Build 4 — Memory-Efficient Data Cache & Profiler

# Build 4: Memory-Efficient Data Cache & Profiler ## Scenario High-throughput services often suffer from memory bloat due to object overhead and unbounded caches. Build a custom, thread-safe LRU cache with strict memory bounds, optimized with `__slots__`. ## Requirements 1. **Optimized Data Structures:** Compare standard classes vs `@dataclass(slots=True)` vs namedtuples. Measure per-instance footprint with `sys.getsizeof` and `pympler`. 2. **Thread-Safe LRU Cache:** Implement an LRU cache using a doubly linked list + hash map (or `collections.OrderedDict`) protected by a reentrant lock (`threading.RLock`). 3. **Profiling Decorator:** Implement `@profile_memory_and_time` that logs elapsed CPU time and heap allocation delta before and after function execution. ## Starter Code Layout ``` build-4-cache-profiler/ ├── src/ │ └── memory_tools/ │ ├── __init__.py │ ├── cache.py # Thread-safe bounded LRU cache │ ├── models.py # Slots-optimized data structures │ └── profiler.py # Memory & execution timing decorator └── tests/ └── test_cache.py ``` ## Verification Command ```bash pytest tests/test_cache.py -v python -m memory_tools.benchmark ``` ## 100-Point Evaluation Rubric - **Cache Eviction Mechanics (30 pts):** Strict O(1) get/put operations and eviction policy. - **Thread Safety (25 pts):** Correct use of RLock during eviction and retrieval. - **Memory Optimization (25 pts):** Demonstrable >40% memory reduction using `__slots__`. - **Profiling Tooling (20 pts):** Decorator reports accurate timing and allocation deltas. ## Grading rubric Your submission passes when **all four** hold: 1. **Works:** Cache is thread-safe and bounded; slots/namedtuple savings measured; profiler decorator reports time + heap delta. 2. **Tested:** a pytest suite covers the happy path plus one failure mode, and all tests pass. 3. **Readable:** a README explains setup, the two most important commands, and one design decision you made. 4. **Production habits:** structured logging on failures and no hardcoded secrets/paths (env or config file). ## Submission contract Submit a public repo URL containing source, tests, README, and `requirements.txt` (or `pyproject.toml`). A reviewer runs your tests and two commands from your README; if either fails on a clean machine, the submission is returned with notes.