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Apache Doris

Apache Doris is a distributed Massively Parallel Processing (MPP) analytical database (analytics database / OLAP engine) designed for real-time data warehousing and interactive query workloads.

  • Distributed MPP columnar analytics database for real-time data warehousing (analytics / OLAP).
  • Supports high-concurrency, low-latency Structured Query Language (SQL) queries on large-scale data (SQL analytics).
  • Provides unified batch and real-time data ingestion from multiple sources (data integration).
  • Implements a shared-nothing architecture with elastic scaling of compute and storage nodes (data platform infrastructure).
  • Offers MySQL-compatible protocol and SQL dialect for ecosystem interoperability (database interoperability).

More About Apache Doris

Apache Doris is an open-source distributed analytical database in the Apache Software Foundation ecosystem, positioned for online analytical processing (OLAP) and real-time data warehousing workloads. It uses a MPP architecture and a columnar storage engine to execute SQL queries over large datasets with low latency, with a focus on interactive analytics scenarios. The project targets use cases such as dashboards, ad hoc queries, multi-dimensional analysis, and user-facing analytics where concurrency levels and response times are important.

The system adopts a shared-nothing architecture (data platform infrastructure) that separates front-end and back-end nodes. Front-end nodes manage metadata, query parsing, optimization, and coordination, while back-end nodes handle data storage, execution of query fragments, and result aggregation. Data is organized into tables, partitions, and buckets, stored in columnar format, and replicated across back-end nodes for availability. The MPP execution engine distributes query tasks across nodes and processes them in parallel, using vectorized execution and cost-based optimization as documented in project materials.

Apache Doris provides a relational model and SQL interface (SQL analytics) with support for standard query constructs such as joins, aggregations, window functions, and subqueries as reflected in the documentation. The project emphasizes high-concurrency query processing for business intelligence, reporting, and interactive analysis. It also supports materialized views (query acceleration) to precompute and store query results, which can reduce latency for common analytical patterns. The storage engine supports columnar compression and indexing techniques that reduce I/O and improve scan performance for analytical workloads.

Data ingestion in Apache Doris covers both batch and streaming scenarios (data integration). Official documentation describes integration with file-based sources such as HDFS or object storage, and streaming sources through connectors and load mechanisms, enabling near real-time data updates. Doris exposes a MySQL-compatible protocol and supports a MySQL-like SQL dialect (database interoperability), which allows reuse of existing MySQL client libraries, JDBC/ODBC drivers, and BI tools that connect via MySQL protocols. This compatibility places Doris within enterprise data stacks that already standardize on SQL-based tools.

In enterprise environments, Apache Doris is deployed as a centralized analytics service that backs dashboards, product analytics, log analysis, and other data-intensive applications. Its distributed design allows horizontal scaling by adding back-end nodes as data volume or query concurrency grows. Role-Based Access Control (RBAC) and integration with external authentication systems described in official materials support multi-tenant or departmental usage. In directory and taxonomy terms, Apache Doris fits under distributed analytical databases, real-time data warehouse engines, and MPP OLAP systems used for interactive business intelligence and user-facing analytics.