# dingodb/dingo

A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data, while meeting the requirements of high concurrency and ultra-low latency.

Repository: https://github.com/dingodb/dingo
Canonical: https://ross.abutalabs.com/products/dingodb-dingo
Homepage: https://www.dingodb.com
Language: Java
License: Apache-2.0
License Family: permissive
Topics: serving, embedding-store, vector-database, mysql-compatibility, embedding-search, key-value-distributed-store, vector-ocean, unified-sql, structured-data, unstructured-data, real-time-semantic-search, hybrid-search
Last push: 2026-07-10T11:13:29+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 8, longevity 100
- inputs: {"age_days": 1785, "days_push": 54, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1701, forks 265 (observed 2026-08-28T04:05:24.297542+00:00)

## What it is
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
- artifact type: service
- maturity: active
- function: vector-database, database, search-engine
- domain: databases, machine-learning, big-data
- platform: self-hosted, cloud, jvm
- tags: distributed-database, mysql-compatible, hybrid-search, embedding-store, real-time-semantic-search, scalar-vector-retrieval, sql, search, retrieval-augmented-generation, linux, docker

## Member repositories
- dingodb/dingo (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:24.297542+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:42.346768+00:00, confidence not recorded.
  - readme: https://github.com/dingodb/dingo (fetched 2026-08-28T04:05:24.297542+00:00, sha 6b6e7bf56e3c)
  - homepage: https://www.dingodb.com (fetched 2026-08-29T11:12:10.911684+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.