How to create a conditional task in Airflow

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Question :

How to create a conditional task in Airflow

I would like to create a conditional task in Airflow as described in the schema below. The expected scenario is the following:

  • Task 1 executes
  • If Task 1 succeed, then execute Task 2a
  • Else If Task 1 fails, then execute Task 2b
  • Finally execute Task 3

Conditional Task
All tasks above are SSHExecuteOperator.
I’m guessing I should be using the ShortCircuitOperator and / or XCom to manage the condition but I am not clear on how to implement that. Could you please describe the solution?

Answer #1:

You have to use airflow trigger rules

All operators have a trigger_rule argument which defines the rule by which the generated task get triggered.

The trigger rule possibilities:

ALL_SUCCESS = 'all_success'
ALL_FAILED = 'all_failed'
ALL_DONE = 'all_done'
ONE_SUCCESS = 'one_success'
ONE_FAILED = 'one_failed'
DUMMY = 'dummy'

Here is the idea to solve your problem:

from airflow.operators.ssh_execute_operator import SSHExecuteOperator
from airflow.utils.trigger_rule import TriggerRule
from airflow.contrib.hooks import SSHHook

sshHook = SSHHook(conn_id=<YOUR CONNECTION ID FROM THE UI>)

task_1 = SSHExecuteOperator(
        task_id='task_1',
        bash_command=<YOUR COMMAND>,
        ssh_hook=sshHook,
        dag=dag)

task_2 = SSHExecuteOperator(
        task_id='conditional_task',
        bash_command=<YOUR COMMAND>,
        ssh_hook=sshHook,
        dag=dag)

task_2a = SSHExecuteOperator(
        task_id='task_2a',
        bash_command=<YOUR COMMAND>,
        trigger_rule=TriggerRule.ALL_SUCCESS,
        ssh_hook=sshHook,
        dag=dag)

task_2b = SSHExecuteOperator(
        task_id='task_2b',
        bash_command=<YOUR COMMAND>,
        trigger_rule=TriggerRule.ALL_FAILED,
        ssh_hook=sshHook,
        dag=dag)

task_3 = SSHExecuteOperator(
        task_id='task_3',
        bash_command=<YOUR COMMAND>,
        trigger_rule=TriggerRule.ONE_SUCCESS,
        ssh_hook=sshHook,
        dag=dag)


task_2.set_upstream(task_1)
task_2a.set_upstream(task_2)
task_2b.set_upstream(task_2)
task_3.set_upstream(task_2a)
task_3.set_upstream(task_2b)
Answered By: Alexis.Rolland

Answer #2:

Airflow has a BranchPythonOperator that can be used to express the branching dependency more directly.

The docs describe its use:

The BranchPythonOperator is much like the PythonOperator except that it expects a python_callable that returns a task_id. The task_id returned is followed, and all of the other paths are skipped. The task_id returned by the Python function has to be referencing a task directly downstream from the BranchPythonOperator task.

If you want to skip some tasks, keep in mind that you can’t have an empty path, if so make a dummy task.

Code Example

def dummy_test():
    return 'branch_a'

A_task = DummyOperator(task_id='branch_a', dag=dag)
B_task = DummyOperator(task_id='branch_false', dag=dag)

branch_task = BranchPythonOperator(
    task_id='branching',
    python_callable=dummy_test,
    dag=dag,
)

branch_task >> A_task 
branch_task >> B_task

EDIT:

If you’re installing an Airflow version >=1.10.3, you can also return a list of task ids, allowing you to skip multiple downstream paths in a single Operator and don’t use a dummy task before joining.

Answered By: Jean S

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