Sole Retriever Memory Leak Resolution

Overview

Sole Retriever, a platform helping sneaker enthusiasts find and enter shoe raffles, was experiencing critical downtime issues due to memory leaks in their GraphQL API. This case study explores how we diagnosed and resolved their performance issues while improving their development infrastructure.

Challenge

• Frequent application crashes due to memory leaks
• Production-only issues difficult to reproduce locally
• Limited visibility into memory usage patterns
• No proper staging environment for testing
• Direct production deployments increasing risk

Solution Architecture

Memory Leak Investigation

We implemented comprehensive memory profiling:

• Node.js Heap Snapshots - Memory allocation analysis
• Continuous Monitoring - Real-time memory tracking
• Load Testing - Stress testing to reproduce issues
• Chrome DevTools - Heap analysis and leak detection

GraphQL Optimization

The core issue was identified in the GraphQL DataLoader implementation:

1. Original Implementation

   • Incorrect cache key generation
   • Memory accumulation across requests
   • Unbounded cache growth

2. Optimized Solution

   • Proper DataLoader cache scoping
   • Request-specific cache boundaries
   • Automated cache cleanup

AWS Infrastructure Improvements

Enhanced the deployment pipeline with:

• Sandbox Environment - Isolated testing infrastructure
• Staging Pipeline - Pre-production validation
• CloudWatch Alarms - Memory usage monitoring
• Auto-scaling Policies - Resource optimization

Results

The optimization efforts delivered significant improvements:

• 95% reduction in memory-related incidents
• Zero unexpected downtimes post-fix
• 40% lower average memory usage
• Improved deployment confidence

Key Benefits

1. Enhanced Stability

   • Eliminated memory leaks
   • Predictable resource usage
   • Improved user experience

2. Better Development Process

   • Proper staging environment
   • Safer deployment pipeline
   • Enhanced testing capabilities

3. Improved Monitoring

   • Early warning system
   • Detailed performance metrics
   • Proactive issue detection

Implementation Process

1. Investigation

   • Memory profiling setup
   • Issue reproduction
   • Root cause analysis

2. Optimization

   • DataLoader refactoring
   • Cache management improvements
   • Infrastructure updates

3. Validation

   • Load testing
   • Memory usage verification
   • Production monitoring

Lessons Learned

1. Proper DataLoader implementation is crucial for GraphQL performance
2. Memory profiling tools are essential for debugging
3. Staging environments are critical for quality assurance
4. Monitoring should include memory metrics

Conclusion

Through careful analysis and optimization, we helped Sole Retriever resolve their critical memory issues while improving their development infrastructure. The combination of technical fixes and process improvements provides a solid foundation for their continued growth.