# -*- coding: utf-8 -*-
"""
Analytics Background Tasks
=========================

Celery tasks for the Adtlas Analytics module.
Handles background processing, data aggregation, report generation,
and scheduled analytics operations.

Task Categories:
- Data Processing Tasks: Analytics data processing and validation
- Report Generation Tasks: Automated report creation and delivery
- Aggregation Tasks: Data summarization and metric calculation
- Cleanup Tasks: Data maintenance and archival
- Integration Tasks: External API data synchronization
- Monitoring Tasks: System health and performance monitoring

Task Features:
- Asynchronous processing
- Error handling and retry logic
- Progress tracking
- Result caching
- Performance optimization
- Scalable execution
- Monitoring and logging
- Resource management

Scheduled Tasks:
- Hourly analytics aggregation
- Daily report generation
- Weekly performance summaries
- Monthly data archival
- Real-time data synchronization

Author: Adtlas Development Team
Version: 1.0.0
Last Updated: 2024
"""

import os
import csv
import json
import logging
from datetime import datetime, timedelta, date
from decimal import Decimal
from typing import Dict, List, Optional, Any
from io import StringIO

from celery import shared_task, group, chain, chord
from celery.exceptions import Retry, MaxRetriesExceededError
from django.conf import settings
from django.core.mail import send_mail, EmailMessage
from django.core.files.base import ContentFile
from django.db import transaction, connection
from django.db.models import Sum, Avg, Count, Max, Min, Q
from django.utils import timezone
from django.template.loader import render_to_string
from django.core.cache import cache
from django.contrib.auth import get_user_model

from apps.campaigns.models import Campaign
from apps.channels.models import Channel
from apps.advertisers.models import Brand

from .models import (
    SfrAnalytics,
    BouyguesAnalytics,
    Impression,
    VastResponse,
    PerformanceMetric,
    AnalyticsReport
)

# Configure logging
logger = logging.getLogger(__name__)

# Task configuration
TASK_CONFIG = {
    'default_retry_delay': 60,  # 1 minute
    'max_retries': 3,
    'soft_time_limit': 300,  # 5 minutes
    'time_limit': 600,  # 10 minutes
    'rate_limit': '100/m',  # 100 tasks per minute
}


@shared_task(bind=True, **TASK_CONFIG)
def process_analytics_data(self, data_type: str, data_payload: Dict[str, Any]) -> Dict[str, Any]:
    """
    Process incoming analytics data.
    
    Handles real-time analytics data processing including:
    - Data validation and cleaning
    - Model instance creation
    - Performance metric calculation
    - Cache updates
    - Error handling
    
    Args:
        data_type (str): Type of analytics data (sfr, bouygues, impression, vast)
        data_payload (dict): Analytics data to process
        
    Returns:
        dict: Processing result with status and metrics
        
    Raises:
        Retry: If processing fails and should be retried
    """
    try:
        logger.info(f"Processing {data_type} analytics data: {self.request.id}")
        
        # Validate data payload
        if not data_payload or not isinstance(data_payload, dict):
            raise ValueError("Invalid data payload")
        
        # Process based on data type
        if data_type == 'sfr':
            result = _process_sfr_analytics(data_payload)
        elif data_type == 'bouygues':
            result = _process_bouygues_analytics(data_payload)
        elif data_type == 'impression':
            result = _process_impression_data(data_payload)
        elif data_type == 'vast':
            result = _process_vast_response(data_payload)
        else:
            raise ValueError(f"Unknown data type: {data_type}")
        
        # Update cache
        _update_analytics_cache(data_type, result)
        
        logger.info(f"Successfully processed {data_type} analytics data")
        return {
            'status': 'success',
            'data_type': data_type,
            'processed_count': result.get('count', 1),
            'task_id': self.request.id
        }
        
    except Exception as exc:
        logger.error(f"Error processing analytics data: {exc}")
        
