V. Advanced Usage Scenarios

Custom Integration Patterns

The KADES system supports sophisticated integration patterns for advanced use cases and custom implementations.

Webhook Integration

WEBHOOK_CONFIG = {
    'retry': {
        'max_attempts': 3,
        'initial_delay': 5,
        'max_delay': 300
    },
    'batch': {
        'size': 100,
        'timeout': 30
    },
    'endpoints': {
        'alerts': {
            'url_template': 'https://api.example.com/webhook/alerts',
            'secret_header': 'X-Webhook-Secret'
        }
    }
}

Custom Model Integration

class CustomModelIntegrator:
    def __init__(
        self,
        model_path: str,
        inference_timeout: int = 30,
        batch_size: int = 32
    ):
        self.model = self._load_custom_model(model_path)
        self.inference_queue = asyncio.Queue()
        self.processing_tasks = set()
        
    async def process_predictions(
        self,
        data: Dict[str, Any]
    ) -> Dict[str, float]:
        """Process custom model predictions."""
        try:
            normalized_data = self._normalize_input(data)
            predictions = await self._run_inference(normalized_data)
            return self._post_process_predictions(predictions)
        except Exception as e:
            logger.error(f"Prediction error: {e}")
            raise

Extension Development

Plugin Architecture

from abc import ABC, abstractmethod

class KADESPlugin(ABC):
    @abstractmethod
    async def initialize(self) -> None:
        """Initialize plugin resources."""
        pass
        
    @abstractmethod
    async def process_event(
        self,
        event: Dict[str, Any]
    ) -> Optional[Dict[str, Any]]:
        """Process system events."""
        pass
        
    @abstractmethod
    async def cleanup(self) -> None:
        """Cleanup plugin resources."""
        pass

Custom Data Source Integration

class CustomDataSource:
    def __init__(
        self,
        connection_params: Dict[str, Any],
        cache_config: Optional[Dict] = None
    ):
        self.connection = self._establish_connection(connection_params)
        self.cache = self._initialize_cache(cache_config)
        
    async def fetch_data(
        self,
        query_params: Dict[str, Any]
    ) -> AsyncGenerator[Dict[str, Any], None]:
        """Fetch data from custom source."""
        async for raw_data in self._stream_data(query_params):
            processed_data = await self._process_raw_data(raw_data)
            if processed_data:
                yield processed_data

Performance Optimization

Memory Management

MEMORY_CONFIG = {
    'cache_size': '4GB',
    'max_batch_size': 1000,
    'gc_interval': 300,
    'memory_limit': '8GB',
    'swap_threshold': '6GB'
}

Query Optimization

-- Optimized query for time-series data
CREATE MATERIALIZED VIEW token_metrics_hourly AS
SELECT
    token_address,
    time_bucket('1 hour', timestamp) as hour,
    avg(price_usd) as avg_price,
    sum(volume_24h) as total_volume,
    count(*) as data_points
FROM token_metrics
GROUP BY token_address, time_bucket('1 hour', timestamp)
WITH DATA;

-- Index creation
CREATE INDEX idx_token_metrics_hour 
ON token_metrics_hourly (token_address, hour DESC);

Advanced Configuration

Feature Flags

FEATURE_FLAGS = {
    'advanced_analytics': {
        'enabled': True,
        'min_data_points': 1000,
        'confidence_threshold': 0.85
    },
    'realtime_processing': {
        'enabled': True,
        'max_latency': 100,
        'batch_timeout': 50
    },
    'custom_models': {
        'enabled': True,
        'max_model_size': '2GB',
        'inference_timeout': 30
    }
}

Performance Tuning

System Tuning:
    Thread Pool:
        min_workers: 4
        max_workers: 16
        queue_size: 1000
        
    Connection Pool:
        min_size: 5
        max_size: 20
        max_queries: 50000
        
    Cache Configuration:
        strategy: 'lru'
        ttl: 300
        max_size: '2GB'
        eviction_policy: 'volatile-lru'

Model Optimization:
    batch_processing: True
    quantization: 'int8'
    cuda_enabled: True
    tensor_parallelism: 2

Security Considerations

SECURITY_POLICIES = {
    'input_validation': {
        'sanitization': True,
        'max_payload_size': '1MB',
        'allowed_content_types': [
            'application/json',
            'application/x-www-form-urlencoded'
        ]
    },
    'rate_limiting': {
        'window_size': 60,
        'max_requests': 100,
        'strategy': 'sliding_window'
    },
    'authentication': {
        'token_expiration': 3600,
        'refresh_window': 300,
        'max_failed_attempts': 5
    }
}

The system supports extensive customization and optimization while maintaining strict security and performance standards.