subsequent_task > job_2_sensor > job_2_operator.This can be done using the bit shift operator > that has the convention, With the tasks defined, we need to define the order in which tasks should be run. from import PythonOperator from import PythonSensor # Define functions def job_1(): print("Perform job 1") def job_2(): print("Perform job 2") def sensor_job(): print("Sensor Job") # Define jobs job_1_operator = PythonOperator( task_id="task_job_1", python_callable=job_1, dag=dag, ) job_2_sensor = PythonSensor( task_id="task_job_2_sensor", python_callable=sensor_job, dag=dag, poke_interval=180, ) job_2_operator = PythonOperator( task_id="task_job_2", python_callable=job_2, dag=dag, ) 3. In the example below, I define 2 Python functions job_1 and job_2, a dummy function sensor_job for sensor, and link it to operator and sensor tasks. Task names are specified in task_id and must be unique, while the Python function is specified with the python_callable argument. Tasks should be linked to their respective Python functions. Below is a sample of how a DAG can be instantiated, import datetime from airflow.models import DAG # Parameteres WORFKLOW_DAG_ID = "my_example_dag" WORFKFLOW_START_DATE = datetime.datetime(2022, 1, 1) WORKFLOW_SCHEDULE_INTERVAL = "* * * * *" WORKFLOW_EMAIL = WORKFLOW_DEFAULT_ARGS = # Initialize DAG dag = DAG( dag_id=WORFKLOW_DAG_ID, schedule_interval=WORKFLOW_SCHEDULE_INTERVAL, default_args=WORKFLOW_DEFAULT_ARGS, ) 2. More parameters can be found in the Apache Airflow documentation. email: email addresses to send emails upon failure or retry.schedule_interval: interval to run DAG, can be defined with datetime.timedelta, or a string following CRON schedule format.start_date: start date to run DAG, this must be a historical date. There are a few important parameters to define, To define the DAG, it is simply a DAG object with parameters passed in default_args. Now, let’s dive into how we can use Apache Airflow with Python! 1. Lastly, you need to define the dependency between tasks, specifying the sequence of tasks. Next, you need to define the operator tasks and sensor tasks by linking the tasks to Python functions. First, you need to define the DAG, specifying the schedule of when the scripts need to be run, who to email in case of task failures, and so on. There are 3 main steps when using Apache Airflow. Now you’re ready to use Apache Airflow! Using Airflow with Python Installing AirflowĪirflow can be installed as a Python package and have its database instantiated with the following commands, $ pip install apache-airflow $ airflow db init DAG is a type of graph that is directed (there is a sequence to task execution) and acyclic (tasks cannot form a cycle such that it is never-ending). Tasks can be chained for execution in a Directed Acyclic Graph (DAG). The two types of tasks are usually executed in sensor > operator sequence where sensor task can pause execution of operator task until a condition is met. Sensor can check the state of any process or data structure Sensor: pause execution of dependent tasks until some criterion is met.PythonOperator can perform any function that can be executed in Python, there are also BashOperator, EmailOperator, SimpleHttpOperator, etc. A task instance runs a single task and has a state such as running, success, failed, skipped, or up for retry, etc. Task is defined as a unit of work and can be thought of as a Python job that runs a script.
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