Example:
DAG structure:
```
default_args = {
    'owner': 'dimon',
    'depends_on_past': False,
    'start_date': datetime(2021, 1, 10)
}

dag = DAG(
    'dummy-dag',
    schedule_interval='21 2 * * *',
    catchup=False,
    default_args=default_args
)

DagContext.push_context_managed_dag(dag)
task1 = BashOperator(task_id='task1', bash_command='echo 1')
task2 = BashOperator(task_id='task2', bash_command='echo 2')
task2 << task1
task3 = BashOperator(task_id='task3', bash_command='echo 3')
```

Let’s say you’ve missed the button and typed `--task-regex task4`.
When the backfill starts, firstly it will create a new empty DagRun and puts it in DB. Then the backfill job will go and try to find tasks that match the regex you’ve entered, will not find any obviously and will be stuck in the “running” state together with newly created DagRun forever.

airflow/cli/commands/dag_command.py
filename_without_ext, _, ext = filename.rpartition('.')
    dot.render(filename=filename_without_ext, format=ext, cleanup=True)
    print(f"File {filename} saved")
@cli_utils.action_logging
def dag_state(args):
    """
    Returns the state (and conf if exists) of a DagRun at the command line.
    >>> airflow dags state tutorial 2015-01-01T00:00:00.000000
    running
    >>> airflow dags state a_dag_with_conf_passed 2015-01-01T00:00:00.000000
    failed, {"name": "bob", "age": "42"}
    """
    if args.subdir:
        dag = get_dag(args.subdir, args.dag_id)
    else:
        dag = get_dag_by_file_location(args.dag_id)
    dr = DagRun.find(dag.dag_id, execution_date=args.execution_date)
    out = dr[0].state if dr else None
    conf_out = ''
    if out and dr[0].conf:
        conf_out = ', ' + json.dumps(dr[0].conf)
    print(str(out) + conf_out)
@cli_utils.action_logging
def dag_next_execution(args):