Caching in System Design: The Rules Interviewers Expect You to Know

Quick primer interviewers expect: caching is a design decision, not a "add Redis and pray" tactic. Know what to cache, which strategy to use, how to expire and invalidate, and how to monitor.

What to cache (start here)

• Sessions and authentication tokens (if safe to cache).
• API responses for idempotent or read-heavy endpoints.
• Static assets (images, JS, CSS) and CDN-cached content.
• "Hot" query results and aggregated counters (e.g., leaderboards, dashboards).

Pick the smallest surface area that gives measurable latency or load reduction.

Popular caching strategies (and when to name-drop them in interviews)

• Cache-Aside (Lazy Loading)

  • Flow: application checks cache -> if miss, load from DB -> write to cache -> return.
  • Pros: simple, widely used, flexible invalidation.
  • Cons: cache warm-up on misses; potential stale reads between DB write and cache update.

• Write-Through

  • Flow: writes go to cache, cache synchronously writes to DB.
  • Pros: strong read-after-write consistency for cached keys.
  • Cons: higher write latency because every write persists synchronously.

• Write-Behind (Write-Back)

  • Flow: write acknowledged when written to cache; cache asynchronously flushes to DB.
  • Pros: very low write latency.
  • Cons: risk of data loss if cache node dies before flush; complexity in retry/ordering.

When discussing, mention trade-offs: consistency vs latency vs complexity.

TTL and invalidation — do not rely on TTL alone

• Always set a sensible TTL (time-to-live) to bound staleness and memory usage.
• Use event-based invalidation when the origin of truth changes (e.g., after a write, invalidate related keys).
• Consider patterns to avoid race conditions and stampedes:
  • Cache stampede mitigation: request coalescing, mutexes, or pre-warming.
  • Stale-while-revalidate: serve slightly stale data while refreshing in background.
  • Use versioned keys (key:v2) to make invalidation atomic.

Distributed cache for multi-instance apps

• Use Redis or Memcached (or cloud-managed equivalents) when you have multiple application instances.
• Choose Redis for advanced features (persistence, replication, data structures, Lua scripting).
• Be mindful of network latency, sharding, and capacity planning.

Monitor and tune

Track these metrics and tune accordingly:

• Hit rate and miss rate (target higher hit rate for the most important keys).
• Eviction rate (indicates insufficient memory or poor key sizing).
• Cache latency (network + serialization costs).
• Thundering herd occurrences and stampede events.

Use dashboards and alerts to detect regressions after deployments.

What not to cache / pitfalls

• Never cache sensitive data (full credit card numbers, secrets, PII) unless encrypted and justified.
• Avoid caching large unbounded collections; prefer paginated or summarized views.
• Watch serialization cost: expensive (de)serialization can negate latency gains.
• Remember cache consistency semantics — eventual consistency may be fine for read-mostly data, but not for critical transactional flows.

Interview checklist — succinct talking points

• Name what you'd cache and why (sessions, hot queries, static assets).
• Choose a strategy and justify trade-offs (cache-aside for simplicity, write-through for strong consistency, write-behind for throughput).
• Explain TTL + event-based invalidation and how you'd prevent stampedes.
• Mention distributed cache choices (Redis vs Memcached) and monitoring metrics.
• Call out security: don’t cache sensitive data.

Short example: cache-aside pseudocode

# read flow
value = cache.get(key)
if not value:
    value = db.read(key)
    cache.set(key, value, ttl=60)
return value

# write flow (simple invalidation)
db.write(key, new_value)
cache.delete(key)  # or update cache atomically

Keep answers concrete in interviews: quantify expected reductions (e.g., reduce DB read load by X%, drop p95 latency from Y->Z ms) and mention monitoring you’d add to validate the change.

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