How to Design APIs Like a Senior Engineer
A Comprehensive Guide to REST, GraphQL, Authentication, and Security
Introduction
This guide addresses the architectural decisions, trade-offs, and implementation patterns that define production-grade API design. We’ll examine protocol selection, authentication strategies, security hardening, and scalability considerations from an engineering perspective.
API design at scale requires understanding not just how to build endpoints, but how to architect systems that are maintainable, secure, performant, and evolvable. This document covers the patterns and practices used in high-traffic, enterprise-grade systems.
Scope and Approach
This guide assumes familiarity with software architecture principles, network protocols, and distributed systems concepts. We’ll focus on:
- Architectural patterns and their trade-offs
- Protocol selection criteria for different use cases
- Security implementation at scale
- Performance optimization and scalability patterns
- Operational considerations and observability
What Senior-Level API Design Requires
Senior engineers are expected to move beyond CRUD endpoints and address the systemic qualities of an API platform. This guide assumes you can handle:
- Trade-off Evaluation: Compare REST vs GraphQL vs gRPC using performance, team expertise, and governance constraints.
- Lifecycle Management: Plan for versioning, deprecation, backward compatibility, and rollout strategies.
- Resiliency Patterns: Apply idempotency, circuit breakers, retries with jitter, and degradation strategies.
- Security & Compliance: Enforce zero-trust principles, token rotation, audit logging, and regulatory requirements (PCI, HIPAA, GDPR).
- Observability & SLOs: Instrument APIs with tracing, metrics, and structured logs tied to measurable objectives.
- Contract Governance: Maintain OpenAPI/GraphQL schemas, consumer-driven contracts, and automated compatibility tests.
- Operational Excellence: Bake in canary deployments, feature flags, automated rollback, and incident response playbooks.
- Performance Engineering: Use caching layers, pagination strategies, payload optimization, and load testing to meet latency budgets.
The rest of this document dives into each of these responsibilities with practical implementation patterns.
APIs in System Architecture
An API (Application Programming Interface) defines the contract for inter-service communication in distributed systems. It establishes the interface boundary between service consumers and providers, encapsulating implementation details while exposing a stable, versioned contract.
At an architectural level, APIs serve as the integration layer that enables service-oriented and microservices architectures. They provide:
Architectural Characteristics
- Abstraction & Service Boundaries: APIs provide encapsulation of business logic and data access patterns. This abstraction enables independent evolution of services, reduces coupling, and facilitates polyglot architectures where services can be implemented in different technology stacks while maintaining interoperability through standardized interfaces.
- Standardization: Adherence to protocol standards (HTTP, gRPC, AMQP) ensures interoperability across heterogeneous systems. Standardization reduces integration complexity and enables ecosystem tooling, monitoring, and security solutions.
- Loose Coupling: Well-designed APIs minimize dependencies between services, enabling independent deployment cycles, technology choices, and scaling strategies. This architectural pattern is fundamental to microservices and distributed system design.
- Reusability & Composition: APIs enable service composition patterns where complex business capabilities are built by orchestrating multiple specialized services. This promotes the DRY principle at the service level and enables API-first development approaches.
- Versioning & Evolution: APIs must support backward compatibility and graceful deprecation strategies. Effective versioning allows services to evolve independently while maintaining contracts with existing consumers.
- Observability: APIs serve as instrumentation points for distributed tracing, metrics collection, and logging. Proper API design incorporates observability patterns from the start.
Protocol Selection: Architectural Considerations
Protocol choice is a fundamental architectural decision that affects system performance, scalability, operational complexity, and integration capabilities. The selection should be driven by:
- Communication Patterns: Request-response (HTTP), streaming (gRPC, WebSockets), pub/sub (AMQP, MQTT)
- Performance Requirements: Latency sensitivity, throughput needs, payload size
- Operational Constraints: Network topology, firewall rules, proxy compatibility
- Ecosystem Integration: Tooling support, monitoring capabilities, security solutions
- Team Expertise: Protocol familiarity, debugging capabilities, operational experience
Protocol Selection Matrix
| Protocol | Use Case | Performance | Complexity | Browser Support |
|---|---|---|---|---|
| HTTP/HTTPS | Public APIs, Web integration | Moderate (text-based) | Low | Full |
| WebSockets | Real-time bidirectional | High (persistent connection) | Medium | Full |
| gRPC | Inter-service communication | Very High (binary, HTTP/2) | Medium-High | Limited (requires proxy) |
| AMQP | Async messaging, event streaming | High (message queuing) | High | N/A |
Common Questions About APIs
API Architectural Styles
API styles represent different architectural approaches to service interface design. Each style embodies distinct principles, trade-offs, and operational characteristics. The selection should align with system requirements, team capabilities, and long-term architectural vision.
Selection Criteria
When evaluating API styles, consider:
- Data Access Patterns: Simple CRUD vs complex queries vs streaming
- Client Diversity: Single client type vs heterogeneous clients with varying needs
- Network Efficiency: Bandwidth constraints, mobile optimization requirements
- Operational Complexity: Team size, monitoring capabilities, debugging requirements
- Ecosystem Maturity: Tooling, libraries, community support, hiring market
- Evolution Strategy: Versioning needs, backward compatibility requirements
Decision Framework
REST (Representational State Transfer)
REST is an architectural style based on HTTP that models resources as URLs and uses standard HTTP methods for state transitions. It’s the de facto standard for public APIs due to its simplicity, wide tooling support, and HTTP ecosystem integration.
Core Principles
- Resource-Oriented: Resources are identified by URIs and manipulated through standard HTTP methods. Resources are decoupled from their representations (JSON, XML, etc.).
- Stateless: Each request contains all necessary context. No server-side session state required, enabling horizontal scaling and simplified caching strategies.
- Uniform Interface: Standard HTTP methods (GET, POST, PUT, PATCH, DELETE) with well-defined semantics. Leverages HTTP status codes and headers for metadata.
- Cacheable: Responses must define cacheability. Enables CDN integration, reduces server load, and improves client performance.
- Layered System: Supports intermediary components (proxies, gateways, load balancers) without affecting client-server communication.
Architectural Trade-offs
Advantages:
- Mature ecosystem with extensive tooling and libraries
- HTTP caching infrastructure (CDNs, reverse proxies)
- Browser-native support, no special clients required
- Simple debugging with standard HTTP tools
- Wide developer familiarity reduces onboarding time
Limitations:
- Over-fetching/under-fetching problems with complex data models
- Multiple round trips for related resources (N+1 problem)
- Limited real-time capabilities without polling or WebSockets
- Versioning requires URL or header-based strategies
Optimal Use Cases
- Public-facing APIs requiring broad compatibility
- Resource-oriented domains with clear CRUD operations
- Scenarios where HTTP caching provides significant value
- Teams prioritizing simplicity and maintainability
- Integration with existing HTTP-based infrastructure
Production Examples: Stripe API, GitHub API v3, AWS S3 API, Twitter API v2
GraphQL
GraphQL is a query language and runtime for APIs that enables clients to request exactly the data they need. Developed by Facebook to address mobile performance issues, it solves the over-fetching and under-fetching problems inherent in REST architectures.
Core Concepts
- Type System: Strongly-typed schema defines the API contract. Types, fields, and relationships are explicitly defined, enabling validation and tooling.
