The Complete Guide to API Communication Patterns: REST, GraphQL, WebSocket, gRPC, and More
A comprehensive guide to API communication patterns including REST, GraphQL, WebSocket, gRPC, webhooks, and message queues. Learn when to use each pattern with diagrams, examples, and decision frameworks.
- tags
- #Api #Rest #Graphql #Websocket #Grpc #Webhook #Rpc #Http #Communication-Patterns #Architecture #Microservices #Real-Time #Server-Sent-Events #Message-Queues #Mqtt #Soap #Webrtc #System-Design #Software-Architecture #Backend #Distributed-Systems #Api-Design
- categories
- Programming Architecture
- published
- reading time
- 24 minutes
Modern applications need to communicate—with browsers, mobile apps, microservices, and third-party systems. But with so many communication patterns available (REST, GraphQL, WebSocket, gRPC, webhooks, message queues, and more), how do you choose the right one?
This guide breaks down 14 communication patterns, explaining what each one is, when to use it, and how it compares to alternatives. Whether you’re building a real-time chat app, a microservices architecture, or a public API, you’ll learn which pattern fits your needs.
The Evolution of API Communication
Before diving into specifics, let’s see how we got here:
Understanding the Landscape
Communication patterns fall into three broad categories:
Part 1: Request-Response Patterns
These patterns follow a simple model: client sends a request, server sends a response, connection closes.
REST (Representational State Transfer)
What it is: Architectural style for building APIs using HTTP and standard methods.
Type: Architectural style (uses HTTP protocol)
{id: 123, name: "Alice"} Client->>Server: POST /users
{name: "Bob"} Note over Server: Create new user Server-->>Client: 201 Created
{id: 124, name: "Bob"} Client->>Server: PUT /users/123
{name: "Alice Smith"} Note over Server: Update user Server-->>Client: 200 OK
{id: 123, name: "Alice Smith"} Client->>Server: DELETE /users/124 Note over Server: Delete user Server-->>Client: 204 No Content
Core Principles:
- Resource-based URLs:
/users,/orders/123, not/getUseror/createOrder - HTTP methods: GET (read), POST (create), PUT (update), DELETE (remove)
- Stateless: Each request contains all needed information
- Standard status codes: 200 OK, 404 Not Found, 500 Internal Server Error
- Multiple representations: JSON, XML, HTML
Example:
| |
When to use:
- Public APIs for web/mobile apps
- Standard CRUD operations
- When you want HTTP caching
- Simple, predictable API design
- Complex query requirements (consider GraphQL)
- High-performance microservices (consider gRPC)
GraphQL
What it is: Query language that lets clients request exactly the data they need.
Type: Query language + protocol (usually over HTTP)
Multiple requests needed Client->>GraphQL Server: GET /users/123 GraphQL Server->>Database: Query user Database-->>GraphQL Server: User data GraphQL Server-->>Client: {id, name, email} Client->>GraphQL Server: GET /users/123/posts GraphQL Server->>Database: Query posts Database-->>GraphQL Server: Posts data GraphQL Server-->>Client: [{title, body}...] Note over Client: GraphQL
Single request Client->>GraphQL Server: query {
user(id: 123) {
name
posts {
title
}
}
} GraphQL Server->>Database: Query user + posts Database-->>GraphQL Server: Combined data GraphQL Server-->>Client: Exactly what was requested
Query Example:
| |
Response:
| |
REST vs GraphQL:
| Aspect | REST | GraphQL |
|---|---|---|
| Endpoints | Multiple (/users, /posts, /comments) | Single (/graphql) |
| Data fetching | Fixed response structure | Client specifies fields |
| Over-fetching | Common (get unneeded fields) | Eliminated |
| Under-fetching | Requires multiple requests | Single request |
| Versioning | URL versioning (/v1/users) | Schema evolution |
| Caching | HTTP caching built-in | More complex |
When to use:
- Mobile apps needing minimal data transfer
- Complex, nested data requirements
- Multiple clients needing different data shapes
- Rapid frontend development
- Simple CRUD (REST is simpler)
- Need HTTP caching out of the box
RPC (Remote Procedure Call)
What it is: Calling functions on a remote server as if they were local.
