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How would you implement a Least Recently Used (LRU) cache in Node.js, and why is eviction necessary for an in-memory cache? What is memoization, and how does it differ from a general-purpose external cache like Redis? How would you cache the result of a database query in Node.js while ensuring the cache is properly invalidated when the underlying data changes? What is the risk of caching sensitive or user-specific data with a shared cache key, and how do you avoid it? What are the main cache invalidation strategies (TTL, write-through, cache-aside), and when is each appropriate? What is cache stampede (thundering herd), and how do you prevent it in a high-traffic Node.js service? What is Redis's EXPIRE and how do TTL-based keys interact with Redis's own memory eviction policies under memory pressure? What is the 'stale-while-revalidate' caching strategy, and how would you implement it for an API response? What is multi-tier caching (combining an in-memory L1 cache with a distributed L2 cache), and what problem does it solve? How would you implement a write-behind (write-back) caching strategy, and what durability risk does it introduce?
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What is negative caching, and why might you deliberately cache the fact that a lookup returned 'not found'?

Intermediate

Answer

Negative caching stores the result of a lookup that found nothing, preventing repeated identical lookups for something known not to exist from hitting the database every time -- without it, requests probing for non-existent resources could generate significant unnecessary load on the underlying data source.
async function getUser(id) {
  const cached = await redis.get(`user:${id}`);
  if (cached === 'NOT_FOUND') return null;
  if (cached) return JSON.parse(cached);
  const user = await db.users.findById(id);
  if (!user) { await redis.set(`user:${id}`, 'NOT_FOUND', { EX: 60 }); return null; }
  await redis.set(`user:${id}`, JSON.stringify(user), { EX: 3600 });
  return user;
}
Real-world example A public API repeatedly queried for invalid or deleted user IDs by a misconfigured integration adds negative caching with a short TTL, absorbing the repeated invalid lookups without hitting the database each time.

Common follow-ups: Why does a negative cache entry typically need a much shorter TTL than a positive cache entry?;Could negative caching be exploited or cause a functional bug if a resource is created shortly after being negatively cached?

Security;Databases & ORMs (MongoDB/Mongoose SQL/Sequelize)
All 15 Caching questions