Milvus is a high-performance, distributed vector database designed for AI applications such as vector similarity search, RAG pipelines, and hybrid search (dense + sparse). It follows a microservices architecture with components like proxy, rootcoord, datacoord, querycoord, datanode, querynode, indexnode, and streamingnode. The system uses a Write-Ahead Log (WAL) with multiple backends (RocksMQ, Kafka, Pulsar, Woodpecker) for real-time ingestion, and a C++ execution engine (segcore) for vector indexing and query processing. It supports a wide range of index types (IVF, HNSW, DiskANN, SCANN, etc.), scalar indices, full-text search via Tantivy (Rust), and advanced features like CDC replication, RBAC, and multi-tenancy. The codebase is primarily Go and C++, with Python test suites and Rust bindings. It is actively maintained with frequent commits, extensive tests, and a robust client SDK.
Milvus Java SDK is a comprehensive client library for the Milvus vector database, supporting both legacy (v1) and modern (v2) APIs. It covers all core operations: collection/index/partition management, vector insert/upsert/delete/search/query, RBAC, resource groups, CDC, snapshots, and bulk import via Parquet/CSV/JSON files. The SDK uses gRPC for communication, includes connection pooling, schema caching, and iterator support for large result sets. A separate bulk writer module handles cloud storage uploads (S3, Azure, GCP) and import job orchestration. The codebase is professionally structured with a builder pattern for all request/response objects, extensive unit and integration tests, and examples demonstrating real-world usage. While dual API versions increase maintenance, the SDK is actively developed with a rapid release cadence aligned to Milvus server versions.