When benchmarking vector databases, three common pitfalls include using unrealistic data distributions, ignoring system resource constraints, and failing to isolate test conditions. These mistakes can lead to misleading performance conclusions and poor decisions in production environments.
First, using synthetic or oversimplified data skews results. Vector databases rely on distance calculations between embeddings, and real-world data often has clusters, outliers, or varying density. For example, testing only with uniformly distributed vectors might make brute-force searches appear faster than they would be on sparse real-world data. Similarly, using small datasets (e.g., 10,000 vectors) hides scalability issues that arise with millions of vectors. To avoid this, use datasets matching your actual data size and distribution. For ANN benchmarks, validate recall rates—if a benchmark uses approximate search but doesn’t report whether results match ground-truth k-NN values, the speed metrics become meaningless. A 95% recall at 10ms might be better than 99% recall at 15ms, depending on the use case, but omitting this detail makes comparisons impossible.
Second, neglecting resource constraints and environmental factors invalidates benchmarks. For instance, running tests on a laptop with shared resources (background processes, thermal throttling) instead of dedicated servers introduces noise. Vector databases often leverage GPU acceleration or parallel CPU threads—failing to allocate sufficient memory or compute power creates bottlenecks unrelated to the database’s actual performance. Additionally, not testing under concurrent loads misses critical latency spikes. For example, a database might handle 100 queries per second in isolation but crash at 150 due to thread contention. Always monitor CPU, memory, and disk I/O during tests. Tools like perf or vmstat can reveal hidden issues, such as memory swapping or cache inefficiencies, that explain inconsistent timing results.
Third, inconsistent test setup and measurement errors distort outcomes. A common mistake is including index-building time in query latency measurements. For example, if a test times queries immediately after database startup, the first few queries might include index-loading overhead. Instead, run warm-up queries before recording measurements. Another error is using single-threaded clients to benchmark databases designed for concurrent access, which underutilizes hardware. To fix this, simulate real-world concurrency levels with tools like wrk or custom multithreaded clients. Finally, relying on a single test run ignores variance caused by OS scheduling or garbage collection. Run benchmarks multiple times, discard outliers (e.g., the first run due to cold caches), and report median or percentile latencies rather than averages. For example, a 99th percentile latency of 200ms reveals more about user experience than an average of 50ms with occasional 2-second spikes.
By addressing these pitfalls—using representative data, isolating resources, and rigorously controlling test conditions—developers can produce reliable benchmarks that reflect real-world performance.