Introduction
SendBird's Chat API message delivery rate drop of 15% over the past week is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for the product.
I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product's user journey and metrics. From there, I'll form data-driven hypotheses, conduct root cause analysis, and propose a comprehensive resolution plan.
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 to the message queueing system. Impact on approach: If confirmed, I'd focus on the update's impact on message delivery.
Why it matters: Helps identify if it's a global issue or specific to certain use cases. Expected answer: The drop is more pronounced for high-volume enterprise customers. Impact on approach: I'd prioritize investigating scalability and load handling for large accounts.
Why it matters: Unusual activity could strain the system and affect delivery rates. Expected answer: There's been a 20% increase in overall message volume. Impact on approach: I'd focus on system capacity and potential bottlenecks under increased load.
Why it matters: External service disruptions could directly impact message delivery. Expected answer: No major outages reported, but there have been some intermittent latency issues. Impact on approach: I'd investigate how these latency spikes might be affecting message delivery and explore redundancy options.
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