Caching
Answer the repeat questions from memory, and most of your traffic never touches the database.
5 lessons · ~51 min · free
By the end you can
- Explain why a small cache absorbs most reads (skewed access patterns)
- Choose between cache-aside, read-through, write-through, and write-behind
- Reason about TTLs, invalidation, and stale reads without hand-waving
- Defend an eviction policy with hit-rate numbers, not vibes
- Place caches at the right layers of a design and say the trade-offs out loud
Lessons
- 1The requests you keep re-answering9 minThe latency ladder, skewed access patterns, and what one small cache does to database load.
- 2Cache-aside and its siblings11 minWho fills the cache and when: cache-aside, read-through, write-through, write-behind.
- 3Staleness, TTLs, and invalidation12 minThe two ways cached data goes wrong, expiry vs explicit invalidation, and the thundering herd.
- 4When the cache is full10 minLRU, LFU, and FIFO racing on the same trace, and what production caches actually run.
- 5Caching in a real design9 minBrowser to CDN to app to database: the layers, what to cache where, and the interview traps.
Design with it after
The design library has full system design prompts on a whiteboard canvas with AI feedback. Every one of them gives you a place to apply this.
Open the design library