Getting Error in airflow DAG unsupported operand type(s) for >>: 'list' and 'list'. Sequential and Parallel execution of tasks

user_4457464 0
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I am new to Apache airflow and DAG. There are total 6 tasks in the DAG (task1, task2, task3, task4, task5, task6). But at the time of running the DAG we are getting the error below.

DAG unsupported operand type(s) for >>: 'list' and 'list'

Below is my code for the DAG. Please help. I am new to airflow.

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, 14)) 


#################### 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 }}'   
        ]
    }
) 

#################### ORDER OF OPERATORS ###########################  
 
    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
airflow-scheduler airflow directed-acyclic-graphs python
5 Answers

What is your desired Task Dependency? Do you want to run task_4 after task_2 only or after task_2 and task_3

Based on that answer, use one of the following:

(use this if task_4 should run after both task_2 and task_3 are completed)

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

OR

(use this if task_4 should run after task_2 is completed and task_5 should run after task_3 is completed)

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

A tip, you don't need to do the following:

    task_1.dag = dag
    task_2.dag = dag
    task_3.dag = dag
    task_4.dag = dag
    task_5.dag = dag
    task_6.dag = dag

You can pass the dag parameter to your task itself, example:

task_6 = DatabricksSubmitRunOperator(
    task_id='task_6',
    dag=dag,
    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 }}'   
        ]
    }
)

or use DAG as your context manager as documented in https://airflow.apache.org/docs/stable/concepts.html#context-manager and Point (1) in https://medium.com/datareply/airflow-lesser-known-tips-tricks-and-best-practises-cf4d4a90f8f

user_56915250

Airflow task dependencies can't handle [list]>>[list]. Easiest way around this is to specify your dependencies over multiple lines:

task_1 >> [task_2 , task_3]
task_2 >> [task_4, task_5]
task_3 >> [task_4, task_5]
[task_4 , task_5 ] >> task_6
user_124860060

Same problem here, running Version : 1.10.14+composer GCP:

Code:

i = 0   # iterator
while i < len(LIST_OF_OPS):   # list of operators which I have aggregated beforehand
    LIST_OF_OPS[i] >> LIST_OF_OPS[i+1:i+8] # one task queues up seven more
    i += 7 # in order not to start all of them in parallel, I increase the iterator to one less than the number of tasks started just now; a chain is formed.

This solution allows me to dynamically generate the tasks, and iterate over them. Currently in use for a workflow with 280 tasks.

enter image description here

user_78104230

taskgroup may be useful to address the grouping of some task and adding dynamic dependencies between taskgroups

user_69348960

My solution was to first encapsulate all the leading tasks with a DummyOperator task, then use the same DummyOperator task to initiate the next list of tasks.

enter image description here

with dag:

    task_1 = DummyOperator(
        task_id = 'real_operation_1'
    )

    task_2 = DummyOperator(
        task_id = 'real_operation_2'
    )

    task_3 = DummyOperator(
        task_id = 'dummy_operation'
    )

    task_4 = DummyOperator(
        task_id = 'real_operation_3'
    )

    task_5 = DummyOperator(
        task_id = 'real_operation_4'
    )

    [task_1, task_2] >> task_3 >> [task_4, task_5]
user_97471380
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