What strategies allow continuous addition of new vectors in a scalable way (streaming data) without reindexing everything from scratch? (e.g., dynamic indexes or periodic rebuilds)

To handle continuous vector additions in scalable systems without full reindexing, developers can use dynamic indexing strategies, partial updates, and periodic optimizations. These approaches balance efficiency and performance by avoiding rebuilding the entire index when new data arrives. The goal is to maintain query speed and accuracy while accommodating streaming data.

One common method is incremental indexing with dynamic data structures. For example, HNSW (Hierarchical Navigable Small World) graphs allow new vectors to be inserted directly into the hierarchy without rebuilding the entire graph. Libraries like FAISS support this through wrappers such as IndexIDMap, which lets you add vectors incrementally by assigning unique IDs. Another approach is using delta indexes, where a smaller, temporary index holds recent additions. Queries combine results from the main index and the delta, with periodic background merges to update the main index. This is similar to how search engines handle document updates. For instance, Vespa or Apache Solr use such strategies for near-real-time updates. Additionally, sharding splits data into smaller, manageable chunks (shards), each with its own index. New vectors go into a dedicated shard, and queries run in parallel across shards. Milvus, a vector database, uses this method, automatically creating new segments as data grows and merging them during low-load periods.

Another strategy is approximate maintenance with quantization or partitioning. Techniques like product quantization (PQ) compress vectors into smaller codes, enabling efficient updates. If the data distribution changes significantly, you can retrain the quantization codebook periodically on a subset of data instead of the entire dataset. Similarly, inverted file (IVF) indexes partition data into clusters. When adding new vectors, they’re assigned to existing clusters, and only affected clusters are reindexed if necessary. For example, FAISS’s IndexIVF allows partial retraining by updating cluster centroids incrementally. However, this requires monitoring cluster quality to avoid drift. Hybrid approaches combine these methods—like using HNSW for fast insertions and IVF for efficient clustering—to balance flexibility and performance.

Developers should consider trade-offs: dynamic updates may slightly reduce query speed over time, while sharding increases infrastructure complexity. Tools like RedisVL or Qdrant automate some of these optimizations, but understanding the underlying mechanics helps tailor solutions. For instance, setting merge intervals for delta indexes or shard size thresholds requires testing based on data velocity and query patterns. The key is to minimize full reindexing while ensuring the system adapts to changing data without degrading user experience.