What are vector database best practices?

Vector database best practices focus on efficient data handling, query optimization, and system scalability. These practices ensure reliable performance when managing high-dimensional data like embeddings from machine learning models. Key areas include data preparation, indexing strategies, and infrastructure design.

First, prioritize data preprocessing and normalization. Vector databases rely on similarity calculations (e.g., cosine similarity), which are sensitive to input scale. For example, text embeddings generated by models like BERT should be normalized to unit length to ensure consistent distance measurements. If working with images, consider dimensionality reduction techniques like PCA to trim unnecessary features without losing critical information. Clean, standardized data reduces computational overhead during queries and improves result accuracy. Additionally, validate embedding quality—poorly trained models or misaligned data will degrade search performance regardless of database tuning.

Next, optimize indexing and query strategies. Choose an indexing method (e.g., HNSW, IVF, or brute-force) based on your latency and recall requirements. HNSW graphs work well for high-recall scenarios, while IVF partitions data for faster but approximate searches. For example, an e-commerce product recommender might use IVF with 1,000 clusters to balance speed and precision. Tune parameters like the number of connections in HNSW or probe counts in IVF through iterative testing. Use batch queries instead of single requests when processing multiple inputs to reduce network overhead. Also, leverage metadata filtering: if searching for similar articles, filter by publication date first to shrink the vector search space.

Finally, design for scalability and monitor performance. Use horizontal scaling by sharding data across nodes—partition vectors by user ID or region to distribute load. Implement caching for frequent queries (e.g., storing top 100 trending video embeddings in memory). Monitor metrics like query latency, error rates, and resource utilization with tools like Prometheus. Rebalance clusters as data grows, and retrain embeddings periodically if your model evolves. For example, updating image embeddings after switching from ResNet-50 to CLIP ensures compatibility with newer representations. Regular backups and versioning of indexes prevent data loss during updates or failures.