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.
Kronotop is a distributed database system built on FoundationDB that provides Redis-compatible (RESP2/RESP3) interfaces for multiple data models including key-value (stash), document (bucket with BSON storage and querying), and sorted sets (zmap). It features ACID transactions, secondary indexing (including vector search via HNSW), clustering, sharding, replication, and namespace isolation for multi-tenancy. The architecture is modular with clear separation between network handling (Netty), protocol processing, service layers (transaction, session, data services), and storage (volume-based segment store). Key entry points include the server entry point (KronotopInstanceStarter), CLI (kronotop-cli), and control tool (kronotop-ctl). The system relies on external dependencies such as FoundationDB, Netty, Jackson, Lettuce, Guava, JVector, and Typesafe Config. The codebase demonstrates strong engineering practices with reusable components like versionstamp utilities, retry mechanisms, and protocol handling.