It has the following code if you check the source code: if self.provide_context:Ĭontext = self. To define **kwargs in your function header. What you can use in your jinja templates. If set to true, Airflow will pass a set of keyword arguments that canīe used in your function. If you check docstring of PythonOperator for provide_context : MySqlToGoogleCloudStorageOperator has no parameter provide_context, hence it is passed in **kwargs and you get Deprecation warning. You would mostly use provide_context with PythonOperator, BranchPythonOperator. Params parameter ( dict type) can be passed to any Operator. Provide_context is not needed for params. How can I access the configuration variables in the TriggerDagRunOperator of the second dag? This pattern does not yield an error but instead passes the parameters through to the next dag as strings ie it doesn't evaluate the expressions. I have succesfully accessed the payload variables in a PythonOperator like so: def run_this_func(ds, **kwargs): Now in this dag I have another TriggerDagRunOperator to start a second dag and would like to pass those same configuration variables through. I have passed through to this dag some configuration variables via the DagRunOrder().payload dictionary in the same way the official example has done. Trigger_dag_id='sparktestingforstandalone', # Or any other DAGĮxecution_date="'.I have a dag that has been triggered by another dag. Trigger_next_iter = TriggerDagRunOperator( # use your context information and add it to the # You can add the data of dag_run.conf in here def dag_run_payload(context, dag_run_obj): Which will trigger a DagRun of your defined DAG. You cant make loops in a DAG Airflow, by definition a DAG is a Directed Acylic Graph.īut you can use TriggerDagRunOperator. I wanted to trigger few tasks single time, few task multiple times in a dag, i dont want to create that many instance as i have done here, need to do it in elegant manner as mentioned. There are multiple other task like t7 also to be triggered. I wanted to run t3,t4,t6 task parallelly in a loop for n times and sleep 30 seconds between each runs. I know this is messy, is there an elegant way to implement the same. Start_op > t7 > s1 > t7 > s2 > t7 > s3 > end_op S12 = PythonOperator(task_id="delay_sleep_task_30sec_12",Įnd_op = DummyOperator(task_id='end_spark_runs', dag=dag) S11 = PythonOperator(task_id="delay_sleep_task_30sec_11", S10 = PythonOperator(task_id="delay_sleep_task_30sec_10", S9 = PythonOperator(task_id="delay_sleep_task_30sec_9", S8 = PythonOperator(task_id="delay_sleep_task_30sec_8", S7 = PythonOperator(task_id="delay_sleep_task_30sec_7", S6 = PythonOperator(task_id="delay_sleep_task_30sec_6", S5 = PythonOperator(task_id="delay_sleep_task_30sec_5", S4 = PythonOperator(task_id="delay_sleep_task_30sec_4", S3 = PythonOperator(task_id="delay_sleep_task_30sec_3", S2 = PythonOperator(task_id="delay_sleep_task_30sec_2", S1 = PythonOperator(task_id="delay_sleep_task_30sec_1", S1 = PythonOperator(task_id="delay_sleep_task_30sec", Start_op = DummyOperator(task_id='start_spark_runs',dag=dag) Linux_command_7 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task7.py ' Linux_command_6 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task6.py ' Linux_command_5 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task5.py ' Linux_command_4 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task4.py ' Linux_command_3 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task3.py ' The pythoncallable argument will be removed and a conf argument will be added to make it explicit that you can pass a. Linux_command_2 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task2.py ' Seems like the TriggerDagRunOperator will be simplified in Airflow 2.0. Linux_command_1 = 'spark-submit -conf "=20" -conf "=2" -executor-memory 1G -driver-memory 2G /hadoopData/bdipoc/poc/python/task1.py ' from airflow import DAGįrom _operator import SSHOperatorįrom import DummyOperatorįrom _operator import PythonOperatorįrom _hook import SSHHookĭag = SSHHook('conn_ssh_sparkstandalone') I am new to airflow and wanted to run a bunch of task in a loop, however i am facing cyclic error.
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