Implement AI-Driven Infrastructure Observability with Prometheus Client and KServe

                      
                     
"""
Production implementation for AI-Driven Infrastructure Observability with Prometheus Client and KServe.
Provides secure and scalable operations using observability metrics.
"""
import os
import logging
import time
from typing import Dict, Any, List, Union
from fastapi import FastAPI, HTTPException
from prometheus_client import CollectorRegistry, Gauge, push_to_gateway, Counter

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    prometheus_gateway: str = os.getenv('PROMETHEUS_GATEWAY_URL', 'http://localhost:9091')
    service_name: str = os.getenv('SERVICE_NAME', 'kserve_service')

# Initialize FastAPI app
app = FastAPI()

# Prometheus metrics registry
registry = CollectorRegistry()

# Define metrics
request_counter = Counter('http_requests_total', 'Total HTTP Requests', ['method', 'endpoint'], registry=registry)
response_time_gauge = Gauge('http_response_time_seconds', 'Response time in seconds', ['endpoint'], registry=registry)

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.

    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'input_data' not in data:
        raise ValueError('Missing input_data')  # Ensure required fields are present
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.

    Args:
        data: Input data to sanitize
    Returns:
        Sanitized input data
    """
    return {k: str(v).strip() for k, v in data.items()}  # Strip whitespace from values

def push_metrics() -> None:
    """Push metrics to Prometheus gateway.
    
    Args:
        None
    Returns:
        None
    """
    try:
        push_to_gateway(Config.prometheus_gateway, job=Config.service_name, registry=registry)
        logger.info('Metrics pushed to Prometheus gateway')  # Log successful push
    except Exception as e:
        logger.error('Failed to push metrics: %s', e)  # Log error on push failure

@app.post('/observe')
async def observe(data: Dict[str, Any]) -> Dict[str, Union[str, int]]:
    """Endpoint to observe metrics.
    
    Args:
        data: Input data for observation
    Returns:
        JSON response with status
    Raises:
        HTTPException: If validation fails
    """
    request_counter.labels(method='POST', endpoint='/observe').inc()  # Increment request counter

    try:
        validate_input(data)  # Validate input
        sanitized_data = sanitize_fields(data)  # Sanitize input
        # Process data here (e.g., call external APIs, perform transformations)
        time.sleep(1)  # Simulate processing delay
        push_metrics()  # Push metrics to Prometheus
        return {'status': 'success', 'input': sanitized_data}  # Return success response
    except ValueError as ve:
        logger.error('Validation error: %s', ve)
        raise HTTPException(status_code=400, detail=str(ve))  # Return bad request on validation error
    except Exception as e:
        logger.error('Unexpected error: %s', e)
        raise HTTPException(status_code=500, detail='Internal server error')  # Handle unexpected errors

def normalize_data(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize raw data for processing.
    
    Args:
        raw_data: List of raw input data
    Returns:
        Normalized data
    """
    return [{'key': d['input_data'].lower()} for d in raw_data]  # Convert input data to lowercase

def aggregate_metrics(data: List[Dict[str, Any]]) -> None:
    """Aggregate metrics from processed data.
    
    Args:
        data: List of processed data
    Returns:
        None
    """
    for item in data:
        response_time_gauge.labels(endpoint='/observe').set(0.5)  # Set dummy response time

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)  # Run FastAPI app in main block