Type: Paradigm (multiple protocol implementations: JSON-RPC, XML-RPC, gRPC)
Comparison with REST:
| |
When to use:
- Internal microservices communication
- When you want function-call semantics
- Backend-to-backend communication
- Public APIs (REST is more standard)
- Need resource-based modeling
gRPC (Google RPC)
What it is: High-performance RPC framework using Protocol Buffers and HTTP/2.
Type: Framework + protocol
Contract"] stub["Generated
Client Stub"] end subgraph network["Network Layer"] http2["HTTP/2
Multiplexing
Streaming"] protobuf["Protocol Buffers
Binary Serialization"] end subgraph server["Server (Any Language)"] proto2[".proto
Contract"] impl["Service
Implementation"] end proto1 -.->|same contract| proto2 stub -->|serialize| protobuf protobuf --> http2 http2 -->|deserialize| impl impl -->|response| http2 http2 --> protobuf protobuf -->|result| stub style client fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style network fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style server fill:#4C4538,stroke:#4a5568,color:#f0f0f0
Contract Definition (.proto file):
| |
Client Code (Python):
| |
gRPC vs REST Performance:
~1000 bytes"] r2["HTTP/1.1
New connection per request"] r3["Text-based parsing"] end subgraph grpc["gRPC/Protobuf"] g1["Protobuf Serialization
~200 bytes
(5x smaller)"] g2["HTTP/2
Multiplexed streams"] g3["Binary parsing"] end rest -->|Latency| slower["Higher latency
More bandwidth"] grpc -->|Latency| faster["7-10x faster
Lower bandwidth"] style rest fill:#4C3A3C,stroke:#4a5568,color:#f0f0f0 style grpc fill:#3A4C43,stroke:#4a5568,color:#f0f0f0
When to use:
- Microservices communication
- High-performance requirements
- Streaming data (logs, metrics, real-time updates)
- Polyglot environments (Go, Python, Java, etc.)
- Browser clients (limited support, use gRPC-Web)
- Simple public APIs (REST is more accessible)
SOAP (Simple Object Access Protocol)
What it is: XML-based protocol with strict contracts (WSDL) for enterprise systems.
Type: Protocol (over HTTP, usually)
Characteristics:
- Extremely verbose (XML for everything)
- Strong typing via WSDL contracts
- Built-in security (WS-Security)
- Transaction support
- Legacy enterprise standard
Example SOAP Message:
| |
When to use:
- Legacy enterprise systems
- Banking/financial systems (compliance requirements)
- When WS-* standards are required
- New projects (use REST or gRPC instead)
- Mobile/web apps (too heavyweight)
Part 2: Real-Time Communication
These patterns enable server-to-client push and low-latency bidirectional communication.
Polling
What it is: Client repeatedly asks “anything new?”
Type: Pattern (uses HTTP)
Example:
| |
Pros:
- Simple to implement
- Works everywhere (just HTTP)
- No special server support needed
Cons:
- Wastes bandwidth (constant requests)
- High latency (up to poll interval)
- Server load (many unnecessary requests)
When to use:
- Simple updates that aren’t time-critical
- Fallback when other methods unavailable
- Real-time needs (use WebSocket/SSE)
- High-frequency updates (too wasteful)
Long Polling
What it is: Server holds request open until data is available.
Type: Pattern (uses HTTP)
No response yet
(connection held open) Note over Server: Event happens! Server-->>Client: {data: "new update"} Note over Client: Process update
Immediately reconnect Client->>Server: GET /api/updates (connection opens) Note over Server: Wait for next event...
Example:
| |
Long Polling vs Regular Polling:
| Aspect | Regular Polling | Long Polling |
|---|---|---|
| Requests | Every N seconds | Only when data available |
| Latency | Up to N seconds | Near-instant |
| Bandwidth | High (many empty responses) | Lower (only meaningful data) |
| Server load | Many short requests | Fewer long-held connections |
When to use:
- Near real-time updates needed
- WebSocket not supported
- Firewall/proxy restrictions
- True real-time (use WebSocket)
- Many concurrent clients (server holds many connections)
Server-Sent Events (SSE)
What it is: Server pushes updates to client over HTTP.