- Single Endpoint: All operations (queries, mutations, subscriptions) go through one endpoint, simplifying client configuration and reducing network complexity.
- Field Selection: Clients specify exact fields needed, eliminating over-fetching. Reduces payload size and improves mobile performance.
- Resolver Pattern: Each field has a resolver function that defines how data is fetched. Enables flexible data aggregation from multiple sources.
- Introspection: Schema is self-documenting and queryable, enabling powerful developer tooling and client code generation.
Architectural Trade-offs
Advantages:
- Eliminates over-fetching/under-fetching, critical for mobile networks
- Single request for complex, nested data structures
- Strong typing enables validation and prevents runtime errors
- Introspection enables powerful tooling (GraphiQL, code generation)
- Flexible schema evolution without versioning URLs
Limitations:
- Complexity: requires resolver design, N+1 query prevention (DataLoader)
- Caching: HTTP caching doesn’t apply; requires custom caching strategies
- Query complexity: unbounded queries can cause performance issues
- Learning curve: steeper than REST, requires schema design expertise
- Overhead: query parsing and validation add latency
Performance Considerations
N+1 Query Problem: GraphQL resolvers can trigger multiple database queries. Solutions include DataLoader (batching/caching), join queries, or GraphQL-specific database libraries.
Query Complexity Analysis: Implement depth limits, field cost analysis, and query timeouts to prevent resource exhaustion.
Caching Strategy: Use field-level caching, persisted queries, and CDN integration for static data. Consider Apollo Client cache for client-side optimization.
Optimal Use Cases
- Mobile applications with bandwidth constraints
- Multiple client types with varying data requirements
- Complex data relationships requiring nested queries
- Rapid frontend iteration where data needs change frequently
- Microservices architectures requiring data aggregation
Production Examples: GitHub GraphQL API, Shopify Storefront API, Facebook Graph API, Yelp GraphQL API
gRPC (Google Remote Procedure Call)
gRPC is a high-performance RPC framework using Protocol Buffers for serialization and HTTP/2 for transport. It provides type-safe, language-agnostic service definitions with support for streaming and code generation.
Core Architecture
- Protocol Buffers: Binary serialization format providing 3-10x smaller payloads than JSON. Schema evolution through backward-compatible field additions. Enables efficient serialization/deserialization.
- HTTP/2 Transport: Multiplexing, header compression, server push capabilities. Reduces latency compared to HTTP/1.1, especially for multiple concurrent requests.
- Code Generation: Service definitions (.proto files) generate client/server code in multiple languages, ensuring type safety and reducing boilerplate.
- Streaming Support: Four communication patterns: Unary (request-response), Server Streaming, Client Streaming, Bidirectional Streaming. Enables efficient real-time data transfer.
- Interceptors: Middleware pattern for cross-cutting concerns (logging, authentication, rate limiting, tracing).
Architectural Trade-offs
Advantages:
- High performance: binary protocol, HTTP/2 multiplexing, efficient serialization
- Type safety: compile-time validation, generated client/server code
- Streaming: efficient for large datasets and real-time scenarios
- Polyglot support: same service definition works across languages
- Built-in features: load balancing, health checking, flow control
Limitations:
- Browser support: requires gRPC-Web proxy (Envoy, etc.)
- Human readability: binary format not inspectable like JSON
- Ecosystem: less tooling than REST (though improving)
- Learning curve: requires understanding Protocol Buffers and HTTP/2
- Debugging: requires specialized tools (grpcurl, gRPC UI)
Streaming Patterns
Unary: Traditional request-response. Use for simple operations.
Server Streaming: Server sends multiple responses to single request. Ideal for large datasets, real-time updates, or progressive data delivery.
Client Streaming: Client sends multiple requests, server responds once. Useful for batch uploads or aggregating client data.
Bidirectional Streaming: Both sides can send messages independently. Enables chat, gaming, or collaborative editing scenarios.
Optimal Use Cases
- Inter-service communication in microservices architectures
- High-throughput, low-latency requirements
- Polyglot environments requiring type-safe contracts
- Streaming scenarios (real-time data, large file transfers)
- Internal APIs where browser compatibility isn’t required
Production Examples: Google Cloud APIs, Netflix microservices, Kubernetes API, CoreOS etcd
REST vs GraphQL: Architectural Decision Framework
The choice between REST and GraphQL is fundamentally about data access patterns, client diversity, and operational trade-offs. Both are valid architectural choices; the decision should be driven by system requirements rather than trends.
Decision Matrix
| Factor | REST Advantage | GraphQL Advantage |
|---|---|---|
| Caching | HTTP caching (CDN, reverse proxy) | Field-level, requires custom implementation |
| Over-fetching | Common with nested resources | Eliminated through field selection |
| Under-fetching | Multiple round trips required | Single request for related data |
| Versioning | URL or header-based | Schema evolution, additive changes |
| Tooling | Mature ecosystem | Growing, GraphiQL/Playground |
| Complexity | Lower operational complexity | Higher (resolvers, N+1, complexity analysis) |
| Mobile Optimization | Requires careful endpoint design | Native field selection optimization |
REST: Optimal When
- Resource-oriented domain model with clear CRUD operations
- HTTP caching provides significant performance benefits
- Public API requiring broad compatibility and tooling
- Team prioritizes operational simplicity
- Well-defined, stable resource model
- Integration with existing HTTP infrastructure
Production Examples: Stripe API, GitHub API v3, AWS S3, Twitter API v2
GraphQL: Optimal When
- Complex data relationships requiring nested queries
- Multiple client types with varying data requirements
- Mobile-first applications with bandwidth constraints
- Rapid frontend iteration with changing data needs
- Microservices requiring data aggregation layer
- Team has GraphQL expertise and tooling
Production Examples: GitHub GraphQL API, Shopify Storefront API, Facebook Graph API
Hybrid Approaches
Many production systems use both REST and GraphQL strategically: REST for public APIs and simple operations, GraphQL for complex queries and mobile clients. Consider API Gateway patterns that route requests to appropriate backends based on client needs and operation complexity.
Architectural Decision Q&A
- Data access patterns: If clients need varying subsets of related data, GraphQL reduces round trips
- Client diversity: Multiple client types with different needs favor GraphQL
- Caching requirements: If HTTP caching is critical (CDN integration), REST has advantages
- Team expertise: GraphQL requires resolver design patterns and N+1 prevention
- Operational complexity: REST has simpler debugging and monitoring
- High throughput and low latency: Binary protocol, HTTP/2 multiplexing
- Type safety is critical: Generated client/server code
- Streaming is needed: Server/client/bidirectional
- Polyglot environment: Benefits from language-agnostic contracts
- Internal services: Where browser compatibility isn’t required
- API Gateway routes requests: REST for public/simple operations, GraphQL for complex queries
- GraphQL resolvers call REST backends: GraphQL as aggregation layer
- Separate services: REST for resource management, GraphQL for query layer
- BFF pattern: GraphQL BFFs for mobile/web, REST for public API
- Maintain single source of truth
- Avoid data duplication
- Implement consistent authentication/authorization
- Ensure observability across both layers
gRPC Communication Types
4 Key Design Principles That Make Great APIs
1. Consistency
- Consistent naming conventions across all endpoints
- Consistent patterns for requests and responses
- Uniform error handling and status codes
2. Simplicity
- Focus on core use cases
- Intuitive design that’s easy to understand
- Avoid over-engineering
3. Security
- Authentication (AuthN) – verifying user identity
- Authorization (AuthZ) – controlling access to resources
- Input validation to prevent malicious data
- Rate limiting to prevent abuse
4. Performance
- Caching strategies to reduce server load
- Pagination for large datasets
- Minimize payload size
- Reduce round trips between client and server
The API Design Process
Key Steps
- Identify core use cases and user stories
- Define scope and boundaries
- Determine performance requirements
- Consider security constraints
Design Approaches
Top-Down: Start with high-level requirements and workflows
Bottom-Up: Begin with existing data models and capabilities
Contract-First: Define the contract before implementation
Lifecycle Management
API Protocols
Application Protocol in the Network Stack
HTTP (HyperText Transfer Protocol)
HTTP is the foundation of data communication on the World Wide Web. It defines how messages are formatted and transmitted, and how web servers and browsers respond to various commands.
