What caused the sudden spike in error rates for Grafana Cloud queries yesterday afternoon?

Introduction

The sudden spike in error rates for Grafana Cloud queries yesterday afternoon 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 implications for our product and users.

I'll outline my approach to addressing this issue by first gathering essential information, then generating and testing hypotheses, and finally proposing a comprehensive solution strategy. My analysis will cover technical, user behavior, and product-related factors to ensure we leave no stone unturned.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this might be related to a recent deployment. Have there been any significant updates or changes to the Grafana Cloud infrastructure in the past 24-48 hours?

Why it matters: Recent changes often correlate with sudden performance issues. Expected answer: Yes, there was a minor update to the query processing engine. Impact on approach: If confirmed, we'd focus on the update's impact and potential rollback.

• Considering the nature of cloud services, I'm wondering about potential scaling issues. Has there been an unusual increase in query volume or user activity leading up to the error spike?

Why it matters: Unexpected load can strain systems and cause errors. Expected answer: Query volume has been steadily increasing over the past week. Impact on approach: We'd investigate our scaling mechanisms and capacity planning.

• Given that it's a query-related issue, I'm curious about data sources. Have there been any changes or issues reported with the data sources Grafana Cloud is connecting to?

Why it matters: Data source problems can propagate and manifest as query errors. Expected answer: No reported issues with data sources. Impact on approach: We'd shift focus to internal query processing and execution.

• Thinking about user experience, I'm interested in the error distribution. Is this affecting all users equally, or are there specific user segments or query types more impacted?

Why it matters: Helps narrow down the problem scope and potential causes. Expected answer: Errors are more prevalent in complex queries involving multiple data sources. Impact on approach: We'd investigate query optimization and multi-source handling.