Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.
When to Use This Skill
- Designing new REST or GraphQL APIs
- Refactoring existing APIs for better usability
- Establishing API design standards for your team
- Reviewing API specifications before implementation
- Migrating between API paradigms (REST to GraphQL, etc.)
- Creating developer-friendly API documentation
- Optimizing APIs for specific use cases (mobile, third-party integrations)
Core Concepts
1. RESTful Design Principles
Resource-Oriented Architecture
- Resources are nouns (users, orders, products), not verbs
- Use HTTP methods for actions (GET, POST, PUT, PATCH, DELETE)
- URLs represent resource hierarchies
- Consistent naming conventions
HTTP Methods Semantics:
GET: Retrieve resources (idempotent, safe)POST: Create new resourcesPUT: Replace entire resource (idempotent)PATCH: Partial resource updatesDELETE: Remove resources (idempotent)
2. GraphQL Design Principles
Schema-First Development
- Types define your domain model
- Queries for reading data
- Mutations for modifying data
- Subscriptions for real-time updates
Query Structure:
- Clients request exactly what they need
- Single endpoint, multiple operations
- Strongly typed schema
- Introspection built-in
3. API Versioning Strategies
URL Versioning:
/api/v1/users
/api/v2/users
Header Versioning:
Accept: application/vnd.api+json; version=1
Query Parameter Versioning:
/api/users?version=1
REST API Design Patterns
Pattern 1: Resource Collection Design
# Good: Resource-oriented endpoints
GET /api/users # List users (with pagination)
POST /api/users # Create user
GET /api/users/{id} # Get specific user
PUT /api/users/{id} # Replace user
PATCH /api/users/{id} # Update user fields
DELETE /api/users/{id} # Delete user
# Nested resources
GET /api/users/{id}/orders # Get user's orders
POST /api/users/{id}/orders # Create order for user
# Bad: Action-oriented endpoints (avoid)
POST /api/createUser
POST /api/getUserById
POST /api/deleteUser
Pattern 2: Pagination and Filtering
from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing import List, Optional
class PaginatedResponse(BaseModel):
items: List[dict]
total: int
page: int
page_size: int
pages: int
@property
def has_next(self) -> bool:
return self.page < self.pages
app = FastAPI()
@app.get("/api/users", response_model=PaginatedResponse)
async def list_users(
page: int = Query(1, ge=1),
page_size: int = Query(20, ge=1, le=100),
status: Optional[str] = Query(None),
search: Optional[str] = Query(None)
):
query = build_query(status=status, search=search)
total = await count_users(query)
offset = (page - 1) * page_size
users = await fetch_users(query, limit=page_size, offset=offset)
return PaginatedResponse(
items=users,
total=total,
page=page,
page_size=page_size,
pages=(total + page_size - 1) // page_size
)
Pattern 3: Error Handling and Status Codes
from fastapi import HTTPException, status
STATUS_CODES = {
"success": 200,
"created": 201,
"no_content": 204,
"bad_request": 400,
"unauthorized": 401,
"forbidden": 403,
"not_found": 404,
"conflict": 409,
"unprocessable": 422,
"internal_error": 500
}
def raise_not_found(resource: str, id: str):
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail={
"error": "NotFound",
"message": f"{resource} not found",
"details": {"id": id}
}
)
Pattern 4: HATEOAS (Hypermedia as the Engine of Application State)
class UserResponse(BaseModel):
id: str
name: str
email: str
_links: dict
@classmethod
def from_user(cls, user: User, base_url: str):
return cls(
id=user.id,
name=user.name,
email=user.email,
_links={
"self": {"href": f"{base_url}/api/users/{user.id}"},
"orders": {"href": f"{base_url}/api/users/{user.id}/orders"},
"update": {"href": f"{base_url}/api/users/{user.id}", "method": "PATCH"},
"delete": {"href": f"{base_url}/api/users/{user.id}", "method": "DELETE"}
}
)
GraphQL Design Patterns
Pattern 1: Schema Design
type User {
id: ID!
email: String!
name: String!
orders(first: Int = 20, after: String): OrderConnection!
}
type OrderConnection {
edges: [OrderEdge!]!
pageInfo: PageInfo!
totalCount: Int!
}
type PageInfo {
hasNextPage: Boolean!
hasPreviousPage: Boolean!
startCursor: String
endCursor: String
}
type Mutation {
createUser(input: CreateUserInput!): CreateUserPayload!
}
input CreateUserInput {
email: String!
name: String!
password: String!
}
type CreateUserPayload {
user: User
errors: [Error!]
}
Pattern 2: DataLoader (N+1 Prevention)
from aiodataloader import DataLoader
class UserLoader(DataLoader):
async def batch_load_fn(self, user_ids: List[str]) -> List[Optional[dict]]:
users = await fetch_users_by_ids(user_ids)
user_map = {user["id"]: user for user in users}
return [user_map.get(user_id) for user_id in user_ids]
Best Practices
REST APIs
- Consistent Naming: Use plural nouns for collections
- Stateless: Each request contains all necessary information
- Use HTTP Status Codes Correctly: 2xx success, 4xx client errors, 5xx server errors
- Version Your API: Plan for breaking changes from day one
- Pagination: Always paginate large collections
- Rate Limiting: Protect your API with rate limits
- Documentation: Use OpenAPI/Swagger for interactive docs
GraphQL APIs
- Schema First: Design schema before writing resolvers
- Avoid N+1: Use DataLoaders for efficient data fetching
- Input Validation: Validate at schema and resolver levels
- Error Handling: Return structured errors in mutation payloads
- Pagination: Use cursor-based pagination (Relay spec)
- Deprecation: Use
@deprecateddirective for gradual migration - Monitoring: Track query complexity and execution time
Common Pitfalls
- Over-fetching/Under-fetching (REST): Fixed in GraphQL but requires DataLoaders
- Breaking Changes: Version APIs or use deprecation strategies
- Inconsistent Error Formats: Standardize error responses
- Missing Rate Limits: APIs without limits are vulnerable to abuse
- Poor Documentation: Undocumented APIs frustrate developers
- Ignoring HTTP Semantics: POST for idempotent operations breaks expectations
- Tight Coupling: API structure shouldn't mirror database schema