HTTP Methods
| Method | Purpose | Idempotent | Safe |
|---|---|---|---|
| GET | Retrieve data from server | Yes | Yes |
| POST | Create new resource or submit data | No | No |
| PUT | Update/replace entire resource | Yes | No |
| PATCH | Partially update resource | No | No |
| DELETE | Delete a resource | Yes | No |
| HEAD | Get headers without body | Yes | Yes |
| OPTIONS | Get allowed methods for resource | Yes | Yes |
HTTP Status Codes
Request succeeded
Resource created successfully
Success with no response body
Invalid request syntax
Authentication required
Access denied
Resource not found
Rate limit exceeded
Server error
Invalid response from upstream
Service temporarily unavailable
Common HTTP Headers
- Content-Type: Specifies the media type of the resource (e.g., application/json, text/html)
- Authorization: Contains credentials for authentication (e.g., Bearer token)
- Accept: Indicates which content types the client can process
- Cache-Control: Directives for caching mechanisms
- ETag: Entity tag for cache validation
- If-None-Match: Conditional request using ETag
HTTPS (HTTP + TLS/SSL Encryption)
Protects data in transit from eavesdropping
Benefits:
- Encrypted communication prevents data interception
- Data integrity ensures data hasn’t been tampered with
- Server authentication verifies you’re connecting to the right server
- Required for modern web applications
Risks without HTTPS:
- Man-in-the-middle attacks
- Data interception and theft
- Privacy violations
- Credential theft
HTTP Connection Pooling
HTTP connection pooling allows multiple requests to reuse the same TCP connection, reducing latency and improving performance.
Issues with HTTP/1.1 Pooling:
- Increased latency: Head-of-line blocking can delay requests
- Wasted bandwidth: Headers sent with every request
- Server resources overhead: Multiple connections consume resources
HTTP/2 Solution: Multiplexing allows multiple requests over a single connection, solving these issues.
WebSockets
WebSocket is a communication protocol that provides full-duplex communication channels over a single TCP connection. Unlike HTTP, which follows a request-response pattern, WebSocket allows both client and server to send messages at any time.
- Real-time Data: Enables bidirectional, real-time communication without polling
- Reduced Bandwidth: More efficient than HTTP polling – no repeated headers
- Bidirectional Communication: Both client and server can initiate messages
- Low Latency: Persistent connection eliminates handshake overhead
- Use Cases: Chat applications, live notifications, real-time gaming, collaborative editing
AMQP (Advanced Message Queuing Protocol)
AMQP is an open standard protocol for message-oriented middleware. It enables reliable, asynchronous messaging between applications and services.
Exchange Types
Direct Exchange: Routes messages to queues based on exact routing key match (1:1 routing)
Fanout Exchange: Broadcasts messages to all bound queues, ignoring routing keys (1:N broadcasting)
Topic Exchange: Routes messages based on pattern matching of routing keys (pattern-based routing)
Headers Exchange: Routes messages based on message header attributes instead of routing keys
Benefits of AMQP
- Reliability: Message persistence and delivery guarantees
- Decoupling: Producers and consumers don’t need to know about each other
- Scalability: Can handle high message volumes
- Flexibility: Supports various messaging patterns
gRPC
Protocol Buffer (messages {...})
Client (Stubs, Type Safety) → (HTTP/2) → Server (Services, Streaming) Protocol Buffers: Schema-first interface definition language that compiles into strongly typed message classes. Messages use compact binary encoding, which keeps payloads small and enforces contracts between producers and consumers.
Client ↔ Server flow: The client uses generated stubs to invoke RPC methods as native function calls. Calls travel over HTTP/2, which provides multiplexed streams, header compression, and flow control. On the server side, generated service skeletons deserialize the request, execute business logic, and stream responses back.
Streaming Patterns
- Unary: Single request → single response (most common)
- Server streaming: Single request → stream of responses (notifications, chunked data)
- Client streaming: Stream of requests → single response (upload batches, telemetry)
- Bidirectional streaming: Both sides send streams simultaneously (real-time collaboration, IoT control)
Common Use Cases
- Microservices communication
- Polyglot systems (different languages)
- Real-time services
Transport Layer: TCP & UDP
TCP (Transmission Control Protocol)
- Connection-oriented: Establishes connection before data transfer
- Reliable: Guarantees delivery and order of packets
- Flow control: Prevents overwhelming the receiver
- Error checking: Detects and retransmits lost packets
- Use cases: HTTP, HTTPS, WebSockets, gRPC, AMQP
UDP (User Datagram Protocol)
- Connectionless: No connection establishment needed
- Fast: Lower overhead than TCP
- Unreliable: No guarantee of delivery or order
- No flow control: Can overwhelm receiver
- Use cases: DNS, real-time gaming, video streaming, IoT sensors
Choosing the Right Protocol
| Factor | Consideration |
|---|---|
| Interaction Pattern | Request-Response vs Real-time |
| Performance | Speed and efficiency requirements |
| Client Compatibility | Browser, Mobile, and Legacy support |
| Payload Size | Data volume and encoding efficiency |
| Security Needs | Authentication and encryption requirements |
| Developer Experience | Tooling and documentation availability |
RESTful API Design
REST Architectural Principles and Constraints
REST (Representational State Transfer) is an architectural style that defines a set of constraints for creating web services.
