diff --git a/airflow/example_dags/tutorial.py b/airflow/example_dags/tutorial.py index 267ae32d7d543..0e31775c7a9a7 100644 --- a/airflow/example_dags/tutorial.py +++ b/airflow/example_dags/tutorial.py @@ -95,7 +95,7 @@ You can document your task using the attributes `doc_md` (markdown), `doc` (plain text), `doc_rst`, `doc_json`, `doc_yaml` which gets rendered in the UI's Task Instance Details page. - ![img](http://montcs.bloomu.edu/~bobmon/Semesters/2012-01/491/import%20soul.png) + ![img](https://imgs.xkcd.com/comics/fixing_problems.png) **Image Credit:** Randall Munroe, [XKCD](https://xkcd.com/license.html) """ ) diff --git a/docs/apache-airflow/core-concepts/dag-run.rst b/docs/apache-airflow/core-concepts/dag-run.rst index d272a1cd0473f..343e50caddd52 100644 --- a/docs/apache-airflow/core-concepts/dag-run.rst +++ b/docs/apache-airflow/core-concepts/dag-run.rst @@ -252,8 +252,16 @@ Using CLI Using UI ^^^^^^^^^^ +In the UI the parameters to trigger a DAG can be better represented via ``params`` definition as described in +:ref:`concepts:params` documentation. Via defined params a proper form for value entry is rendered. + +If the DAG does not define ``params``the form is usually skipped, via the configuration option ``show_trigger_form_if_no_params`` +it is possible to force the display the classic form of a dict-only entry to pass configuration options. + .. image:: ../img/example_passing_conf.png +Please consider to convert such usage to ``params`` as this is the more convenient way and allows also validation of user input. + To Keep in Mind '''''''''''''''' * Marking task instances as failed can be done through the UI. 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-196,7 +196,7 @@ We've developed our tasks, now we need to wrap them in a DAG, which enables us t * only run once in the event that days are missed, and * timeout after 60 minutes -And from the last line in the definition of the ``process-employees`` DAG, we see: +And from the last line in the definition of the ``process_employees`` DAG, we see: .. code-block:: python @@ -221,7 +221,7 @@ Putting all of the pieces together, we have our completed DAG. @dag( - dag_id="process-employees", + dag_id="process_employees", schedule_interval="0 0 * * *", start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, @@ -308,15 +308,15 @@ Putting all of the pieces together, we have our completed DAG. dag = ProcessEmployees() -Save this code to a python file in the ``/dags`` folder (e.g. ``dags/process-employees.py``) and (after a `brief delay `_), the ``process-employees`` DAG will be included in the list of available DAGs on the web UI. +Save this code to a python file in the ``/dags`` folder (e.g. ``dags/process_employees.py``) and (after a `brief delay `_), the ``process_employees`` DAG will be included in the list of available DAGs on the web UI. .. image:: ../img/tutorial-pipeline-1.png -You can trigger the ``process-employees`` DAG by unpausing it (via the slider on the left end) and running it (via the Run button under **Actions**). +You can trigger the ``process_employees`` DAG by unpausing it (via the slider on the left end) and running it (via the Run button under **Actions**). .. image:: ../img/tutorial-pipeline-2.png -In the ``process-employees`` DAG's **Grid** view, we see all that all tasks ran successfully in all executed runs. Success! +In the ``process_employees`` DAG's **Grid** view, we see all that all tasks ran successfully in all executed runs. Success! What's Next? -------------