Gabriel
49bfbdecff
### What problem does this PR solve? Related PR: #69836 Selecting an external Iceberg REST view can fail during query binding with `BadRequestException: ... is not an Iceberg compatible table`, even when the catalog supports reading the view definition. `BindRelation` loads statement snapshots before expanding the view SQL. The snapshot loader calls `PluginDrivenExternalTable.resolveConnectorTableHandle`, which unconditionally asks the connector for a table handle using the view name. A REST catalog can reject that table request with HTTP 400 instead of reporting a missing table. The Iceberg connector only converts `NoSuchTableException` to an empty handle, so the error escapes before the existing view-existence fallback can run. Resolve the object's view classification before requesting a table handle. Known views return no table handle and retain the live view-existence check, so a view deleted outside Doris still invalidates its cached lookup. Snapshot loading can then return an empty view pin and continue to view expansion; the referenced base tables keep their normal snapshot handling. Ordinary table handles and table-load errors retain their existing behavior. This addresses the shared handle-resolution boundary, including callers outside the snapshot loader. #69836 proposes skipping the snapshot-loader entry instead. ### Release note Fix external view queries failing when an Iceberg REST catalog rejects table metadata requests for view names. ### Check List (For Author) - Test - [ ] Regression test - [x] Unit Test - [ ] Manual test - [ ] No need to test or manual test. - Behavior changed: - [ ] No. - [x] Yes. External views no longer issue a table-handle request during resolution. - Does this need documentation? - [x] No. - [ ] Yes. Validation: - `run-fe-ut.sh --run` with `PluginDrivenExternalViewHandleTest`, `PluginDrivenExternalTableTest`, `PluginDrivenMvccExternalTableTest`, and `PluginDrivenMissingTableTest`: **164 tests passed**, with no failures, errors, or skips. - Before the fix, both view cases failed with the reproduced table-endpoint error; the ordinary table cases passed. - `mvn checkstyle:check -pl fe-core`: **0 violations**. - `git diff --check` passed. No live Databricks integration test was run.
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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 Cases
| 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. |
🚀 Core Capabilities
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. |
🔌 Ecosystem
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.
👣 Get Started
🧱 Architecture
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.
📣 Project Updates
| 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. |
🧩 Components
Doris provides connectors and tools for common data engineering workflows.
- Doris Flink Connector
- Doris Spark Connector
- Doris Kafka Connector
- Doris Stream Loader
- Doris Kubernetes Operator
👨👩👧👦 Users
Apache Doris is used in production by thousands of companies worldwide across internet services, finance, retail, logistics, manufacturing, energy, telecommunications, AI, and other industries.
🙌 Contributors
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.
🌈 Community and Support
💬 Contact Us
🧰 Links
- Apache Doris official website: doris.apache.org
- X: @doris_apache
- Medium: @ApacheDoris
📜 License
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