jason810496 opened a new pull request, #68696:
URL: https://github.com/apache/airflow/pull/68696
## Why
A Java task's SLF4J logs (e.g. `logger.info(...)`) fell through to stderr,
which the supervisor tags as `ERROR`, so every application log showed up as
`ERROR` in the UI regardless of its real level.
Java-Task code:
```java
@Task(
id = "extract"
)
public long extractValue(Client client) throws InterruptedException {
logger.info("Hello from task");
Object pythonXcom = client.getXCom("python_task_1");
logger.info("Got XCom from Python Task 'python_task_1' {}", pythonXcom);
Connection connection = client.getConnection("test_http");
logger.info("Got con {}", connection);
for(int i = 0; i < 3; ++i) {
logger.info("Beep {}, next time will be {}", i, new Date());
Thread.sleep(2000L);
}
logger.info("Goodbye from task");
return (new Date()).getTime();
}
```
What Airflow UI got:
<img width="1499" height="703" alt="Screenshot 2026-06-18 at 1 31 12 PM"
src="https://github.com/user-attachments/assets/75dbbb4a-a7bd-44a9-9fa1-85deb5fc1678";
/>
## What
- Ship an SLF4J binding inside the SDK (`AirflowSlf4jServiceProvider`)
registered via `META-INF/services`, so `LoggerFactory.getLogger(...)` routes
through the Airflow logs socket carrying each message's real level instead of
writing to stderr.
- Map SLF4J levels to the strings the supervisor (structlog `NAME_TO_LEVEL`)
understands; `TRACE` maps to `debug` since there is no TRACE level on the
Python side.
---
##### Was generative AI tooling used to co-author this PR?
- [x] Yes, with help of Claude Code (Opus 4.8) following [the
guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
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