Amazon Managed Workflows for Apache Airflow (MWAA)¶

Amazon Managed Workflows for Apache Airflow (MWAA) is a managed service for Apache Airflow that lets you use your current, familiar Apache Airflow platform to orchestrate your workflows. You gain improved scalability, availability, and security without the operational burden of managing underlying infrastructure.

Note: Unlike Airflow’s built-in operators, these operators are meant for interaction with external Airflow environments hosted on AWS MWAA.

Prerequisite Tasks¶

To use these operators, you must do a few things:

Create necessary resources using AWS Console or AWS CLI.
Install API libraries via pip.
pip install 'apache-airflow[amazon]'
Detailed information is available Installation of Airflow®
Setup Connection.

Generic Parameters¶

aws_conn_id

Reference to Amazon Web Services Connection ID. If this parameter is set to None then the default boto3 behaviour is used without a connection lookup. Otherwise use the credentials stored in the Connection. Default: aws_default

region_name

AWS Region Name. If this parameter is set to None or omitted then region_name from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

verify

Whether or not to verify SSL certificates.

False - Do not validate SSL certificates.
path/to/cert/bundle.pem - A filename of the CA cert bundle to use. You can specify this argument if you want to use a different CA cert bundle than the one used by botocore.

If this parameter is set to None or is omitted then verify from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

botocore_config

The provided dictionary is used to construct a botocore.config.Config. This configuration can be used to configure Avoid Throttling exceptions, timeouts, etc.

Example, for more detail about parameters please have a look botocore.config.Config¶

{
    "signature_version": "unsigned",
    "s3": {
        "us_east_1_regional_endpoint": True,
    },
    "retries": {
      "mode": "standard",
      "max_attempts": 10,
    },
    "connect_timeout": 300,
    "read_timeout": 300,
    "tcp_keepalive": True,
}

If this parameter is set to None or omitted then config_kwargs from AWS Connection Extra Parameter will be used. Otherwise use the specified value instead of the connection value. Default: None

Note

Specifying an empty dictionary, {}, will overwrite the connection configuration for botocore.config.Config

Operators¶

Trigger a DAG run in an Amazon MWAA environment¶

To trigger a DAG run in an Amazon MWAA environment you can use the MwaaTriggerDagRunOperator

In the following example, the task trigger_dag_run triggers a DAG run for the DAG hello_world in the environment MyAirflowEnvironment.

amazon/tests/system/amazon/aws/example_mwaa.py

trigger_dag_run = MwaaTriggerDagRunOperator(
    task_id="trigger_dag_run",
    env_name=env_name,
    trigger_dag_id=trigger_dag_id,
)

Sensors¶

Wait on the state of an AWS MWAA DAG Run¶

To wait for a DAG Run running on Amazon MWAA until it reaches one of the given states, you can use the MwaaDagRunSensor

In the following example, the task wait_for_dag_run waits for the DAG run created in the above task to complete.

amazon/tests/system/amazon/aws/example_mwaa.py

wait_for_dag_run = MwaaDagRunSensor(
    task_id="wait_for_dag_run",
    external_env_name=env_name,
    external_dag_id=trigger_dag_id,
    external_dag_run_id="{{ task_instance.xcom_pull(task_ids='trigger_dag_run')['RestApiResponse']['dag_run_id'] }}",
    poke_interval=5,
)

References¶

AWS boto3 library documentation for MWAA