Type: Protocol (uses HTTP with text/event-stream)
Accept: text/event-stream Note over Server: Connection established Server-->>Client: HTTP 200
Content-Type: text/event-stream Note over Server: Connection stays open Server->>Client: data: {temperature: 72} Note over Client: Update UI Server->>Client: data: {temperature: 73} Note over Client: Update UI Server->>Client: data: {temperature: 74} Note over Client: Update UI Note over Server,Client: Server can push anytime!
Client Code:
| |
Server Code (Python/FastAPI):
| |
SSE vs WebSocket:
| Feature | Server-Sent Events | WebSocket |
|---|---|---|
| Direction | Server → Client only | Bidirectional |
| Protocol | HTTP (text/event-stream) | WebSocket protocol |
| Complexity | Simple | More complex |
| Reconnection | Automatic | Manual |
| Use case | Live feeds, notifications | Chat, gaming, collaboration |
When to use:
- Live dashboards (stock prices, metrics)
- Progress updates (file uploads, long tasks)
- Notifications
- News/activity feeds
- Need client-to-server messages (use WebSocket)
- Binary data (use WebSocket)
WebSocket
What it is: Persistent bidirectional connection between client and server.
Type: Protocol (RFC 6455)
Upgrade: websocket
Connection: Upgrade Server-->>Client: HTTP/1.1 101 Switching Protocols
Upgrade: websocket Note over Client,Server: 2. WebSocket Connection Established rect rgb(128, 170, 221, 0.1) Note over Client,Server: Bidirectional Communication Client->>Server: {"type": "message", "text": "Hello"} Server->>Client: {"type": "message", "text": "Hi there!"} Server->>Client: {"type": "notification", "text": "User joined"} Client->>Server: {"type": "message", "text": "Welcome"} end Note over Client,Server: 3. Connection Close Client->>Server: Close frame Server-->>Client: Close frame
Client Code:
| |
Server Code (Python/FastAPI):
| |
When to use:
- Chat applications
- Live collaboration (Google Docs style)
- Multiplayer games
- Live dashboards with user interaction
- Real-time trading platforms
- Simple one-way updates (use SSE)
- Occasional API calls (use REST)
WebRTC
What it is: Peer-to-peer real-time communication for audio, video, and data.
Type: Protocol suite
(WebSocket/HTTP)"] signal["Exchange connection info
SDP offers/answers
ICE candidates"] end subgraph browser2["Browser 2"] app2["Web App"] rtc2["WebRTC API"] end subgraph p2p["Peer-to-Peer Connection"] media["Audio/Video Streams"] data["Data Channels"] end app1 <-->|Signaling| signal signal <-->|Signaling| app2 rtc1 <-.->|Direct P2P| p2p p2p <-.->|Direct P2P| rtc2 style signaling fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style p2p fill:#3A4C43,stroke:#4a5568,color:#f0f0f0
Key Characteristics:
- Peer-to-peer: Direct browser-to-browser connection (no server in the middle)
- Media streams: Audio and video
- Data channels: Arbitrary data transfer
- NAT traversal: Works across firewalls/routers (STUN/TURN servers)
Use Cases:
- Video conferencing (Zoom, Google Meet)
- Screen sharing
- Peer-to-peer file transfer
- Multiplayer gaming (low latency)
- IoT device communication
When to use:
- Video/audio calling
- Need lowest possible latency
- Want to minimize server bandwidth
- Need server-side processing of media
- Simple messaging (use WebSocket)
Part 3: Event-Driven Patterns
These patterns enable asynchronous, decoupled communication based on events.
Webhooks
What it is: HTTP callback where a server notifies your app when events happen.