Resource Modeling and URL Design
Resource Modeling
Business Domain: product – order – reviews
REST Resources: products – orders – reviews
URL Pattern: /api/v1/products, /api/v1/orders, /api/v1/reviews
URL Features
- Filtering: /api/v1/products?category=electronics&price_min=100
- Sorting: /api/v1/products?sort=price&order=desc
- Pagination: /api/v1/products?page=1&limit=20
Pagination Strategies
Offset-Based Pagination: ?page=1&limit=20
- Simple to implement, works with SQL OFFSET/LIMIT
- Issues: Performance degrades with large offsets, inconsistent results if data changes during pagination
- Use for: Small datasets, simple use cases
Cursor-Based Pagination: ?cursor=eyJpZCI6MTIzfQ&limit=20
- Uses opaque cursor (typically base64-encoded ID or timestamp)
- Advantages: Consistent results, better performance at scale, no offset calculation
- Use for: Large datasets, real-time data, social media feeds
Keyset Pagination: ?since_id=123&limit=20
- Uses resource ID or timestamp as pagination key
- Best for: Time-ordered data, infinite scroll, Twitter-style feeds
Benefits of Filtering, Sorting, and Pagination
- Save bandwidth by returning only needed data
- Improve performance by reducing data transfer and database load
- Give frontend more flexibility in data presentation
- Enable efficient infinite scroll and lazy loading patterns
Status Codes and Error Handling
Proper use of HTTP status codes is crucial for API design:
- 2xx Success: 200 (OK), 201 (Created), 204 (No Content)
- 4xx Client Error: 400 (Bad Request), 401 (Unauthorized), 403 (Forbidden), 404 (Not Found)
- 5xx Server Error: 500 (Internal Server Error), 502 (Bad Gateway), 503 (Service Unavailable)
Practical REST API Examples
Example: E-Commerce API
Let’s see how to implement CRUD operations for a products API:
GET /api/v1/products?category=electronics&price_min=100&page=1&limit=20
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…
Response (200 OK):
{
“data”: [
{
“id”: 1,
“name”: “Laptop”,
“price”: 999.99,
“category”: “electronics”
}
],
“pagination”: {
“page”: 1,
“limit”: 20,
“total”: 150,
“totalPages”: 8
}
}
GET /api/v1/products/123
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…
Response (200 OK):
{
“id”: 123,
“name”: “Gaming Laptop”,
“description”: “High-performance gaming laptop”,
“price”: 1499.99,
“category”: “electronics”,
“inStock”: true,
“createdAt”: “2024-01-15T10:30:00Z”
}
POST /api/v1/products
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…
Content-Type: application/json
Request Body:
{
“name”: “Wireless Mouse”,
“description”: “Ergonomic wireless mouse”,
“price”: 29.99,
“category”: “accessories”,
“inStock”: true
}
Response (201 Created):
{
“id”: 456,
“name”: “Wireless Mouse”,
“description”: “Ergonomic wireless mouse”,
“price”: 29.99,
“category”: “accessories”,
“inStock”: true,
“createdAt”: “2024-01-20T14:22:00Z”
}
PATCH /api/v1/products/123
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…
Content-Type: application/json
Request Body:
{
“price”: 1299.99,
“inStock”: false
}
Response (200 OK):
{
“id”: 123,
“name”: “Gaming Laptop”,
“price”: 1299.99,
“inStock”: false,
“updatedAt”: “2024-01-20T15:30:00Z”
}
DELETE /api/v1/products/123
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…
Response (204 No Content)
Error Handling Examples
POST /api/v1/products
Request Body: { “name”: “”, “price”: -10 }
Response (400 Bad Request):
{
“error”: {
“code”: “VALIDATION_ERROR”,
“message”: “Validation failed”,
“details”: [
{
“field”: “name”,
“message”: “Name cannot be empty”
},
{
“field”: “price”,
“message”: “Price must be greater than 0”
}
]
}
}
GET /api/v1/products
Authorization: Bearer invalid_token
Response (401 Unauthorized):
{
“error”: {
“code”: “UNAUTHORIZED”,
“message”: “Invalid or expired token”
}
}
cURL Examples
curl -X GET “https://api.example.com/v1/products?page=1&limit=20” \
-H “Authorization: Bearer YOUR_TOKEN” \
-H “Content-Type: application/json”
# Create a product
curl -X POST “https://api.example.com/v1/products” \
-H “Authorization: Bearer YOUR_TOKEN” \
-H “Content-Type: application/json” \
-d ‘{“name”:”Laptop”,”price”:999.99,”category”:”electronics”}’
Best Practices
- Use plural nouns for resources (e.g., /products, not /product)
- Keep URLs consistent and hierarchical
- Support filtering, sorting, and pagination
- Version your APIs (e.g., /api/v1/, /api/v2/)
- Use appropriate HTTP methods for operations
- Return consistent error response formats
- Include pagination metadata in list responses
- Use ISO 8601 format for dates (YYYY-MM-DDTHH:mm:ssZ)
- Implement proper caching headers (ETag, Cache-Control)
REST API Design Questions
- Use proper HTTP status codes: 200 for success, 400 for bad request, 401 for unauthorized, 404 for not found, 500 for server error
- Consistent error format: Always return errors in a consistent format like {“error”: {“code”: “VALIDATION_ERROR”, “message”: “Name is required”}}
- Clear error messages: Don’t just return “Error” – tell the developer what went wrong
- Security: Never expose internal details (like database errors) to the client
- Large datasets (millions of records)
- Real-time data where results change frequently
- Infinite scroll patterns
- When consistent ordering is critical
- Small datasets (< 10K records)
- When total count is needed
- Simple use cases
Implementation: Encode cursor (ID + timestamp) in base64, use WHERE id > last_seen_id for database queries. Cursor-based avoids performance degradation at high offsets and prevents duplicate/missing results when data changes.
Advanced API Patterns
Idempotency
Idempotent operations can be safely retried without side effects. Critical for distributed systems where network failures can cause duplicate requests.
Implementation Strategies
Idempotency Keys: Client sends unique key (UUID) in Idempotency-Key header. Server stores key with response, returns cached response for duplicate keys.
POST /api/v1/orders
Idempotency-Key: 550e8400-e29b-41d4-a716-446655440000
Content-Type: application/json
Request Body:
{
“productId”: 123,
“quantity”: 2
}
// Server-side (Node.js)
async function createOrder(req, res) {
const idempotencyKey = req.headers[‘idempotency-key’];
if (idempotencyKey) {
const cached = await redis.get(`idempotency:${idempotencyKey}`);
if (cached) {
return res.status(200).json(JSON.parse(cached));
}
}
// … create order logic …
if (idempotencyKey) {
await redis.setex(
`idempotency:${idempotencyKey}`,
3600, // 1 hour TTL
JSON.stringify(order)
);
}
}
Natural Idempotency: Use resource identifiers or timestamps. Example: PUT /api/v1/products/123 is naturally idempotent.
Trade-offs: Idempotency keys require storage (Redis) and add complexity, but enable safe retries and prevent duplicate operations.
Webhooks
Webhooks enable event-driven architectures by allowing APIs to push events to external systems. Common for payment processing, CI/CD, and real-time notifications.
Webhook Implementation
Flow: Client registers webhook URL → API stores URL → Event occurs → API POSTs event to URL → Client acknowledges receipt
POST /api/v1/webhooks
{
“url”: “https://client.com/webhooks/payment”,
“events”: [“payment.completed”, “payment.failed”],
“secret”: “webhook_secret_for_verification”
}
# Webhook delivery (from API to client)
POST https://client.com/webhooks/payment
X-Webhook-Signature: sha256=abc123…
X-Webhook-Id: evt_1234567890
Content-Type: application/json
{
“event”: “payment.completed”,
“data”: {
“paymentId”: “pay_123”,
“amount”: 100.00,
“status”: “completed”
},
“timestamp”: “2024-01-15T10:30:00Z”
}
Best Practices: Sign webhooks (HMAC), implement retries with exponential backoff, use idempotency keys, provide webhook management API, log all delivery attempts, support webhook verification.
Structured Error Responses (RFC 7807)
Standard format for API error responses improves client error handling and debugging.
