Apache Doris is an open-source analytical database built on a massively parallel processing architecture, designed to deliver sub-second query response times on large datasets while maintaining ease of use. Written primarily in Java, it functions as a unified analytics platform supporting both real-time and batch analytical workloads, making it suitable for report generation, ad-hoc queries, data warehouse consolidation, and data lake acceleration scenarios.
The system employs a storage-compute integrated architecture consisting of two core process types: Frontend nodes that handle user requests, query parsing, planning, and metadata management, and Backend nodes responsible for data storage and query execution. Frontend nodes operate in a three-role hierarchy with Master nodes managing metadata writes, Follower nodes handling reads and providing failover capability, and Observer nodes increasing query concurrency without participating in leadership elections. Both Frontend and Backend processes scale horizontally, enabling single clusters to support hundreds of machines and tens of petabytes of storage. Data is partitioned across Backend nodes with multiple replicas, and consistency is maintained through quorum protocols, supporting both same-city and cross-region disaster recovery configurations.
The database maintains high compatibility with MySQL protocol and standard SQL syntax, supporting most MySQL and Hive functions. This compatibility enables seamless integration with existing MySQL client tools and business intelligence platforms. Doris supports multiple data modeling approaches including wide table models, pre-aggregation models, and star or snowflake schemas, with flexibility to flatten data during import through compute engines like Flink or Spark, or to perform modeling operations through views and materialized views.
Core technical capabilities include vectorized query execution, Pipeline execution engines, and real-time data ingestion at second-level latency from upstream transactional databases. The platform supports federated queries across multiple data sources, enabling lakehouse analytics that bridge data lakes and data warehouses while eliminating data silos. Use cases span real-time reporting and decision-making, multidimensional ad-hoc analysis, user behavior and profiling analysis, lakehouse query acceleration, and SQL-based observability for log and event analysis.
The project maintains multilingual documentation with README translations in over twenty languages, reflecting its global user base and commitment to accessibility.
