Introduction

When working with AWS Glue, one common frustration is that jobs might report as succeeded even when underlying issues, such as SQL errors, occur. This post will discuss a simple yet effective solution using sys.exit to ensure your job statuses accurately reflect their outcomes.

In AWS Glue, a job may complete with a status of 'succeeded' even if there are hidden failures in your SQL queries or other parts of the script. This often happens because the script does not exit with a failure status code, leading to misleading job reports.

Using sys.exit and logging

There is a quick solution for this. sys.exit is universally recognized and built into the runtime. Taken from the python docs, zero is considered successful termination while any other code is considered to be an error.

sys.exit is particularly useful in exception handling blocks where you catch errors but still need the job status to show as failed. This is good for us because it no longer shows the job as successful unless its entire run was successful.

The only limitation is that AWS glue will give a generic error for the glue job like Command failed with exit code 1 which we can't customize. For this reason, we're left with logging statements. Using logging statements, we can also target that we want our cloudwatch logs to go to /aws-glue/python-jobs/error. Furthermore, we're able to send a custom error.

import sys
import logging

# Set up logging
logging.basicConfig(level=logging.ERROR)
logger = logging.getLogger()

try:
    # Potentially problematic code
    execute_sql_query()
except Exception as e:
    logger.error(f"Failed to execute job: {str(e)}")
    sys.exit(1)  # Exit with status 1 to indicate failure

The way that I use this is that I see that failure appear in the glue job and use it as a signal to check the cloudwatch logs. This also gives me confidence that if a job has succeeded then it has truly succeeded.

Conclusion

In conclusion, while AWS Glue's default behavior may lead to misleading job success statuses, using sys.exit combined with robust logging provides a reliable way to ensure job outcomes are accurately reported. This approach not only helps in quickly identifying issues through detailed logs but also reinforces the reliability of your data processing workflows. Always ensure your logs are detailed enough to provide insights into failures, and consider integrating monitoring tools to enhance error visibility further.