Apache Doris is an open-source, real-time analytical database that blends the speed of a columnar MPP (Massively Parallel Processing) engine with the simplicity of a two-process architecture. If you have ever wrestled with slow dashboards, complex ETL to pre-aggregate everything, or an ever-growing menagerie of systems for lakehouse, BI, and observability, Doris offers a clean counterpoint: fast-by-default queries, sub-second fresh ingest, and a MySQL-compatible interface that plugs into the tools you already use. This post explores the repository, the engine beneath it, and how teams deploy Doris for real-world analytics at scale.
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Organization
doris
Apache Doris is an easy-to-use, high performance and unified analytics database.The problem and the solution
Modern analytics stacks often trade simplicity for flexibility: a lake, multiple compute engines, a caching layer, a serving layer, and a separate OLAP system for interactive BI. Each component has tuning knobs, moving parts, and operational cost. Apache Doris tackles this complexity by collapsing the critical path into one system that is easy to operate and fast for both high-concurrency point lookups and heavy analytical scans.
The repository's top-level organization mirrors that focus: fe/ (Frontends) for SQL parsing, planning, metadata, and coordination; be/ (Backends) for columnar storage and vectorized execution; and first-class connectors and tools in docker/, tools/, and docs/.
The README.md lays out the value proposition plainly: extreme speed, high compatibility via the MySQL protocol, and deployments that scale to tens of petabytes.
Key features
- Real-time analytics: Second-level ingestion via stream/batch paths (Kafka, Binlog, HTTP) with upsert-friendly storage and materialized views for low-latency reads.
- Lightning-fast queries: Columnar storage, vectorized execution, MPP parallelism, and runtime filters to cut scanned data; pipeline execution avoids thread explosion
- Federated lakehouse: Query data in Hive, Iceberg, and Hudi and join it with in-cluster tables for unified analytics
- Flexible data models: Duplicate/Unique/Aggregate key models plus strong materialized views and multiple index types (min/max, BloomFilter, inverted) tailored to workloads (README.md).
- MySQL compatibility: Use standard SQL and MySQL-compatible protocol, enabling seamless BI integration and simple client access.
- Elastic, simple operations: Horizontally scale FE and BE; automatic replica recovery; HA across regions; built-in docker/ assets for easy trials.
Why I like it
Doris marries pragmatic design choices with serious performance engineering. Its two-process model (FE/BE) is operationally friendly, while the BE's columnar storage and vectorized pipeline execution deliver the kind of latency and throughput usually reserved for specialized systems.
Because the wire protocol is MySQL, you can point existing BI tools and SQL clients at it without a new driver dance. And when you need a lakehouse, Doris's federated queries over Hive, Iceberg, and Hudi let you span data wherever it sits without extra copy jobs (Apache Doris Docs, 2025).
Under the hood
The FE parses and plans queries, manages metadata and roles (Master/Follower/Observer), and coordinates execution. While the BE stores data in a columnar format and executes vectorized operators across an MPP cluster.
Doris's pipeline execution engine breaks plans into fine-grained tasks to better saturate CPUs while controlling thread counts. Runtime filters (IN, Min/Max, Bloom) are pushed down to scans to prune I/O; adaptive execution uses runtime stats to reshape parts of the plan on the fly.
Materialized views can be auto-refreshed (single-table) or scheduled (multi-table) and are chosen transparently by the optimizer. For storage, Doris supports primary-key upserts and aggregate precomputation to tame hot writes and expensive group-bys.
Build and test helpers live in top-level scripts like build.sh and run-regression-test.sh.
The repository highlights these concerns cleanly: fe/ contains Java code for SQL and metadata; be/ contains the C++ execution engine and storage; docs/ captures architecture guides and operational how-tos; and thirdparty/ and LICENSE.txt make license compliance explicit.
-- Connect with any MySQL-compatible client
-- Example DDL for a real-time events table in Doris
CREATE TABLE demo.events (
event_id BIGINT,
user_id BIGINT,
ts DATETIME,
props JSON
) DUPLICATE KEY(event_id)
DISTRIBUTED BY HASH(event_id) BUCKETS 8
PROPERTIES ("replication_num" = "3");
-- A materialized view for fast daily rollups
CREATE MATERIALIZED VIEW mv_user_daily AS
SELECT user_id, DATE(ts) AS d, COUNT(*) AS events
FROM demo.events
GROUP BY user_id, DATE(ts);
-- Fresh data in, fast queries out
SELECT user_id, d, events
FROM mv_user_daily
WHERE d >= CURDATE() - INTERVAL 7 DAY
ORDER BY events DESC
LIMIT 10;Use cases
Apache Doris excels in scenarios demanding fast, concurrent analytics on fresh data, with impressive real-world deployments across diverse industries showcasing its versatility and performance.
