如何通过条件任务运行气流DAG [英] How to run airflow DAG with conditional tasks

问题描述

总共有6个任务，这些任务需要根据输入json中出现的一个字段的( flag_value )值执行. 如果 flag_value 的值为true，则所有任务都需要以以下方式执行: 然后，第一任务1平行于(任务2和任务3在一起)，平行于任务4，平行于任务5. 完成所有步骤后，再执行task6. 由于是气流和DAG的新手，我不知道如何在这种情况下运行.

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.

如果 flag_value 的值为false，则该顺序仅是顺序的
task_1>> task_4>> task_5>> task_6.

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

下面是我的DAG代码.

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

    

        
        
    

推荐答案

首先，依赖关系不正确，这应该可以工作:

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

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

不可能用list_1 >> list_2来订购任务，但是有提供此帮助的方法，请参见:

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

对于分支，您可以使用BranchPythonOperator更改触发规则您的任务.不确定以下代码，可能会有一些小错误，但是这里的想法可行.

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

可能会有更好的方法.

这篇关于如何通过条件任务运行气流DAG的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！