Type: Pattern (uses HTTP POST)
{url: "https://yourapp.com/webhook"} External Service-->>Your App: 200 OK
{webhook_id: "abc123"} Note over External Service: User makes payment... Note over External Service,Your Webhook Endpoint: 2. Event Notification External Service->>Your Webhook Endpoint: POST /webhook
{event: "payment.success",
amount: 99.99} Note over Your Webhook Endpoint: Process payment
Update database
Send email Your Webhook Endpoint-->>External Service: 200 OK Note over External Service: Another event... External Service->>Your Webhook Endpoint: POST /webhook
{event: "refund.processed"} Your Webhook Endpoint-->>External Service: 200 OK
Example: GitHub Webhook
| |
Common Webhook Providers:
(Payments)"] github["GitHub
(Code events)"] twilio["Twilio
(SMS/calls)"] sendgrid["SendGrid
(Email events)"] shopify["Shopify
(E-commerce)"] end subgraph your["Your Application"] endpoint["/webhook endpoint"] logic["Event Handler
Business Logic"] end stripe -->|payment.succeeded| endpoint github -->|push, pull_request| endpoint twilio -->|message.received| endpoint sendgrid -->|delivered, opened| endpoint shopify -->|order.created| endpoint endpoint --> logic style providers fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style your fill:#3A4C43,stroke:#4a5568,color:#f0f0f0
Security Best Practices:
| |
When to use:
- Payment notifications (Stripe, PayPal)
- CI/CD triggers (GitHub, GitLab)
- Form submissions (Typeform, Google Forms)
- Email events (SendGrid, Mailgun)
- Any “notify me when X happens” scenario
- Need immediate response (webhooks are async)
- High-frequency events (consider message queues)
Message Queues
What it is: Asynchronous message passing to decouple services.
Type: Pattern + various implementations (RabbitMQ, Kafka, AWS SQS, Redis)
Core Patterns:
1. Point-to-Point Queue
to ONE consumer
2. Pub/Sub (Publish/Subscribe)
a COPY of message
Example: RabbitMQ (Python)
| |
Example: AWS SQS (Python/boto3)
| |
Benefits of Message Queues:
| Benefit | Description |
|---|---|
| Decoupling | Services don’t need to know about each other |
| Load leveling | Queue absorbs traffic spikes |
| Reliability | Messages persist if consumer is down |
| Scalability | Add more consumers to process faster |
| Async processing | Don’t block web requests with slow tasks |
When to use:
- Background jobs (email, image processing)
- Microservices communication
- Event-driven architectures
- Handle traffic spikes
- Retry failed operations
- Need immediate response (use synchronous APIs)
- Simple request-response (use REST)
MQTT (Message Queuing Telemetry Transport)
What it is: Lightweight pub/sub protocol for IoT and constrained devices.
Type: Protocol (binary, over TCP)
home/bedroom/temp
home/living/motion
home/camera/alert"] end subgraph subscribers["Subscribers"] app["Mobile App"] dashboard["Dashboard"] automation["Automation Engine"] end temp -->|publish| topics motion -->|publish| topics camera -->|publish| topics topics -->|subscribe| app topics -->|subscribe| dashboard topics -->|subscribe| automation automation -->|publish| light style devices fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style broker fill:#4C4538,stroke:#4a5568,color:#f0f0f0 style subscribers fill:#3A4C43,stroke:#4a5568,color:#f0f0f0
Characteristics:
- Extremely lightweight (header ~2 bytes vs HTTP ~100+ bytes)
- Pub/sub model with topics
- Quality of Service (QoS) levels (0, 1, 2)
- Retained messages (new subscribers get last value)
- Low power consumption
- Designed for unreliable networks
Example (Python/paho-mqtt):
| |
MQTT vs HTTP REST:
| Aspect | MQTT | HTTP REST |
|---|---|---|
| Overhead | ~2 bytes | ~100+ bytes |
| Pattern | Pub/Sub | Request-Response |
| Power | Very low | Higher |
| Use case | IoT sensors | Web APIs |
| Network | Works on unreliable networks | Needs stable connection |
When to use:
- IoT devices (sensors, smart home)
- Battery-powered devices
- Unreliable/low bandwidth networks
- Real-time telemetry
- Web APIs (use REST/WebSocket)
- Large payloads (use HTTP)
Part 4: Decision Framework
How do you choose the right communication pattern? Use these decision trees and comparisons.