Problem Details Format
Content-Type: application/problem+json
Status: 400 Bad Request
{
“type”: “https://api.example.com/problems/validation-error”,
“title”: “Validation Error”,
“status”: 400,
“detail”: “The request body contains invalid data”,
“instance”: “/api/v1/products”,
“errors”: [
{
“field”: “price”,
“message”: “Price must be greater than 0”
}
]
}
Benefits: Standardized format, machine-readable, includes context (type, instance), enables better error handling in clients.
Rate Limiting Algorithms
Token Bucket Algorithm
Maintains a bucket of tokens. Each request consumes a token. Tokens refill at a fixed rate. Allows bursts up to bucket capacity.
Use Cases: APIs that need to allow bursts, variable request rates
Implementation: Redis with atomic operations, or dedicated rate limiting service
Sliding Window Log
Maintains a log of request timestamps. Counts requests within time window. More accurate than fixed window but requires more storage.
Use Cases: When precise rate limiting is required, distributed systems
Implementation: Redis sorted sets (ZSET) with timestamps as scores
Fixed Window
Divides time into fixed windows (e.g., 1 minute). Counts requests per window. Simple but allows bursts at window boundaries.
Use Cases: Simple rate limiting, when burst tolerance is acceptable
Implementation: Redis counters with TTL
Caching Strategies
Multi-Layer Caching
CDN/Edge Cache: Static content, public API responses. Use Cache-Control headers, ETags for validation.
API Gateway Cache: Cache responses at gateway level. Reduces backend load, improves latency.
Application Cache: In-memory cache (Redis, Memcached). Cache database queries, computed results.
Database Query Cache: Cache frequent queries. Use with care – invalidation complexity.
Cache Invalidation: TTL-based (simple), event-based (complex but accurate), version-based (ETags).
Contract Testing
Consumer-Driven Contracts
Clients define expected API contract. Provider tests against consumer expectations. Ensures backward compatibility.
Tools: Pact (consumer-driven contracts), Spring Cloud Contract, OpenAPI contract testing
Benefits: Prevents breaking changes, enables independent deployment, improves confidence in API changes
{
“consumer”: {“name”: “MobileApp”},
“provider”: {“name”: “ProductAPI”},
“interactions”: [{
“request”: {
“method”: “GET”,
“path”: “/api/v1/products/123”
},
“response”: {
“status”: 200,
“body”: {“id”: 123, “name”: “Product”}
}
}]
}
GraphQL Design
Core Concepts of GraphQL and Why It Exists
GraphQL is a query language for APIs that allows clients to request exactly the data they need, nothing more, nothing less. It was developed by Facebook to solve the problem of over-fetching and under-fetching data.
Schema Design and Types
The schema is a contract between client and server. It defines the types, queries, mutations, and subscriptions available in your API.
Queries and Mutations
- Queries: Read operations to fetch data
- Mutations: Write operations to modify data
- Subscriptions: Real-time data updates
Handling Errors
GraphQL provides a structured way to handle errors through the errors field in responses, allowing partial data to be returned even when some fields fail.
Practical GraphQL Examples
Example: E-Commerce GraphQL Schema
type Product {
id: ID!
name: String!
description: String
price: Float!
category: Category!
reviews: [Review!]!
inStock: Boolean!
}
type Category {
id: ID!
name: String!
}
type Query {
products(category: String, limit: Int): [Product!]!
product(id: ID!): Product
}
type Mutation {
createProduct(input: CreateProductInput!): Product!
updateProduct(id: ID!, input: UpdateProductInput!): Product!
deleteProduct(id: ID!): Boolean!
}
input CreateProductInput {
name: String!
description: String
price: Float!
categoryId: ID!
}
GraphQL Query Examples
query GetProducts {
products(category: “electronics”, limit: 10) {
id
name
price
inStock
}
}
Response:
{
“data”: {
“products”: [
{
“id”: “1”,
“name”: “Laptop”,
“price”: 999.99,
“inStock”: true
}
]
}
}
query GetProductWithReviews {
product(id: “123”) {
id
name
price
reviews {
id
rating
comment
author {
name
}
}
}
}
GraphQL Mutation Examples
mutation CreateProduct {
createProduct(
input: {
name: “Wireless Mouse”,
description: “Ergonomic wireless mouse”,
price: 29.99,
categoryId: “cat-1”
}
) {
id
name
price
}
}
GraphQL Error Handling
query GetProduct {
product(id: “999”) {
id
name
}
}
Response:
{
“data”: {
“product”: null
},
“errors”: [
{
“message”: “Product not found”,
“path”: [“product”],
“extensions”: {
“code”: “NOT_FOUND”
}
}
]
}
GraphQL vs REST: Practical Comparison
REST Approach (Multiple Requests):
GET /api/v1/products/123
GET /api/v1/products/123/reviews
GET /api/v1/products/123/category
GraphQL Approach (Single Request):
product(id: “123”) {
name
reviews { rating, comment }
category { name }
}
}
Best Practices
- Keep schema small and focused
- Avoid deeply nested queries (limit depth to 3-4 levels)
- Implement query depth limits to prevent abuse
- Use meaningful naming conventions
- Use Input types for mutations
- Implement proper error handling
- Use DataLoader to prevent N+1 query problems
- Implement query complexity analysis
- Use fragments for reusable field sets
GraphQL Questions
gRPC Design
Service Definition Strategy
Proto File Organization
- Group services by bounded context (`payments/payment_service.proto`, `orders/order_service.proto`).
- Keep message definitions close to services; share only cross-domain contracts in a `common` package.
- Use explicit package names (`package payments.v1;`) to control namespace collisions and code generation.
- Adopt semantic versioning for proto packages to support concurrent major versions.
Schema Design and Evolution
- Field Number Governance: Never reuse field numbers; reserve deprecated fields to avoid accidental reuse (`reserved 4, 6;`).
- Backward Compatibility: Add new fields as optional with sensible defaults; prefer additive changes.
- Breaking Changes: Introduce new services or messages under `v2` package; avoid editing in-place.
- Shared Types: Use separate proto for domain primitives (Money, Address) to ensure consistency.
Example Service Definition
syntax = "proto3";
package inventory.v1;
import "google/protobuf/timestamp.proto";
service InventoryService {
rpc GetProduct (GetProductRequest) returns (GetProductResponse) {
option (google.api.http) = {
get: "/v1/products/{product_id}"
};
}
rpc StreamStock (StreamStockRequest) returns (stream StockUpdate);
rpc UpdateStock (stream StockMutation) returns (StockSummary);
rpc BidirectionalAdjust (stream StockMutation) returns (stream StockUpdate);
}
message GetProductRequest {
string product_id = 1;
bool include_historical = 2;
}
message StockUpdate {
string product_id = 1;
int64 quantity_on_hand = 2;
google.protobuf.Timestamp updated_at = 3;
}Streaming Pattern Selection
Error Handling
- Always return canonical status codes (`INVALID_ARGUMENT`, `UNAUTHENTICATED`, `PERMISSION_DENIED`).
- Use `google.rpc.Status` with `Any` details for structured errors (e.g., validation metadata).
- Surface domain-specific error enums in proto to ensure code generation includes typed errors.
- Attach correlation IDs via metadata for observability (`x-request-id`).
Performance Optimization
- Connection Reuse: Keep channels warm; use connection pooling with backoff for reconnects.
- Compression: Enable gzip for large payloads; evaluate `brotli` if supported across languages.