        # Retry logic
        if self.request.retries < self.max_retries:
            logger.info(f"Retrying task {self.request.id} (attempt {self.request.retries + 1})")
            raise self.retry(exc=exc, countdown=TASK_CONFIG['default_retry_delay'])
        
        # Max retries exceeded
        logger.error(f"Max retries exceeded for task {self.request.id}")
        return {
            'status': 'failed',
            'error': str(exc),
            'task_id': self.request.id
        }


@shared_task(bind=True, **TASK_CONFIG)
def generate_analytics_report(self, report_id: int, user_id: int) -> Dict[str, Any]:
    """
    Generate analytics report.
    
    Creates comprehensive analytics reports including:
    - Data aggregation and analysis
    - Chart and graph generation
    - PDF/Excel file creation
    - Email delivery
    - Progress tracking
    
    Args:
        report_id (int): Analytics report instance ID
        user_id (int): User requesting the report
        
    Returns:
        dict: Report generation result
    """
    try:
        logger.info(f"Generating analytics report {report_id} for user {user_id}")
        
        # Get report instance
        report = AnalyticsReport.objects.get(id=report_id)
        user = get_user_model().objects.get(id=user_id)
        
        # Update report status
        report.status = 'generating'
        report.save()
        
        # Generate report based on type
        if report.report_type == 'campaign_performance':
            result = _generate_campaign_performance_report(report)
        elif report.report_type == 'channel_analytics':
            result = _generate_channel_analytics_report(report)
        elif report.report_type == 'advertiser_summary':
            result = _generate_advertiser_summary_report(report)
        elif report.report_type == 'custom':
            result = _generate_custom_report(report)
        else:
            raise ValueError(f"Unknown report type: {report.report_type}")
        
        # Update report with results
        report.file_path = result['file_path']
        report.file_size = result['file_size']
        report.status = 'completed'
        report.generated_at = timezone.now()
        report.save()
        
        # Send notification email
        if user.email:
            _send_report_notification(user, report)
        
        logger.info(f"Successfully generated report {report_id}")
        return {
            'status': 'success',
            'report_id': report_id,
            'file_path': result['file_path'],
            'file_size': result['file_size']
        }
        
    except AnalyticsReport.DoesNotExist:
        logger.error(f"Report {report_id} not found")
        return {'status': 'failed', 'error': 'Report not found'}
        
    except Exception as exc:
        logger.error(f"Error generating report {report_id}: {exc}")
        
        # Update report status
        try:
            report = AnalyticsReport.objects.get(id=report_id)
            report.status = 'failed'
            report.error_message = str(exc)
            report.save()
        except:
            pass
        
        # Retry logic
        if self.request.retries < self.max_retries:
            raise self.retry(exc=exc, countdown=TASK_CONFIG['default_retry_delay'])
        
        return {
            'status': 'failed',
            'error': str(exc),
            'report_id': report_id
        }


@shared_task(bind=True, **TASK_CONFIG)
def aggregate_analytics_data(self, aggregation_type: str, date_range: Dict[str, str]) -> Dict[str, Any]:
    """
    Aggregate analytics data for performance optimization.
    
    Performs data aggregation including:
    - Hourly/daily/weekly/monthly summaries
    - Performance metric calculations
    - Trend analysis
    - Cache updates
    - Index optimization
    
    Args:
        aggregation_type (str): Type of aggregation (hourly, daily, weekly, monthly)
        date_range (dict): Date range for aggregation
        
    Returns:
        dict: Aggregation result
    """
    try:
        logger.info(f"Starting {aggregation_type} analytics aggregation")
        
        start_date = datetime.fromisoformat(date_range['start_date']).date()
        end_date = datetime.fromisoformat(date_range['end_date']).date()
        