Communication Pattern Decision Tree
or Async?} sync -->|Synchronous| crud{Standard
CRUD API?} sync -->|Async/Event-driven| event{Who initiates?} crud -->|Yes| rest["REST"] crud -->|No, complex queries| graphql["GraphQL"] crud -->|No, function calls| perf{Performance
critical?} perf -->|Yes| grpc["gRPC"] perf -->|No| rpc["JSON-RPC"] event -->|Server notifies client| webhook["Webhook"] event -->|Services communicate| mq["Message Queue"] event -->|IoT devices| mqtt["MQTT"] start --> realtime{Real-time
needed?} realtime -->|Yes| direction{Communication
direction?} direction -->|Server → Client only| sse["Server-Sent Events"] direction -->|Bidirectional| ws["WebSocket"] direction -->|Peer-to-peer media| webrtc["WebRTC"] realtime -->|No, occasional| polling["Polling/REST"] style rest fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style graphql fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style grpc fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style ws fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style webhook fill:#4C4538,stroke:#4a5568,color:#f0f0f0 style mq fill:#4C4538,stroke:#4a5568,color:#f0f0f0
Performance Comparison
~1ms"] l2["gRPC
~5ms"] l3["WebRTC
~10ms"] l4["REST
~50ms"] l5["Long Polling
~100ms"] l6["SSE
~150ms"] l7["Polling
~5000ms"] l8["Webhook
Variable"] end subgraph throughput["Throughput (Higher is better)"] t1["gRPC
100k msg/s"] t2["WebSocket
50k msg/s"] t3["MQTT
30k msg/s"] t4["REST
10k msg/s"] t5["GraphQL
8k msg/s"] t6["SOAP
1k msg/s"] end style l1 fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style l2 fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style l7 fill:#4C3A3C,stroke:#4a5568,color:#f0f0f0 style l8 fill:#4C3A3C,stroke:#4a5568,color:#f0f0f0 style t1 fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style t2 fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style t6 fill:#4C3A3C,stroke:#4a5568,color:#f0f0f0
Complexity vs Capability
Use Case Mapping
| Use Case | Recommended Pattern | Why? |
|---|---|---|
| Public API for mobile/web | REST | Standard, cacheable, simple |
| Complex data requirements | GraphQL | Flexible queries, avoid over-fetching |
| Microservices internal communication | gRPC | High performance, type safety |
| Real-time chat | WebSocket | Bidirectional, low latency |
| Live dashboard (one-way) | Server-Sent Events | Simple server push |
| Payment notifications | Webhook | Event-driven, reliable delivery |
| Background job processing | Message Queue | Async, decoupled, scalable |
| IoT sensor data | MQTT | Lightweight, low power |
| Video conferencing | WebRTC | Peer-to-peer, low latency |
| Stock ticker | Server-Sent Events | Continuous updates, one-way |
| Multiplayer game | WebSocket or WebRTC | Low latency, bidirectional |
Part 5: Hybrid Architectures
Real-world systems rarely use just one pattern. Here’s how to combine them effectively.
E-Commerce System Example
/api/products
/api/orders"] ws["WebSocket
/ws/cart
/ws/notifications"] end subgraph services["Microservices"] order["Order Service"] inventory["Inventory Service"] payment["Payment Service"] notification["Notification Service"] end subgraph async["Async Layer"] queue["Message Queue"] events["Event Bus"] end subgraph external["External Services"] stripe["Stripe
(Payments)"] shippo["Shippo
(Shipping)"] end web -->|GET /products| rest mobile -->|POST /orders| rest admin -->|WebSocket connection| ws rest <-->|gRPC| order rest <-->|gRPC| inventory order -->|publish event| queue queue -->|consume| payment queue -->|consume| notification payment <-->|HTTP API| stripe stripe -.->|webhook| payment order <-->|HTTP API| shippo shippo -.->|webhook| notification notification -->|push| ws ws -->|real-time updates| admin style clients fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style api fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style services fill:#4C4538,stroke:#4a5568,color:#f0f0f0 style async fill:#4C3A3C,stroke:#4a5568,color:#252627 style external fill:#2c5282,stroke:#4a5568,color:#f0f0f0
Breaking down this architecture:
REST API - Public-facing endpoints for standard operations
GET /products- Browse catalogPOST /orders- Place orderGET /orders/{id}- Check order status
WebSocket - Real-time updates for admin dashboard
- Live order notifications
- Inventory alerts
- Customer activity feed
gRPC - Internal microservice communication
- Order service ↔ Inventory service (high performance)
- Order service ↔ Payment service (type safety)
Message Queue - Async processing
- Order created → Send confirmation email
- Order created → Update inventory
- Order created → Trigger analytics
Webhooks - External service integration
- Stripe → Payment confirmation
- Shippo → Shipping updates
- SendGrid → Email delivery status
Social Media Platform Example
Flexible queries"] wsserver["WebSocket Server
Real-time updates"] rest["REST API
Simple endpoints"] end subgraph backend["Backend Services"] user["User Service"] post["Post Service"] feed["Feed Service"] messaging["Messaging Service"] end subgraph storage["Data Layer"] postgres[("PostgreSQL
User data")] redis[("Redis
Cache/Sessions")] s3[("S3
Media storage")] end subgraph realtime["Real-Time Layer"] pubsub["Pub/Sub"] wsconnections["WebSocket
Connection Pool"] end webapp -->|Complex queries| graphql mobileapp -->|Simple endpoints| rest webapp <-->|Chat, notifications| wsserver mobileapp <-->|Chat, notifications| wsserver graphql --> user graphql --> post graphql --> feed rest --> user rest --> post wsserver <--> messaging messaging --> pubsub pubsub --> wsconnections wsconnections --> webapp wsconnections --> mobileapp user --> postgres post --> postgres feed --> redis post --> s3 style frontend fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style gateway fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style backend fill:#4C4538,stroke:#4a5568,color:#f0f0f0 style storage fill:#2c5282,stroke:#4a5568,color:#f0f0f0 style realtime fill:#4C3A3C,stroke:#4a5568,color:#252627
Why this combination?
