To design a benchmark test for a vector database that reflects real production conditions, start by replicating realistic data distributions and query patterns. Use synthetic or real-world datasets that mimic the expected data characteristics, such as varying vector dimensions, clustering patterns, and data densities. For example, if the database is intended for image retrieval, generate vectors with clusters representing common object categories and outliers for rare cases. Introduce dynamic data updates (e.g., adding/removing 10% of vectors daily) to simulate live environments. Avoid uniform distributions—real data often has skewed access patterns, such as frequent queries targeting specific clusters (e.g., popular products in e-commerce). Tools like FAISS or ANN Benchmarks can help generate synthetic datasets with configurable distributions.
Next, design query workloads that mirror actual usage. Define a mix of search types (e.g., exact nearest-neighbor, approximate, filtered searches) and parameter distributions (e.g., varying k values in k-NN queries). For instance, if the application involves hybrid search, include metadata filtering in 30% of queries. Introduce concurrency to simulate multiple users or services querying the database simultaneously. Measure latency under realistic throughput (e.g., 1,000 queries per second) and include bursty traffic patterns. Also, test edge cases like high-dimensional queries or low-selectivity filters. Capture query logs from staging environments if available, or use tools like Locust to model traffic. Ensure the benchmark includes a warm-up phase to preload indexes and caches, avoiding cold-start distortions.
Finally, replicate production infrastructure and track metrics that align with operational goals. Deploy the benchmark on hardware matching production specs (e.g., AWS EC2 instances with similar CPU/RAM/storage). Measure latency percentiles (p95/p99), throughput, recall rates, and resource utilization (CPU, memory, disk I/O). For example, if recall drops below 95% when throughput exceeds 500 QPS, it indicates scalability limits. Include scalability tests by incrementally increasing dataset size (e.g., from 1M to 100M vectors) and observing performance degradation. Test fault tolerance by killing nodes during queries and measuring recovery time. Compare results against baseline systems (e.g., Pinecone vs. Milvus) and document configuration details (index type, distance metric). Run tests multiple times to account for variability and publish raw data for reproducibility.