Why are customers reporting slower query performance in Fabric's lakehouse product compared to the previous quarter?

Introduction

The reported slower query performance in Fabric's lakehouse product compared to the previous quarter is a critical issue that demands immediate attention. This problem directly impacts user experience and could potentially lead to customer churn if not addressed promptly. I'll approach this analysis systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a seasonal impact. Have we seen similar performance dips in previous years during this quarter?

Why it matters: Helps distinguish between cyclical patterns and new issues. Expected answer: No significant seasonal patterns observed in previous years. Impact on approach: If seasonal, we'd focus on capacity planning; if not, we'd investigate recent changes.

• Considering the nature of lakehouse architecture, I'm wondering about data volume changes. Has there been a significant increase in data ingestion or query complexity recently?

Why it matters: Data volume and complexity directly impact query performance. Expected answer: 20% increase in data volume over the last quarter. Impact on approach: If confirmed, we'd focus on optimizing data storage and indexing strategies.

• Given the comparison to the previous quarter, I'm curious about any recent product updates. Have there been any significant changes to the query engine or data processing pipeline in the last 3-6 months?

Why it matters: Recent changes could introduce performance regressions. Expected answer: A major update to the query optimizer was released two months ago. Impact on approach: If confirmed, we'd prioritize investigating the impact of this update.

• Thinking about user behavior, I'm wondering if the reported slowdown is uniform across all users. Are we seeing this issue across all customer segments or is it more pronounced for specific user types or query patterns?

Why it matters: Helps narrow down the scope of the problem and identify potential user-specific issues. Expected answer: The slowdown is more significant for users running complex analytical queries. Impact on approach: If confirmed, we'd focus on optimizing performance for specific query types.