Series · 10 parts · 34 min · Intermediate

Scaling to 1M Requests

Take a web backend from one server to a million requests: capacity maths, auto scaling, caching, databases, queues, rate limiting, serverless and observability.

What you'll build

A mental model and a toolkit for taking an ordinary Node.js API on AWS from one server to a fleet that serves a million requests.

Before you start

Some experience building a backend API. No prior AWS or scaling experience needed.

What you'll learn

Turning traffic goals into peak RPS and concurrency with Little's Law
Stateless app servers behind a load balancer with EC2 Auto Scaling
Caching at the CDN, application and Redis layers
Database indexes, read replicas and connection pooling
Keeping the Node.js event loop healthy under load
Queues, rate limiting and load shedding
When serverless fits, load testing with k6, and observability

(01)The parts

Your progress0/10 parts

Start at part 01

01The Anatomy of a Million RequestsWhat a million requests really means in requests per second, how to size for it, and where systems usually break first.4 min

02Horizontal Scaling with EC2 Auto Scaling GroupsMake app servers stateless, put them behind an ALB, and let target-tracking policies add and remove EC2 instances as traffic moves.4 min

03Caching Layers: CDN, Redis, and MemoryThe fastest request is the one your servers never see. How to layer CDN, Redis and in-memory caching without serving stale nonsense.3 min

04Database Scaling: Indexes, Replicas, and PoolingMost scaling problems end at the database. Indexes, connection pooling, read replicas and cursor pagination keep it standing.3 min

05Node.js Under Load: Event Loop, Clustering, BackpressureKeep the event loop free, use every core, reuse outbound connections and respect backpressure so Node stays fast at high RPS.3 min

06Queues and Async WorkMove slow work off the request path with queues, idempotent jobs, retries with jitter and dead-letter queues, so your API stays fast under load.4 min

07Rate Limiting and Load SheddingProtect your API with Redis-backed rate limits, honest 429 responses, load shedding, circuit breakers and timeouts before traffic takes it down.3 min

08Serverless at Scale: When AWS Lambda FitsHow AWS Lambda behaves under heavy traffic: concurrency quotas, cold starts, database connections, and when containers or EC2 are the better deal.3 min

09Load Testing Before Launch with k6Find your breaking point before your users do: smoke, load, stress, spike and soak tests with k6, arrival-rate scenarios and percentile thresholds.3 min

10Observability for High-Traffic SystemsMetrics, structured logs and distributed traces for high traffic: RED and USE methods, percentiles, OpenTelemetry, SLOs and alerts that matter.4 min

(10)Let's talk

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