Root Cause Analysis Report

Root Cause Analysis Report: Mobile App Performance Issues

Executive Summary

Our new mobile app is suffering from poor performance, with slow load times and frequent crashes, particularly in low-connectivity environments. This has resulted in negative app store reviews and high user churn, significantly impacting our user growth and retention KPIs. The problem began after our last major release a month ago, which introduced several new features. This report analyzes the root causes and provides recommendations to address the issues and prevent future occurrences.

Problem Statement

• The mobile app is experiencing slow load times and frequent crashes, especially in low-connectivity areas.
• The issue was first observed immediately after the last major release a month ago, which included several new features.
• User reviews in the app store are overwhelmingly negative, citing poor performance and stability.
• High user churn rates are impacting user growth and retention KPIs.
• The app uses a microservices architecture hosted on AWS.

Data Collection and Analysis

• Analyzed app performance metrics (load times, crash rates, API response times) for the past 3 months.
  • Average load times increased by 150% after the last release.
  • Crash rates spiked by 400% post-release, particularly on low-end devices and slow networks.
  • API response times for key endpoints doubled, with high latency and timeout rates.
• Reviewed app store reviews and user feedback.
  • 70% of reviews post-release are 1-2 stars, citing slow performance and crashes.
  • Sentiment analysis shows a sharp decline in user satisfaction and increased frustration.
• Analyzed server logs and monitoring dashboards.
  • Spikes in server CPU and memory usage correlate with increased traffic post-release.
  • Database query times have increased, with some queries taking 5x longer than before.
  • Timeouts and 5xx errors have increased by 200% for several microservices.

Root Cause Determination

1. Unoptimized new features: The new features introduced in the last release were not properly optimized for performance, particularly on low-end devices and slow networks. Inefficient code and resource-intensive operations are causing slowdowns and crashes.

2. Inadequate performance testing: The new features were not thoroughly tested for performance under realistic conditions (low-connectivity, high concurrency, diverse devices). Performance regressions were not caught before release.

3. Microservices scalability issues: Some of the microservices are not scaling well to handle the increased load post-release. Inefficient inter-service communication, lack of caching, and resource contention are causing slowdowns and timeouts.

4. Database performance problems: Slow database queries are a major contributor to increased API response times. Lack of indexing, inefficient queries, and increased data volume are overloading the database.

5. Insufficient monitoring and alerting: The existing monitoring and alerting setup did not provide early warning signs of the performance degradation. Key metrics and thresholds were not properly defined and monitored.

Recommended Corrective Actions

1. Optimize new features:

   • Conduct code reviews and profiling to identify performance bottlenecks in the new features.
   • Optimize resource-intensive operations, reduce network calls, and implement caching where possible.
   • Progressively enhance features based on device capabilities and network conditions.

2. Enhance performance testing:

   • Develop comprehensive performance test suites covering various scenarios (low-connectivity, high concurrency, diverse devices).
   • Integrate performance testing into the CI/CD pipeline to catch regressions early.
   • Conduct load testing to identify scalability limits and bottlenecks.

3. Improve microservices scalability:

   • Review microservice architecture and identify scalability bottlenecks.
   • Optimize inter-service communication, introduce caching, and minimize resource contention.
   • Implement auto-scaling policies based on key metrics to handle variable load.

4. Optimize database performance:

   • Analyze slow queries and optimize them with proper indexing and query restructuring.
   • Introduce caching layers to reduce database load for frequently accessed data.
   • Consider database sharding or partitioning to improve scalability.

5. Enhance monitoring and alerting:

   • Define key performance metrics and set appropriate thresholds for alerts.
   • Implement comprehensive monitoring for app, server, and database performance.
   • Set up proactive alerting and escalation processes to identify and respond to issues quickly.

Lessons Learned

• Performance should be a key consideration throughout the development lifecycle, not just before release.
• Thorough performance testing under realistic conditions is crucial to catch regressions early.
• Microservices architecture requires careful design and optimization for scalability and performance.
• Database performance is often a critical bottleneck and requires continuous optimization.
• Comprehensive monitoring and alerting are essential for quickly identifying and resolving performance issues.

To prevent similar issues in the future, we recommend: - Incorporating performance requirements and testing into the development process. - Regularly reviewing and optimizing microservice architecture for scalability and efficiency. - Continuously monitoring and optimizing database performance. - Investing in robust monitoring and alerting infrastructure to proactively identify issues. - Conducting regular performance audits and capacity planning exercises.

By addressing the identified root causes and implementing the recommended corrective actions, we can improve the mobile app's performance, restore user satisfaction, and mitigate the impact on our growth and retention KPIs. Continued focus on performance optimization and proactive monitoring will be essential to prevent similar issues in the future.