- Deadlines & Timeouts: Set explicit deadlines on every call to prevent resource leaks.
- Load Balancing: Use xDS or service mesh (Envoy) for client-side load balancing and health checks.
- Reflection & Discovery: Enable server reflection for tooling, but disable in production clusters if not needed.
Security and Governance
TLS and Authentication
- Enable mTLS between services; rotate certificates via automated CA (SPIRE, cert-manager).
- Propagate identity via x509 SAN or JWT metadata; enforce authorization with interceptors.
- Use interceptors for rate limiting, auditing, and policy enforcement (OPA, AWS Cedar).
Observability
- Inject tracing metadata (B3/W3C) through gRPC metadata; export traces via OpenTelemetry.
- Log request/response metadata (method, status, latency) with structured logging.
- Expose health checks via `grpc.health.v1.Health/Check` for orchestration systems.
Gateway and Interoperability
gRPC-Gateway: Generate REST/JSON facade for browser/mobile clients using `google.api.http` annotations. Ensure consistency by generating both server and gateway from the same proto.
gRPC-Web: For browser-based clients, deploy Envoy or similar proxy that translates gRPC-Web to HTTP/2 gRPC.
Versioning Strategy: Maintain REST and gRPC versions in lockstep or use translation layer to avoid breaking clients.
Authentication Strategies
Authentication establishes the identity of API consumers. In distributed systems, this requires stateless, scalable mechanisms that don’t rely on server-side session storage. Modern authentication patterns balance security, performance, and operational complexity.
Authentication vs Authorization
Authentication (AuthN): Verifies identity – “Who is making this request?”
Authorization (AuthZ): Determines permissions – “What is this identity allowed to do?”
These are distinct concerns that should be implemented as separate layers. Authentication typically happens first, then authorization checks are performed based on the authenticated identity and requested resource.
Authentication Design Principles
- Stateless: Avoid server-side session storage to enable horizontal scaling
- Token-based: Use signed tokens (JWT) or opaque tokens with token store lookups
- Short-lived Access Tokens: Minimize exposure window if tokens are compromised
- Refresh Token Pattern: Long-lived refresh tokens for obtaining new access tokens
- Revocation Support: Ability to invalidate tokens (blacklists, token stores, or short TTLs)
Basic Authentication
HTTP Basic Authentication transmits credentials (username:password) as Base64-encoded strings in the Authorization header. While simple to implement, it has significant security limitations and should only be used over HTTPS.
Implementation Details
Credentials are sent in the format: Authorization: Basic base64(username:password)
Security Considerations
Limitations:
- Credentials sent with every request (no token expiration)
- Base64 encoding is not encryption (easily decoded)
- No built-in revocation mechanism
- Vulnerable to replay attacks if not over HTTPS
Use Cases: Internal APIs, development environments, or when combined with HTTPS and short-lived credentials. Not recommended for production public APIs.
Basic Auth Mechanism
- Username + Password
- Base64 encoding of username:password
- Sent in Authorization header:
Authorization: Basic base64(username:password)
Practical Example
GET /api/v1/users/me
Authorization: Basic dXNlcm5hbWU6cGFzc3dvcmQ=
# Base64(“username:password”) = “dXNlcm5hbWU6cGFzc3dvcmQ=”
# Server-side validation (Node.js example)
const authHeader = req.headers.authorization;
const base64Credentials = authHeader.split(‘ ‘)[1];
const credentials = Buffer.from(base64Credentials, ‘base64’).toString();
const [username, password] = credentials.split(‘:’);
// Validate username and password
Important Notes:
- Base64 is simple encoding that is easily reversible
- Rarely used today outside internal tools
- Simple but insecure unless wrapped in HTTPS
- Never use in production for public APIs
Bearer Tokens
Client → Token → API → (Success) → Client
Authorization: Bearer <access_token> This is the standard approach in API design today. It’s fast and stateless, making it ideal for modern applications.
How bearer tokens work:
- Client obtains token: After authenticating (username/password, OAuth2, etc.), the identity provider issues a signed access token.
- Client calls API: Every request adds the header
Authorization: Bearer <access_token>. “Bearer” means whoever “bears” the token is treated as the authenticated user. - API validates token: The service verifies the token signature/expiry, checks scopes/claims, and skips any server-side session lookup (stateless).
- Response returned: If the token is valid and authorized, the API executes the operation and returns a response; otherwise it returns 401/403.
Because the token is self-contained, servers can scale horizontally without sharing session state. Always use HTTPS so tokens are never exposed in transit, and set short expirations plus refresh tokens to limit risk.
OAuth2 + JWT
Think of OAuth2 Like a Hotel Key Card
When you check into a hotel, you get a key card that gives you access to your room, but not other rooms. OAuth2 works similarly – when you “Login with Google,” Google gives the app a special key card (token) that says “This person is allowed to use this app, but only with these specific permissions.” The app never sees your Google password – it just gets a temporary key card.
A secure way to let one app access your information in another app without sharing your password. Like giving a friend a spare key to your house instead of your main key – they can enter, but you can take it back anytime.
Think of it like a digital ID card. It contains information about you (like your user ID and permissions) in a secure, compact format. It’s like a driver’s license – it proves who you are and what you’re allowed to do, all in one card.
OAuth2 is an authorization framework that allows applications to obtain limited access to user accounts. JWT (JSON Web Token) is a compact, URL-safe token format used to securely transmit information between parties.
OAuth2 Authorization Code Flow
- Used in “Login with Google”, “Login with GitHub”, “Login with Facebook”
- JWT tokens are stateless – you don’t need to store sessions
- OAuth2 provides secure authorization framework
- Delegated authorization – user grants permission without sharing password
JWT Structure
{
“alg”: “HS256”,
“typ”: “JWT”
}
{
“sub”: “1234567890”,
“name”: “John Doe”,
“iat”: 1516239022,
“exp”: 1516242622
}
HMACSHA256(
base64UrlEncode(header) + “.” +
base64UrlEncode(payload),
secret
)
Format: header.payload.signature
Benefits: Stateless, self-contained, compact, verifiable
JWT Implementation Example (Node.js)
// Generate JWT Token
const jwt = require(‘jsonwebtoken’);
function generateAccessToken(user) {
return jwt.sign(
{
userId: user.id,
email: user.email,
role: user.role
},
process.env.JWT_SECRET,
{ expiresIn: ’15m’ }
);
}
// Verify JWT Token (Middleware)
function authenticateToken(req, res, next) {
const authHeader = req.headers[‘authorization’];
const token = authHeader && authHeader.split(‘ ‘)[1];
if (!token) {
return res.sendStatus(401);
}
jwt.verify(token, process.env.JWT_SECRET, (err, user) => {
if (err) {
return res.sendStatus(403);
}
req.user = user;
next();
});
}
Login Flow Example
POST /api/v1/auth/login
Content-Type: application/json
Request Body:
{
“email”: “[email protected]”,
“password”: “securePassword123”
}
Response (200 OK):
{
“accessToken”: “eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…”,
“refreshToken”: “dGhpcyBpcyBhIHJlZnJlc2ggdG9rZW4…”,
“expiresIn”: 900,
“tokenType”: “Bearer”
}
Access and Refresh Tokens
Short-lived (15min-1hr)
Long-lived (days/weeks)
Access Token
Refresh Token:
- Lives longer (days to weeks)
- Used less often
- Generally stored server-side for security reasons
- Can be revoked if compromised
Access Token:
- Expires fast (15 minutes to 1 hour)
- Used for API calls
- Contains user permissions and claims
- Sent with every API request
Token Refresh Flow: When access token expires, the refresh token is used to get a new access token behind the scenes. Users don’t need to login again, and the system stays secure.
