How can we explain the sudden 50% spike in API errors for Instabase's natural language processing service over the last 48 hours?

Introduction

The sudden 50% spike in API errors for Instabase's natural language processing service over the last 48 hours is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for our service.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into our product ecosystem. We'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, we'll develop a comprehensive plan to resolve the issue and prevent future occurrences.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to a recent deployment. Have there been any significant changes or updates to the NLP service in the past week?

Why it matters: Recent changes often correlate with sudden performance shifts. Expected answer: Yes, a new model version was deployed 72 hours ago. Impact on approach: If confirmed, we'd focus on the new model's performance and rollback considerations.

• Considering the scale of the issue, I'm wondering about its distribution. Is this spike uniform across all API endpoints, or are certain endpoints more affected?

Why it matters: Helps narrow down the problem scope and potential causes. Expected answer: The errors are concentrated in sentiment analysis and entity recognition endpoints. Impact on approach: We'd prioritize investigating these specific endpoints and their underlying models.

• Given the nature of NLP tasks, I'm curious about input data. Have there been any notable changes in the types or sources of text being processed recently?

Why it matters: Unusual input can sometimes trigger unexpected behavior in NLP models. Expected answer: No significant changes in input sources, but there's been an increase in non-English text processing. Impact on approach: We'd examine how our model handles multilingual inputs and potential preprocessing issues.

• Considering system health, I'm wondering about our infrastructure. Have there been any recent changes in our cloud resources or scaling policies?

Why it matters: Infrastructure changes can sometimes lead to unexpected performance issues. Expected answer: No major infrastructure changes, but there was a minor update to our auto-scaling policy. Impact on approach: We'd investigate how the new scaling policy might be affecting our service's performance under load.