Caching Strategies

Caching is one of the most impactful performance techniques in data engineering. By storing the results of expensive computations or database queries, you avoid repeating work. Python provides built-in caching tools, and understanding how to build custom caches using data structures gives you fine-grained control over eviction policies, size limits, and expiration. Using functools.lru_cache Building a Custom LRU Cache The LRU eviction policy works well for workloads with temporal locality - recently accessed items tend to be accessed again soon. For frequency-based workloads, consider LFU (Least Frequently Used) strategies instead.