Introduction
The sudden spike in processing time for Datavant's TokenizR service last Tuesday presents 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 implications for the service.
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a minor update deployed on Monday. Impact on approach: If confirmed, we'd focus on the recent changes as a primary area of investigation.
Why it matters: Unusual load patterns can strain the system and cause performance degradation. Expected answer: Usage has been relatively stable, with a slight increase in API calls. Impact on approach: If usage patterns are normal, we'd shift focus to internal system issues rather than external factors.
Why it matters: Changes in data characteristics can impact processing time. Expected answer: No significant changes in data types, but volume has increased by 15%. Impact on approach: A volume increase would lead us to investigate scaling and capacity issues.
Why it matters: Issues in connected systems can ripple through and affect TokenizR's performance. Expected answer: One of our data providers reported some latency issues last week. Impact on approach: This would prompt us to investigate the interaction between TokenizR and its dependencies.
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