**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/www/views.py
"""Display DAG dependencies"""
        title = "DAG Dependencies"
        if timezone.utcnow() > self.last_refresh + self.refresh_interval:
            if SerializedDagModel.get_max_last_updated_datetime() > self.last_refresh:
                self._calculate_graph()
            self.last_refresh = timezone.utcnow()
        return self.render_template(
            "airflow/dag_dependencies.html",
            title=title,
            nodes=self.nodes,
            edges=self.edges,
            last_refresh=self.last_refresh.strftime("%Y-%m-%d %H:%M:%S"),
            arrange=conf.get("webserver", "dag_orientation"),
            width=request.args.get("width", "100%"),
            height=request.args.get("height", "800"),
        )
    def _calculate_graph(self):