How to use Boto3 to start a workflow in AWS Glue Data Catalog

In this article, we will see how to start a workflow in AWS Glue Data Catalog using the boto3 library. AWS Glue workflows help orchestrate ETL jobs and crawlers in a defined sequence.

Problem Statement

Use the boto3 library in Python to programmatically start an AWS Glue workflow and handle potential errors during execution.

Prerequisites

Before running this code, ensure you have:

• AWS credentials configured (via AWS CLI, environment variables, or IAM roles)

• boto3 library installed: pip install boto3

• An existing workflow in AWS Glue Data Catalog

• Appropriate IAM permissions for Glue operations

Approach/Algorithm

• Step 1: Import boto3 and botocore exceptions to handle exceptions.

• Step 2: Define a function that takes workflow_name as parameter.

• Step 3: Create an AWS session using boto3. Ensure region_name is configured in your default profile.

• Step 4: Create an AWS client for glue service.

• Step 5: Use start_workflow_run() method with the workflow name.

• Step 6: Return the response containing RunId and metadata.

• Step 7: Handle exceptions for robust error management.

Example

The following code demonstrates how to start a workflow in AWS Glue Data Catalog ?

import boto3
from botocore.exceptions import ClientError

def start_a_workflow(workflow_name):
    session = boto3.session.Session()
    glue_client = session.client('glue')
    try:
        response = glue_client.start_workflow_run(Name=workflow_name)
        return response
    except ClientError as e:
        raise Exception("boto3 client error in start_a_workflow: " + e.__str__())
    except Exception as e:
        raise Exception("Unexpected error in start_a_workflow: " + e.__str__())

# Example usage
print(start_a_workflow("test-daily"))

Output

{'RunId': 'wr_64e880240692fddd5e1b19aed587f856bc20a96f54bc', 'ResponseMetadata': {'RequestId': '782e953b-8ee3-4876-9b2c-cd35e147b513', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Sun, 28 Mar 2021 08:11:02 GMT', 'content-type': 'application/x-amz-json-1.1', 'content-length': '79', 'connection': 'keep-alive', 'x-amzn-requestid': '782e953b-********************************13'}, 'RetryAttempts': 0}}

Key Points

• RunId: Unique identifier for the workflow run instance

• Error Handling: ClientError catches AWS-specific errors, while generic Exception catches other issues

• Session Management: Using boto3.session.Session() provides better control over AWS configurations

• Workflow Status: You can use the returned RunId to monitor workflow progress using get_workflow_run()

Alternative Approach with Explicit Region

If you need to specify the AWS region explicitly ?

import boto3
from botocore.exceptions import ClientError

def start_workflow_with_region(workflow_name, region_name='us-east-1'):
    try:
        glue_client = boto3.client('glue', region_name=region_name)
        response = glue_client.start_workflow_run(Name=workflow_name)
        return response
    except ClientError as e:
        print(f"AWS Error: {e}")
        return None
    except Exception as e:
        print(f"Unexpected error: {e}")
        return None

# Example usage
result = start_workflow_with_region("my-etl-workflow", "us-west-2")
if result:
    print(f"Workflow started with RunId: {result['RunId']}")

Conclusion

Use boto3's start_workflow_run() method to programmatically trigger AWS Glue workflows. Always implement proper error handling and use the returned RunId to track workflow execution status.