Measuring Query Throughput (QPS) in Vector Search
QPS for vector search is measured by sending a controlled workload of search queries to the database and counting how many complete successfully per second. This is typically done using benchmarking tools (e.g., locust, custom scripts) that simulate multiple clients querying the system concurrently. The test runs for a fixed duration, and QPS is calculated as the total queries completed divided by the test time. To ensure accuracy, tests account for network latency, client overhead, and server warm-up periods. For example, a database handling 10,000 queries in 10 seconds achieves 1,000 QPS. Latency percentiles (e.g., p99) are also tracked to ensure consistent performance under load.
Key Factors Impacting High QPS
- Search Algorithm Efficiency: Approximate Nearest Neighbor (ANN) algorithms like HNSW or IVF reduce computational complexity compared to exact search. For example, HNSW’s graph-based approach skips unnecessary distance calculations, enabling faster queries.
- Hardware Resources: GPUs accelerate vector operations (e.g., matrix multiplications), while fast storage (NVMe SSDs) reduces index loading times. Memory bandwidth also matters: a 512-dimensional vector requires ~2KB of data per query, so higher bandwidth allows more parallel computations.
- Concurrency and Parallelism: Multithreaded query execution and distributed architectures split workloads across nodes. A system with 8 threads per node might handle 8x more queries than a single-threaded setup, provided there’s no resource contention.
Optimization Trade-offs and Practical Considerations Achieving high QPS often involves trade-offs. For instance, reducing vector dimensionality (e.g., from 1024 to 256 via PCA) cuts computation time but may lower accuracy. Caching frequent queries (e.g., using Redis) improves QPS but requires memory overhead. Network optimizations like gRPC (instead of HTTP/JSON) reduce serialization latency. Additionally, batch processing—submitting multiple queries in a single request—can improve throughput by leveraging hardware parallelism. However, these optimizations must align with use-case requirements: a recommendation system needing 99% recall might prioritize algorithm choice over raw QPS gains.