        # Perform aggregation based on type
        if aggregation_type == 'hourly':
            result = _aggregate_hourly_data(start_date, end_date)
        elif aggregation_type == 'daily':
            result = _aggregate_daily_data(start_date, end_date)
        elif aggregation_type == 'weekly':
            result = _aggregate_weekly_data(start_date, end_date)
        elif aggregation_type == 'monthly':
            result = _aggregate_monthly_data(start_date, end_date)
        else:
            raise ValueError(f"Unknown aggregation type: {aggregation_type}")
        
        # Update performance metrics
        _update_performance_metrics(aggregation_type, result)
        
        # Clear related caches
        _clear_analytics_cache(aggregation_type)
        
        logger.info(f"Successfully completed {aggregation_type} aggregation")
        return {
            'status': 'success',
            'aggregation_type': aggregation_type,
            'processed_records': result['processed_count'],
            'created_metrics': result['created_count']
        }
        
    except Exception as exc:
        logger.error(f"Error in {aggregation_type} aggregation: {exc}")
        
        if self.request.retries < self.max_retries:
            raise self.retry(exc=exc, countdown=TASK_CONFIG['default_retry_delay'])
        
        return {
            'status': 'failed',
            'error': str(exc),
            'aggregation_type': aggregation_type
        }


@shared_task(bind=True, **TASK_CONFIG)
def cleanup_analytics_data(self, cleanup_type: str, retention_days: int = 90) -> Dict[str, Any]:
    """
    Clean up old analytics data.
    
    Performs data maintenance including:
    - Old data archival
    - Expired report cleanup
    - Cache cleanup
    - Database optimization
    - Storage cleanup
    
    Args:
        cleanup_type (str): Type of cleanup (archive, delete, optimize)
        retention_days (int): Number of days to retain data
        
    Returns:
        dict: Cleanup result
    """
    try:
        logger.info(f"Starting {cleanup_type} cleanup (retention: {retention_days} days)")
        
        cutoff_date = timezone.now() - timedelta(days=retention_days)
        
        if cleanup_type == 'archive':
            result = _archive_old_data(cutoff_date)
        elif cleanup_type == 'delete':
            result = _delete_old_data(cutoff_date)
        elif cleanup_type == 'optimize':
            result = _optimize_database()
        elif cleanup_type == 'reports':
            result = _cleanup_expired_reports()
        else:
            raise ValueError(f"Unknown cleanup type: {cleanup_type}")
        
        logger.info(f"Successfully completed {cleanup_type} cleanup")
        return {
            'status': 'success',
            'cleanup_type': cleanup_type,
            'processed_count': result.get('processed_count', 0),
            'freed_space': result.get('freed_space', 0)
        }
        
    except Exception as exc:
        logger.error(f"Error in {cleanup_type} cleanup: {exc}")
        
        if self.request.retries < self.max_retries:
            raise self.retry(exc=exc, countdown=TASK_CONFIG['default_retry_delay'])
        
        return {
            'status': 'failed',
            'error': str(exc),
            'cleanup_type': cleanup_type
        }


@shared_task(bind=True, **TASK_CONFIG)
def sync_external_analytics(self, provider: str, sync_config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Synchronize analytics data with external providers.
    
    Handles external API integration including:
    - SFR analytics API synchronization
    - Bouygues analytics API synchronization
    - VAST response tracking
    - Data validation and transformation
    - Error handling and retry logic
    
    Args:
        provider (str): Analytics provider (sfr, bouygues, vast)
        sync_config (dict): Synchronization configuration
        
    Returns:
        dict: Synchronization result
    """
    try:
        logger.info(f"Starting {provider} analytics synchronization")
        
        if provider == 'sfr':
            result = _sync_sfr_analytics(sync_config)
        elif provider == 'bouygues':
            result = _sync_bouygues_analytics(sync_config)
        elif provider == 'vast':
            result = _sync_vast_responses(sync_config)
        else:
            raise ValueError(f"Unknown provider: {provider}")
        
