What's causing the sudden 30% increase in failed transactions on Groww's stock trading platform during peak hours?

Introduction

The sudden 30% increase in failed transactions on Groww's stock trading platform during peak hours 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 strategic implications.

I'll approach this problem by first clarifying key details, ruling out external factors, and then diving deep into the product ecosystem, user journey, and relevant metrics. From there, I'll generate data-driven hypotheses, conduct root cause analysis, and propose a comprehensive validation and resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to recent platform changes. Have there been any significant updates or deployments to the trading platform in the past week?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, a new feature was deployed last week. Impact on approach: If true, I'd focus on regression testing and rollback options.

• Considering the specificity of "peak hours," I'm wondering about load-related issues. Can you provide more details on what constitutes peak hours and how they compare to off-peak in terms of transaction volume?

Why it matters: Understanding load patterns is crucial for capacity planning. Expected answer: Peak hours are 9:30 AM to 11:30 AM, with 5x normal volume. Impact on approach: High load during specific times would lead me to investigate scalability solutions.

• Given the 30% increase in failed transactions, I'm curious about the nature of these failures. Are we seeing any specific error messages or patterns in these failed transactions?

Why it matters: Error patterns can quickly narrow down potential causes. Expected answer: Most failures show a "timeout" error. Impact on approach: Timeout errors would direct my focus to backend processing capacity or database query optimization.

• Considering user segments, I'm wondering if this issue affects all users equally. Have we noticed any differences in failure rates across different user types or account levels?

Why it matters: Segmented issues can indicate problems with specific features or user flows. Expected answer: Premium users seem less affected than standard users. Impact on approach: Differences between user segments would lead me to investigate feature-specific issues or capacity allocation.