Caching strategies

Caching Strategies

Cache data at the right layer to reduce latency, database load, and compute costs. The two hardest problems in computer science are cache invalidation and naming things. This guide focuses on getting invalidation right.

When to Use

• API endpoints that serve the same data to many users
• Database queries that are expensive and don't change frequently
• Static assets and CDN configuration
• Computed values that are expensive to recalculate
• Reducing latency for frequently accessed data

Core Patterns

In-Memory Memoization

Cache computed values within a single process. Simplest form of caching.

// Simple memoization with Map
function memoize<Args extends unknown[], Result>(
  fn: (...args: Args) => Result,
  keyFn: (...args: Args) => string = (...args) => JSON.stringify(args),
): (...args: Args) => Result {
  const cache = new Map<string, Result>();

  return (...args: Args): Result => {
    const key = keyFn(...args);
    if (cache.has(key)) {
      return cache.get(key)!;
    }
    const result = fn(...args);
    cache.set(key, result);
    return result;
  };
}

// For bounded caches, use an LRU eviction policy (e.g., lru-cache npm package)
// to prevent unbounded memory growth in long-running processes.
import { LRUCache } from 'lru-cache';
const userCache = new LRUCache<string, User>({ max: 1000 });

Redis Caching Patterns

Distributed caching for multi-instance deployments.

import Redis from 'ioredis';

const redis = new Redis(process.env.REDIS_URL);

// Cache-aside pattern (most common)
async function getUserById(id: string): Promise<User> {
  const cacheKey = `user:${id}`;

  // 1. Check cache
  const cached = await redis.get(cacheKey);
  if (cached) {
    return JSON.parse(cached);
  }

  // 2. Cache miss - fetch from database
  const user = await db.users.findById(id);
  if (!user) {
    throw new Error('User not found');
  }

  // 3. Populate cache with TTL
  await redis.set(cacheKey, JSON.stringify(user), 'EX', 3600); // 1 hour

  return user;
}

// Write-through: update cache when data changes
async function updateUser(id: string, data: UpdateUserDto): Promise<User> {
  const user = await db.users.update(id, data);
  const cacheKey = `user:${id}`;
  await redis.set(cacheKey, JSON.stringify(user), 'EX', 3600);
  return user;
}

// Cache invalidation on delete
async function deleteUser(id: string): Promise<void> {
  await db.users.delete(id);
  await redis.del(`user:${id}`);
  // Also invalidate related caches
  await redis.del(`user:${id}:posts`);
  await redis.del(`user:${id}:settings`);
}

// Pattern-based invalidation
async function invalidateUserCaches(userId: string): Promise<void> {
  const keys = await redis.keys(`user:${userId}:*`);
  if (keys.length > 0) {
    await redis.del(...keys);
  }
}

HTTP Cache Headers

Control browser and CDN caching with proper headers.

// Express middleware for cache control

// Static assets: cache aggressively (use content hashing in filenames)
app.use('/assets', express.static('dist/assets', {
  maxAge: '1y',           // Cache for 1 year
  immutable: true,        // Never revalidate (filename changes on content change)
}));

// API responses: vary by use case
app.get('/api/products', (req, res) => {
  res.set({
    'Cache-Control': 'public, max-age=300, stale-while-revalidate=60',
    'Vary': 'Accept-Encoding',
  });
  res.json(products);
});

// User-specific data: private caching only
app.get('/api/profile', authenticate, (req, res) => {
  res.set({
    'Cache-Control': 'private, max-age=60, must-revalidate',
  });
  res.json(req.user);
});

// Never cache sensitive data
app.get('/api/auth/session', (req, res) => {
  res.set({
    'Cache-Control': 'no-store',
  });
  res.json(sessionData);
});

Cache-Control Directives Cheat Sheet:

public          - CDN and browser can cache
private         - Only browser can cache (user-specific data)
max-age=N       - Fresh for N seconds
s-maxage=N      - CDN-specific max-age (overrides max-age for CDN)
no-cache        - Must revalidate with server before using cached copy
no-store        - Never cache (sensitive data)
must-revalidate - After max-age expires, must revalidate (no stale serving)
immutable       - Content will never change (use with content-hashed filenames)
stale-while-revalidate=N - Serve stale for N seconds while fetching fresh copy

Stale-While-Revalidate Pattern

Serve stale data immediately while refreshing in the background. The HTTP header Cache-Control: stale-while-revalidate=60 does this at the CDN/browser level. For server-side SWR, use libraries like swr (React) or implement a cache wrapper that returns stale data and triggers a background refresh when the TTL expires.

### CDN Caching (Cloudflare / Vercel / AWS CloudFront)

Configure CDN edge caching for global performance.

```typescript
// Next.js ISR (Incremental Static Regeneration)
export async function getStaticProps() {
  const posts = await fetchPosts();
  return {
    props: { posts },
    revalidate: 60, // Regenerate page every 60 seconds
  };
}

// Vercel Edge Config / Cloudflare Cache API
app.get('/api/popular-products', (req, res) => {
  res.set({
    'Cache-Control': 'public, s-maxage=300, stale-while-revalidate=600',
    'CDN-Cache-Control': 'max-age=300',     // CDN-specific
    'Surrogate-Control': 'max-age=3600',    // Varnish/Fastly
  });
  res.json(products);
});

Anti-Patterns

• Caching without a TTL (data grows forever, never expires)
• Using redis.keys('*') in production (blocks Redis, O(N) scan)
• Caching user-specific data with public Cache-Control
• Invalidating cache on every write when reads vastly outnumber writes
• Not varying cache by relevant headers (Accept-Language, Authorization)
• Caching error responses (propagates failures even after recovery)
• Using unbounded in-memory caches (memory leak in long-running processes)
• Setting very long cache TTLs without a cache-busting mechanism

Quick Reference

Layer	Tool	Best For
In-process	Map / LRU Cache	Computed values, hot path data
Distributed	Redis / Memcached	Shared state across instances
CDN edge	Cloudflare / CloudFront	Static assets, public API responses
Browser	Cache-Control headers	Repeat visits, static assets
Database	Query result cache	Expensive queries, materialized views

Pattern	When to Use
Cache-aside	Read-heavy, tolerance for occasional stale data
Write-through	Need cache always in sync with database
Write-behind	High write volume, eventual consistency OK
SWR	User-facing reads where freshness < speed
TTL-based	Simple expiration, no explicit invalidation needed