E-commerce and Search Analytics: JD.com processes over 10 billion rows daily with 10,000+ QPS and maintains 150ms average query latency (Li, 2022). Their search platform handles 600 million records in 10-minute windows while supporting real-time A/B testing across multiple dimensions. The implementation replaced their previous Storm-based architecture, achieving better flexibility and data consistency while reducing resource costs.
Enterprise Data Platforms: Major technology companies report significant operational improvements. China Unicom processes 15 billion log entries daily with 1-second query response times, while Tencent achieved substantial cost reductions by consolidating their analytics infrastructure (Apache Doris Users, 2025). Tencent Music has also pioneered LLM-powered OLAP using their SuperSonic framework, which translates natural language queries into SQL and integrates seamlessly with Doris for music analytics (Zhang & Luo, 2023).
Cloud-Scale Operations: Leading cloud providers operate massive deployments with 50+ clusters, 3,000+ nodes, and over 15 petabytes of managed data (Apache Doris Users, 2025). These implementations demonstrate Doris's capability to handle enterprise-scale workloads while maintaining operational simplicity.
Manufacturing and IoT: The platform supports industrial applications requiring real-time monitoring and analytics. Manufacturing companies use Doris for 5G-connected factory data platforms, processing sensor data and production metrics in real-time to optimize operations and predict maintenance needs.
Telecommunications: Telecom operators leverage Doris for network analytics and customer intelligence. One major operator migrated from ClickHouse to Apache Doris to handle 13PB in a single table, citing better MySQL compatibility and superior concurrent query performance as key factors in their decision.
Unified Lakehouse Architecture: Teams increasingly deploy Doris as a unified analytics gateway over data lakes, accelerating queries over Iceberg, Hudi, and Hive formats. The doris-flink-connector and doris-spark-connector enable seamless integration with existing Flink and Spark pipelines, making Doris a natural choice for organizations standardizing on these platforms.
Impact and future potential
Consolidating fast analytics into one operationally simple system is compelling. Doris already powers thousands of production deployments, from consumer internet to finance and manufacturing, with clusters spanning thousands of nodes. The near-term trajectory is clear: deeper lakehouse integrations, richer semi-structured support, and more workload isolation and tiered storage to balance latency and cost. Given its open ecosystem and MySQL compatibility, Doris is well-positioned to be the default real-time warehouse sitting between streaming systems and BI/ML consumers.
Community and contribution
Doris is a top-level Apache Software Foundation project with an active community: hundreds of contributors, a steady release cadence, and detailed discussions in issues and the dev mailing list. The repository includes CONTRIBUTING.md, code of conduct, and links to Doris Improvement Proposals (DSIPs) for major features. You can join via GitHub Discussions, dev@doris.apache.org, and Slack invites on their website.
Usage and license terms
Doris is licensed under the Apache License 2.0, which allows commercial and open-source use, modification, distribution, and patent grants, with standard attribution and NOTICE requirements. See LICENSE.txt and NOTICE.txt. Some optional third-party components carry different licenses; the project maintains thirdparty/ disclosures and guidance in the README.
About the Apache Software Foundation
The Apache Software Foundation (ASF) is a nonprofit steward for hundreds of open-source projects, providing vendor-neutral governance, licensing, and infrastructure. ASF projects are guided by community-over-code principles and meritocratic contribution processes. Doris's top-level status means it has a stable governance home and a clear path for sustained evolution.
Conclusion
If your analytics stack is creaking under latency and complexity, Apache Doris is worth a serious look. Start with the README, scan the architecture in docs/, then trial a cluster with the assets in docker/. Point your BI tool at Doris's MySQL endpoint, ingest a live stream, and try materialized views on a wide table. The experience—fast queries on fresh data, minus the operational sprawl—speaks for itself.
Apache Doris: A Real-Time Data Warehouse That Stays Simple