To set a latency target for a vector search in an SLA, start by defining the maximum acceptable response time based on your application’s use case and user expectations. For example, a real-time recommendation system might require sub-50ms latency, while a batch analytics tool could tolerate 200ms. Use percentile-based metrics (e.g., 95th or 99th percentile) to account for variability, ensuring most requests meet the target even under stress. Factors like dataset size (e.g., 10M vectors vs. 1B), query complexity (exact vs. approximate search), and hardware constraints (CPU/GPU, memory bandwidth) directly influence this target. Benchmarking on representative data and load helps validate realistic goals. If your search involves filtering (e.g., metadata constraints), test how added processing impacts latency to avoid surprises.
Architectural choices are critical to hitting latency targets. Use approximate nearest neighbor (ANN) indexes like HNSW or IVF-PQ, which trade slight accuracy for faster queries. For example, HNSW works well for low-dimensional vectors (e.g., 100-300 dimensions) with high recall, while IVF-PQ scales better for high-dimensional data. Distribute the workload horizontally using sharding—split the dataset across nodes to parallelize queries. A load balancer (e.g., round-robin or least connections) routes traffic evenly, preventing hotspots. Cache frequently accessed vectors or results (e.g., with Redis) to reduce compute overhead. Optimize hardware by using SSDs for large indices that don’t fit in RAM and GPUs for compute-heavy embedding models. Pre-warm caches and indexes during off-peak times to avoid cold-start delays.
To maintain performance under load, implement auto-scaling (e.g., Kubernetes horizontal pod autoscaler) that adds nodes during traffic spikes. Use monitoring tools (e.g., Prometheus, Grafana) to track latency, query throughput, and error rates in real time. Set alerts for breaches (e.g., 95th percentile exceeding 50ms) to trigger investigations. Stress-test the system with tools like Locust or JMeter to identify bottlenecks—for example, slow disk I/O when swapping index partitions. Fine-tune ANN parameters (e.g., HNSW’s “efSearch” or IVF’s “nprobe”) to balance speed and accuracy. Apply rate limiting or request queuing to prevent overload, and prioritize critical queries (e.g., paid users) during congestion. Regularly reindex and retrain models to adapt to data drift, which can degrade search efficiency over time.