4.1.2.1 Implement Cosmos DB Data Models
First Principle: Azure Cosmos DB’s multi-model APIs provide flexibility in data modeling, letting you choose the best fit for your application's needs. The API you select determines your data modeling approach, query language, and SDKs, aligning the database structure with application requirements.
What It Is: Cosmos DB data models refer to the different ways data can be structured and accessed within Azure Cosmos DB, corresponding to its various APIs.
Visual: "Cosmos DB Multi-Model APIs"
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Primary APIs and Their Data Models:
| API | Data Model | Use Cases | Modeling Tip |
|---|---|---|---|
| "Core (SQL) API" | Document (JSON) | Flexible schema, rapid development, rich querying with SQL-like syntax. Ideal for web, mobile, and product catalog applications. | Design containers for logical groupings (e.g., users, products). Leverage JSON's flexibility to store diverse attributes. |
| "MongoDB API" | Document (BSON, MongoDB-compatible) | Migrating existing MongoDB-compatible apps, using familiar drivers and tools. | Collections and documents mirror MongoDB; supports MongoDB query language. |
| "Cassandra API" | Wide-column store (tables, column families) | High-throughput, large-scale workloads, time-series data, eventual consistency. Ideal for IoT or archival data. | Model for queries; denormalize data for performance. |
| "Gremlin API" | Graph (vertices and edges) | Highly connected data, such as social networks, recommendation engines, or fraud detection. | Define entities as nodes, relationships as edges; use Gremlin traversal queries. |
| "Table API" | Key-value (entities with partition and row keys) | Simple, fast lookups; compatible with Azure Table storage SDKs. | Design for access patterns; partition key impacts scalability. |
Key Point: The API you select shapes your data modeling, query capabilities, and integration options. Choose based on your application's data structure, access patterns, and existing technology stack.
Scenario: You are developing a new application that needs to store user profiles with a flexible schema, as new attributes might be added frequently. Separately, you need to store highly interconnected data for a social network feature, tracking user friendships and interactions.
Reflection Question: How does implementing different Cosmos DB data models (e.g., Core (SQL) API for flexible documents, Gremlin API for graph data) fundamentally align the database structure with your application's unique data structures and access patterns, ensuring optimal performance and flexibility?