To create an operator with the move_data_mssql function, we must write the function under the execute function and set all the variables as operator parameters like below. ui_color: it is color of the operator on the DAG graph execute function: task will be implemented under this function. Template_fields: they are parameters that we use templates to define them when we call the operator. class AnAirflowOperator(BaseOperator): template_fields = ('param1', 'param2') ui_color = '#A6E6A6' def _init_( self, param1, param2, *args, **kwargs): super(AirflowOperator, self)._init_(*args, **kwargs) self.param1 = param1 self.param2 = param2 def execute(self, context): # the task what we want to execute A general format of an operator can be found below. All operators has an execute function and some helper functions that are related to its task.
In an operator the most important part is execute function. Sensor operators derive from BaseSensorOperator that derive from BaseOperator. As you can see, it is a very common requirement for a pipeline and creating an operator is going to reduce developer development time.Īll operators derive from BaseOperator except sensor operators. The move_data_mysql function has two parts, the first part converts the hive table into text format and the second part runs the apache sqoop to move data into a MsSQL table. In this article we will create an operator for the move_data_mysql task that we used on article “Building Data Pipeline with Airflow” If we have a PythonOperator task that we use in more than one project or use more than once in the same project, we should write it in an operator. If there is no operator to implement a task we use PythonOperator to implement the task in a python function.
If there is no way to combine them we can use cross communication ( XCom) to share information between them.Īn Airflow DAG consists of operators to implement tasks. The RedshiftResumeClusterOperator is the Airflow Redshift Operator that can be used to resume a ‘paused. The Airflow Redshift Operators which come under the category of Redshift Cluster Management operators are as follows: A) Resume a Redshift Cluster. In general if two operators need to share information we should combine them into a single operator. Airflow Redshift Operator: S3ToRedshiftOperator 1) Redshift Cluster Management Operators.
We have to call them in correct certain order on the DAG. They generally implement a single assignment and do not need to share resources with any other operators. An operator in airflow is a dedicated task.
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