Using a secret key in Amazon Secrets Manager for an Apache Airflow connection - Amazon Managed Workflows for Apache Airflow
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Using a secret key in Amazon Secrets Manager for an Apache Airflow connection

The following sample calls Amazon Secrets Manager to get a secret key for an Apache Airflow connection on Amazon Managed Workflows for Apache Airflow. It assumes you've completed the steps in Configuring an Apache Airflow connection using a Amazon Secrets Manager secret.

Version

  • The sample code on this page can be used with Apache Airflow v1 in Python 3.7.

  • You can use the code example on this page with Apache Airflow v2 in Python 3.10.

Prerequisites

To use the sample code on this page, you'll need the following:

Permissions

Requirements

  • To use this code example with Apache Airflow v1, no additional dependencies are required. The code uses the Apache Airflow v1 base install on your environment.

  • To use this code example with Apache Airflow v2, no additional dependencies are required. The code uses the Apache Airflow v2 base install on your environment.

Code sample

The following steps describe how to create the DAG code that calls Secrets Manager to get the secret.

Apache Airflow v2
  1. In your command prompt, navigate to the directory where your DAG code is stored. For example:

    cd dags
  2. Copy the contents of the following code sample and save locally as secrets-manager.py.

    """ Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from airflow import DAG, settings, secrets from airflow.operators.python import PythonOperator from airflow.utils.dates import days_ago from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook from datetime import timedelta import os ### The steps to create this secret key can be found at: https://docs.aws.amazon.com/mwaa/latest/userguide/connections-secrets-manager.html sm_secretId_name = 'airflow/connections/myconn' default_args = { 'owner': 'airflow', 'start_date': days_ago(1), 'depends_on_past': False } ### Gets the secret myconn from Secrets Manager def read_from_aws_sm_fn(**kwargs): ### set up Secrets Manager hook = AwsBaseHook(client_type='secretsmanager') client = hook.get_client_type(region_name='us-east-1') response = client.get_secret_value(SecretId=sm_secretId_name) myConnSecretString = response["SecretString"] return myConnSecretString ### 'os.path.basename(__file__).replace(".py", "")' uses the file name secrets-manager.py for a DAG ID of secrets-manager with DAG( dag_id=os.path.basename(__file__).replace(".py", ""), default_args=default_args, dagrun_timeout=timedelta(hours=2), start_date=days_ago(1), schedule_interval=None ) as dag: write_all_to_aws_sm = PythonOperator( task_id="read_from_aws_sm", python_callable=read_from_aws_sm_fn, provide_context=True )
Apache Airflow v1
  1. In your command prompt, navigate to the directory where your DAG code is stored. For example:

    cd dags
  2. Copy the contents of the following code sample and save locally as secrets-manager.py.

    from airflow import DAG, settings, secrets from airflow.operators.python_operator import PythonOperator from airflow.utils.dates import days_ago from airflow.contrib.hooks.aws_hook import AwsHook from datetime import timedelta import os ### The steps to create this secret key can be found at: https://docs.aws.amazon.com/mwaa/latest/userguide/connections-secrets-manager.html sm_secretId_name = 'airflow/connections/myconn' default_args = { 'owner': 'airflow', 'start_date': days_ago(1), 'depends_on_past': False } ### Gets the secret myconn from Secrets Manager def read_from_aws_sm_fn(**kwargs): ### set up Secrets Manager hook = AwsHook() client = hook.get_client_type('secretsmanager') response = client.get_secret_value(SecretId=sm_secretId_name) myConnSecretString = response["SecretString"] return myConnSecretString ### 'os.path.basename(__file__).replace(".py", "")' uses the file name secrets-manager.py for a DAG ID of secrets-manager with DAG( dag_id=os.path.basename(__file__).replace(".py", ""), default_args=default_args, dagrun_timeout=timedelta(hours=2), start_date=days_ago(1), schedule_interval=None ) as dag: write_all_to_aws_sm = PythonOperator( task_id="read_from_aws_sm", python_callable=read_from_aws_sm_fn, provide_context=True )

What's next?

  • Learn how to upload the DAG code in this example to the dags folder in your Amazon S3 bucket in Adding or updating DAGs.