Overview

Apache Doris is an open‑source, real‑time analytics and hybrid search database designed specifically for AI agents and applications that need quick, relevant context. It lives at https://doris.apache.org/ and markets itself as a data foundation for the AI era, offering fast SQL access across a range of data types.

In practice, Doris lets you store structured, semi‑structured and unstructured data, such as vectors, text, JSON and log streams, together in a way that can be queried with standard SQL. For example, an AI model might push embeddings and related metadata into a table, and then your application can retrieve the nearest vectors with a single SQL statement that returns the matching IDs, scores and supporting fields. The system also exposes a profile analysis workspace, where you can open a slow‑query log file and the tool marks the problematic steps, giving you visibility into what parts of a query cost time.

The tool is aimed at data teams that run interactive analytics for customers, monitor business health, or analyze telemetry from large‑scale services. A portfolio of case studies shows use by companies like Baidu for log analytics, Xiaomi for device telemetry, and miHoYo for player‑behavior analysis. The database works best when your workloads need sub‑second response times and need to combine structured analytics with search across vectors and text. It is less suited to batch‑heavy ETL pipelines that involve complex transformations outside the scope of a traditional SQL query.

Your typical input to Doris is an SQL query that may include JOINs, aggregations, filters and a nearest‑neighbor clause on vectors. The output is a result set delivered to your client or application in row format, just as you would receive from any relational database. The system integrates with standard JDBC/ODBC drivers, so existing BI and analytics tools can connect without additional adapters.

The site also mentions a free 101 course to learn the fundamentals, and a Slack channel for community support. The roadmap page lists planned features but does not provide release dates or pricing. No details are given about data ingestion frameworks, index types, or higher‑level APIs beyond SQL.