What it is

Apache Doris is an open-source, real-time analytics and search database built on a massively parallel processing (MPP) architecture. It runs on the JVM and is written in Java, licensed under Apache-2.0, and has been developed in the open for roughly nine years, with the repository showing about 15,900 stars and 3,900 forks. It lives in the data infrastructure ecosystem, where it sits alongside streaming systems, lakehouse table formats, and business intelligence tooling rather than replacing any single one of them. The project describes itself as a real-time analytics and hybrid search database for AI agents.

The concrete problem it solves is the fragmentation that appears when analytical workloads span several kinds of data. Structured rows, JSON documents, full-text fields, and vector embeddings traditionally require different engines, which means separate clusters, separate query languages, and separate operational stories. Apache Doris puts those into one SQL engine so that hybrid search across structured, text, and vector data is expressed as SQL rather than as a pipeline between systems. It also addresses latency: streaming ingestion, incremental transformation, and sub-second queries under high concurrency are handled inside the same database, which removes the need for a separate serving layer between a warehouse and a customer-facing application.

Key capabilities

• Real-time analytics: streaming ingestion, incremental transformation, and sub-second query response under high concurrency.
• Lakehouse analytics: fast SQL analytics directly over open table formats such as Iceberg, Delta Lake, and Hudi.
• Hybrid search: SQL-native analytics across JSON, full-text, and vector data in a single engine.
• MPP query engine, the architectural basis for the parallel execution described across the capability list.
• AI-oriented search, positioned for AI agents through the Doris for AI use case.
• Observability workloads, with support for analyzing high-throughput logs, events, and metrics using SQL.
• Query acceleration over lakehouse storage, listed as a distinct capability alongside analytics and search.

Who uses it and how

• Teams building customer-facing analytics, shipping sub-second interactive analytics to external users.
• Data warehousing groups constructing one real-time warehouse that spans multiple business domains.
• Observability teams analyzing high-throughput logs, events, and metrics through SQL instead of a separate log store.
• AI and search workloads that need vector, text, JSON, and structured search combined inside one SQL engine.
• Data platform teams connecting upstream databases, streaming systems, and lakehouse storage to downstream BI, AI, analytics, and observability tools.

Getting started

The README points to an official download page,