What caused the sudden spike in error rates for FinQuery's stock screener API calls yesterday afternoon?

Introduction

The sudden spike in error rates for FinQuery's stock screener API calls yesterday afternoon is a critical issue that demands immediate attention and thorough analysis. As we delve into this problem, we'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

I'll begin by gathering essential information, formulating data-driven hypotheses, and conducting a comprehensive root cause analysis. Throughout this process, we'll prioritize user experience, system stability, and the overall integrity of FinQuery's services.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to market volatility. Was there any significant market event yesterday that might have triggered an unusual volume of API calls?

Why it matters: Unusual market conditions could strain our system capacity. Expected answer: There was a major earnings report release. Impact on approach: If confirmed, we'd need to investigate our system's ability to handle peak loads.

• Considering the specificity of "stock screener" API calls, I'm wondering about recent changes. Have we deployed any updates to the stock screener functionality in the past week?

Why it matters: Recent changes could introduce bugs or performance issues. Expected answer: A minor update was pushed two days ago. Impact on approach: If true, we'd prioritize reviewing the recent code changes and their potential impact.

• Given that it's an API issue, I'm curious about our third-party data providers. Have we received any notifications about service disruptions from our data sources?

Why it matters: External data issues could propagate through our system. Expected answer: No reported issues from data providers. Impact on approach: If confirmed, we'd focus more on internal systems and code.

• Thinking about system health, I'm interested in our monitoring tools. Did we observe any unusual patterns in server load or network traffic leading up to the error spike?

Why it matters: Gradual changes in system metrics could indicate underlying issues. Expected answer: There was a gradual increase in CPU usage over the past 24 hours. Impact on approach: This would prompt us to investigate potential resource constraints or inefficiencies.

• Considering user behavior, I'm wondering about any changes in usage patterns. Have we seen any significant shifts in the types of queries or the volume of requests from specific user segments?

Why it matters: Changes in user behavior could stress certain parts of our system. Expected answer: There's been an increase in complex queries from institutional clients. Impact on approach: We'd need to examine our query optimization and potentially adjust our service tiers.