Query latency in vector databases refers to the time taken to process a search query and return results. It is measured from the moment a query is submitted to the database until the final response is delivered. Latency is typically tracked using metrics like average, 95th percentile (p95), and 99th percentile (p99). For example, average latency calculates the mean response time across all queries, while p95 or p99 represent the maximum latency experienced by 95% or 99% of queries, respectively. These metrics are collected by timing individual queries and aggregating results over a period, often using monitoring tools like Prometheus or custom logging.
Average latency provides a general sense of system performance but can mask outliers. For instance, if 99% of queries take 10ms but 1% take 500ms, the average might appear acceptable (e.g., 15ms), even though some users face severe delays. In contrast, p95 and p99 latency highlight worst-case scenarios, which are critical for applications requiring consistent performance, such as real-time recommendation systems. A vector database serving an e-commerce platform might prioritize p99 latency to ensure most users receive product recommendations swiftly, even during traffic spikes. High percentile metrics are especially important for SLAs (Service Level Agreements) where guaranteeing a predictable user experience is mandatory.
Several factors influence query latency in vector databases. The choice of indexing algorithm (e.g., HNSW, IVF) impacts search speed, as some methods prioritize recall accuracy over speed. Dataset size and hardware (e.g., GPU acceleration) also play roles. To measure latency effectively, developers use tools that capture per-query timestamps and compute statistical aggregates. For example, a developer might benchmark a vector database by running thousands of queries under varying loads and plotting latency distributions. Monitoring p95/p99 helps identify bottlenecks, such as inefficient index configurations or resource contention, enabling targeted optimizations. This approach ensures the system meets both average and edge-case performance requirements.