CODEWITHSUNDEEP
pythonairflowdirected-acyclic-graphs
Joe1988
Joe1988

Reputation: 161

How to run airflow DAG with conditional tasks

There are total 6 tasks are there.These tasks need to get execute based on one field's(flag_value) value which is coming in input json. If the value of flag_value is true then all tasks need to get execute in such a way that , First task1 then parallell to (task2 & task3 together), parallell to task4, parallell to task5. Once all this finishes then task6. Since am new to airflow and DAG i dont know how to run for this condition.

If the value of flag_value is false then the order is sequential only
task_1 >> task_4 >> task_5 >> task_6.

Below is my code for the DAG.

from airflow import DAG
from datetime import datetime
from airflow.providers.databricks.operators.databricks import DatabricksSubmitRunOperator


default_args = {
    'owner': 'airflow',
    'depends_on_past': False
}

dag = DAG('DAG_FOR_TEST',default_args=default_args,schedule_interval=None,max_active_runs=3, start_date=datetime(2020, 7, 8)) 


#################### CREATE TASK #####################################   

task_1 = DatabricksSubmitRunOperator(
    task_id='task_1',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',
    libraries= [
        {
        'jar': 'dbfs:/task_1/task_1.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_1.driver.TestClass1',
        'parameters' : [
            '{{ dag_run.conf.json }}'       
        ]
    }
)



    
task_2 = DatabricksSubmitRunOperator(
    task_id='task_2',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',   
    libraries= [
        {
        'jar': 'dbfs:/task_2/task_2.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_2.driver.TestClass2',
        'parameters' : [
            '{{ dag_run.conf.json }}'                               
        ]
    }
)
    
task_3 = DatabricksSubmitRunOperator(
    task_id='task_3',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',   
    libraries= [
        {
        'jar': 'dbfs:/task_3/task_3.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_3.driver.TestClass3',
        'parameters' : [
            '{{ dag_run.conf.json }}'   
        ]
    }
) 

task_4 = DatabricksSubmitRunOperator(
    task_id='task_4',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',
    libraries= [
        {
        'jar': 'dbfs:/task_4/task_4.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_4.driver.TestClass4',
        'parameters' : [
            '{{ dag_run.conf.json }}'   
        ]
    }
) 

task_5 = DatabricksSubmitRunOperator(
    task_id='task_5',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',
    libraries= [
        {
        'jar': 'dbfs:/task_5/task_5.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_5.driver.TestClass5',
        'parameters' : [
            'json ={{ dag_run.conf.json }}' 
        ]
    }
) 

task_6 = DatabricksSubmitRunOperator(
    task_id='task_6',
    databricks_conn_id='connection_id_details',
    existing_cluster_id='{{ dag_run.conf.clusterId }}',
    libraries= [
        {
        'jar': 'dbfs:/task_6/task_6.jar'
        }        
        ],
    spark_jar_task={
        'main_class_name': 'com.task_6.driver.TestClass6',
        'parameters' : ['{{ dag_run.conf.json }}'   
        ]
    }
) 


flag_value='{{ dag_run.conf.json.flag_value }}'

#################### ORDER OF OPERATORS ###########################  

if flag_value == 'true':
    
    task_1.dag = dag
    task_2.dag = dag
    task_3.dag = dag
    task_4.dag = dag
    task_5.dag = dag
    task_6.dag = dag
    
    task_1  >> [task_2 , task_3] >> [task_4] >> [task_5]  >> task_6    // Not sure correct 
else:
    task_1.dag = dag
    task_4.dag = dag
    task_5.dag = dag
    task_6.dag = dag
    
    task_1 >> task_4 >> task_5 >> task_6

Upvotes: 1

Views: 11145

Answers (1)

mustafagok
mustafagok

Reputation: 1802

First of all, dependency is not correct, this should work:

task_1 >> [task_2 , task_3] >> task_4 >> task_5  >> task_6

It is not possible to order tasks with list_1 >> list_2, but there are helper methods to provide this, see: cross_downstream.

For branching, you can use BranchPythonOperator with changing trigger rules of your tasks. Not sure about the following code, it could have minor errors, but the idea here works.

task_4.trigger_rule = "none_failed"

dummy = DummyOperator(task_id="dummy", dag=dag)

branch = BranchPythonOperator(
    task_id="branch",
    # jinja template returns string "True" or "False"
    python_callable=lambda f: ["task_2" , "task_3"] if f == "True" else "dummy",
    op_kwargs={"f": flag_value},
    dag=dag)

task_1 >> branch
branch >> [task_2 , task_3, dummy] >> task_4
task_4 >> task_5 >> task_6

There could be better ways to do it.

Upvotes: 3

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