#TRIGGER AIRFLOW DAG FROM PYTHON HOW TO#
#TRIGGER AIRFLOW DAG FROM PYTHON CODE#

#TRIGGER AIRFLOW DAG FROM PYTHON CODE#

On the function page, copy the following code and replace the following with the names of your resources: YOURENVIRONMENTNAME The name of your Amazon MWAA environment. to use Airflow's REST API, to trigger a specific dag (externalĮvent system directly calls airflow's REST API)ī. Open the AWS Lambda console at Choose your Lambda function from the Functions list. This approach has a downside - it will hold airflow's resources forever, and while it can be good for several lone use case, it's definitely NOT scalalble to use with hundreds of dags.Ī. As suggested in the answer by you can run a sensor (there are many supported, HTTP, FTP, FTPS and etc.) in a endless loop in a pre-defined interval (every 30s, every mintute and such.) and when sensor is fired up (task successfully completes), you can trigger a specific dag (with TriggerDagRunOperator).Nervertheless, it's definitely achievable, in multiple ways:

#TRIGGER AIRFLOW DAG FROM PYTHON HOW TO#

You also know how to transfer data between tasks with XCOMs a must-know concept in Airflow. We’ve gone through the most common PythonOperator, and now you know how to run any Python function in a DAG task. Then you can call the connection by matching the _CONN_ID.Īs already mentioned in the question itself, airflow is not an event based triggered system, and it's main paradigm is to do a pre-scheduled batch processing. As you’ve seen today, Apache Airflow is incredibly easy for basic ETL pipeline implementations. Popular Use Case It’s a great tool for orchestrating ETL pipelines and monitor them as they run. High Level What does it do It allows you to schedule tasks to run, run them in a particular order, and monitor / manage all of your tasks. Task_id='run_process_with_external_files', What is Airflow It is a platform to programmatically author, schedule and monitor workflows. Invoke_lambda_function = AwsLambdaInvokeFunctionOperator( If 'Content-Length' not in recent_header.keys():Ĭonn.hmset('header_dict', current_header) Recent_header = conn.hgetall('header_dict') Key.decode() if isinstance(key, bytes) else key: code() if isinstance(value, bytes) else valueįor key, value in ()Ĭonn = redis.Redis(host='redis', port=6379) ''' uses redis to check if a new file is on the server''' ''' DAG for operational District heating '''įrom .operators.aws_lambda import AwsLambdaInvokeFunctionOperatorįrom .http import HttpSensor In my current example, I am using redis to persist the current file state. HttpSensor works only once during the scheduled time window. This article aims to provide an overview of Apache Airflow along with presenting multiple examples in Python that. )īut Airflow requires a defined data_interval. 1 Apache Airflow is an open-source Workflow Automation & Scheduling platform. The basic concept of Airflow does not allow triggering a Dag on an irregular interval.Īctually, I want to trigger a dag every time a new file is placed on a remote server (like HTTPS, sftp, s3. Parameters: triggerdagid (str) the dagid to trigger pythoncallable (python callable) a reference to a python function that will be called while.

Trigger airflow dag from python