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Level 8 / Project 02 - Query Cache Layer

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— This project — — Flashcards — —

Focus

  • LRU (Least Recently Used) cache with OrderedDict
  • TTL (Time-To-Live) expiration with lazy invalidation
  • Cache statistics: hit rate, evictions, expirations
  • Decorator pattern for transparent function caching

Why this project exists

Every production system uses caching — from database query results to API responses. This project builds an LRU cache from scratch so you understand eviction policies, TTL management, and hit-rate metrics before using tools like Redis or functools.lru_cache.

Run (copy/paste)

cd <repo-root>/projects/level-8/02-query-cache-layer
python project.py --capacity 5 --ttl 10
pytest -q

Expected terminal output

{
  "queries_executed": 10,
  "cache_stats": {"hits": 4, "misses": 6, ...},
  ...
}
7 passed

Expected artifacts

  • Console output with cache stats
  • Passing tests
  • Updated notes.md

Alter it (required)

Extend this project in a meaningful way — add a feature that addresses a real use case.

Break it (required)

Introduce a subtle bug and see if your tests catch it. If they don't, write a test that would.

Fix it (required)

Review your code critically — is there a design pattern that would improve it?

Explain it (teach-back)

Could you explain the architectural trade-offs to a colleague?

Mastery check

You can move on when you can: - explain why OrderedDict.move_to_end is O(1), - describe the tradeoff between eager and lazy TTL expiration, - implement a cache warming strategy, - calculate hit rate and explain what a "good" rate looks like.

Mastery Check

  • Can you explain the architectural trade-offs in your solution?
  • Could you refactor this for a completely different use case?
  • Can you identify at least two alternative approaches and explain why you chose yours?
  • Could you debug this without print statements, using only breakpoint()?
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