What actually matters in production: A practical guide to building high-performance applications
This repository provides practical examples and benchmarks for the four most critical aspects of production performance:
- Understanding and preventing N+1 query problems
- Query planning and execution analysis
- Efficient indexing strategies
- Batch processing and bulk operations
- MongoDB-specific optimizations
- Redis implementation patterns
- Cache invalidation strategies
- Multi-level caching
- Cache-aside vs Write-through patterns
- Distributed caching considerations
- Request batching and aggregation
- GraphQL vs REST considerations
- Connection pooling
- Keep-alive and persistent connections
- Circuit breakers and fallbacks
- Common memory leak patterns
- Memory profiling tools
- Garbage collection optimization
- Resource cleanup patterns
- Memory-efficient data structures
# Clone the repository
git clone https://github.com/your-username/production-performance-patterns.git
# Install dependencies
cd production-performance-patterns
npm install
# Run benchmarks
cd benchmarks
npm install
npm run benchmark:mongo
npm run benchmark:cache.
├── database-optimization/ # Database query optimization examples
│ ├── mongodb-optimization.md
│ └── mongodb-examples/
├── caching-strategies/ # Redis and caching implementations
├── network-optimization/ # Network call optimization demos
├── memory-management/ # Memory leak prevention examples
└── benchmarks/ # Performance comparison benchmarks
Each directory contains:
- Theory: README with concept explanations
- Practice: Real-world code examples
- Exercises: Hands-on tasks to apply concepts
- Common Pitfalls: What to avoid
- Best Practices: Industry-standard approaches
- Basic understanding of backend development
- Familiarity with any programming language (examples in Python/Node.js)
- Docker for running examples locally
- Basic understanding of databases (MySQL/PostgreSQL/MongoDB)
- Real-world examples from production systems
- Performance benchmarks with actual metrics
- Practical implementation patterns
- Memory and CPU profiling examples
- Scalability considerations
Contributions are always welcome! See CONTRIBUTING.md for ways to get started.
Please adhere to this project's Code of Conduct.
Implementing these patterns has shown significant improvements in production:
- 10-100x faster database queries
- 5-10x reduction in API response times
- 60-80% reduction in memory usage
- 40-50% reduction in server costs
While algorithmic complexity (Big O notation) is important, real-world performance often depends more on:
- Efficient database access patterns
- Smart caching strategies
- Optimized network calls
- Proper memory management
This repository focuses on these practical aspects that directly impact production performance.
- Database Optimization Guide
- Caching Strategies Guide
- Network Optimization Guide
- Memory Management Guide
- Benchmarks Guide
- Database Performance Tuning Guide
- Redis Documentation
- Network Performance Best Practices
- Memory Management in Modern Applications
This project is licensed under the MIT License - see the LICENSE file for details.
If you find this repository helpful, please consider giving it a star! It helps others discover these important concepts.
performance optimization, database optimization, caching strategies, redis, mongodb, network optimization, memory management, n+1 queries, production patterns, scalability, backend development, web performance, nodejs, python, benchmarks