dingodb/dingo

Health v2 · maintenance only

Adoption not part of the score

1701 stars · 265 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DingoDB is an open-source distributed multi-modal vector database that combines relational (SQL) and vector semantics in a unified platform with MySQL protocol compatibility. It supports scalar-vector hybrid retrieval, real-time index optimization, elastic sharding, and built-in high availability for enterprise-grade deployments.

Use cases

• store and query vector embeddings with SQL
• build semantic search over structured and unstructured data
• run scalar-vector hybrid retrieval queries
• serve low-latency vector search at high concurrency
• deploy a MySQL-compatible vector database cluster
• power RAG applications with an embedding store
• perform real-time semantic search on large datasets

When to choose

• you need combined relational and vector data in one database
• you want MySQL protocol compatibility for existing tooling
• you require horizontal scalability and high availability out of the box
• you need hybrid scalar-plus-vector filtering in queries
• you want disk-based tiered retrieval for massive datasets

When to avoid

• you only need a lightweight embedded vector index for a single app
• your workload is purely relational with no vector search
• you need a fully managed cloud database service
• your team cannot operate a distributed Java-based cluster

Facets

service · maturity active

vector-database database search-engine databases machine-learning big-data self-hosted cloud jvm distributed-database mysql-compatible hybrid-search embedding-store real-time-semantic-search scalar-vector-retrieval sql search retrieval-augmented-generation linux docker

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