Horizontal scaling in a vector database refers to the ability to increase capacity by adding more machines (nodes) to the system, rather than upgrading existing hardware (vertical scaling). This approach allows the database to handle larger datasets and higher query throughput by distributing data and computation across multiple nodes. The primary goal is to maintain performance as data grows, ensuring low-latency similarity searches even when indexes exceed the memory or processing limits of a single machine.
To achieve horizontal scaling, vector databases typically partition the vector index into smaller segments (shards) distributed across nodes. Sharding strategies vary but often involve splitting the index based on the vector data’s properties. For example, in inverted file (IVF) indexing, vectors are grouped into clusters during preprocessing. Each cluster can be assigned to a separate shard, allowing queries to target only the most relevant clusters’ shards instead of scanning the entire dataset. This reduces the number of nodes involved in each query, minimizing network overhead. Systems like Milvus use this approach, where query nodes handle specific index segments, and a coordinator routes requests to the appropriate shards. Consistent hashing or dynamic resharding may also be employed to balance data distribution and avoid hotspots as the dataset grows.
Distributed query execution is another key component. When a query arrives, a coordinator node identifies relevant shards, sends parallel requests to them, and aggregates the results. For example, Pinecone’s architecture processes queries across distributed pods, each managing a subset of the index. To optimize this, systems often replicate high-traffic shards or cache frequently accessed vectors, reducing latency. Challenges include ensuring consistency during updates and avoiding bottlenecks in the coordinator. However, by decoupling storage (e.g., using cloud object storage for vector data) from compute (nodes handling queries), modern systems like Weaviate or Elasticsearch’s vector search capabilities achieve scalable performance without sacrificing accuracy. The result is a system that can grow linearly with demand while maintaining efficient nearest-neighbor search operations.