What factors are contributing to the unexpected 30% spike in error rates for Starburst's data lake connectivity module this month?

Introduction

The unexpected 30% spike in error rates for Starburst's data lake connectivity module this month presents a critical challenge that demands immediate attention and a thorough root cause analysis. As we delve into this issue, we'll employ a systematic approach to identify, validate, and address the underlying factors contributing to this significant performance degradation.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in the system. Has there been any significant update or deployment to the data lake connectivity module in the past month?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update two weeks ago. Impact on approach: If confirmed, we'd focus on the changes made in that update.

• Considering the scale of the issue, I'm wondering about its distribution. Is this 30% spike uniform across all users and data sources, or are there specific segments more affected?

Why it matters: Helps narrow down potential causes and affected areas. Expected answer: The spike is more pronounced for users accessing larger datasets. Impact on approach: We'd investigate scalability issues and large dataset handling.

• Given the nature of data lake connectivity, I'm curious about any changes in data volume or types. Have there been any significant increases in data ingestion or new data types added recently?

Why it matters: Changes in data characteristics can strain existing systems. Expected answer: There's been a 20% increase in semi-structured data ingestion. Impact on approach: We'd focus on how the system handles increased semi-structured data.

• Thinking about external factors, I'm considering potential changes in user behavior. Have we seen any shifts in usage patterns or peak times for data access?

Why it matters: Unusual usage patterns can reveal underlying issues or changes in user needs. Expected answer: There's been a 15% increase in concurrent users during peak hours. Impact on approach: We'd investigate system performance under increased concurrent load.