jerryzhou196 opened a new pull request, #71318:
URL: https://github.com/apache/airflow/pull/71318
`DataprocSubmitJobOperator.__init__` built `start_trigger_args` from `job`,
`project_id`,
`region`, `gcp_conn_id`, `impersonation_chain` and `request_id` — all of
them template
fields, so in the constructor they still hold the un-rendered Jinja
expression. Those raw
strings were then what the operator advertised (and what got serialized) as
the arguments
for starting directly in the triggerer.
Reproducer on `main`:
```python
op = DataprocSubmitJobOperator(
task_id="submit",
region="{{ params.region }}",
project_id="{{ params.project }}",
job={"reference": {"job_id": "job-{{ ds }}"}},
deferrable=True,
start_from_trigger=True,
)
op.start_trigger_args.trigger_kwargs
# {'job': {'reference': {'job_id': 'job-{{ ds }}'}}, 'project_id': '{{
params.project }}',
# 'region': '{{ params.region }}', ...}
```
Rendering the fields afterwards does not help — `trigger_kwargs` keeps the
parse-time copy.
`start_trigger_args` is now built in `render_template_fields()`, the first
method that runs
after rendering, so it holds the rendered values. It is created with
`dataclasses.replace`
so the class-level template is left untouched for the next task built from
the operator.
Regression tests are added for the rendered values and for the per-task
isolation; the first
fails on `main`.
Removes the class from
`scripts/ci/prek/validate_operators_init_exemptions.txt`.
related: #70296
---
##### Was generative AI tooling used to co-author this PR?
- [X] Yes — Claude Code (Opus 5)
Generated-by: Claude Code (Opus 5) following [the
guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
