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PythonOperator

Метки: airflow apache airflow

Use the PythonOperator to execute Python callables.

airflow/example_dags/example_python_operator.pyVIEW SOURCE

def print_context(ds, **kwargs):
    pprint(kwargs)
    print(ds)
    return 'Whatever you return gets printed in the logs'


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

Passing in arguments

Use the op_args and op_kwargs arguments to pass additional arguments to the Python callable.

airflow/example_dags/example_python_operator.pyVIEW SOURCE

def my_sleeping_function(random_base):
    """This is a function that will run within the DAG execution"""
    time.sleep(random_base)


# Generate 5 sleeping tasks, sleeping from 0.0 to 0.4 seconds respectively
for i in range(5):
    task = PythonOperator(
        task_id='sleep_for_' + str(i),
        python_callable=my_sleeping_function,
        op_kwargs={'random_base': float(i) / 10},
        dag=dag,
    )

    run_this >> task

Templating

When you set the provide_context argument to True, Airflow passes in an additional set of keyword arguments: one for each of the Jinja template variables and a templates_dict argument.

The templates_dict argument is templated, so each value in the dictionary is evaluated as a Jinja template.