Gabriel
035a843648
### What problem does this PR solve? Parquet output relies on implicit page-index and data-page-size defaults, so users cannot explicitly omit indexes or tune the target page size. Add two validated session variables shared by OUTFILE, EXPORT, Hive and Iceberg Parquet writers: - `enable_parquet_write_page_index`: write ColumnIndex/OffsetIndex metadata; defaults to `true`, preserving the current master behavior. - `parquet_write_data_page_size`: target uncompressed data-page size in bytes; defaults to `1048576`, accepts positive 32-bit integers, and applies with indexes enabled or disabled. Keep the existing 128 KiB target cap for Hadoop-framed LZ4. Other codecs use the requested target. Row-group size, dictionary-page size, and data-page/format versions are unchanged. Optional query fields preserve existing defaults when omitted by older FEs. ### Release note Add session controls for Parquet page-index output and target data-page size. ### Validation - 16 BE ASAN tests passed, including real file metadata/readback, index on/off, dictionary on/off, null/all-null columns, Parquet versions, Data Page V1/V2, page offset bounds, Hadoop-framed LZ4 limits, and Hive/Iceberg partition writers. - 9 FE tests passed for defaults, variable forwarding, Thrift serialization, valid bounds, and invalid input. - The new tests failed for the missing settings before implementation. - FE Checkstyle, clang-format 16, build hygiene, and clang-tidy passed. - No cluster SQL run or performance benchmark was performed locally; CI validation is pending. ### Check List (For Author) - Test: - [x] Unit Test - Behavior changed: - [x] Yes. Explicit controls are available; current defaults are preserved. - Does this need documentation? - [x] Yes. English and Chinese documentation: https://github.com/apache/doris-website/pull/4581 ### Check List (For Reviewer who merge this PR) - [ ] Confirm the release note - [ ] Confirm test cases - [ ] Confirm document - [ ] Add branch pick label
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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