that don't fit neatly into fixed fields. This avoids creating many sparsely populated fields.
• Example:

{ "_id": 1, "name": "Product A", "attributes": { "color": "red", "size": "large", "material": "cotton" } }
{ "_id": 2, "name": "Product B", "attributes": { "weight": "2kg", "power": "100W" } }

3. Bucket Pattern:

• Concept: Groups related data into a single document (the "bucket").
• Use Case: When you have a one-to-many relationship where the "many" side is relatively small and frequently accessed together with the "one" side. This reduces the number of reads required.
• Example: Storing blog posts with their comments within the same document.

{ "_id": 1, "title": "My Blog Post", "content": "...", "comments": [
    { "author": "User A", "text": "Great post!" },
    { "author": "User B", "text": "I agree." }
]}

4. Outlier Pattern:

• Concept: Stores rarely accessed or very large data separately from the main document.
• Use Case: When you have some data that is infrequently used or would significantly increase the size of the main document, impacting performance. For example, storing large images or detailed product descriptions separately.
• Implementation: Often implemented using GridFS for very large files or by storing a reference to a separate document.

5. Computed Pattern:

• Concept: Stores pre-calculated values to avoid expensive calculations during read operations.
• Use Case: When you have frequently accessed data that requires complex computations. Storing the computed value improves read performance at the cost of increased write complexity (you need to update the computed value whenever the source data changes).
• Example: Storing the total price of an order, which is calculated from the individual item prices.

6. Subset Pattern:

• Concept: Stores a subset of frequently accessed fields within an embedded document for quick retrieval.
• Use Case: When you have a large document but often only need a small set of fields. Embedding this subset reduces the amount of data read from disk.

7. Extended Reference Pattern:

• Concept: Combines referencing with some key fields from the referenced document.
• Use Case: When you frequently need some information from a related document but don't want to perform a separate lookup every time. This reduces the number of queries but introduces some data duplication.

8. Approximation Pattern:

• Concept: Stores an approximate value instead of an exact one to improve performance.
• Use Case: When absolute precision is not required and performance is critical. For example, storing an estimated count instead of performing an expensive count operation.

9. Tree Pattern:

• Concept: Represents hierarchical data structures (like categories or organizational charts) using nested documents or references.
• Use Case: When you need to model tree-like relationships. Different approaches exist, like parent referencing, child referencing, or using an array of ancestors.

10. Preallocation Pattern:

• Concept: Reserves space for future data growth within a document.
• Use Case: When you know a document will grow over time (e.g., an array of events) and you want to avoid frequent document resizing, which can impact performance.

11. Document Versioning Pattern:

• Concept: Stores different versions of a document, allowing you to track changes over time.
• Use Case: When you need to maintain a history of changes to your data. This can be implemented by creating new documents for each version or by storing versions within an array in the main document.

Choosing the right pattern depends heavily on your specific application's requirements, data structure, and access patterns. Often, you'll use a combination of these patterns to achieve optimal performance and maintainability.

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