wudi
c0274bfb4a
### What problem does this PR solve? When an unsupported upstream DDL pauses a streaming job, the existing `ErrorMsg` contains the unsupported-change reason, source table, and observed added/removed columns, but **does not contain the original upstream SQL or identify the mapped Doris target table**. For MySQL, the original DDL is available in the CDC logs only. Users therefore need to find that statement and resolve the target mapping before deciding how to adjust the Doris schema. This PR puts that diagnostic context directly in `ErrorMsg` and makes the recovery guidance consistent between the initial failure and a failed RESUME. | Information | Before | After | | --- | --- | --- | | Original MySQL DDL | Written to CDC logs; absent from `ErrorMsg` | Complete statement appended as `Source DDL: [...]`, including all clauses of a mixed DDL | | Table identification | Source table only | `source_table -> doris_db.actual_mapped_table` | | Same-name MODIFY/CHANGE | `(amount -> amount)` | `(amount)`; real renames retain both names | | PostgreSQL change details | Observed types/OIDs and column differences | Those details are retained, with the mapped target table added; no original SQL is available from the change event | | RESUME guidance | Confirmation explained on the initial failure, but omitted from the resumed failure hint | Both failure paths explain schema review and RESUME confirmation | | Target validation failure | Concrete error such as `missing columns [city]` | Retained alongside the diagnostic context and common recovery hint |
🌍 Read this in other language
English • العربية • বাংলা • Deutsch • Español • فارسی • Français • हिन्दी • Bahasa Indonesia • Italiano • 日本語 • 한국어 • Polski • Português • Română • Русский • Slovenščina • ไทย • Türkçe • Українська • Tiếng Việt • 简体中文 • 繁體中文
?style=for-the-badge){kind=icon}
?style=for-the-badge){kind=icon}
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