Latency and throughput trade-offs in benchmarks reflect how a system balances responsiveness against capacity under varying loads. Latency measures the time to complete a single operation (e.g., a database query), while throughput quantifies how many operations the system can handle per second (e.g., queries per second, QPS). When a system exhibits low latency at low QPS but rising latency under higher QPS, it often indicates resource contention. For example, at low load, requests are processed immediately with minimal queueing, but as QPS increases, bottlenecks like CPU saturation, memory pressure, or I/O limits force requests to wait, increasing latency. This behavior is inherent in most systems because scaling throughput typically requires parallel processing or batching, which can introduce delays.
To interpret this trade-off, focus on the workload requirements. If the application prioritizes fast individual responses (e.g., real-time user interactions), aim for a latency target that remains acceptable even if it limits maximum throughput. Conversely, if the system prioritizes bulk processing (e.g., data pipelines), higher throughput at the cost of increased latency may be acceptable. For instance, a payment gateway might target 99% of requests completing under 100ms, even if that caps throughput at 1,000 QPS. Meanwhile, a log-processing service might tolerate 500ms latency to achieve 10,000 QPS. Benchmarks should map latency percentiles (e.g., P50, P99) against throughput to identify the "knee" where latency degrades sharply, indicating the practical operating limit.
Developers can address these trade-offs by optimizing critical paths (e.g., reducing database locks) or scaling resources (e.g., adding threads, nodes). For example, a web server might use connection pooling to reduce per-request overhead, improving throughput without drastically increasing latency. Alternatively, rate limiting or load shedding can prevent overload scenarios where latency becomes unpredictable. Testing under realistic load patterns (e.g., sudden spikes) helps uncover whether latency increases linearly with load or exhibits non-linear degradation, informing architectural decisions like caching strategies or autoscaling policies.