**Description**

For a DAG that looks like this:

```
A >> B
```

If A fails, B goes into "upstream_failed" state. If a user then marks A "success", B will remain in "upstream_failed" state. It will not automatically start running. This scenario often happens if the failure of A is dealt with outside of Airflow and the user does not want Airflow to run A again. But he usually expect Airflow to run B after A is marked "success".

**Use case / motivation**

After A is marked "success", its downstream tasks B should be cleared automatically and get ready to be scheduled. To avoid changing this behaviour completely, there can be a toggle button next to "Success" that lets the user decide if he wants Airflow to automatically clear downstream tasks that are in "upstream_failed" state.
airflow/models/dag.py
"""A tag name per dag, to allow quick filtering in the DAG view."""
    __tablename__ = "dag_tag"
    name = Column(String(100), primary_key=True)
    dag_id = Column(String(ID_LEN), ForeignKey('dag.dag_id'), primary_key=True)
    def __repr__(self):
        return self.name
class DagModel(Base):
    """Table containing DAG properties"""
    __tablename__ = "dag"
    """
    These items are stored in the database for state related information
    """
    dag_id = Column(String(ID_LEN), primary_key=True)
    root_dag_id = Column(String(ID_LEN))
    
    
    is_paused_at_creation = conf.getboolean('core', 'dags_are_paused_at_creation')
    is_paused = Column(Boolean, default=is_paused_at_creation)
    
    is_subdag = Column(Boolean, default=False)
    
    is_active = Column(Boolean, default=False)
    
    last_parsed_time = Column(UtcDateTime)
    
    last_pickled = Column(UtcDateTime)
    
    
    last_expired = Column(UtcDateTime)