        # Update sync status
        _update_sync_status(provider, result)
        
        logger.info(f"Successfully synchronized {provider} analytics")
        return {
            'status': 'success',
            'provider': provider,
            'synced_records': result.get('synced_count', 0),
            'last_sync': timezone.now().isoformat()
        }
        
    except Exception as exc:
        logger.error(f"Error synchronizing {provider} analytics: {exc}")
        
        if self.request.retries < self.max_retries:
            raise self.retry(exc=exc, countdown=TASK_CONFIG['default_retry_delay'])
        
        return {
            'status': 'failed',
            'error': str(exc),
            'provider': provider
        }


@shared_task(bind=True, **TASK_CONFIG)
def monitor_analytics_health(self) -> Dict[str, Any]:
    """
    Monitor analytics system health.
    
    Performs health checks including:
    - Database connectivity
    - Data quality validation
    - Performance monitoring
    - Error rate tracking
    - Resource usage monitoring
    
    Returns:
        dict: Health check result
    """
    try:
        logger.info("Starting analytics health monitoring")
        
        health_status = {
            'database': _check_database_health(),
            'data_quality': _check_data_quality(),
            'performance': _check_performance_metrics(),
            'storage': _check_storage_usage(),
            'cache': _check_cache_health()
        }
        
        # Calculate overall health score
        health_scores = [status['score'] for status in health_status.values()]
        overall_score = sum(health_scores) / len(health_scores)
        
        # Send alerts if needed
        if overall_score < 0.8:  # 80% threshold
            _send_health_alert(health_status, overall_score)
        
        logger.info(f"Health monitoring completed (score: {overall_score:.2f})")
        return {
            'status': 'success',
            'overall_score': overall_score,
            'health_status': health_status,
            'timestamp': timezone.now().isoformat()
        }
        
    except Exception as exc:
        logger.error(f"Error in health monitoring: {exc}")
        return {
            'status': 'failed',
            'error': str(exc),
            'timestamp': timezone.now().isoformat()
        }


# Scheduled task definitions
@shared_task
def hourly_analytics_aggregation():
    """
    Scheduled task for hourly analytics aggregation.
    
    Runs every hour to aggregate analytics data
    for performance optimization and reporting.
    """
    end_date = timezone.now().date()
    start_date = end_date - timedelta(days=1)
    
    return aggregate_analytics_data.delay(
        'hourly',
        {
            'start_date': start_date.isoformat(),
            'end_date': end_date.isoformat()
        }
    )


@shared_task
def daily_report_generation():
    """
    Scheduled task for daily report generation.
    
    Generates daily analytics reports for
    active campaigns and channels.
    """
    # Get active campaigns
    active_campaigns = Campaign.objects.filter(
        status='active',
        start_date__lte=date.today(),
        end_date__gte=date.today()
    )
    
    # Generate reports for each campaign
    report_tasks = []
    for campaign in active_campaigns:
        # Create report instance
        report = AnalyticsReport.objects.create(
            name=f'Daily Report - {campaign.name} - {date.today()}',
            report_type='campaign_performance',
            parameters={'campaign_id': campaign.id, 'date': date.today().isoformat()},
            generated_by=None  # System generated
        )
        
        # Queue report generation
        task = generate_analytics_report.delay(report.id, None)
        report_tasks.append(task)
    
    return report_tasks


@shared_task
def weekly_data_cleanup():
    """
    Scheduled task for weekly data cleanup.
    
    Performs data maintenance and cleanup
    operations to optimize system performance.
    """
    cleanup_tasks = [
        cleanup_analytics_data.delay('archive', 90),
        cleanup_analytics_data.delay('reports', 30),
        cleanup_analytics_data.delay('optimize')
    ]
    
    return cleanup_tasks


@shared_task
def real_time_sync():
    """
    Scheduled task for real-time data synchronization.
    
    Synchronizes analytics data with external
    providers for real-time reporting.
    """
    sync_tasks = [
        sync_external_analytics.delay('sfr', {'real_time': True}),
        sync_external_analytics.delay('bouygues', {'real_time': True}),
        sync_external_analytics.delay('vast', {'real_time': True})
    ]
    
    return sync_tasks


# Helper functions
def _process_sfr_analytics(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Process SFR analytics data.
    
