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API Communication & Data Fetching

1. REST (Representational State Transfer)​

Theory​

REST is an architectural style that uses HTTP methods (GET, POST, PUT, DELETE) to interact with resources. It follows a stateless, client-server model where each request contains all necessary information.

Use Cases​

✅ Simple, well-structured APIs for CRUD operations.
✅ Public APIs and microservices.
✅ Scalable systems with caching mechanisms.

Limitations​

❌ Over-fetching or under-fetching of data.
❌ Multiple round trips needed for complex data queries.

2. GraphQL​

Theory​

GraphQL is a query language that allows clients to request only the data they need. Unlike REST, which relies on multiple endpoints, GraphQL operates on a single endpoint with flexible queries.

How It Works?​

  • Clients specify what data they need in a structured query.
  • The server responds with only the requested data.

Use Cases​

✅ Optimized data fetching (avoids over-fetching & under-fetching).
✅ Suitable for complex relationships (e.g., social media platforms).
✅ Ideal for mobile applications with limited bandwidth.

Limitations​

❌ Requires additional setup (GraphQL server, schema).
❌ Caching is more complex than REST.

3. API Caching​

Theory​

Caching stores frequently requested data to reduce load times and improve performance. It can be applied at different levels (client-side, server-side, or CDN).

Types of Caching​

  • Client-side caching: Uses browser storage (LocalStorage, IndexedDB, etc.).
  • Server-side caching: Stores responses in memory (Redis, Memcached).
  • CDN caching: Stores static resources closer to users.

Use Cases​

✅ Improving API performance and reducing server load.
✅ Optimizing repeated queries (e.g., user profile data, product listings).
✅ Reducing network latency.

Limitations​

❌ Stale data if caching is not invalidated properly.
❌ Complexity in maintaining consistency.

4. WebSockets​

Theory​

WebSockets provide a persistent, full-duplex communication channel over TCP, allowing real-time data exchange between the client and server.

How It Works?​

  1. The client establishes a WebSocket connection.
  2. Both client and server can send and receive messages at any time.
  3. The connection remains open until explicitly closed.

Use Cases​

✅ Real-time chat applications.
✅ Live notifications and stock price updates.
✅ Multiplayer gaming, collaborative tools (Google Docs-style editing).

Limitations​

❌ Not suitable for one-time requests.
❌ Requires maintaining open connections, which increases server load.

5. gRPC (Google Remote Procedure Call)​

Theory​

gRPC is a high-performance, language-agnostic framework that enables communication between services using protocol buffers (protobufs) instead of JSON.

How It Works?​

  • Uses HTTP/2 for faster, multiplexed connections.
  • Defines service methods using .proto files.
  • Generates client and server code automatically.

Use Cases​

✅ Microservices communication.
✅ High-performance, low-latency APIs.
✅ Streaming data and real-time messaging.

Limitations​

❌ Not human-readable (uses binary format instead of JSON).
❌ Requires gRPC clients for non-browser environments.

Comparison Table​

FeatureRESTGraphQLAPI CachingWebSocketsgRPC
Real-time❌ No❌ No❌ No✅ Yes✅ Yes
Data FetchingFixed endpointsFlexible queriesCached responsesEvent-basedRPC calls
Best ForTraditional APIsComplex queriesPerformance boostsLive updatesMicroservices
TransportHTTPHTTPHTTPTCPHTTP/2
Performance⚠️ Medium✅ Optimized✅ Fast✅ Low latency✅ Very fast

🚀 Conclusion Choosing the right API communication method depends on the application’s needs. REST is great for simplicity, GraphQL is best for efficient data fetching, WebSockets enable real-time communication, gRPC excels in microservices, and API Caching enhances performance by reducing redundant queries.