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description: Performance optimization guidelines
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# Performance Optimization Guidelines

## Memory Management
- Use appropriate data structures for the use case
- Implement proper cleanup for long-running services
- Monitor memory usage and implement limits
- Use memory pools for frequently allocated objects

## I/O Optimization
- Use buffered I/O for better performance
- Implement connection pooling for HTTP clients
- Use appropriate buffer sizes (64KB for HTTP)
- Minimize system calls and context switches

## Concurrency Patterns
- Use worker pools for CPU-intensive tasks
- Implement proper backpressure with semaphores
- Use channels for coordination between goroutines
- Avoid excessive goroutine creation

## Caching Strategies
- Use tiered caching (memory + disk) for optimal performance
- Implement intelligent cache eviction policies
- Use cache warming for predictable access patterns
- Monitor cache hit ratios and adjust strategies

## Network Optimization
- Use HTTP/2 when available
- Enable connection keep-alives
- Use appropriate timeouts for different operations
- Implement request coalescing for duplicate requests

## Data Structures
- Choose appropriate data structures for access patterns
- Use sync.RWMutex for read-heavy operations
- Consider lock-free data structures where appropriate
- Minimize memory allocations in hot paths

## Algorithm Selection
- Choose GC algorithms based on access patterns
- Use LRU for general gaming workloads
- Use LFU for gaming cafes with popular content
- Use Hybrid algorithms for mixed workloads

## Monitoring and Profiling
- Implement performance metrics collection
- Use structured logging for performance analysis
- Monitor key performance indicators
- Profile the application under realistic loads

## Resource Management
- Implement proper resource cleanup
- Use context.Context for cancellation
- Set appropriate limits on resource usage
- Monitor resource consumption over time

## Scalability Considerations
- Design for horizontal scaling where possible
- Use sharding for large datasets
- Implement proper load balancing
- Consider distributed caching for large deployments

## Bottleneck Identification
- Profile the application to identify bottlenecks
- Focus optimization efforts on the most critical paths
- Use appropriate tools for performance analysis
- Test performance under realistic conditions