API Performance Optimization Techniques

This repository demonstrates 7 proven techniques to optimize API performance, offering concrete implementations, comprehensive benchmarks, and practical insights for production applications.

Project Structure

.
├── api/                    # Main API service
│   ├── routes/            # API route handlers
│   ├── techniques/        # Optimization implementations
│   ├── app.py            # FastAPI application
│   ├── models.py         # SQLAlchemy models
│   └── requirements.txt   # API dependencies
├── benchmarks/            # Benchmarking suite
│   ├── data/             # Sample data for tests
│   ├── techniques/       # Individual benchmark implementations
│   ├── requirements.txt  # Benchmark dependencies
│   └── run.py           # Benchmark runner
├── databases/            # Database configurations
│   ├── postgres/        # PostgreSQL init scripts
│   └── redis/           # Redis configuration
└── docker-compose.yml    # Service orchestration

Optimization Techniques

Technique	Description	Average Improvement
Connection Pooling	Reuse database connections	~113x faster
Caching	Redis-based data caching	~20x faster
Pagination	Efficient data chunking	~2.6x faster
Async Logging	Non-blocking logging	~1.4x faster
N+1 Query Prevention	Optimized query patterns	~1.5x faster
Compression	Response payload reduction	~1.1x faster
JSON Serialization	Optimized data transformation	Stable baseline

Quick Start

1. Clone the repository:

   git clone https://github.com/zvdy/api-performance.git
   cd api-performance

2. Start the services:

   docker-compose up -d

3. Run benchmarks:

   # Run all benchmarks
   python benchmarks/run.py
   
   # Run specific benchmark
   python benchmarks/run.py --technique compression --iterations 10 --concurrency 10

Benchmarking

The project includes a comprehensive benchmarking suite that measures:

• Response time (ms)
• Requests per second
• Improvement factor vs baseline
• Technique-specific metrics (e.g., compression ratio)

Example benchmark command:

python benchmarks/run.py --technique compression --iterations 10 --concurrency 10

Parameters:

• --technique: Specific technique to benchmark (optional)
• --iterations: Number of test iterations (default: 3)
• --concurrency: Number of concurrent requests (default: 10)
• --output-dir: Custom output directory (optional)

Results are saved in the reports/ directory with timestamps.

Implementation Details

1. Connection Pooling

• Configurable connection pool size
• Connection health monitoring
• Automatic connection recycling
• AsyncSession support with SQLAlchemy

2. Caching

• Redis-based LRU caching
• Configurable TTL
• Automatic cache invalidation
• Circuit breaker pattern

3. Pagination

• Cursor-based pagination
• Efficient COUNT queries
• HATEOAS-compliant responses
• Optimized for large datasets

4. Async Logging

• Non-blocking log operations
• Structured logging format
• Performance monitoring
• Configurable log levels

5. N+1 Query Prevention

• Strategic JOIN operations
• Relationship loading optimization
• Query performance monitoring
• Efficient indexing strategy

6. Compression

• Content-Encoding negotiation
• Brotli compression (quality 11)
• Size-based compression decisions
• Compression ratio monitoring

7. JSON Serialization

• orjson for optimal performance
• Custom serializers for complex types
• Memory optimization
• Content-type negotiation

Development

Prerequisites

• Docker and Docker Compose
• Python 3.10+
• PostgreSQL 15
• Redis 7

Local Setup

1. Install API dependencies:

   cd api
   pip install -r requirements.txt

2. Install benchmark dependencies:

   cd benchmarks
   pip install -r requirements.txt

Running Tests

# Run all benchmarks
python benchmarks/run.py

# Run specific benchmark
python benchmarks/run.py --technique compression

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.