MILVUS-SPEC

Milvus LF AI Graduate vector DB billions vectors.

Definition

Milvus 2.x cloud-native architecture: separation Compute (Query Nodes + Index Nodes + Data Nodes) + Storage (Object Storage S3/MinIO + Message Queue Kafka/Pulsar + Metadata Etcd). Components: Proxy (handles requests), Coordinators (Root, Data, Query, Index coordinators), Worker Nodes (Query, Data, Index, Streaming Nodes). Index types: 11+ (FLAT brute force exact, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, RHNSW_FLAT/SQ/PQ, ANNOY, DiskANN, AUTOINDEX automated selection, GPU indexes RAFT). Distance: L2, IP, Cosine, Jaccard, Hamming, Tanimoto. Hybrid search: multi-vector field search + scalar field filter integrated query DSL. Scalar fields: INT, FLOAT, DOUBLE, BOOL, VARCHAR, JSON, ARRAY. Multi-tenancy: Database concept (~Kubernetes namespace) + Collections + Partitions. SDKs: Python, Java, Go, Node.js, Ruby, .NET. Customers: eBay, Shopee, Wal-Mart, IKEA, Bilibili. Zilliz Cloud SaaS Free Tier (1M vectors) + Standard $0.30/CU (Compute Unit) hour pricing. Milvus Lite (embedded SQLite-like 2024).

Origin

Milvus open-source initial 2019 ; Zilliz founded 2017 by Charles Xie + ex-Oracle engineers ; Milvus 2.0 cloud-native architecture 2022 ; LF AI Foundation Graduate 2023 ; $103M Series B August 2022.

Example in context

Walmart e-commerce uses Milvus 2.x cluster ~50 Kubernetes nodes: ~1.5B product images embedded via custom CLIP model fine-tuned retail, visual similar products search ~100ms latency, IVF_PQ index 3x compression, Zilliz Cloud managed offering for production reliability.

Related terms

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