What factors are causing the sudden spike in API errors for Geotab's MyGeotab platform this week?

Introduction

The sudden spike in API errors for Geotab's MyGeotab platform this week presents a critical issue that demands immediate attention and a systematic approach to resolution. As we delve into this product root cause analysis, we'll methodically examine potential factors, gather relevant data, and formulate hypotheses to identify the underlying cause of these errors. Our goal is not only to address the immediate problem but also to implement long-term solutions that enhance the platform's reliability and performance.

Step 1

Clarifying Questions (3 minute)

• Given the sudden nature of the spike, I'm wondering about recent changes. Have there been any significant updates or deployments to the MyGeotab platform in the past week?

Why it matters: Recent changes often correlate with sudden performance issues. Expected answer: Yes, a minor update was pushed last Thursday. Impact on approach: If confirmed, we'd prioritize investigating that update's impact.

• Considering the platform's architecture, I'm curious about the error distribution. Are these API errors concentrated in specific endpoints or spread across the entire system?

Why it matters: Localized errors might indicate a specific component issue, while widespread errors could suggest a more systemic problem. Expected answer: Errors are primarily affecting data retrieval endpoints. Impact on approach: This would focus our investigation on data access and processing components.

• Thinking about external factors, has there been any unusual spike in user activity or data volume that could be overwhelming the system?

Why it matters: Unexpected load can often trigger cascading failures in complex systems. Expected answer: User activity has been within normal ranges. Impact on approach: If confirmed, we'd shift focus from scaling issues to internal system problems.

• Reflecting on the error patterns, I'm wondering about the nature of these errors. Are they consistent (e.g., timeout errors) or varied in type?

Why it matters: The error type can provide crucial clues about the underlying issue. Expected answer: Mostly timeout errors with some data integrity issues. Impact on approach: This would guide us to look at both performance bottlenecks and data consistency mechanisms.