Distributed Systems · core
Caching
Cache-aside, write-through, eviction policies.
Mental model
A cache trades memory and staleness for speed. The hard parts are not the hit — they are invalidation, the stampede when a hot key expires, and deciding how stale is acceptable.
How to study Caching
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use HTTP caching (MDN) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Caching with Object Storage, Load Balancing. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Cache stampede and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Where it matters
Redis/Memcached, CDN edges, HTTP caching.
Common mistakes
- Cache-aside with no stampede protection on a hot key
- No TTL, so stale data lives forever
- Caching per-user data in a shared key
Learn from primary sources
- HTTP caching (MDN) (doc)
Practice and explain it back
Cache stampede
Hot key expires, 1000 requests miss together. Name two mitigations.
Expected evidence: Probabilistic early expiry, request coalescing/singleflight, mutex per key.
Open the interactive drill →Review prompts
- What is a cache stampede and how do you prevent it?
Build evidence
Synthesize: Distributed Systems
Reason about coordination, data placement, logs, durable workflows, consistency, and recovery under partial failure. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks