How can you re-run upstream task if a downstream task fails in Airflow (using Sub Dags)

问题

I have an airflow dag that extracts data and performs validation. If the validation fails, it needs to re-run the extract. If the validation succeeds its continues.

I've read people saying that sub dags can solve this problem, but I can't see any example of this. I've tried using a sub dag, but come across the same problem as trying to do it in one DAG.

How can I get all tasks in the Sub DAG to re-run if one of them fails?

I have the following DAG/sub dag details:

maindag.py

default_args = {
  'owner': 'airflow',
  'depends_on_past': False,
  'start_date': start_date,
  'retries': 3,
  'retry_delay': timedelta(minutes=5),
  'sla': timedelta(hours=sla_hours)
}

main_dag = DAG(
  dag_id,
  default_args=default_args,
  schedule_interval='30 14 * * *',
  max_active_runs=1,
  concurrency=1)

task1 = BashOperator(...)

task2 = SubDagOperator(
  task_id=sub_dag_task_id,
  subdag=sub_dag(dag_id, sub_dag_task_id, start_date, main_dag.schedule_interval),
  dag=main_dag)

task3 = BashOperator(...)

subdag.py

def sub_dag(parent_dag_name, task_id, start_date, schedule_interval):
  dag = DAG(
    '%s.%s' % (parent_dag_name, task_id),
    schedule_interval=schedule_interval,
    start_date=start_date,
    )

  task1 = BashOperator(...)

  task2 = BashOperator(...)

  task3 = BashOperator(...)

  task1 >> task2 >> task3

  return dag

In the sub dag, if task 3 fails, I want task 1 to run again even though it has succeeded. Why is this so hard to do??!

回答1:

I've found a solution to this by creating a retry on callback method in main dag:

(original source: https://gist.github.com/nathairtras/6ce0b0294be8c27d672e2ad52e8f2117 )

from airflow.models import DagBag

def callback_subdag_clear(context):
    """Clears a subdag's tasks on retry."""
    dag_id = "{}.{}".format(
        context['dag'].dag_id,
        context['ti'].task_id
)
    execution_date = context['execution_date']
    sdag = DagBag().get_dag(dag_id)
    sdag.clear(
        start_date=execution_date,
        end_date=execution_date,
        only_failed=False,
        only_running=False,
        confirm_prompt=False,
        include_subdags=False)

Then for my task that runs subdagoperator, it has:

on_retry_callback=callback_subdag_clear,

It now clears out the task instance history of each task and re-runs each task in the sub dag up to the number of retries in the main dag.

回答2:

There's a simpler alternative. Full snippet

Instead of

dag_id = "{}.{}".format(
    context['dag'].dag_id,
    context['ti'].task_id
)
sdag = DagBag().get_dag(dag_id)

you can do

task = context['task']
sdag = task.subdag

Why?

Because (most likely) your task is related to a SubDagOperator which has a subdag attribute.

I had issues using the solution by Alistair. When I was trying to call clear on the sdag variable I will get an exception because it was None.

I drilled down the issue to improper parsing of Dags while filling the DagBag, which I could not figure out. Instead, I found a workaround by looking into what was passed in the context and noticing that it has a reference to the task which has the subdag attribute as long as it comes from a SubDag operator

来源：https://stackoverflow.com/questions/49008716/how-can-you-re-run-upstream-task-if-a-downstream-task-fails-in-airflow-using-su

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