Why has Firebolt's query performance for large datasets declined by 15% over the past month?

Introduction

Firebolt's query performance decline for large datasets is a critical issue that demands immediate attention. As we analyze this 15% performance drop over the past month, we'll follow a systematic approach to identify, validate, and address the root cause. Our framework will cover issue identification, hypothesis generation, validation, and solution development, considering both short-term fixes and long-term strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in our data processing pipeline. Have there been any updates to our ETL processes or data ingestion methods in the last 1-2 months?

Why it matters: Changes in data processing could directly impact query performance. Expected answer: Yes, we implemented a new data compression algorithm. Impact on approach: If confirmed, we'd focus on optimizing the new compression method.

• Considering the specificity of "large datasets," I'm wondering about our definition. What's the threshold for classifying a dataset as "large" in Firebolt's context?

Why it matters: Ensures we're analyzing the correct segment of queries. Expected answer: Datasets over 1TB are considered large. Impact on approach: We'd narrow our investigation to queries on 1TB+ datasets.

• Given the 15% decline, I'm curious about the baseline. What was the average query performance for large datasets before this decline?

Why it matters: Establishes the severity of the issue and sets a benchmark for improvement. Expected answer: Average query time was 2 seconds for large datasets. Impact on approach: We'd aim to bring performance back to at least 2.3 seconds or better.

• Thinking about potential changes in usage patterns, has there been a significant increase in concurrent queries or users over the past month?

Why it matters: Increased load could explain performance degradation. Expected answer: User base grew by 20% but concurrent queries remained stable. Impact on approach: We'd focus less on scaling issues and more on query optimization.