What factors are contributing to the increased error rates in Domino Data Lab's Kubernetes-based workspaces over the last two weeks?

Introduction

The increased error rates in Domino Data Lab's Kubernetes-based workspaces over the last two weeks present 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 product ecosystem.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into our product's user journey and metrics. We'll generate data-driven hypotheses, conduct root cause analysis, and develop a comprehensive plan for validation and resolution.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent deployment or configuration change. Has there been any significant update to our Kubernetes infrastructure or workspace configurations in the past month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update to our Kubernetes version. Impact on approach: If confirmed, we'd focus on compatibility issues and rollback options.

• Considering user segments, I'm curious about the distribution of errors. Are we seeing these increased error rates across all user types, or is it concentrated in specific segments or use cases?

Why it matters: Helps narrow down if it's a global issue or specific to certain workflows. Expected answer: The errors are more prevalent in data science workspaces with heavy computational loads. Impact on approach: We'd prioritize investigating resource allocation and scaling mechanisms.

• Given the nature of Kubernetes, I'm wondering about resource utilization. Have we observed any unusual spikes in CPU, memory, or network usage coinciding with these errors?

Why it matters: Resource constraints often lead to increased error rates in containerized environments. Expected answer: There have been some memory pressure alerts, but CPU usage seems normal. Impact on approach: We'd focus on memory management and potential memory leaks in our application.

• Thinking about our monitoring systems, I'm curious if there have been any changes to how we're collecting or interpreting error data. Has our error logging or monitoring setup been modified recently?

Why it matters: Ensures we're not dealing with a false positive due to changes in our observability stack. Expected answer: No recent changes to monitoring, but we did update our error classification rules. Impact on approach: We'd need to validate our error classification and potentially revert recent changes.