What are common pitfalls or mistakes to avoid when benchmarking vector databases (such as not using enough queries, or not accounting for initialization overhead in timing)?

When benchmarking vector databases, common mistakes include using insufficient query volumes, ignoring initialization overhead, and overlooking system-level factors like resource allocation. These errors can lead to misleading performance metrics, making it hard to compare databases accurately or predict real-world behavior. For example, testing with only 100 queries when the system is designed for 10,000 queries per second might hide scalability issues or bottlenecks that emerge under sustained load. Similarly, failing to account for the time it takes to load indexes into memory or warm up caches can skew latency measurements, making a database appear faster than it would be in production.

Another pitfall is using unrealistic or poorly structured datasets and queries. Vector databases often handle high-dimensional data (e.g., embeddings from text or images), and performance can vary drastically based on data distribution, dimensionality, and query types. For instance, testing only with synthetic, uniformly distributed vectors might not reveal how the database handles clusters or sparse regions in real-world data. Similarly, benchmarking only nearest-neighbor searches while neglecting range queries or filtered searches can lead to an incomplete picture. A database optimized for approximate nearest neighbors (ANN) might excel at speed but struggle with precision under certain conditions, which would go unnoticed if queries aren’t diverse enough.

Finally, ignoring system-level factors like hardware constraints, network latency, or configuration settings can invalidate results. For example, running benchmarks on a local machine with limited RAM might cause disk thrashing, which wouldn’t occur in a cloud environment with sufficient memory. Similarly, not tuning parameters like index type (e.g., HNSW vs. IVF) or batch size during ingestion can lead to unfair comparisons. A common oversight is neglecting to isolate the benchmark environment—background processes or other services consuming CPU or I/O resources can introduce noise. To avoid this, use dedicated instances, monitor resource usage, and document configurations (e.g., “we tested with 4 vCPUs and 16GB RAM, index built with 32 segments”) to ensure reproducibility.