# Address Formatter: Enterprise Features and Performance Optimizations

This document provides an overview of the enterprise features and performance optimizations implemented in the address formatter project, covering sections 7 and 8 of the implementation checklist.

## Enterprise Features (Section 7)

### 7.1 API Implementation

We've implemented a complete REST API using FastAPI, providing the following endpoints:

- **Format Endpoint (`/format`)**: Formats a single address according to country-specific rules
- **Batch Endpoint (`/batch`)**: Processes multiple addresses in a single request
- **Validation Endpoint (`/validate`)**: Validates address components and country support
- **Health Check Endpoint (`/health`)**: Provides service health status
- **Metrics Endpoint (`/metrics`)**: Exposes Prometheus metrics
- **Stats Endpoint (`/stats`)**: Shows formatter statistics

Key features of the API implementation:

- **Request Validation**: Using Pydantic models for request validation
- **Response Formatting**: Structured JSON responses with proper error handling
- **Middleware**: Rate limiting and metrics collection middleware
- **Documentation**: Auto-generated OpenAPI documentation
- **Server Configuration**: Flexible server configuration with environment variables

### 7.2 Monitoring

We've implemented a comprehensive monitoring system using Prometheus metrics:

- **Request Metrics**: Count of requests by status and country code
- **Error Metrics**: Count of errors by error type
- **Performance Metrics**: Histogram of formatting time
- **Resource Metrics**: Gauge of cache size and template count
- **Component Metrics**: Frequency of address components

Key features of the monitoring system:

- **Decorator-Based**: Easy to apply to functions with `@monitor_formatting`
- **Collector Class**: `MetricsCollector` for gathering formatter statistics
- **Integration with API**: Metrics exposed through API endpoint
- **Middleware**: Middleware for API request metrics

### 7.3 Type System

We've implemented a comprehensive type system using Python type hints and Pydantic models:

- **Basic Types**: Type aliases for common types (e.g., `CountryCode`, `LanguageCode`)
- **Component Types**: `AddressComponent` TypedDict for address components
- **Formatting Options**: `FormattingOptions` TypedDict for formatting options
- **Template Types**: `TemplateData` TypedDict for template data
- **Protocol Classes**: Protocol classes for formatter, normalizer, and renderer
- **API Models**: Pydantic models for API requests and responses
- **Event Types**: Enum for event types
- **Plugin Types**: Protocol for plugins and metadata model

Key features of the type system:

- **Static Type Checking**: Support for mypy static type checking
- **Documentation**: Type-based documentation for better IDE support
- **Validation**: Type-based validation for API requests
- **Interfaces**: Clear interfaces for components

## Performance Optimizations (Section 8)

### 8.1 Memory Optimization

We've implemented several memory optimization techniques:

- **Memoization**: Custom memoization decorator for caching results
- **Object Pooling**: Object pool for reusing instances of immutable objects
- **String Interning**: String interning utility for reducing memory usage
- **Dictionary Optimization**: Function for optimizing dictionaries
- **Cache Management**: Functions for monitoring and clearing caches

Key features of memory optimization:

- **Reduced Memory Footprint**: Less memory usage for repeated strings and objects
- **Configurable Cache Size**: Configurable cache sizes for different components
- **Monitoring Integration**: Memory usage metrics exposed through monitoring

### 8.2 Computational Optimization

We've implemented several computational optimization techniques:

- **Regex Caching**: Caching of compiled regex patterns
- **JIT Compilation**: Optional JIT compilation using Numba
- **Performance Profiling**: Simple timer for performance profiling
- **Optimized Algorithms**: More efficient implementations of key algorithms

Key features of computational optimization:

- **Faster Processing**: Reduced processing time for formatting operations
- **Reduced Overhead**: Less overhead for regex compilation and function calls
- **Performance Monitoring**: Tools for measuring and improving performance
- **Configurable Optimizations**: Enable/disable optimizations as needed

### 8.3 Async Support

We've implemented comprehensive asynchronous processing support:

- **Async API**: Asynchronous API with async/await support
- **Batch Processing**: Efficient batch processing of multiple addresses
- **Thread Pool**: Thread pool for CPU-bound operations
- **Async Template Loading**: Asynchronous template loading for faster startup

Key features of async support:

- **Higher Throughput**: Process more addresses concurrently
- **Reduced Latency**: Lower latency for batch operations
- **Configurable**: Enable/disable async support as needed
- **Integration with FastAPI**: Seamless integration with FastAPI async endpoints

## Integration and Deployment

To tie everything together, we've implemented several integration and deployment features:

- **CLI Tool**: Command-line interface for the address formatter
- **Docker Container**: Containerization with Docker
- **Package Management**: Proper package management with setuptools
- **Environment Configuration**: Configuration through environment variables
- **Installation Options**: Various installation options (pip, requirements.txt)

The CLI tool provides the following commands:

- **format**: Format a single address
- **batch**: Format multiple addresses
- **server**: Run the API server
- **stats**: Show formatter statistics

## Usage Examples

### Using the API

```python
import requests

# Format a single address
response = requests.post("http://localhost:8000/format", json={
    "components": {
        "houseNumber": "123",
        "road": "Main St",
        "city": "Anytown",
        "state": "CA",
        "countryCode": "US"
    },
    "options": {
        "abbreviate": True
    }
})
print(response.json()["formatted"])

# Process a batch of addresses
batch_response = requests.post("http://localhost:8000/batch", json={
    "addresses": [
        {
            "houseNumber": "123",
            "road": "Main St",
            "city": "Anytown",
            "state": "CA",
            "countryCode": "US"
        },
        {
            "houseNumber": "456",
            "road": "High St",
            "city": "Othertown",
            "state": "NY",
            "countryCode": "US"
        }
    ],
    "options": {
        "abbreviate": True
    }
})
print(batch_response.json()["results"])
```

### Using the CLI

```bash
# Format a single address
address-formatter format --input address.json --abbreviate

# Process a batch of addresses
address-formatter batch --input addresses.csv --output formatted.csv --format csv

# Run the API server
address-formatter server --port 8000

# Show formatter statistics
address-formatter stats
```

### Using Async API

```python
import asyncio
from address_formatter import format_address_async, format_batch_async

async def main():
    # Format a single address asynchronously
    formatted = await format_address_async({
        "houseNumber": "123",
        "road": "Main St",
        "city": "Anytown",
        "state": "CA",
        "countryCode": "US"
    })
    print(formatted)
    
    # Process a batch of addresses asynchronously
    addresses = [
        {
            "houseNumber": "123",
            "road": "Main St",
            "city": "Anytown",
            "state": "CA",
            "countryCode": "US"
        },
        {
            "houseNumber": "456",
            "road": "High St",
            "city": "Othertown",
            "state": "NY",
            "countryCode": "US"
        }
    ]
    results = await format_batch_async(addresses)
    print(results)

asyncio.run(main())
```

## Installation Instructions

### Basic Installation

```bash
pip install pyaddress
```

### Installation with Extra Features

```bash
# Install with API support
pip install pyaddress[api]

# Install with async support
pip install pyaddress[async]

# Install with ML support
pip install pyaddress[ml]

# Install with optimization support
pip install pyaddress[optimize]

# Install with development tools
pip install pyaddress[dev]

# Install with all features
pip install pyaddress[api,async,ml,optimize,dev]
```

### Using Docker

```bash
# Build the Docker image
docker build -t address-formatter .

# Run the Docker container
docker run -p 8000:8000 address-formatter
``` 