airflow/bin/cli.py
def task_failed_deps(args):
    """
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow task_failed_deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    """
    dag = get_dag(args)
    task = dag.get_task(task_id=args.task_id)
    ti = TaskInstance(task, args.execution_date)

    dep_context = DepContext(deps=SCHEDULER_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    # TODO, Do we want to print or log this
    if failed_deps:
        print("Task instance dependencies not met:")
        for dep in failed_deps:
            print("{}: {}".format(dep.dep_name, dep.reason))
    else:
        print("Task instance dependencies are all met.")
def task_failed_deps(args):
    """
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow task_failed_deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    """
    dag = get_dag(args)
    task = dag.get_task(task_id=args.task_id)
    ti = TaskInstance(task, args.execution_date)

    dep_context = DepContext(deps=SCHEDULER_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    # TODO, Do we want to print or log this
    if failed_deps:
        print("Task instance dependencies not met:")
        for dep in failed_deps:
            print("{}: {}".format(dep.dep_name, dep.reason))
    else:
        print("Task instance dependencies are all met.")
def task_failed_deps ( args ) : dag = get_dag ( args ) task = dag . get_task ( task_id = args . task_id ) ti = TaskInstance ( task , args . execution_date ) dep_context = DepContext ( deps = SCHEDULER_DEPS ) failed_deps = list ( ti . get_failed_dep_statuses ( dep_context = dep_context ) ) # TODO, Do we want to print or log this if failed_deps : print ( "Task instance dependencies not met:" ) for dep in failed_deps : print ( "{}: {}" . format ( dep . dep_name , dep . reason ) ) else : print ( "Task instance dependencies are all met." )
Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow task_failed_deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
Returns the unmet dependencies for a task instance from the perspective of the scheduler ( i . e . why a task instance doesn t get scheduled and then queued by the scheduler and then run by an executor ) . >>> airflow task_failed_deps tutorial sleep 2015 - 01 - 01 Task instance dependencies not met : Dagrun Running : Task instance s dagrun did not exist : Unknown reason Trigger Rule : Task s trigger rule all_success requires all upstream tasks to have succeeded but found 1 non - success ( es ) .