Redis is often introduced as a very fast key-value store. In production, the interesting question is not whether Redis is fast. It is whether the entire path around Redis is fast enough for the workload.
The network is part of the cache
A cache lookup has a client, a connection, a network hop, a server, a response, and application work around it. If the application makes thousands of tiny round trips, the latency budget can disappear even when Redis itself is doing very little work.
Pipelining, batching, connection management, and data layout can matter more than micro-optimizing application code.
Measure the workload you actually have
Hit rate alone is not enough. I care about latency distributions, command frequency, payload size, memory behavior, eviction behavior, and the shape of hot keys.
Averages hide the tails. If a small percentage of requests become very slow, users experience those outliers directly.
Optimize for the bottleneck
The useful sequence is simple: establish a baseline, identify the dominant cost, change one thing, measure again, and keep the change only if it improves the system.
Performance engineering is less about clever tricks than disciplined feedback loops.