airflow/utils/cli.py
def action_logging(f):
    """
    Decorates function to execute function at the same time submitting action_logging
    but in CLI context. It will call action logger callbacks twice,
    one for pre-execution and the other one for post-execution.

    Action logger will be called with below keyword parameters:
        sub_command : name of sub-command
        start_datetime : start datetime instance by utc
        end_datetime : end datetime instance by utc
        full_command : full command line arguments
        user : current user
        log : airflow.models.log.Log ORM instance
        dag_id : dag id (optional)
        task_id : task_id (optional)
        execution_date : execution date (optional)
        error : exception instance if there's an exception

    :param f: function instance
    :return: wrapped function
    """
    @functools.wraps(f)
    def wrapper(*args, **kwargs):
        """
        An wrapper for cli functions. It assumes to have Namespace instance
        at 1st positional argument
        :param args: Positional argument. It assumes to have Namespace instance
        at 1st positional argument
        :param kwargs: A passthrough keyword argument
        """
        assert args
        assert isinstance(args[0], Namespace), \
            "1st positional argument should be argparse.Namespace instance, " \
            "but {}".format(args[0])
        metrics = _build_metrics(f.__name__, args[0])
        cli_action_loggers.on_pre_execution(**metrics)
        try:
            return f(*args, **kwargs)
        except Exception as e:
            metrics['error'] = e
            raise
        finally:
            metrics['end_datetime'] = datetime.utcnow()
            cli_action_loggers.on_post_execution(**metrics)

    return wrapper
def action_logging(f):
    """
    Decorates function to execute function at the same time submitting action_logging
    but in CLI context. It will call action logger callbacks twice,
    one for pre-execution and the other one for post-execution.

    Action logger will be called with below keyword parameters:
        sub_command : name of sub-command
        start_datetime : start datetime instance by utc
        end_datetime : end datetime instance by utc
        full_command : full command line arguments
        user : current user
        log : airflow.models.log.Log ORM instance
        dag_id : dag id (optional)
        task_id : task_id (optional)
        execution_date : execution date (optional)
        error : exception instance if there's an exception

    :param f: function instance
    :return: wrapped function
    """
    @functools.wraps(f)
    def wrapper(*args, **kwargs):
        """
        An wrapper for cli functions. It assumes to have Namespace instance
        at 1st positional argument
        :param args: Positional argument. It assumes to have Namespace instance
        at 1st positional argument
        :param kwargs: A passthrough keyword argument
        """
        assert args
        assert isinstance(args[0], Namespace), \
            "1st positional argument should be argparse.Namespace instance, " \
            "but {}".format(args[0])
        metrics = _build_metrics(f.__name__, args[0])
        cli_action_loggers.on_pre_execution(**metrics)
        try:
            return f(*args, **kwargs)
        except Exception as e:
            metrics['error'] = e
            raise
        finally:
            metrics['end_datetime'] = datetime.utcnow()
            cli_action_loggers.on_post_execution(**metrics)

    return wrapper
Decorates function to execute function at the same time submitting action_logging
    but in CLI context. It will call action logger callbacks twice,
    one for pre-execution and the other one for post-execution.

    Action logger will be called with below keyword parameters:
        sub_command : name of sub-command
        start_datetime : start datetime instance by utc
        end_datetime : end datetime instance by utc
        full_command : full command line arguments
        user : current user
        log : airflow.models.log.Log ORM instance
        dag_id : dag id (optional)
        task_id : task_id (optional)
        execution_date : execution date (optional)
        error : exception instance if there's an exception

    :param f: function instance
    :return: wrapped function