Providing context in TriggerDagRunOperator

Question

I have a dag that has been triggered by another dag. I have passed through to this dag some configuration variables via the DagRunOrder().payload dictionary in the same way the official example has done.

Now in this dag I have another TriggerDagRunOperator to start a second dag and would like to pass those same configuration variables through.

I have succesfully accessed the payload variables in a PythonOperator like so:

def run_this_func(ds, **kwargs):
    print("Remotely received value of {} for message and {} for day".format(
        kwargs["dag_run"].conf["message"], kwargs["dag_run"].conf["day"])
    )

run_this = PythonOperator(
    task_id='run_this',
    provide_context=True,
    python_callable=run_this_func,
    dag=dag
)

But the same pattern does not work in the TriggerDagRunOperator:

def trigger(context, dag_run_obj, **kwargs):
    dag_run_obj.payload = {
        "message": kwargs["dag_run"].conf["message"],
        "day": kwargs["dag_run"].conf["day"]
    }
    return dag_run_obj

trigger_step = TriggerDagRunOperator(
    task_id="trigger_modelling",
    trigger_dag_id="Dummy_Modelling",
    provide_context=True,
    python_callable=trigger,
    dag=dag
)

It yields a warning regarding the use of provide_context:

INFO - Subtask: /usr/local/lib/python2.7/dist-packages/airflow/models.py:1927: PendingDeprecationWarning: Invalid arguments were passed to TriggerDagRunOperator. Support for passing such arguments will be dropped in Airflow 2.0. Invalid arguments were:
INFO - Subtask: *args: ()
INFO - Subtask: **kwargs: {'provide_context': True}
INFO - Subtask:   category=PendingDeprecationWarning

And this error suggesting I haven't passed the conf :

INFO - Subtask: Traceback (most recent call last):
INFO - Subtask:   File "/usr/local/lib/python2.7/dist-packages/airflow/models.py", line 1374, in run
INFO - Subtask:     result = task_copy.execute(context=context)
INFO - Subtask:   File "/usr/local/lib/python2.7/dist-packages/airflow/operators/dagrun_operator.py", line 64, in execute
INFO - Subtask:     dro = self.python_callable(context, dro)
INFO - Subtask:   File "/home/user/airflow/dags/dummy_responses.py", line 28, in trigger
INFO - Subtask:     "message": kwargs["dag_run"].conf["message"],
INFO - Subtask: KeyError: 'dag_run'

---

A second pattern that I've tried which also hasn't worked is using the params argument like so:

def trigger(context, dag_run_obj):
    dag_run_obj.payload = {
        "message": context['params']['message'],
        "day": context['params']['day']
    }
    return dag_run_obj

trigger_step = TriggerDagRunOperator(
    task_id="trigger_modelling",
    trigger_dag_id="Dummy_Modelling",
    python_callable=trigger,
    params={
        "message": "{{ dag_run.conf['message'] }}",
        "day": "{{ dag_run.conf['day'] }}"
    },
    dag=dag
)

This pattern does not yield an error but instead passes the parameters through to the next dag as strings ie it doesn't evaluate the expressions.

---

How can I access the configuration variables in the TriggerDagRunOperator of the second dag?

Answer 1

In Airflow2.0.x, the equivalent of @efbbrown's answer is:

from airflow.operators.trigger_dagrun import TriggerDagRunOperator

trigger_step = TriggerDagRunOperator(
    task_id="trigger_modelling",
    trigger_dag_id="Dummy_Modelling",
    conf={"message": "{{ dag_run.conf['message'] }}", "day":"{{ 
    dag_run.conf['day'] }}"},
    dag=dag
)

The pull request is described here on GitHub.

See the documentation for external-triggers and for trigger_dagrun.

Here is a YouTube video on the topic that shows the correct imports.

Answer 2

Solved:

The dag_run object is stored in the context and so the configuration variables can be accessed in the python_callable of the TriggerDagRunOperator with this pattern:

def trigger(context, dag_run_obj):
    dag_run_obj.payload = {
        "message": context["dag_run"].conf["message"],
        "day": context["dag_run"].conf["day"]
    }
    return dag_run_obj

trigger_step = TriggerDagRunOperator(
    task_id="trigger_modelling",
    trigger_dag_id="Dummy_Modelling",
    python_callable=trigger,
    dag=dag
)