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Apache Doris

Real-time analytics database with hybrid vector search and full-text search for AI agents

★ 15,672 GitHub Apache-2.0 databaseanalyticsvector-searcholapreal-timesqlai-agents RAG & Knowledge

Overview

Apache Doris is a real-time analytics and hybrid search database built for AI workloads. It combines OLAP analytics with vector search and full-text search in a single engine, making it ideal for AI agents that need to combine structured data queries with semantic search. With its MySQL-compatible protocol and real-time ingestion capabilities, Doris enables AI-powered applications to query billions of rows with sub-second latency while simultaneously performing vector similarity searches across high-dimensional embeddings.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  apache-doris:
    image: apache/doris:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/apache-doris:/data

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Guides & articles

How to Deploy Apache Doris for AI-Powered Vector Search and Analytics

A practical step-by-step tutorial on deploying Apache Doris with Docker, creating vector search tables, and running hybrid analytics plus similarity queries.

Jul 26, 2026

Apache Doris: Real-Time Analytics and Hybrid Search Database for AI Agents

Discover Apache Doris, the open-source real-time analytics database with built-in vector search and full-text search. Learn how it unifies OLAP, semantic search, and SQL for AI applications.

Jul 26, 2026