Using a DAG to import variables in the CLI - Amazon Managed Workflows for Apache Airflow
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Using a DAG to import variables in the CLI

The following sample code imports variables using the CLI on Amazon Managed Workflows for Apache Airflow.


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


  • No additional permissions are required to use the code example on this page.


Your Amazon account needs access to the AmazonMWAAAirflowCliAccess policy. To learn more, see Apache Airflow CLI policy: AmazonMWAAAirflowCliAccess.


  • 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 sample code takes three inputs: your Amazon MWAA environment name (in mwaa_env), the Amazon Region of your environment (in aws_region), and the local file that contains the variables you want to import (in var_file).

import boto3 import json import requests import base64 import getopt import sys argv = sys.argv[1:] mwaa_env='' aws_region='' var_file='' try: opts, args = getopt.getopt(argv, 'e:v:r:', ['environment', 'variable-file','region']) #if len(opts) == 0 and len(opts) > 3: if len(opts) != 3: print ('Usage: -e MWAA environment -v variable file location and filename -r aws region') else: for opt, arg in opts: if opt in ("-e"): mwaa_env=arg elif opt in ("-r"): aws_region=arg elif opt in ("-v"): var_file=arg boto3.setup_default_session(region_name="{}".format(aws_region)) mwaa_env_name = "{}".format(mwaa_env) client = boto3.client('mwaa') mwaa_cli_token = client.create_cli_token( Name=mwaa_env_name ) with open ("{}".format(var_file), "r") as myfile: fileconf ='\n', '') json_dictionary = json.loads(fileconf) for key in json_dictionary: print(key, " ", json_dictionary[key]) val = (key + " " + json_dictionary[key]) mwaa_auth_token = 'Bearer ' + mwaa_cli_token['CliToken'] mwaa_webserver_hostname = 'https://{0}/aws_mwaa/cli'.format(mwaa_cli_token['WebServerHostname']) raw_data = "variables set {0}".format(val) mwaa_response = mwaa_webserver_hostname, headers={ 'Authorization': mwaa_auth_token, 'Content-Type': 'text/plain' }, data=raw_data ) mwaa_std_err_message = base64.b64decode(mwaa_response.json()['stderr']).decode('utf8') mwaa_std_out_message = base64.b64decode(mwaa_response.json()['stdout']).decode('utf8') print(mwaa_response.status_code) print(mwaa_std_err_message) print(mwaa_std_out_message) except: print('Use this script with the following options: -e MWAA environment -v variable file location and filename -r aws region') print("Unexpected error:", sys.exc_info()[0]) sys.exit(2)

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.