Redis Caching Strategies for High-Traffic APIs

A well-placed cache can cut database load by 90% and shave hundreds of milliseconds off responses. Redis is the default tool — but caching is easy to get subtly wrong. Here are the patterns that work.

Cache-Aside (The Default)

async function getUser(id) {
  const cached = await redis.get(`user:${id}`);
  if (cached) return JSON.parse(cached);

  const user = await db.users.findById(id);
  await redis.set(`user:${id}`, JSON.stringify(user), "EX", 300); // 5 min TTL
  return user;
}

Invalidate on Write

The hard part of caching. When data changes, delete the key so the next read repopulates it:

async function updateUser(id, data) {
  await db.users.update(id, data);
  await redis.del(`user:${id}`); // next read refreshes the cache
}

Choosing TTLs

• Short TTLs (seconds) for fast-changing data — accept slight staleness for safety.
• Long TTLs with explicit invalidation for stable data like product catalogs.
• Add jitter to TTLs to avoid a 'thundering herd' when many keys expire at once.