Production Readiness: Error Handling, Monitoring, and Performance

[spacegoatai]

Overview

Implement robust error handling, monitoring, and performance optimization for production-ready deployment.

Error Handling & Resilience

• Graceful Degradation: Continue functioning when AI models are unavailable
• Connection Recovery: Auto-reconnect to Pixeltable on connection loss
• Model Fallbacks: Switch between available models when primary fails
• Resource Management: Handle memory/disk space constraints gracefully
• Timeout Handling: Prevent hanging operations on slow AI models

Performance Optimization

• Connection Pooling: Efficient Pixeltable database connections
• Model Caching: Keep frequently used models loaded in memory
• Batch Processing: Optimize bulk operations
• Lazy Loading: Load AI models only when needed
• Resource Monitoring: Track CPU, memory, disk usage

Logging & Monitoring

• Structured Logging: JSON logs with correlation IDs
• Performance Metrics: Track operation times and success rates
• Health Checks: Endpoint for monitoring service status
• Debug Mode: Detailed logging for troubleshooting
• Usage Analytics: Optional telemetry for optimization insights

Configuration Management

• Environment-based Config: Development, staging, production settings
• Runtime Configuration: Change settings without restart
• Validation: Ensure configuration is valid on startup
• Secrets Management: Secure handling of API keys and credentials

Testing Infrastructure

• Unit Tests: Core functionality coverage
• Integration Tests: End-to-end workflow testing
• Performance Tests: Benchmark critical operations
• Mock Services: Test without external dependencies
• CI/CD Pipeline: Automated testing and deployment

Security Considerations

• Input Validation: Sanitize all user inputs
• Rate Limiting: Prevent abuse and resource exhaustion
• Access Control: Basic authentication and authorization
• Audit Logging: Track sensitive operations
• Data Privacy: Ensure local processing maintains privacy