Milvus facilitates scaling by providing a distributed architecture with components designed for horizontal scalability, fault tolerance, and efficient resource utilization. It separates compute and storage, allowing each layer to scale independently based on workload demands. The system uses a microservices design where key components—like coordinators, worker nodes, and object storage—can be deployed across multiple machines, enabling clustering and distributed operations without single points of failure.
For clustering and coordination, Milvus uses a set of coordinator nodes (root coordinators, data coordinators, etc.) to manage metadata, node health, and task scheduling. Worker nodes (query nodes, data nodes) handle specific operations like indexing or search. These components communicate via etcd for distributed consensus and service discovery. For example, adding more query nodes increases parallel search capacity, while data nodes scale ingestion throughput. This decoupling allows teams to allocate resources precisely—like scaling memory-heavy query nodes for latency-sensitive searches while keeping storage nodes optimized for disk I/O.
Load balancing is achieved through dynamic request routing and partitioning. The proxy layer distributes incoming queries across available worker nodes, while sharding splits datasets into partitions managed by different nodes. Milvus automatically redistributes shards when nodes join or leave the cluster, ensuring even load distribution. For instance, a large vector dataset might be split into 16 shards, with each shard processed by a separate query node during a search. Built-in metrics and integration with Kubernetes enable auto-scaling: if CPU usage spikes, the system can spin up additional pods to handle the load.
Distributed index storage relies on object storage (e.g., S3, MinIO) for durability and shared access. Milvus separates hot data (frequently accessed, stored in memory/SSD) from cold data (archived in object storage), optimizing cost and performance. Indexes like IVF_FLAT or HNSW are built in parallel across nodes, with each node handling a subset of the data. During queries, partial results from distributed nodes are merged and reranked. Developers can configure parameters like nlist (number of clusters in IVF indexes) to balance accuracy and scalability—higher nlist values improve precision but require more compute resources during training. This flexibility allows tailoring the system to datasets ranging from millions to billions of vectors.