System Design: APIs, Caching & Scalability
Master the core pillars of modern backend architecture - API design, caching strategies, and scalability patterns used at production scale. You will build a rate-limited REST API with caching, load balancing, and horizontal scaling applied end-to-end.
Course Content
6 modules · 30 lessonsAPI Design Fundamentals
Design clean, versioned REST APIs that handle errors and communicate contracts clearly.
What is REST API and how does it work
REST constraints, statelessness, client-server model, uniform interface, HTTP verbs, resource naming
REST API versioning strategies explained
URI versioning, header versioning, query param versioning, backward compatibility, versioning trade-offs, deprecation strategy
HTTP status codes every backend developer must know
2xx success codes, 4xx client errors, 5xx server errors, 201 vs 200, 400 vs 422, 401 vs 403, idempotency signals
How to design API error responses
error response schema, problem details RFC 7807, machine-readable errors, error codes vs status codes, validation error format, developer experience
API authentication: API keys vs JWT vs OAuth2
API key authentication, JWT structure, OAuth2 flows, bearer tokens, token expiry, refresh tokens, stateless vs stateful auth
Caching Fundamentals
Implement effective caching strategies at every layer of the stack to reduce latency and backend load.
What is caching and why does it matter for performance
cache definition, cache hit vs miss, cache hit ratio, latency reduction, origin offloading, cost reduction, when not to cache
Cache eviction policies: LRU, LFU, and TTL explained
LRU eviction, LFU eviction, TTL-based expiry, cache size limits, eviction vs expiration, Redis maxmemory-policy, choosing the right policy
Cache invalidation strategies: how to handle stale data
cache invalidation problem, write-through, write-behind, cache-aside, invalidation on write, event-driven invalidation, cache stampede
HTTP caching with Cache-Control headers
Cache-Control header, max-age directive, no-cache vs no-store, ETag and conditional requests, stale-while-revalidate, CDN caching, browser caching
Redis as a cache: patterns and best practices
Redis data structures, key naming conventions, TTL management, Redis Cluster, connection pooling, avoiding hot keys, Redis vs Memcached
Scalability Patterns
Apply horizontal scaling, load balancing, and stateless design to build systems that handle traffic growth without re-architecture.
Horizontal vs vertical scaling: when to use each
vertical scaling limits, horizontal scaling, shared nothing architecture, stateless services, scaling trade-offs, cost comparison, cloud elasticity
How load balancers work: algorithms and types
round-robin, least connections, IP hash, layer 4 vs layer 7 load balancing, health checks, sticky sessions, load balancer as SPOF
Database scaling: read replicas and sharding explained
read replicas, replication lag, write path vs read path, horizontal sharding, shard key selection, cross-shard queries, consistent hashing
What is a CDN and how does it reduce latency
CDN edge nodes, Points of Presence, origin server, cache-control for CDN, CDN invalidation, dynamic vs static content, CDN for API responses
Stateless vs stateful services: design trade-offs
stateless service definition, stateful service risks, session externalization, sticky sessions as anti-pattern, idempotent operations, twelve-factor app principles
Rate Limiting and Throttling
Protect your APIs from abuse and overload by implementing server-side rate limiting with accurate, Redis-backed algorithms.
Why APIs need rate limiting and how it works
rate limiting definition, abuse prevention, DDoS mitigation, fair usage, cost control, rate limiting vs throttling, 429 status code
Fixed window vs sliding window rate limiting algorithms
fixed window algorithm, sliding window log algorithm, sliding window counter, boundary spike problem, memory trade-offs, algorithm selection
Token bucket and leaky bucket rate limiting explained
token bucket algorithm, burst capacity, refill rate, leaky bucket algorithm, smooth output rate, API burst allowance, traffic shaping
Distributed rate limiting with Redis across multiple servers
distributed rate limiting, Redis atomic operations, MULTI/EXEC, Lua scripts in Redis, race conditions in distributed systems, consistency trade-offs
API gateway rate limiting vs application-level rate limiting
API gateway rate limiting, Kong rate limiting plugin, application middleware, latency impact, centralized vs distributed enforcement, per-route limits
Message Queues and Async Processing
Decouple services and handle background work reliably using message queues, job workers, and event-driven patterns.
Why use a message queue: sync vs async API patterns
synchronous request problems, async decoupling, message queue definition, producer consumer model, durability, back-pressure, use cases for queues
Message queue guarantees: at-least-once vs exactly-once delivery
at-most-once delivery, at-least-once delivery, exactly-once delivery, idempotent consumers, message acknowledgment, dead letter queue, duplicate handling
How BullMQ and Redis-backed job queues work
BullMQ architecture, job states, worker concurrency, job priority, scheduled jobs, job events, Redis Streams, failed job handling
Event-driven architecture: pub/sub pattern explained
pub/sub model, event topics, fan-out, loose coupling, Redis Pub/Sub, event ordering, pub/sub vs message queue, real-time use cases
Job status polling vs webhook callbacks for async APIs
polling pattern, webhook callbacks, 202 Accepted pattern, job status endpoint, webhook security, HMAC signatures, polling vs push trade-offs
System Design End-to-End
Synthesize every module concept into coherent system designs, trade-off analysis, and production-readiness patterns.
How to approach a system design interview question
requirements gathering, capacity estimation, API design first, component selection, trade-off articulation, back-of-envelope calculation, iterative design
Designing a URL shortener system end-to-end
URL shortener architecture, base62 encoding, hash collision handling, redirect performance, read vs write path optimization, analytics counting, database choice
Designing a notification system that scales to millions
notification system architecture, fan-out on write vs read, push vs pull delivery, user preference service, notification templates, delivery receipts, rate limiting notifications
CAP theorem and consistency trade-offs in distributed systems
CAP theorem, consistency, availability, partition tolerance, CP vs AP systems, eventual consistency, strong consistency, PACELC model, practical examples
Observability in production: metrics, logging, and tracing
observability pillars, structured logging, distributed tracing, metrics vs logs, SLO and SLA, alerting on symptoms not causes, correlation IDs, OpenTelemetry