Network latencies directly affect database benchmarks by adding overhead to query response times and limiting throughput. When a client application sends a request to a database, the time it takes for the request to travel over the network (and for the response to return) is part of the total measured latency. For example, if a query takes 5ms to execute on the database but 20ms to travel over the network each way, the client will observe a 45ms response time instead of the actual 5ms. This skews benchmarks, making the database appear slower than it is. High network latency also reduces the maximum achievable queries per second (QPS), as clients spend more time waiting for data to traverse the network rather than processing results.
The impact varies by workload type and database architecture. Read-heavy benchmarks with small result sets (e.g., key-value lookups) are more sensitive to network latency because each query involves a round trip. For instance, a Redis benchmark running over a high-latency connection might report half the QPS of a local test, even if the server itself is fast. Distributed databases face additional challenges: cross-node communication (like replication or consensus protocols) can amplify latency effects. A Cassandra cluster spanning multiple regions might see slower write times due to inter-data-center delays, even if individual nodes perform well. Network latency also complicates benchmarks for cloud databases, where clients and servers are often in different physical locations.
To minimize these effects, benchmarks should isolate network factors. One approach is to run the client and database on the same physical machine or local network, reducing external latency. Tools like tc (Traffic Control) on Linux can simulate real-world network conditions for more accurate testing. Additionally, using connection pooling or batch requests reduces round trips—for example, a PostgreSQL benchmark using prepared statements and batch inserts will be less affected by network delays than single-row inserts. When interpreting results, developers should distinguish between database processing time and network overhead by measuring client-side latency versus server-side metrics (e.g., database engine execution time). Ignoring network latency risks misdiagnosing performance bottlenecks and optimizing the wrong part of the system.