P95 latency: What it is, why averages lie & how to reduce it

P95 latency is a critical metric in assessing the performance of production systems, as it highlights the slowest 5% of requests that average latency often obscures. Unlike averages that mask outliers, P95 provides insight into the user experience by exposing the requests that linger due to specific causes like connection pool exhaustion, garbage collection pauses, and upstream dependency slowdowns. Calculated at the 95th percentile of request durations, P95 belongs to the family of tail latency metrics, which are essential for understanding the degradation that averages fail to capture. It is particularly important in distributed systems where fan-out can amplify latency issues, turning backend latency spikes into frontend user pain. Choosing the right percentile for monitoring depends on the system's architecture and the level of fan-out, with P95 often serving as a balanced threshold for service-level objectives. Identifying and addressing P95 spikes involves targeted interventions, such as optimizing connection pools or setting timeouts for slow dependencies, rather than overhauling entire systems. Tools like Redis can help mitigate P95 latency by reducing variability in data access, making it a valuable component in latency-sensitive infrastructures.

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