Token Refresh Implementation
POST /api/v1/auth/refresh
Content-Type: application/json
Request Body:
{
“refreshToken”: “dGhpcyBpcyBhIHJlZnJlc2ggdG9rZW4…”
}
Response (200 OK):
{
“accessToken”: “eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9…”,
“expiresIn”: 900
}
// Server-side refresh token validation (Node.js)
async function refreshAccessToken(req, res) {
const { refreshToken } = req.body;
// Verify refresh token
const storedToken = await db.refreshTokens.findOne({ token: refreshToken });
if (!storedToken || storedToken.expiresAt < new Date()) {
return res.status(401).json({ error: ‘Invalid refresh token’ });
}
// Generate new access token
const user = await db.users.findById(storedToken.userId);
const newAccessToken = generateAccessToken(user);
res.json({
accessToken: newAccessToken,
expiresIn: 900
});
}
Single Sign-On (SSO) and Identity Protocols
SSO enables users to authenticate once and access multiple applications without re-entering credentials. This reduces password fatigue, improves security through centralized identity management, and simplifies user experience.
SAML 2.0 (Security Assertion Markup Language)
XML-based protocol for enterprise SSO. Common in B2B scenarios and enterprise identity providers.
- Use Cases: Enterprise SSO, B2B integrations, legacy systems
- Architecture: Identity Provider (IdP) issues SAML assertions, Service Provider (SP) validates them
- Flow: SP redirects to IdP → User authenticates → IdP returns SAML assertion → SP validates and grants access
- Trade-offs: XML overhead, complex setup, but mature and widely supported in enterprise
OAuth2 / OpenID Connect (OIDC)
OAuth2 is an authorization framework; OIDC extends it for authentication. JSON-based, modern standard for consumer and enterprise applications.
- OAuth2: Authorization delegation – allows apps to access resources on behalf of users
- OIDC: Authentication layer on top of OAuth2 – provides identity information (ID tokens)
- Use Cases: “Login with Google/Facebook”, API authorization, mobile app authentication
- Flows: Authorization Code (web), Implicit (deprecated), Client Credentials (service-to-service), Device Code (IoT)
- Advantages: JSON-based, RESTful, mobile-friendly, widely adopted
Protocol Selection
SAML 2.0: Enterprise SSO, B2B integrations, when XML-based protocols are required
OAuth2/OIDC: Consumer applications, modern web/mobile apps, API authorization, when JSON/REST is preferred
Hybrid: Many organizations support both – SAML for enterprise customers, OAuth2/OIDC for consumer-facing applications
Authentication Architecture Q&A
7 Techniques to Protect Your APIs
1. Rate Limiting
Rate limiting controls the number of requests a client can make to your API within a specific time period. This prevents abuse and ensures fair usage.
- Per endpoint: Different limits for different endpoints (e.g., login: 5/min, search: 100/min)
- Per IP/User: Limit requests from specific sources to prevent abuse
- Overall: Global rate limits to mitigate DDoS attacks
- Strategies: Token bucket, sliding window, fixed window
Common Limits: 100 requests/minute per user, 1000 requests/hour per IP
Response Headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset
Rate Limiting Implementation (Node.js with express-rate-limit)
const rateLimit = require(‘express-rate-limit’);
// General API rate limiter
const apiLimiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 100, // Limit each IP to 100 requests per windowMs
message: ‘Too many requests from this IP, please try again later.’,
standardHeaders: true, // Return rate limit info in headers
legacyHeaders: false,
});
// Stricter rate limiter for login endpoint
const loginLimiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 5, // Limit each IP to 5 login requests per windowMs
message: ‘Too many login attempts, please try again later.’,
skipSuccessfulRequests: true,
});
// Apply to routes
app.use(‘/api/’, apiLimiter);
app.post(‘/api/v1/auth/login’, loginLimiter, loginHandler);
2. CORS (Cross-Origin Resource Sharing)
CORS controls which domains can access your API from browsers. It’s a security mechanism that prevents unauthorized cross-origin requests.
- Same-Origin Policy: Browsers block requests from different origins by default
- CORS Headers: Access-Control-Allow-Origin, Access-Control-Allow-Methods, Access-Control-Allow-Headers
- Preflight Requests: OPTIONS requests to check if actual request is allowed
- Best Practice: Whitelist specific origins, not “*”
Security Note: CORS doesn’t prevent server-to-server requests. Always validate on the server side.
CORS Implementation (Node.js with Express)
const cors = require(‘cors’);
// Basic CORS configuration
const corsOptions = {
origin: [
‘https://example.com’,
‘https://www.example.com’,
‘http://localhost:3000’ // Development
],
methods: [‘GET’, ‘POST’, ‘PUT’, ‘DELETE’, ‘PATCH’],
allowedHeaders: [‘Content-Type’, ‘Authorization’],
credentials: true,
maxAge: 86400 // 24 hours
};
app.use(cors(corsOptions));
// Dynamic origin validation
const allowedOrigins = [‘https://example.com’, ‘https://app.example.com’];
const corsOptionsDynamic = {
origin: function (origin, callback) {
if (allowedOrigins.indexOf(origin) !== –1 || !origin) {
callback(null, true);
} else {
callback(new Error(‘Not allowed by CORS’));
}
}
};
3. SQL Injection Prevention
SQL injection occurs when attackers insert malicious SQL code into input fields, potentially gaining unauthorized access to your database.
- Parameterized Queries: Use prepared statements with placeholders
- Input Validation: Validate and sanitize all user inputs
- Least Privilege: Database users should have minimal required permissions
- ORM Usage: Object-Relational Mapping tools provide built-in protection
query = “SELECT * FROM users WHERE id = ” + userId;
// GOOD – Parameterized Query
query = “SELECT * FROM users WHERE id = ?”;
params = [userId];
4. Firewalls
Firewalls provide network-level protection by filtering traffic based on security rules. They can block malicious requests before they reach your API.
- Web Application Firewall (WAF): Protects against common web exploits
- Network Firewall: Controls traffic at network boundaries
- IP Whitelisting: Allow only specific IP addresses
- DDoS Protection: Mitigate distributed denial-of-service attacks
5. VPN (Virtual Private Network)
VPNs encrypt network traffic and provide secure remote access. They’re useful for protecting API communication in corporate environments.
- Encryption: All traffic is encrypted in transit
- Secure Tunneling: Creates secure connection over public networks
- Access Control: Restricts API access to authorized users
- Use Cases: Internal APIs, B2B integrations, remote access
6. CSRF (Cross-Site Request Forgery)
CSRF attacks trick authenticated users into performing unwanted actions on a web application where they’re authenticated.
- CSRF Tokens: Unique tokens for each session/request
- SameSite Cookies: Prevents cookies from being sent in cross-site requests
- Origin Validation: Check Origin and Referer headers
- Double Submit Cookie: Cookie and form field must match
Example Attack: Attacker’s website makes a request to your API using the victim’s authenticated session.
