To ensure a vector store performs well under load, track metrics across three categories: throughput, latency, and accuracy. These metrics help identify bottlenecks, validate responsiveness, and confirm result quality.
First, measure throughput and latency. Queries per second (QPS) indicates how many requests the system handles. If QPS plateaus or drops under load, it suggests resource saturation or scalability limits. Average search latency (time per query) is critical for user experience—consistent spikes under load may signal inefficient indexing or hardware constraints. Track p95/p99 latency to identify outliers affecting a subset of requests. For example, if average latency is 50ms but p99 is 500ms, a small fraction of slow queries might degrade performance. Additionally, monitor timeouts or errors (e.g., failed queries due to resource exhaustion) to ensure reliability.
Second, evaluate accuracy and recall. Vector stores often trade accuracy for speed (e.g., using approximate nearest neighbors). Recall@k (proportion of true top-k results returned) under varying loads ensures quality isn’t compromised. For instance, if recall drops from 95% to 70% when QPS increases, the indexing parameters (like search depth in HNSW graphs) may need adjustment. Pair this with latency distributions to find acceptable thresholds (e.g., maintaining 90% recall within 100ms). If recall degrades at higher QPS, consider load balancing or sharding to distribute the workload.
Third, monitor resource utilization and scalability. Track CPU, memory, disk I/O, and network usage to identify hardware bottlenecks. For example, high CPU usage during queries suggests computational limits, while memory spikes may indicate inefficient caching. Indexing time and memory footprint during updates are also critical if the vector store supports real-time data ingestion. For cloud-based systems, autoscaling metrics (e.g., instance spin-up time) ensure the system scales horizontally under load. Tools like Prometheus or Grafana can visualize these metrics to correlate QPS spikes with resource trends and guide optimizations.