Web Stack Outage Postmortem: Lessons Learned from the Great Downtime Debacle

Issue Summary:

Duration: Start Time: January 21, 2024, 03:15 AM (GMT-5) End Time: January 21, 2024, 05:30 AM (GMT-5)

Impact: The outage impacted our primary user authentication service, rendering it inaccessible for 45% of our users. Affected users experienced login failures, leading to disruptions in accessing critical features.

Root Cause: The root cause was identified as an unforeseen surge in traffic due to a sudden increase in user registrations, causing the authentication service to overload and fail.

Timeline:

• Detection Time:

  • January 21, 2024, 03:15 AM (GMT-5)

• Detection Method:

  • Automated monitoring alerts triggered due to a sharp spike in error rates on the authentication service.

• Actions Taken:

  • Immediate investigation initiated on the authentication service logs to identify the cause.

  • Assumed initial hypothesis of a database connection issue and began investigating that avenue.

  • Parallel investigation into server health metrics to rule out performance bottlenecks.

• Misleading Paths:

  • Initial focus on the database connection issue proved misleading, consuming valuable time.

  • Temporary scaling of server resources based on assumptions rather than concrete evidence, exacerbating the issue.

• Escalation:

  • Incident escalated to the DevOps and Database teams as the issue persisted and impacted a growing number of users.

• Resolution:

  • Identified and alleviated the bottleneck by optimizing database queries and scaling the authentication service horizontally.

  • Implemented rate limiting on the registration endpoint to prevent similar surges in the future.

Root Cause and Resolution:

• Cause:

  • Unanticipated traffic surge overwhelming the authentication service.

• Resolution:

  • Optimized database queries to handle increased load efficiently.

  • Horizontal scaling of the authentication service to distribute traffic and improve overall system resilience.

Corrective and Preventative Measures:

• Improvements/Fixes:

  • Implement automated scaling policies to dynamically adjust resources based on traffic.

  • Enhance monitoring to detect unusual spikes in traffic and proactively address potential issues.

  • Conduct regular load testing to identify and mitigate potential performance bottlenecks.

• Tasks:

  • Implement caching mechanisms to reduce database load during peak periods.

  • Conduct a thorough review of the current monitoring setup and enhance alerting thresholds.

  • Document and communicate incident response procedures to streamline future escalations.

In conclusion, the outage served as a valuable lesson in preparing for unforeseen traffic spikes and the importance of accurate root cause analysis. By implementing the identified corrective and preventative measures, we aim to fortify our system against similar incidents and ensure a seamless user experience. This postmortem emphasizes the significance of continuous improvement in monitoring, incident response, and system scalability.