Redis Caching Patterns for Next.js: Cache-Aside, Tag Invalidation, and Stampede Prevention

Redis caching is the difference between a slow app and a fast one. But bad cache invalidation is worse than no cache at all -- stale data that never updates.

Here are the patterns that actually work in production Next.js apps.

Setup: Upstash Redis

Upstash works in Edge Runtime and serverless -- unlike ioredis which requires persistent connections.

npm install @upstash/redis

// lib/redis.ts
import { Redis } from '@upstash/redis'

export const redis = Redis.fromEnv()
// Requires UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN in .env

Pattern 1: Cache-Aside (Read-Through)

Check cache first, fetch from DB on miss, populate cache:

import { redis } from '@/lib/redis'
import { db } from '@/lib/db'

async function getUser(userId: string) {
  const cacheKey = `user:${userId}`

  // Check cache
  const cached = await redis.get<User>(cacheKey)
  if (cached) return cached

  // Fetch from DB
  const user = await db.user.findUnique({ where: { id: userId } })
  if (!user) return null

  // Populate cache with 5-minute TTL
  await redis.setex(cacheKey, 300, user)
  return user
}

// Invalidate on update
async function updateUser(userId: string, data: Partial<User>) {
  const user = await db.user.update({ where: { id: userId }, data })
  await redis.del(`user:${userId}`) // Bust the cache
  return user
}

Pattern 2: Cache Tags for Group Invalidation

When one update should invalidate multiple cache entries:

// Store tag-to-key mappings
async function cacheWithTags(
  key: string,
  value: unknown,
  tags: string[],
  ttl = 300
) {
  await redis.setex(key, ttl, JSON.stringify(value))
  // Add key to each tag set
  for (const tag of tags) {
    await redis.sadd(`tag:${tag}`, key)
    await redis.expire(`tag:${tag}`, ttl)
  }
}

async function invalidateTag(tag: string) {
  const keys = await redis.smembers(`tag:${tag}`)
  if (keys.length > 0) {
    await redis.del(...keys, `tag:${tag}`)
  }
}

// Usage
await cacheWithTags(
  'products:all',
  products,
  ['products', `org:${orgId}`]
)

// When a product is updated:
await invalidateTag('products') // Busts all product caches

Pattern 3: Stale-While-Revalidate

Serve stale data immediately, refresh in background:

async function getWithSWR<T>(
  key: string,
  fetcher: () => Promise<T>,
  opts = { ttl: 60, staleTtl: 300 }
): Promise<T> {
  const staleKey = `stale:${key}`
  const freshKey = `fresh:${key}`

  // Check if fresh
  const fresh = await redis.get<T>(freshKey)
  if (fresh) return fresh

  // Return stale while refreshing
  const stale = await redis.get<T>(staleKey)
  if (stale) {
    // Refresh in background (don't await)
    fetcher().then(async (data) => {
      await redis.setex(freshKey, opts.ttl, JSON.stringify(data))
      await redis.setex(staleKey, opts.staleTtl, JSON.stringify(data))
    }).catch(console.error)
    return stale
  }

  // No cache at all -- fetch synchronously
  const data = await fetcher()
  await redis.setex(freshKey, opts.ttl, JSON.stringify(data))
  await redis.setex(staleKey, opts.staleTtl, JSON.stringify(data))
  return data
}

Pattern 4: Distributed Lock

Prevent cache stampede (multiple requests fetching the same thing simultaneously on cache miss):

async function getWithLock<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttl = 300
): Promise<T> {
  const cached = await redis.get<T>(key)
  if (cached) return cached

  const lockKey = `lock:${key}`
  const acquired = await redis.set(lockKey, '1', { nx: true, ex: 10 })

  if (!acquired) {
    // Another process is fetching -- wait and retry
    await new Promise(r => setTimeout(r, 100))
    return getWithLock(key, fetcher, ttl)
  }

  try {
    const data = await fetcher()
    await redis.setex(key, ttl, JSON.stringify(data))
    return data
  } finally {
    await redis.del(lockKey)
  }
}

What to Cache

Good candidates:

• Public data (product listings, blog posts, docs)
• Expensive aggregations (analytics, counts)
• External API responses (weather, exchange rates)
• User profile data (changes infrequently)

Don't cache:

• Auth tokens or session data (use NextAuth session store)
• User-specific financial data
• Real-time data that must be accurate
• Data that changes on every request

Cache TTL Guidelines

Data Type	TTL
Static content	24h
Product catalog	5-15min
User profile	5min
Analytics	1min
External API	30-60s
Real-time data	0 (no cache)

Pre-Configured in the Starter

The AI SaaS Starter Kit includes Redis caching pre-configured with Upstash:

• Cache-aside helpers
• Tag-based invalidation
• Rate limiting (same Redis instance)
• Session storage

AI SaaS Starter Kit -- $99 one-time -- Redis, auth, Stripe, all pre-wired. Clone and ship.

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