What does it mean for a vector database to scale horizontally, and how do systems achieve this (for example, through sharding the vector index across multiple nodes or partitions)?

Horizontal scaling in a vector database refers to the ability to handle increased workloads by adding more machines or nodes to the system, rather than upgrading the hardware of a single server (vertical scaling). This approach distributes data and computational load across multiple nodes, allowing the database to manage larger datasets and higher query throughput. For vector databases, which specialize in storing and querying high-dimensional vectors (like embeddings from machine learning models), horizontal scaling is critical because similarity searches—such as finding the nearest neighbors to a query vector—are computationally intensive. By spreading the workload, the system can maintain performance as data grows and user demands increase.

To achieve horizontal scaling, vector databases typically use sharding, a method that splits the dataset into smaller subsets (shards) and distributes them across nodes. For vector data, sharding often involves partitioning the vector index. One common approach is to group vectors into clusters using algorithms like k-means or hierarchical navigable small worlds (HNSW). Each cluster is assigned to a shard, and queries are routed to the most relevant shards based on proximity to the query vector. For example, a system might precompute cluster centroids and compare the query vector to these centroids to determine which shards to search. This reduces the number of nodes involved in each query, improving efficiency. Some databases, like Milvus, use a “proxy-coordinator” architecture where a coordinator node manages shard locations and orchestrates distributed queries.

When a query is executed, the system sends it to relevant shards in parallel. Each shard processes its portion of the index and returns local results, which are then merged and ranked to produce the final output. While this approach boosts scalability, it introduces trade-offs. For instance, query accuracy might decrease if the initial shard selection misses relevant clusters, leading to incomplete results. To mitigate this, systems often search slightly more shards than strictly necessary (a technique called over-fetching). Additionally, maintaining cluster assignments as new data is added requires periodic rebalancing, which can be resource-intensive. Despite these challenges, horizontal scaling via sharding remains a foundational strategy for vector databases, enabling them to support applications like recommendation systems or semantic search at scale.