To handle continuous vector additions without full reindexing, three primary strategies are used: dynamic indexing, delta indexing with periodic merges, and partitioning/sharding. Each balances scalability, query performance, and update efficiency.
1. Dynamic Indexes Dynamic indexes allow incremental updates by design. For example, HNSW (Hierarchical Navigable Small World) graphs insert new vectors by connecting them to existing nodes in logarithmic time, avoiding full rebuilds. Similarly, tree-based structures like ANNOY or IVF (Inverted File Index) can extend clusters or branches incrementally. Systems like FAISS or Milvus support such dynamic updates by maintaining mutable index segments. However, frequent updates may degrade query performance over time due to fragmented structures. To mitigate this, some systems periodically optimize portions of the index in the background (e.g., rebalancing graph layers in HNSW) without disrupting availability.
2. Delta Indexes with Periodic Merges Here, new vectors are added to a smaller "delta" index (e.g., in-memory or lightweight on-disk structures), while the primary index remains static. Queries search both indexes, and the delta is merged into the primary during off-peak intervals (e.g., nightly). This approach, used in Elasticsearch’s Lucene-based indices, minimizes rebuild frequency. However, merging can still be costly for large deltas. Optimizations include tiered merging (combining smaller deltas first) or using write-ahead logs to batch updates. Trade-offs include temporary inconsistencies and increased query latency during merges.
3. Partitioning/Sharding Data is split into shards (e.g., by time or hash), and new vectors are appended to a dedicated "active" shard. Queries fan out to all shards, and older shards remain static. Platforms like Vespa or Pinecone use this method to scale horizontally. Sharding reduces rebuild costs since only the active shard is updated, and older shards are rarely modified. Downsides include higher query latency (due to scatter-gather overhead) and the need for distributed coordination. Some systems optimize by reindexing individual shards asynchronously when they reach size thresholds.
Key Considerations:
- Accuracy vs. Speed: Dynamic indexes offer low-latency updates but may compromise recall. Delta indexes and sharding trade consistency for scalability.
- Resource Overhead: Delta indexes require memory/disk for temporary storage, while sharding needs distributed infrastructure.
- Tooling: Libraries like FAISS or frameworks like Milvus and Qdrant provide built-in support for these strategies, reducing implementation complexity.
The choice depends on update frequency, query latency requirements, and infrastructure constraints. Hybrid approaches (e.g., dynamic indexes for recent data + periodic shard optimization) are common in production systems.