Accessing Configuration Parameters Passed To Airflow Through Cli

I am trying to pass the following configuration parameters to Airflow CLI while triggering a dag run. Following is the trigger_dag command I am using. airflow trigger_dag -c '{'a

Solution 1:

This is probably a continuation of the answer provided by devj.

1. At airflow.cfg the following property should be set to true: dag_run_conf_overrides_params=True

2. While defining the PythonOperator, pass the following argument provide_context=True. For example:

get_row_count_operator = PythonOperator(task_id='get_row_count', python_callable=do_work, dag=dag, provide_context=True)

3. Define the python callable (Note the use of **kwargs):

def do_work(**kwargs):    
    table_name = kwargs['dag_run'].conf.get('table_name')    
    # Rest of the code

4. Invoke the dag from command line:

airflow trigger_dag read_hive --conf '{"table_name":"my_table_name"}'

I have found this discussion to be helpful.

Solution 2:

There are two ways in which one can access the params passed in airflow trigger_dag command.

1. In the callable method defined in PythonOperator, one can access the params as kwargs['dag_run'].conf.get('account_list')

2. given the field where you are using this thing is templatable field, one can use {{ dag_run.conf['account_list'] }}

The schedule_interval for the externally trigger-able DAG is set as None for the above approaches to work

Solution 3:

In the case you are trying to access the Airflow system-wide config (instead of a DAG config), the following might help:

Firstly, import this

from airflow.configurationimport conf

Secondly, get the value somewhere

conf.get("core", "my_key")

Possible, set a value with

conf.set("core", "my_key", "my_val")

Solution 4:

For my use case, I had to pass arguments to the airflow workflow(or task) using the API. My workflow is as follows: Lambda is triggered when a new file lands in the S3 bucket, the Lambda in turn triggered an airflow DAG and passed the bucket name and the key of the file.

Here's my solution:

s3 = boto3.client('s3')
mwaa = boto3.client('mwaa')

deflambda_handler(event, context):
    # print("Received event: " + json.dumps(event, indent=2))# Get the object from the event and show its content type
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8')
    
    mwaa_cli_token = mwaa.create_cli_token(
        Name=mwaa_env_name
    )
    
    mwaa_auth_token = 'Bearer ' + mwaa_cli_token['CliToken']
    mwaa_webserver_hostname = 'https://{0}/aws_mwaa/cli'.format(mwaa_cli_token['WebServerHostname'])
    
    conf = {'bucket': bucket, 'key': key}
    raw_data = """{0} {1} --conf '{2}'""".format(mwaa_cli_command, dag_name, json.dumps(conf))
    
    # pass the key and bucket name to airflow to initiate the workflow
    requests.post(
            mwaa_webserver_hostname,
            headers={
                'Authorization': mwaa_auth_token,
                'Content-Type': 'text/plain'
                },
            data=raw_data
            )