docs-archive/apache-airflow/2.2.5/_sources/logging-monitoring/callbacks.rst.txt

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Callbacks
=========

A valuable component of logging and monitoring is the use of task callbacks to act upon changes in state of a given task, or across all tasks in a given DAG.
For example, you may wish to alert when certain tasks have failed, or have the last task in your DAG invoke a callback when it succeeds.

.. note::

    Callback functions are only invoked when the task state changes due to execution by a worker.
    As such, task changes set by the command line interface (:doc:`CLI <../usage-cli>`) or user interface (:doc:`UI <../ui>`) do not
    execute callback functions.

Callback Types
--------------

There are five types of task events that can trigger a callback:

=========================================== ================================================================
Name                                        Description
=========================================== ================================================================
``on_success_callback``                     Invoked when the task :ref:`succeeds <concepts:task-instances>`
``on_failure_callback``                     Invoked when the task :ref:`fails <concepts:task-instances>`
``sla_miss_callback``                       Invoked when a task misses its defined :ref:`SLA <concepts:slas>`
``on_retry_callback``                       Invoked when the task is :ref:`up for retry <concepts:task-instances>`
``on_execute_callback``                     Invoked right before the task begins executing.
=========================================== ================================================================


Example
-------

In the following example, failures in any task call the ``task_failure_alert`` function, and success in the last task calls the ``dag_success_alert`` function:

.. code-block:: python

    import datetime
    import pendulum

    from airflow import DAG
    from airflow.operators.dummy import DummyOperator


    def task_failure_alert(context):
        print(f"Task has failed, task_instance_key_str: {context['task_instance_key_str']}")


    def dag_success_alert(context):
        print(f"DAG has succeeded, run_id: {context['run_id']}")


    with DAG(
        dag_id="example_callback",
        schedule_interval=None,
        start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
        dagrun_timeout=datetime.timedelta(minutes=60),
        catchup=False,
        on_success_callback=None,
        on_failure_callback=task_failure_alert,
        tags=["example"],
    ) as dag:

        task1 = DummyOperator(task_id="task1")
        task2 = DummyOperator(task_id="task2")
        task3 = DummyOperator(task_id="task3", on_success_callback=dag_success_alert)
        task1 >> task2 >> task3