Why Vector Databases Matter and How Milvus Stacks Up Against OceanBase

Vector databases have become indispensable in the AI era because massive amounts of unstructured data—images, video, audio, and text—must be stored and queried efficiently, and traditional exact‑match databases cannot handle high‑dimensional similarity searches.

Why a Vector Database Is Needed

Data format shift: billions of non‑text files are generated daily, requiring storage beyond plain text.

Query method change: Unstructured data is represented as high‑dimensional vectors, so similarity search relies on distance metrics such as Manhattan, Euclidean, and cosine rather than exact matches.

High‑performance demand: Vector data consumes large storage and indexing resources, making real‑time retrieval and scalability critical.

Current Vector Database Landscape

Professional Vector Databases

Milvus (open‑source)

Pinecone

Qdrant

Traditional Databases with Vector Extensions

OceanBase (vector‑enhanced version)

Elasticsearch (vector‑enhanced version)

These extensions integrate vector types and indexes into mature relational or distributed database architectures, enabling unified storage and query of structured and vector data.

Why Milvus Is Strong

Functionality: supports similarity search, sparse vectors, batch vectors, filtered and hybrid searches.

Flexibility: multiple deployment modes and SDKs within a unified ecosystem.

Performance: uses HNSW, DiskANN and GPU acceleration for high throughput and low latency.

Scalability: distributed design handles collections exceeding 100 billion vectors.

Milvus vs. OceanBase Vector‑Enhanced Edition

Technical Route

Milvus follows a dedicated vector‑database path with a four‑layer, cloud‑native, storage‑compute separated architecture (access layer, coordinator, worker nodes, storage). OceanBase extends its native distributed engine by adding a Vector data type and vector indexes while preserving its relational core.

Architecture Details

Milvus builds on libraries such as Faiss, HNSW, DiskANN, and SCANN, offering stateless components, dynamic schema, and mixed‑vector‑scalar searches. OceanBase integrates vector types into its storage layer and provides a rich set of index types (HNSW, IVFFlat, DiskANN) and distance algorithms (L2, IP, COSINE, Jaccard).

Feature Comparison

Milvus excels in raw vector‑search performance, GPU support, and handling up to 32 768‑dimensional vectors, though it lacks native SQL. OceanBase supports up to 16 000‑dimensional vectors but offers full SQL compatibility, enabling standard DML operations on vector tables.

Product Positioning

Milvus targets high‑throughput, low‑latency AI workloads such as recommendation systems, finance risk control, and biomedical similarity search. OceanBase positions itself as an AI‑native data foundation that unifies structured and vector data, suitable for enterprise scenarios requiring strong consistency and transactional guarantees.

大家要特别注意，我们拿Milvus和OceanBase向量版做对比只是一个把两种技术路线的向量化存储做对比，不代表A就一定强于B。需要根据具体的业务场景选择合适的引擎，比如我们没有提到的ES，ES的核心优势是全文检索与向量检索的深度融合，更加适合需要同时匹配关键词与语义的场景。

Both solutions have distinct strengths; the optimal choice depends on specific application requirements such as query latency, data consistency, and ecosystem integration.