- GraphQL - Web app needs flexible queries (user profile + posts + comments in one request)
- REST - Mobile app needs simple, cacheable endpoints
- WebSocket - Real-time chat and notifications
- Pub/Sub - Distribute messages to all connected users
Monitoring & Observability System
(IoT devices)"] grpc["gRPC
(High throughput)"] http["HTTP
(Legacy systems)"] end subgraph processing["Processing"] kafka["Kafka
Message Stream"] processor["Stream Processor"] end subgraph storage["Storage"] timeseries[("Time-series DB")] elasticsearch[("Elasticsearch")] end subgraph ui["User Interface"] dashboard["Dashboard"] alerts["Alert Manager"] end app1 --> grpc app2 --> grpc app3 --> http mqtt --> kafka grpc --> kafka http --> kafka kafka --> processor processor --> timeseries processor --> elasticsearch timeseries --> dashboard elasticsearch --> dashboard processor -->|SSE| dashboard processor -->|Webhook| alerts style sources fill:#3A4A5C,stroke:#4a5568,color:#f0f0f0 style ingestion fill:#3A4C43,stroke:#4a5568,color:#f0f0f0 style processing fill:#4C4538,stroke:#4a5568,color:#f0f0f0 style storage fill:#2c5282,stroke:#4a5568,color:#f0f0f0 style ui fill:#4C3A3C,stroke:#4a5568,color:#252627
Pattern choices:
- gRPC - High-throughput metrics ingestion (10k+ msg/s)
- MQTT - Lightweight IoT device telemetry
- Kafka - Message stream for processing
- Server-Sent Events - Live dashboard updates
- Webhooks - Alert notifications (PagerDuty, Slack)
Conclusion
Modern applications require multiple communication patterns working together. Here’s your decision framework:
Quick Reference
Need to fetch/update data? → REST (simple) or GraphQL (complex queries)
Need to call remote functions? → RPC (simple) or gRPC (high performance)
Need real-time bidirectional communication? → WebSocket
Need server-to-client push only? → Server-Sent Events
Need to be notified of external events? → Webhook
Need async background processing? → Message Queue (RabbitMQ, Kafka, SQS)
Building IoT system? → MQTT
Need video/audio calling? → WebRTC
Key Takeaways
Not all patterns are protocols - REST is a style, WebSocket is a protocol, webhook is a pattern, gRPC is a framework
Patterns are complementary - Use REST for your API, webhooks for external events, WebSocket for real-time, gRPC for microservices
Choose based on requirements - Consider latency, throughput, complexity, and existing infrastructure
Start simple - REST covers most needs. Add complexity only when required.
Security matters - Verify webhook signatures, use WSS:// for WebSocket, implement authentication for all patterns
Further Reading
- REST: Roy Fielding’s Dissertation
- WebSocket: RFC 6455
- gRPC: gRPC Documentation
- GraphQL: GraphQL Specification
- MQTT: MQTT.org
Have questions or suggestions? Found an error? Open an issue on GitHub or connect on Twitter/X .