Why has Sisense's Elastic Data Hub seen a 30% drop in query performance over the past month?

Introduction

Sisense's Elastic Data Hub has experienced a significant 30% drop in query performance over the past month, raising concerns about the product's efficiency and user satisfaction. To address this critical issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate and long-term implications for the product and its users.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent update or change. Has there been any significant product or infrastructure changes in the last 1-2 months?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a major update to the data processing pipeline. Impact on approach: If confirmed, we'd focus on the update's impact on query performance.

• Considering user segments, I'm wondering if this is affecting all users equally. Are we seeing this performance drop across all user types and query complexities?

Why it matters: Helps isolate whether it's a global issue or specific to certain use cases. Expected answer: The drop is more pronounced for complex queries and power users. Impact on approach: We'd investigate factors affecting complex query execution and power user workflows.

• Given the specific 30% figure, I'm curious about our measurement accuracy. Has there been any change in how we measure or define query performance in the last month?

Why it matters: Ensures we're dealing with a real performance issue, not a measurement anomaly. Expected answer: No changes in measurement methodology. Impact on approach: Confirms the issue is with actual performance, not metrics.

• Thinking about external factors, have we seen any significant changes in data volume or user behavior over this period?

Why it matters: External changes could explain performance drops without internal issues. Expected answer: Data volume has increased by 15% but user behavior remains consistent. Impact on approach: We'd need to consider if the system is scaling appropriately with data growth.