Optimize with a cache/hash map

##### Question This is a follow-up to your existing implementation (for example, a script that repeatedly resolves file paths or parses license strings into computed results). Propose and analyze an optimization that adds an in-memory cache backed by a dictionary/hash map to speed up repeated lookups. Address each part: 1. **Where to cache.** Identify the spot in your code where caching or a hash map would actually help — i.e. a pure, repeated, deterministic computation whose inputs recur. Justify why that call is a good candidate and why other calls are not. 2. **Keys and values.** Specify the cache key (what uniquely identifies a computation, e.g. the file path or the normalized license string) and the cached value (the computed result). Note any normalization needed so logically-equal inputs map to the same key. 3. **Expected hit rate.** Estimate the expected cache hit rate given the access pattern, and explain what assumptions drive it (input cardinality vs. number of lookups, temporal locality). 4. **Eviction policy.** Choose and justify an eviction policy (LRU / LFU / TTL, or a bounded `dict` with no eviction) based on working-set size and memory budget. 5. **Invalidation / staleness.** Describe how you keep cached values correct when the underlying source changes — e.g. invalidating an entry when a file's `mtime` or content hash changes, or expiring via TTL — and the consistency vs. staleness trade-off you are accepting. 6. **Concurrency safety.** Discuss what happens if multiple threads or processes run concurrently: races on get/put, the thundering-herd / cache-stampede problem on a miss, and how you'd make it safe (locking, per-key locks, or a process-local cache). 7. **Pseudocode.** Provide `get`/`put` pseudocode that includes the validation/invalidation check on read. 8. **Complexity.** State the time and space complexity of the relevant operation before and after adding the cache.