In a distributed vector database, a search query is executed by splitting the workload across multiple machines, each handling a subset of the data. When a query is received, a coordinator node broadcasts the query vector to all nodes (or shards) in the cluster. Each node independently searches its local subset of vectors using an indexing structure, such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index), to efficiently find the nearest neighbors within its partition. For example, if the database is split into 10 shards, each node scans its own 1 million vectors, returning the top 100 candidates from its local results. This parallel processing reduces latency, as each machine operates on a smaller dataset rather than scanning the entire collection centrally.
After all nodes return their partial results, the coordinator merges these lists to produce the final nearest neighbors. The merging process typically involves aggregating all candidate vectors from each node, recalculating their distances to the query vector (if necessary), and then sorting the combined list to select the top k results. To optimize this step, a priority queue or heap structure is often used to efficiently track the closest vectors without fully sorting all candidates. For instance, if each node returns 100 candidates, the coordinator might process 1,000 total candidates (from 10 nodes) to extract the final top 10. Some systems also apply filtering—like distance thresholds or deduplication—to refine accuracy. This step ensures that the merged results are as precise as if the search had run on a single machine, but with significantly faster execution.
Key considerations include balancing accuracy, latency, and resource usage. Increasing the number of candidates per node (e.g., returning 200 instead of 100) improves result quality but increases network overhead and merge time. Systems may also employ load balancing to prevent slower nodes from becoming bottlenecks, or use redundancy (e.g., replicating data across nodes) to tolerate failures. Trade-offs between approximate and exact search methods also play a role: approximate techniques speed up local searches but may require larger candidate lists during merging to maintain accuracy. Developers must configure parameters like shard count, candidate list size, and indexing algorithms based on their specific latency and precision requirements. For example, a real-time application might prioritize low latency by using smaller candidate lists, while a batch analytics system could favor accuracy with larger lists and stricter merging logic.