Using a DAG to write custom metrics in CloudWatch - Amazon Managed Workflows for Apache Airflow
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Using a DAG to write custom metrics in CloudWatch

You can use the following code example to write a directed acyclic graph (DAG) that runs a PythonOperator to retrieve OS-level metrics for an Amazon MWAA environment. The DAG then publishes the data as custom metrics to Amazon CloudWatch.

Custom OS-level metrics provide you with additional visibility about how your environment workers are utilizing resources such as virtual memory and CPU. You can use this information to select the environment class that best suits your workload.


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


To use the code example on this page, you need the following:


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


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

Code example

  1. In your command prompt, navigate to the folder where your DAG code is stored. For example:

    cd dags
  2. Copy the contents of the following code example and save it locally as Replace MWAA-ENV-NAME with your environment name.

    from airflow import DAG from airflow.operators.python_operator import PythonOperator from airflow.utils.dates import days_ago from datetime import datetime import os,json,boto3,psutil,socket def publish_metric(client,name,value,cat,unit='None'): environment_name = os.getenv("MWAA_ENV_NAME") value_number=float(value) hostname = socket.gethostname() ip_address = socket.gethostbyname(hostname) print('writing value',value_number,'to metric',name) response = client.put_metric_data( Namespace='MWAA-Custom', MetricData=[ { 'MetricName': name, 'Dimensions': [ { 'Name': 'Environment', 'Value': environment_name }, { 'Name': 'Category', 'Value': cat }, { 'Name': 'Host', 'Value': ip_address }, ], 'Timestamp':, 'Value': value_number, 'Unit': unit }, ] ) print(response) return response def python_fn(**kwargs): client = boto3.client('cloudwatch') cpu_stats = psutil.cpu_stats() print('cpu_stats', cpu_stats) virtual = psutil.virtual_memory() cpu_times_percent = psutil.cpu_times_percent(interval=0) publish_metric(client=client, name='virtual_memory_total', cat='virtual_memory',, unit='Bytes') publish_metric(client=client, name='virtual_memory_available', cat='virtual_memory', value=virtual.available, unit='Bytes') publish_metric(client=client, name='virtual_memory_used', cat='virtual_memory', value=virtual.used, unit='Bytes') publish_metric(client=client, name='virtual_memory_free', cat='virtual_memory',, unit='Bytes') publish_metric(client=client, name='virtual_memory_active', cat='virtual_memory',, unit='Bytes') publish_metric(client=client, name='virtual_memory_inactive', cat='virtual_memory', value=virtual.inactive, unit='Bytes') publish_metric(client=client, name='virtual_memory_percent', cat='virtual_memory', value=virtual.percent, unit='Percent') publish_metric(client=client, name='cpu_times_percent_user', cat='cpu_times_percent', value=cpu_times_percent.user, unit='Percent') publish_metric(client=client, name='cpu_times_percent_system', cat='cpu_times_percent', value=cpu_times_percent.system, unit='Percent') publish_metric(client=client, name='cpu_times_percent_idle', cat='cpu_times_percent', value=cpu_times_percent.idle, unit='Percent') return "OK" with DAG(dag_id=os.path.basename(__file__).replace(".py", ""), schedule_interval='*/5 * * * *', catchup=False, start_date=days_ago(1)) as dag: t = PythonOperator(task_id="memory_test", python_callable=python_fn, provide_context=True)
  3. Run the following Amazon CLI command to copy the DAG to your environment's bucket, then trigger the DAG using the Apache Airflow UI.

    $ aws s3 cp s3://your-environment-bucket/dags/
  4. If the DAG runs successfully, you should see something similar to the following in your Apache Airflow logs:

    [2022-08-16, 10:54:46 UTC] {{}} INFO - cpu_stats scpustats(ctx_switches=3253992384, interrupts=1964237163, soft_interrupts=492328209, syscalls=0)
    [2022-08-16, 10:54:46 UTC] {{}} INFO - writing value 16024199168.0 to metric virtual_memory_total
    [2022-08-16, 10:54:46 UTC] {{}} INFO - {'ResponseMetadata': {'RequestId': 'fad289ac-aa51-46a9-8b18-24e4e4063f4d', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'fad289ac-aa51-46a9-8b18-24e4e4063f4d', 'content-type': 'text/xml', 'content-length': '212', 'date': 'Tue, 16 Aug 2022 17:54:45 GMT'}, 'RetryAttempts': 0}}
    [2022-08-16, 10:54:46 UTC] {{}} INFO - writing value 14356287488.0 to metric virtual_memory_available
    [2022-08-16, 10:54:46 UTC] {{}} INFO - {'ResponseMetadata': {'RequestId': '6ef60085-07ab-4865-8abf-dc94f90cab46', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': '6ef60085-07ab-4865-8abf-dc94f90cab46', 'content-type': 'text/xml', 'content-length': '212', 'date': 'Tue, 16 Aug 2022 17:54:45 GMT'}, 'RetryAttempts': 0}}
    [2022-08-16, 10:54:46 UTC] {{}} INFO - writing value 1342296064.0 to metric virtual_memory_used
    [2022-08-16, 10:54:46 UTC] {{}} INFO - {'ResponseMetadata': {'RequestId': 'd5331438-5d3c-4df2-bc42-52dcf8d60a00', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'd5331438-5d3c-4df2-bc42-52dcf8d60a00', 'content-type': 'text/xml', 'content-length': '212', 'date': 'Tue, 16 Aug 2022 17:54:45 GMT'}, 'RetryAttempts': 0}}
    [2022-08-16, 10:54:46 UTC] {{}} INFO - Done. Returned value was: OK
    [2022-08-16, 10:54:46 UTC] {{}} INFO - Marking task as SUCCESS. dag_id=dag-custom-metrics, task_id=memory_test, execution_date=20220816T175444, start_date=20220816T175445, end_date=20220816T175446
    [2022-08-16, 10:54:46 UTC] {{}} INFO - Task exited with return code 0