What caused the sudden spike in error rates for OpenTrons's Opentrons Protocol Library API last week?

Introduction

The sudden spike in error rates for OpenTrons's Opentrons Protocol Library API last week presents a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Our analysis will follow a structured framework, beginning with clarifying questions to establish context, followed by a comprehensive examination of potential causes, data analysis, hypothesis formation, and ultimately, a robust plan for resolution and future prevention.

Step 1

Clarifying Questions (3 minutes)

• I'm noticing the specificity of the API mentioned. Would you say this issue is isolated to the Protocol Library API, or are other OpenTrons APIs experiencing similar problems?

Why it matters: This helps us determine the scope of the problem and whether it's a localized or systemic issue. Expected answer: The issue is specific to the Protocol Library API. Impact on approach: If isolated, we'll focus on that API's unique characteristics; if widespread, we'll investigate common infrastructure or deployment processes.

• Given the sudden nature of the spike, I'm thinking there might have been a recent deployment or change. Has there been any significant update to the Protocol Library API in the past week?

Why it matters: Recent changes are often the culprit in sudden performance shifts. Expected answer: There was a minor update deployed three days before the spike. Impact on approach: If confirmed, we'll scrutinize the recent changes and their potential impact on error rates.

• Considering user behavior, I'm curious about usage patterns. Has there been any unusual spike in API calls or changes in user behavior coinciding with the error rate increase?

Why it matters: Unusual user activity could strain the system and lead to increased errors. Expected answer: Usage patterns have remained relatively consistent. Impact on approach: If usage is stable, we'll focus more on internal system issues rather than external factors.

• Looking at the error types, I'm wondering about the nature of these errors. Are we seeing a particular type of error dominating the increase, or is it a general rise across various error types?

Why it matters: The nature of the errors can point us towards specific components or issues within the API. Expected answer: There's a significant increase in timeout errors. Impact on approach: This would lead us to investigate performance bottlenecks or resource constraints.