7. XSS (Cross-Site Scripting)
XSS attacks inject malicious scripts into web pages viewed by other users. These scripts can steal data, hijack sessions, or deface websites.
- Input Sanitization: Remove or encode dangerous characters
- Content Security Policy (CSP): Restrict which scripts can run
- Output Encoding: Encode user input when displaying
- HttpOnly Cookies: Prevent JavaScript access to cookies
Types of XSS:
- Stored XSS: Malicious script stored in database
- Reflected XSS: Malicious script reflected in response
- DOM-based XSS: Vulnerability in client-side code
Additional Security Best Practices
- Input Validation: Validate all inputs on both client and server side
- Output Encoding: Encode outputs to prevent injection attacks
- HTTPS Only: Always use HTTPS, never HTTP
- Security Headers: Use security headers like HSTS, X-Frame-Options, X-Content-Type-Options
- Regular Updates: Keep dependencies and frameworks updated
- Security Audits: Regular security audits and penetration testing
- Logging & Monitoring: Monitor for suspicious activities
- Error Handling: Don’t expose sensitive information in error messages
Security Architecture Q&A
- Network: WAF, DDoS protection, firewall rules
- Transport: TLS 1.3, certificate pinning
- Application: Authentication, authorization, input validation, output encoding
- Data: Encryption at rest, field-level encryption for PII
- Monitoring: Intrusion detection, anomaly detection, security logging
- Broken authentication (weak tokens, no expiration)
- Excessive data exposure (returning full objects)
- Lack of rate limiting
- Broken authorization (object-level access control)
- Mass assignment
- Security misconfiguration
- Injection (SQL, NoSQL, command)
- Improper asset management (deprecated APIs)
- Insufficient logging/monitoring
- Use managed WAF services (AWS WAF, Cloudflare, Azure Application Gateway)
- Configure rules for OWASP Top 10
- Set up custom rate limiting
- Enable geo-blocking and bot detection
- Use cloud provider DDoS protection (AWS Shield, Cloudflare)
- Implement rate limiting at multiple layers (API Gateway, application, per-endpoint)
- Use CDN for static content
- Implement circuit breakers
- Consider dedicated DDoS mitigation services for high-value targets
- Data in transit: Use TLS 1.3
- Data at rest: Database encryption (TDE), application-level encryption for PII/PCI data
- Key management: Use key management services (AWS KMS, HashiCorp Vault) for key rotation
- Field-level encryption: Implement for sensitive fields (credit cards, SSNs)
- Passwords: Use hashing (bcrypt, Argon2), never encryption
- Tokenization: Consider for PCI data to reduce scope
- Structured security logging: Authentication failures, authorization denials, rate limit violations, suspicious patterns
- SIEM tools: Use Splunk, Datadog Security for correlation and alerting
- Anomaly detection: Set up for unusual traffic patterns
- Security metrics dashboards: Implement for visibility
- Incident response playbooks: Establish clear procedures
- Distributed tracing: Use for security forensics
- Bug bounty programs: Consider for external validation
Architecture and Implementation Q&A
Common questions about API architecture, scalability, and production considerations.
General API Questions
- Circuit breakers: Prevent cascading failures
- Retries: With exponential backoff and jitter
- Health checks: And graceful degradation
- Rate limiting: To prevent overload
- Distributed tracing: For observability
- Infrastructure: Load balancers, multiple availability zones, database replication
- Idempotency: Implement for critical operations
- Partial failures: Design services to degrade gracefully rather than fail completely
- URL versioning: (/api/v1/) – most common and explicit
- Header-based versioning: (Accept header) – RESTful but less discoverable
- GraphQL: Uses schema evolution
- Maintain backward compatibility during transition
- Implement deprecation policies (6-12 months)
- Use feature flags for gradual rollouts
- Document breaking changes clearly
- Provide migration guides
- Consider semantic versioning for internal APIs
- Distributed rate limiting: Use Redis with sliding window or token bucket algorithm for consistency across instances
- Multiple tiers: Per-user, per-IP, per-API-key
- Rate limit headers: Use X-RateLimit-* for client awareness
- Adaptive rate limiting: Consider based on system load
- High-scale systems: Use dedicated rate limiting services (Kong, AWS API Gateway) or implement at API Gateway/load balancer level
Summary: Production API Architecture
This guide covered the architectural patterns, trade-offs, and implementation strategies for building production-grade APIs at scale.
Key Architectural Decisions
- API Style Selection: REST for public APIs and simple operations, GraphQL for complex queries and mobile optimization, gRPC for inter-service communication
- Authentication Strategy: Stateless JWT with refresh tokens for scalability, OAuth2/OIDC for third-party integrations
- Authorization Models: RBAC for most cases, ABAC for complex requirements, policy-based for externalized logic
- Security Hardening: Rate limiting, input validation, CORS, SQL injection prevention, HTTPS enforcement
- Protocol Selection: HTTP for public APIs, WebSockets for real-time, gRPC for performance, AMQP for async messaging
Production Checklist
Define clear service boundaries, implement API versioning strategy, design for backward compatibility, establish deprecation policies, implement idempotency for critical operations.
Implement stateless authentication (JWT with refresh tokens), enforce HTTPS, implement rate limiting (distributed with Redis), validate and sanitize all inputs, implement CORS policies, use parameterized queries to prevent SQL injection.
Implement distributed tracing, structured logging, metrics collection (latency, error rates, throughput), health check endpoints, alerting on anomalies.
Design for horizontal scaling (stateless services), implement caching strategies (CDN, application cache), use connection pooling, implement circuit breakers, design for graceful degradation.
Implement API documentation (OpenAPI/Swagger), establish SLAs and SLOs, implement feature flags for gradual rollouts, automate testing (unit, integration, load), establish incident response procedures.
Architectural Patterns for Scale
API Gateway Pattern: Central entry point for routing, authentication, rate limiting, and request transformation. Enables microservices architecture.
BFF (Backend for Frontend): Separate API layer optimized for specific client types (mobile, web). Reduces client complexity.
Circuit Breaker: Prevents cascading failures by stopping requests to failing services. Enables graceful degradation.
Idempotency: Design operations to be safely retryable. Critical for distributed systems and eventual consistency.
Event-Driven Architecture: Use message queues (AMQP, Kafka) for async communication, decoupling services and improving resilience.
Final Considerations
API design is an iterative process. Start with clear requirements, choose patterns that fit your constraints, and evolve based on operational feedback. Monitor production metrics, gather client feedback, and iterate on the design. The best APIs are those that balance simplicity, performance, and maintainability while meeting business requirements.
Remember: there’s no one-size-fits-all solution. The patterns discussed here are tools in your architectural toolkit. Choose based on your specific requirements, team capabilities, and operational constraints.
Build production-grade APIs with confidence.
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Thanks for any other great post. The place else could anyone get that type of info
in such a perfect method of writing? I have a presentation next week,
and I am on the look for such information.
Nice blog.
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An interesting discussion is worth comment. I think that
you should write more about this issue, it may not
be a taboo matter butt usually folks don’t discuss these subjects.
To the next! Kind regards!!
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Really interesting read! The focus on localized payment options, like GCash with ninong gaming legit, is smart for the Philippine market. Building trust is key – that ‘Ninong’ branding feels right. Definitely a platform to watch!
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