[fix](fe) Reject static partition assignments for OLAP tables (#70287) Doris accepts both partition-name lists and static partition assignments in INSERT statements. Static assignments such as `PARTITION(dt='2026-11-04')` are supported by external-table write paths; OLAP tables use partition names, or `PARTITION(*)` for automatic overwrite partition detection. ### What problem does this PR solve? Issue Number: None Related PR: #58396 (introduced static partition overwrite syntax) PR Type: Bugfix Problem Summary: An INSERT OVERWRITE on an OLAP table accepts `PARTITION(dt='2026-11-04')` but silently discards the static partition assignments when creating the table sink. The resulting empty partition-name list is treated as a whole-table overwrite, so data in unrelated partitions is removed. Reject static partition assignments before constructing an OLAP sink, for both INSERT INTO and INSERT OVERWRITE, including remote Doris targets. The error directs users to partition names or automatic overwrite partition detection. ### Details - **Frequency:** Reproducible whenever an OLAP INSERT OVERWRITE specifies static partition assignments; ordinary INSERT INTO silently ignores the same assignments. - **Root cause:** The parser preserves assignments separately from partition names, but both OLAP sink creation entry points ignore the assignments. - **Why existing tests missed it:** Parser tests verify that assignments are accepted, and external-table tests exercise supported static partition writes. They do not check that OLAP targets reject the unsupported syntax or preserve unrelated partitions. - **Fix:** Share an admission check between both OLAP sink creation entry points and raise an analysis error before any write or partition replacement. - **Other paths and branches:** External connector sinks retain their static partition assignments. Partition-name inserts, partition-name overwrites, automatic partition detection, and explicit whole-table overwrites retain their behavior.
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Apache Doris is an open-source, real-time analytics and search database built on MPP architecture. It provides fast SQL analytics, lakehouse query acceleration, and hybrid search across structured, text, and vector data.
Explore the official website for the latest product overview, use cases, ecosystem updates, blogs, and user stories. For version updates, see all release notes.
| Use Case | What it provides |
|---|---|
| Customer-Facing Analytics | Ship sub-second interactive analytics to external users. |
| Data Warehousing | Build one real-time warehouse across business domains. |
| Observability | Analyze high-throughput logs, events, and metrics with SQL. |
| Doris for AI | Use vector, text, JSON, and structured search in one SQL engine. |
Apache Doris is built around three core capabilities. The website is the source of truth for detailed product descriptions and examples.
| Capability | What it provides |
|---|---|
| Real-Time Analytics | Streaming ingestion, incremental transformation, and sub-second queries under high concurrency. |
| Lakehouse Analytics | Fast SQL analytics over open table formats such as Iceberg, Delta Lake, and Hudi. |
| Hybrid Search | SQL-native analytics across JSON, full-text, and vector data for AI and search workloads. |
Doris sits at the center of the modern data stack. It connects upstream databases, streaming systems, and lakehouse storage with downstream BI, AI, analytics, and observability tools.
For the latest ecosystem coverage, visit the official website and the connection and integration documentation.
Apache Doris supports both compute-storage coupled and compute-storage decoupled deployments. In decoupled mode, stateless compute groups run over shared object storage, so you can scale compute on demand and isolate workloads.
Learn more in the deployment guide and deployment mode guide.
| Resource | What it provides |
|---|---|
| Community Report | Weekly updates on community activity, merged PRs, contributors, and feature progress. |
| Roadmap 2026 | The 2026 planning discussion for AI and hybrid search, query engine, storage, and data lake work. |
Doris provides connectors and tools for common data engineering workflows.
Apache Doris is used in production by thousands of companies worldwide across internet services, finance, retail, logistics, manufacturing, energy, telecommunications, AI, and other industries.
Apache Doris graduated from the Apache Incubator and became an Apache Top-Level Project in June 2022. Thanks to all community contributors who help build Doris.
Note Some licenses of the third-party dependencies are not compatible with Apache 2.0 License. So you need to disable some Doris features to comply with Apache 2.0 License. For details, refer to the
thirdparty/LICENSE.txt