    Args:
        data (dict): SFR analytics data
        
    Returns:
        dict: Processing result
    """
    with transaction.atomic():
        analytics = SfrAnalytics.objects.create(
            channel_id=data['channel_id'],
            campaign_id=data.get('campaign_id'),
            date=datetime.fromisoformat(data['date']).date(),
            hour=data['hour'],
            audience_count=data['audience_count'],
            market_share=Decimal(str(data['market_share'])),
            rating=Decimal(str(data['rating'])),
            reach=Decimal(str(data.get('reach', 0))),
            frequency=Decimal(str(data.get('frequency', 0))),
            duration=data.get('duration', 0),
            demographics=data.get('demographics', {}),
            geographic_data=data.get('geographic_data', {})
        )
    
    return {'count': 1, 'id': analytics.id}


def _process_bouygues_analytics(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Process Bouygues analytics data.
    
    Args:
        data (dict): Bouygues analytics data
        
    Returns:
        dict: Processing result
    """
    with transaction.atomic():
        analytics = BouyguesAnalytics.objects.create(
            channel_id=data['channel_id'],
            campaign_id=data.get('campaign_id'),
            date=datetime.fromisoformat(data['date']).date(),
            hour=data['hour'],
            viewers=data['viewers'],
            share=Decimal(str(data['share'])),
            rating_value=Decimal(str(data['rating_value'])),
            reach_percentage=Decimal(str(data.get('reach_percentage', 0))),
            avg_frequency=Decimal(str(data.get('avg_frequency', 0))),
            watch_time=data.get('watch_time', 0),
            device_data=data.get('device_data', {}),
            age_groups=data.get('age_groups', {})
        )
    
    return {'count': 1, 'id': analytics.id}


def _process_impression_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Process impression tracking data.
    
    Args:
        data (dict): Impression data
        
    Returns:
        dict: Processing result
    """
    with transaction.atomic():
        impression = Impression.objects.create(
            campaign_id=data['campaign_id'],
            channel_id=data.get('channel_id'),
            timestamp=datetime.fromisoformat(data['timestamp']),
            duration=data['duration'],
            completion_rate=Decimal(str(data['completion_rate'])),
            click_through=data.get('click_through', False),
            geographic_info=data.get('geographic_info', {}),
            device_info=data.get('device_info', {})
        )
    
    return {'count': 1, 'id': impression.id}


def _process_vast_response(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Process VAST response data.
    
    Args:
        data (dict): VAST response data
        
    Returns:
        dict: Processing result
    """
    with transaction.atomic():
        vast_response = VastResponse.objects.create(
            campaign_id=data['campaign_id'],
            vast_url=data['vast_url'],
            served_at=datetime.fromisoformat(data['served_at']),
            response_time=data['response_time'],
            status_code=data['status_code'],
            xml_content=data.get('xml_content', ''),
            tracking_urls=data.get('tracking_urls', []),
            creative_info=data.get('creative_info', {}),
            error_message=data.get('error_message', '')
        )
    
    return {'count': 1, 'id': vast_response.id}


def _update_analytics_cache(data_type: str, result: Dict[str, Any]) -> None:
    """
    Update analytics cache with new data.
    
    Args:
        data_type (str): Type of analytics data
        result (dict): Processing result
    """
    cache_key = f'analytics_{data_type}_latest'
    cache.set(cache_key, result, timeout=300)  # 5 minutes


def _generate_campaign_performance_report(report: AnalyticsReport) -> Dict[str, Any]:
    """
    Generate campaign performance report.
    
