To set a latency target for a vector search in an SLA, start by understanding the application’s requirements and user expectations. For example, a real-time recommendation system might require sub-100ms latency to avoid disrupting user interactions, while a batch analytics tool could tolerate 1-2 seconds. Benchmark the current system under varying loads to establish a baseline, using percentile metrics (e.g., P99, P95) to account for tail latency. Collaborate with stakeholders to align on acceptable trade-offs between speed, accuracy, and cost. For instance, using approximate nearest neighbor (ANN) algorithms like HNSW or IVF may reduce latency but slightly lower recall. Define the SLA as a percentile-based threshold (e.g., “95% of requests complete within 50ms”) to balance consistency and performance.
Configuration decisions focus on optimizing the vector search engine. Choose an ANN algorithm that fits the latency-accuracy trade-off: HNSW excels in low-latency scenarios, while IVF scales better for large datasets. Tune parameters like HNSW’s efSearch (higher values improve accuracy but increase latency) or IVF’s nprobe (more clusters checked slow queries but improve results). Use in-memory storage for indices and vectors to avoid disk I/O delays. Employ quantization (e.g., 8-bit integers instead of 32-bit floats) to reduce memory footprint and speed up distance calculations. For hardware, leverage GPUs or vectorized CPU instructions (e.g., AVX-512) to accelerate computations. Pre-filtering or partitioning data (e.g., by metadata) can also reduce search space and latency.
Architecturally, design for horizontal scalability and fault tolerance. Shard the dataset across nodes to parallelize queries and distribute load. Use a load balancer to route requests evenly and prevent hotspots. Deploy caching (e.g., Redis) for frequent or repeated queries to bypass full searches. Optimize network layers by colocating services in the same cloud region, using protocols like gRPC for lower overhead, and minimizing serialization costs (e.g., with binary formats like Protobuf). Implement autoscaling to add nodes during traffic spikes and ensure resource headroom. Continuously monitor latency with tools like Prometheus and set up alerts for SLA breaches. Stress-test the system using tools like Locust or custom scripts to validate performance under load and iterate on configurations.