📈 Level 2 — Scalability

⚡Intermediate · 1–3 yrsIntermediate1–3 yrs experience Builds on the foundations. Expect trade-off discussions, light math, and short code snippets.Prerequisites: Solid grasp of networking, databases, and at least one web framework.

Learn how to design systems that grow from 100 users to 100 million.

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2.1 Vertical vs Horizontal Scaling

Visual Comparison

Comparing

Vertical Scaling

Scale Up — bigger machine

Horizontal Scaling

Scale Out — more machines

0 of 4 reasoning panels open

• Cost

  Expensive hardware

  Commodity servers
• Limit

  Hardware ceiling

  Nearly unlimited
• Fault Tolerance

  Single point of failure

  Redundant
• Complexity

  Simple

  Requires coordination

💻 JS Example: Horizontal Scaling with Node.js Cluster

In Node.js, you can scale horizontally across multiple CPU cores on a single machine using the cluster module.

const cluster = require("cluster");
const http = require("http");
const numCPUs = require("os").cpus().length;

if (cluster.isMaster) {
  console.log(`Master ${process.pid} is running`);

  // Fork workers for each CPU core
  for (let i = 0; i < numCPUs; i++) {
    cluster.fork();
  }

  cluster.on("exit", (worker) => {
    console.log(`worker ${worker.process.pid} died. Respawning...`);
    cluster.fork();
  });
} else {
  // Workers can share any TCP connection
  http
    .createServer((req, res) => {
      res.writeHead(200);
      res.end("Hello from scaled server!\n");
    })
    .listen(8000);
}

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2.2 CAP Theorem

A distributed system can only guarantee 2 out of 3 properties simultaneously.

System Type	Guarantees	Example Scenario
CP	Correct data, may be unavailable	Banking: Reject transaction if network split to avoid double spending.
AP	Always responds, may return stale data	Social Media: Show old feed rather than an error page.
CA	Consistent + Available	Local DB: High reliability on a single machine, but fails on network split.

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2.3 Consistency Models

Eventual vs Strong Consistency

💻 JS Example: Handling Eventual Consistency

When dealing with eventual consistency, clients often need to retry or "poll" for the latest state.

async function getConsistentData(id, expectedValue, retries = 3) {
  for (let i = 0; i < retries; i++) {
    const data = await fetch(`/api/data/${id}`).then((r) => r.json());
    if (data.value === expectedValue) return data;

    console.log(`Stale data detected, retry ${i + 1}...`);
    await new Promise((resolve) => setTimeout(resolve, 1000)); // Wait 1s
  }
  throw new Error("Failed to reach consistency");
}

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2.4 How to Scale: Step by Step

The 5-Stage Journey

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2.5 Rate Limiting

Protects your system from being overwhelmed by too many requests.

💻 JS Example: Basic Token Bucket Implementation

A conceptual look at how a rate limiter might track requests in memory.

class RateLimiter {
  constructor(limit, windowMs) {
    this.limit = limit;
    this.requests = new Map(); // Store counts per IP
  }

  isAllowed(ip) {
    const now = Date.now();
    const count = this.requests.get(ip) || 0;

    if (count >= this.limit) {
      return false; // Rate limit exceeded
    }

    this.requests.set(ip, count + 1);
    // Reset limit after window expires
    setTimeout(() => this.requests.delete(ip), 60000);
    return true;
  }
}

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✅ Checklist Before Moving On

• [ ] I can explain CAP theorem with a real-world example
• [ ] I know when to scale vertically vs horizontally
• [ ] I understand eventual vs strong consistency
• [ ] I can draw the 5-stage scaling journey

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➡️ Next: Level 3 — Databases