In a distributed vector database, a search query is executed by distributing the workload across multiple machines and then merging partial results to produce the final nearest neighbors list. Here’s how it works:
Query Distribution and Parallel Execution When a search query is received, the database first determines which nodes in the cluster store the relevant vectors. This is done using a partitioning strategy, such as sharding (e.g., hash-based, range-based, or learned partitioning). For example, if vectors are sharded by range, the coordinator node identifies which shards contain vectors closest to the query vector’s value range. The query is then broadcast to all relevant nodes in parallel. Each node executes the search locally using algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to find approximate nearest neighbors within its subset of data. This parallel execution reduces latency by leveraging multiple machines.
Local Search and Partial Results Each node processes the query independently, scanning its local index to generate a list of candidate vectors ranked by similarity (e.g., cosine or Euclidean distance). For efficiency, nodes often return a larger set of candidates (e.g., top 200 results) than the final required count (e.g., top 100) to account for overlaps and improve accuracy during merging. For instance, a node using HNSW might traverse its graph structure to find local nearest neighbors, then return these candidates along with their similarity scores. This step balances speed and precision, as nodes prioritize reducing computation time while ensuring sufficient candidate diversity.
Merging Partial Results The coordinator node aggregates all candidate vectors from the distributed nodes and reranks them globally. This is typically done using a priority queue or a heap structure to select the top-K nearest neighbors based on their similarity scores. For example, if 10 nodes each return 200 candidates, the coordinator evaluates all 2,000 candidates, sorts them by distance, and selects the top 100. To optimize performance, some systems use early termination (e.g., stopping once the top-K results stabilize) or distributed merging (e.g., merging results in a tree-like hierarchy across nodes). The final list ensures consistency by applying the same distance metric used locally, even if nodes employed approximations during local searches.
This approach balances scalability and accuracy, allowing the system to handle large datasets while maintaining low latency. Trade-offs include the choice of sharding strategy (affecting query distribution accuracy) and the candidate count per node (impacting merge overhead and result quality).