linrrarity
3ba29643fa
### What problem does this PR solve? Problem Summary: UDF cache cleanup has two problems: 1. Cloud mode does not register a worker for `CLEAN_UDF_CACHE`, so `DROP FUNCTION` cannot clean the cached UDF classloader. 2. UDF caches are cleaned by function signature. If a function is dropped and recreated with the same signature, a delayed cleanup task may delete the new cache, or the recreated function may reuse the stale cache. The FE removes the function metadata first and then submits `CleanUDFCacheTask` asynchronously. It does not wait for the BE cache cleanup result. In addition, the cleanup task does not report a completion result back to the FE. Therefore, even if the task cannot be submitted or the JNI cache cleanup fails, `DROP FUNCTION` still returns success to the client. ```sql CREATE FUNCTION test_udf(INT) RETURNS INT ...; -- implementation V1 SELECT test_udf(1); -- cache V1 DROP FUNCTION test_udf(INT); -- cache cleanup fails or is delayed CREATE FUNCTION test_udf(INT) RETURNS INT ...; -- implementation V2 SELECT test_udf(1); ```` Before this PR, the last query could reuse V1's cached classloader because V1 and V2 had the same signature. A delayed cleanup task for V1 could also remove V2's cache. ### How does this PR fix the problem? - Register the CLEAN_UDF_CACHE worker in cloud mode. - Use the function ID as the key for Java UDF cache lookup, insertion, and cleanup. = Fall back to signature-based cleanup when no valid function ID is provided for compatibility with older FEs. A recreated function receives a new function ID. Therefore, even if the previous function's cache is not successfully removed, the recreated function does not reuse it and can load and execute the correct implementation. A delayed cleanup task also removes only the old function's cache without affecting the recreated function.
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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