Pad71 opened a new issue, #72519:
URL: https://github.com/apache/airflow/issues/72519
### Description
# Task-level deadline alerts
Airflow 3 introduced **Deadline Alerts**, replacing the previous SLA
mechanism. This is a useful improvement, but currently deadlines can only be
defined at the **DAG run level**.
I would like to propose extending Deadline Alerts to support **task-level
deadlines** as well.
### Use case/motivation
## Motivation
A DAG run can consist of many tasks with very different expected completion
times. A single deadline for the entire DAG does not provide enough granularity
to detect delays in individual parts of the pipeline.
For example:
```text
DAG: daily_etl
extract_data → expected by 01:00
transform_data → expected by 02:00
load_data → expected by 03:00
publish_report → expected by 04:00
```
With a DAG-level deadline of 04:00, a problem with `extract_data` might only
become visible much later. If the task has its own deadline, the problem can be
detected and alerted immediately.
This is particularly useful for:
* long-running ETL/ELT pipelines
* data availability monitoring
* pipelines with different deadlines for individual stages
* operational monitoring where downstream tasks depend on upstream data
* detecting bottlenecks before they cause the entire DAG run to miss its
deadline
## Proposed API
The existing DAG-level mechanism could be extended with an optional
task-level deadline configuration.
For example:
```python
@dag(
deadline=DeadlineAlert(
interval=timedelta(hours=4),
callback=...
)
)
def my_dag():
extract = PythonOperator(
task_id="extract",
...,
deadline=DeadlineAlert(
interval=timedelta(hours=1),
callback=...
)
)
```
Alternatively, the deadline could be defined directly on the task:
```python
extract = PythonOperator(
task_id="extract",
...,
deadline=timedelta(hours=1),
deadline_callback=...
)
```
The exact API is of course open for discussion.
## Expected behavior
A task-level deadline should be evaluated independently for each task
instance.
For example, if:
```text
extract_data deadline = 01:00
transform_data deadline = 02:00
DAG run deadline = 04:00
```
and `extract_data` has not completed by 01:00, the configured deadline
callback should be triggered, even though the overall DAG run is still within
its deadline.
The DAG-level deadline should continue to work independently.
## Why not use execution_timeout?
`execution_timeout` is useful, but it solves a different problem.
`execution_timeout` answers:
> How long is a task allowed to run?
A deadline answers:
> By what point should the task have completed?
For example, a task might normally run for 10 minutes but must be completed
by 01:00 because downstream processing starts afterwards. An execution timeout
does not express this requirement.
## Backwards compatibility
This should be backwards compatible:
* existing DAG-level deadlines continue to work unchanged
* task-level deadlines are optional
* a DAG can use DAG-level deadlines, task-level deadlines, or both
* no deadline configuration means no deadline alert
I believe task-level deadlines would make the new Deadline Alert
functionality significantly more useful for real-world production pipelines,
especially for data engineering and operational workloads.
### Related issues
_No response_
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of
Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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