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.
Why Apache Doris for AI Workloads?
Apache Doris is an open-source, real-time analytics database that has evolved beyond traditional OLAP to become a hybrid search and analytics engine purpose-built for the age of AI. At 15.7K GitHub stars and backed by the Apache Software Foundation, it offers a unified platform for structured querying, vector similarity search, and full-text search — all through a familiar MySQL-compatible interface.
🚀 Want to deploy Apache Doris yourself?
Docker configs, system requirements, and installation guides — all on one page.
View Apache Doris Tool Page →Key Capabilities
| ⚡ Real-Time OLAP | Sub-second query latency on billions of rows with real-time data ingestion. |
| 🔍 Vector Search | Native vector similarity search over high-dimensional embeddings (cosine, dot product, L2). |
| 📝 Full-Text Search | Integrated full-text indexing alongside structured and vector columns. |
| 🔄 Hybrid Search | Combine keyword, vector, and structured filters in a single query — perfect for RAG pipelines. |
| 🐬 MySQL Compatible | Drop-in MySQL protocol support — use any MySQL client or ORM to connect. |
What Makes It Different from Other Databases?
Most databases excel at one thing: ClickHouse is great at analytics, Elasticsearch at search, Pinecone at vectors. Apache Doris unifies all three. When building AI agents that need to answer questions like "Find me products similar to X that sold well last quarter", you need vector similarity and structured filtering and keyword matching — Doris handles it in one query, one engine, one deployment.
Use Cases for AI Agents
- RAG Pipelines: Store document embeddings alongside metadata, query by vector similarity with SQL filters.
- AI Observability: Ingest logs, traces, and metrics in real time; search by text, vector, or both.
- Semantic Product Search: Combine user intent vectors with category and price filters.
- Agent Memory: Maintain structured conversation history with semantic recall.
🚀 Ready to try Apache Doris?
Get the Docker Compose file, system requirements, and deployment guide.
View Apache Doris Tool Page →