What factors contributed to the sudden 30% decrease in Lusha's API integration success rate last week?

Introduction

The sudden 30% decrease in Lusha's API integration success rate last week is a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, we'll follow a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to a recent deployment. Has there been any significant update or change to the API in the past week or two?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update to the API authentication process. Impact on approach: If confirmed, we'd focus on the authentication flow and related components.

• Considering the scale of the decrease, I'm wondering about the distribution across different user segments. Is this 30% decrease uniform across all API users, or are certain segments more affected?

Why it matters: Helps identify if the issue is global or specific to certain user groups. Expected answer: The decrease is more pronounced among enterprise users. Impact on approach: We'd prioritize investigating enterprise-specific integrations or usage patterns.

• Given the nature of API integrations, I'm curious about any changes in error messages or types. Have you noticed any shift in the types of errors being reported by users or logged in our systems?

Why it matters: Error patterns can provide crucial clues about the underlying issue. Expected answer: There's been an increase in timeout errors and authentication failures. Impact on approach: We'd focus on network infrastructure and authentication mechanisms.

• Considering potential external factors, have there been any significant changes in API usage patterns or volume in the days leading up to the decrease?

Why it matters: Unusual usage patterns could indicate user behavior changes or external factors. Expected answer: Usage volume has remained relatively stable. Impact on approach: We'd shift focus from capacity issues to internal system problems.