Automated Remediation for User Transaction Errors

This blog outlines a high-level system for automating the detection, logging. triaging and remediation of user transaction errors in an application. 

Components

• Datadog RUM: Real User Monitoring tool that identifies and alerts on user transaction errors within web applications.
• ServiceNow: IT service management platform used for ticketing, workflow automation, and credential management.
• Ansible: Open-source automation tool for deploying and managing configurations.
• AWS KMS: AWS Key Management Service for secure storage and retrieval of credentials.
• Git Repository: Version control system for storing Ansible playbooks.

Workflow

1. Error Detection: Datadog RUM detects a user transaction error within a web application.
2. Alert Trigger: Datadog RUM sends a webhook notification to ServiceNow containing details of the error.
3. Ticket Creation: ServiceNow automatically creates a ticket upon receiving the webhook notification.
4. Inventory Retrieval: ServiceNow retrieves details of the affected user journey from Datadog.
5. Playbook Selection: ServiceNow analyzes the error details and selects the appropriate Ansible playbook from the Git repository based on predefined rules (e.g., error code, impacted functionality).
6. Credential Retrieval: ServiceNow securely retrieves the required credentials for Ansible execution from AWS KMS.
7. Playbook Execution: ServiceNow triggers the chosen Ansible playbook using an integration tool (e.g., Ansible Tower, custom script) with the retrieved credentials and inventory details.
8. Remediation: The Ansible playbook executes automated tasks to fix the code or configuration causing the user transaction error.

Benefits

• Faster Resolution: Automates error response, reducing time to resolution for user experience issues.
• Improved Efficiency: Streamlines workflow by eliminating manual intervention.
• Increased Accuracy: Reduces human error in identifying and fixing errors.
• Enhanced Security: Utilizes secure communication channels and credential management with AWS KMS.

Considerations

• Error Handling: Design mechanisms to handle failures during ticket creation, inventory retrieval, credential retrieval, and playbook execution.
• Testing: Rigorously test the entire workflow to ensure functionality and error handling.

Conclusion

This high-level design provides a framework for automating the remediation of user transaction errors detected by Datadog RUM. By leveraging ServiceNow and Ansible, this solution streamlines the workflow, improves efficiency, and enhances user experience by expediting issue resolution. The implementation of secure communication channels and credential management with AWS KMS ensures the overall security of the process. This approach empowers organizations to proactively maintain a smooth user experience and minimize downtime caused by user transaction errors.