How can we explain the sudden spike in API errors for impact.com's tracking and attribution system over the past week?

Introduction

The sudden spike in API errors for impact.com's tracking and attribution system over the past week is a critical issue that demands immediate attention. As we delve into this product root cause analysis, we'll systematically investigate potential factors contributing to this unexpected surge in errors. Our approach will involve clarifying the situation, examining both internal and external factors, and developing data-driven hypotheses to identify the root cause.

Step 1

Clarifying Questions (3 minutes)

• I'm noticing the issue is specific to the tracking and attribution system. Could you confirm if this spike is affecting all clients or a specific subset?

Why it matters: This helps us narrow down whether it's a system-wide issue or isolated to certain integrations. Expected answer: It's affecting a majority of clients, but not all. Impact on approach: If it's affecting all clients, we'd focus on core system issues; if it's specific, we'd investigate client-specific factors.

• Given the sudden nature of the spike, I'm wondering about recent deployments. Have there been any significant updates or changes to the API or related systems in the past two weeks?

Why it matters: Recent changes are often culprits in sudden performance shifts. Expected answer: A minor update was pushed to production last week. Impact on approach: If there was a recent update, we'd prioritize reviewing those changes and their potential impact.

• Considering the nature of tracking and attribution, I'm curious about any changes in traffic patterns. Have you noticed any unusual spikes or drops in overall API usage during this period?

Why it matters: Unusual traffic patterns could indicate external factors or potential abuse. Expected answer: There's been a slight increase in overall API usage, but nothing dramatic. Impact on approach: Significant traffic changes would lead us to investigate capacity issues or potential DDoS attacks.

• Looking at the error types, I'm thinking there might be a pattern. Can you share the most common error codes or messages you're seeing?

Why it matters: Error patterns can point us directly to specific system components or issues. Expected answer: There's an increase in timeout errors and data inconsistency reports. Impact on approach: Specific error types would guide our technical investigation, focusing on relevant system components.