BLISS — A Billion Scale Index using Iterative Re-partitioning

Milvus is a high-performance, open-source vector database designed for managing massive-scale embedding vectors in AI applications. It excels in similarity search using advanced indexing like HNSW and IVF, supports distributed deployment for billions of vectors, GPU acceleration, and hybrid search combining vector and scalar filters. Key use cases include RAG pipelines, recommendation engines, and image/video retrieval; it outperforms single-node DBs like Faiss in scalability but requires more setup compared to managed options like Pinecone.

Apache's distributed graph database from Baidu with vector search capabilities via HNSW and DiskANN indexes, supporting billion-scale graph + vector workloads for fraud detection and knowledge graphs.

VLDB 2022 paper introducing Manu, a cloud-native vector database management system designed for scalable similarity search in cloud environments with separated storage and compute architecture.

TKDE 2024 paper presenting DIMS, a distributed indexing method for efficient similarity search across metric spaces. The approach enables parallel processing of vector similarity queries at scale.

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

VLDB 2022 paper introducing Hercules, an approach for efficient data series (time series) similarity search at scale, leveraging advanced indexing and pruning techniques for billion-scale sequence datasets.