How should one interpret latency vs. throughput trade-offs in benchmarks (e.g., a system might achieve low latency at low QPS, but latency rises under higher QPS)?

Latency (the time to complete a single request) and throughput (the number of requests processed per second) often trade off against each other in system benchmarks. When a system handles low query-per-second (QPS) loads, it can prioritize quick responses because resources like CPU, memory, or network bandwidth aren’t fully saturated. For example, a web server might process 50 requests per second with 100ms latency by dedicating full resources to each request. But as QPS increases—say to 200 requests per second—the same system might see latency rise to 500ms. This happens because incoming requests start competing for limited resources, leading to queuing delays, context switching, or bottlenecks in shared components like databases or caches. The relationship isn’t linear: latency might stay manageable until a critical QPS threshold, after which it degrades rapidly.

Several factors influence this trade-off. Hardware constraints (e.g., CPU cores, memory bandwidth) set a hard limit on throughput, while software design choices like concurrency models (thread pools vs. event loops) determine how efficiently resources are used. For instance, a system using a fixed-size thread pool might handle low QPS with low latency, but under high load, threads become blocked waiting for I/O, causing requests to pile up. Conversely, an asynchronous event-driven architecture (like Node.js) might maintain higher throughput with gradual latency increases by avoiding thread contention. Another example is buffering or batch processing: a database might buffer writes to process them in batches, improving throughput but adding latency as individual writes wait to be grouped. The trade-off depends on whether the system prioritizes speed per request (latency) or overall capacity (throughput).

Choosing the right balance depends on the application’s requirements. Real-time systems (e.g., gaming, trading platforms) prioritize low latency even if it means limiting throughput, as delayed responses are unacceptable. Batch processing systems (e.g., data pipelines) focus on maximizing throughput, accepting higher latency to process large volumes efficiently. To optimize, developers can test systems under realistic loads to identify bottlenecks—like a database connection pool maxing out—and adjust configurations (e.g., increasing pool size) or scale horizontally by adding more servers. Monitoring tools can track metrics like 95th percentile latency to catch degradation early. For example, an e-commerce site might auto-scale servers during peak traffic to maintain latency under 200ms, even if throughput per server drops slightly. The key is to align design and infrastructure with the workload’s specific needs rather than chasing theoretical maxima.