apache/doris

Apache Doris is a real-time analytics and hybrid search database for AI agents.

What it solves

Apache Doris is a real-time analytics and search database designed to handle high-concurrency, sub-second queries across diverse data types. It eliminates the need for multiple specialized databases by providing a single SQL engine for structured data, full-text search, and vector search, which is particularly useful for AI workloads.

How it works

Built on a Massively Parallel Processing (MPP) architecture, Doris supports both compute-storage coupled and decoupled deployments. In decoupled mode, stateless compute groups run over shared object storage, allowing for independent scaling of compute resources and workload isolation.

Who it’s for

It is intended for data engineers and architects building customer-facing analytics, real-time data warehouses, observability platforms (logs, events, metrics), and AI-powered search applications.

Highlights

  • Hybrid Search: Combines SQL-native analytics across JSON, full-text, and vector data.
  • Lakehouse Analytics: Accelerates SQL queries over open table formats like Iceberg, Delta Lake, and Hudi.
  • Real-Time Analytics: Supports streaming ingestion and incremental transformations for fast interactive queries.
  • Broad Ecosystem: Includes connectors for Flink, Spark, Kafka, and a Kubernetes Operator.

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