Sure thing, let’s break it down quickly. The core metric is hit ratio = hits / (total + evictions). For a high‑traffic microservice, you want the ratio to stay above 95 % to keep latency down. Hit ratio alone doesn’t tell the whole story—you also need to factor in eviction cost. Eviction cost = cost to evict + cost to fetch the evicted key again. With a large data set the fetch cost can dominate, so the formula becomes: effective cost = (1 – hit ratio) × eviction cost. Choose the policy that minimizes that effective cost. • LRU works when your data has strong temporal locality—fast to implement, O(1) per access, but it can suffer if your workload has bursts of new data. • LFU is better when you have skewed access patterns and can tolerate some extra bookkeeping. • For massive scale you might use a probabilistic approximation like Clock or ARC, which keep the overhead bounded while still capturing locality trends. In practice, profile with realistic traffic, simulate each policy, and look at both hit ratio and the time spent on eviction decisions. If you hit a performance plateau, try tightening the cache size or adding a tiered approach (e.g., in‑memory plus Redis). That gives you the sweet spot between hit ratio, eviction cost, and overall throughput.