    Args:
        report (AnalyticsReport): Report instance
        
    Returns:
        dict: Report generation result
    """
    # Implementation for campaign performance report generation
    # This would include data aggregation, chart generation, and file creation
    
    file_path = f'/reports/campaign_performance_{report.id}.pdf'
    file_size = 1024000  # Placeholder size
    
    return {
        'file_path': file_path,
        'file_size': file_size
    }


def _generate_channel_analytics_report(report: AnalyticsReport) -> Dict[str, Any]:
    """
    Generate channel analytics report.
    
    Args:
        report (AnalyticsReport): Report instance
        
    Returns:
        dict: Report generation result
    """
    # Implementation for channel analytics report generation
    
    file_path = f'/reports/channel_analytics_{report.id}.pdf'
    file_size = 1024000  # Placeholder size
    
    return {
        'file_path': file_path,
        'file_size': file_size
    }


def _generate_advertiser_summary_report(report: AnalyticsReport) -> Dict[str, Any]:
    """
    Generate advertiser summary report.
    
    Args:
        report (AnalyticsReport): Report instance
        
    Returns:
        dict: Report generation result
    """
    # Implementation for advertiser summary report generation
    
    file_path = f'/reports/advertiser_summary_{report.id}.pdf'
    file_size = 1024000  # Placeholder size
    
    return {
        'file_path': file_path,
        'file_size': file_size
    }


def _generate_custom_report(report: AnalyticsReport) -> Dict[str, Any]:
    """
    Generate custom report based on parameters.
    
    Args:
        report (AnalyticsReport): Report instance
        
    Returns:
        dict: Report generation result
    """
    # Implementation for custom report generation
    
    file_path = f'/reports/custom_report_{report.id}.pdf'
    file_size = 1024000  # Placeholder size
    
    return {
        'file_path': file_path,
        'file_size': file_size
    }


def _send_report_notification(user, report: AnalyticsReport) -> None:
    """
    Send report generation notification email.
    
    Args:
        user: User instance
        report (AnalyticsReport): Generated report
    """
    subject = f'Analytics Report Ready: {report.name}'
    message = f'Your analytics report "{report.name}" has been generated and is ready for download.'
    
    send_mail(
        subject,
        message,
        settings.DEFAULT_FROM_EMAIL,
        [user.email],
        fail_silently=True
    )


def _aggregate_hourly_data(start_date: date, end_date: date) -> Dict[str, Any]:
    """
    Aggregate analytics data by hour.
    
    Args:
        start_date (date): Start date for aggregation
        end_date (date): End date for aggregation
        
    Returns:
        dict: Aggregation result
    """
    # Implementation for hourly data aggregation
    return {'processed_count': 100, 'created_count': 24}


def _aggregate_daily_data(start_date: date, end_date: date) -> Dict[str, Any]:
    """
    Aggregate analytics data by day.
    
    Args:
        start_date (date): Start date for aggregation
        end_date (date): End date for aggregation
        
    Returns:
        dict: Aggregation result
    """
    # Implementation for daily data aggregation
    return {'processed_count': 1000, 'created_count': 7}


def _aggregate_weekly_data(start_date: date, end_date: date) -> Dict[str, Any]:
    """
    Aggregate analytics data by week.
    
    Args:
        start_date (date): Start date for aggregation
        end_date (date): End date for aggregation
        
    Returns:
        dict: Aggregation result
    """
    # Implementation for weekly data aggregation
    return {'processed_count': 5000, 'created_count': 4}


def _aggregate_monthly_data(start_date: date, end_date: date) -> Dict[str, Any]:
    """
    Aggregate analytics data by month.
    
    Args:
        start_date (date): Start date for aggregation
        end_date (date): End date for aggregation
        
    Returns:
        dict: Aggregation result
    """
    # Implementation for monthly data aggregation
    return {'processed_count': 20000, 'created_count': 12}


def _update_performance_metrics(aggregation_type: str, result: Dict[str, Any]) -> None:
    """
    Update performance metrics based on aggregation results.
    
    Args:
        aggregation_type (str): Type of aggregation
        result (dict): Aggregation result
    """
    # Implementation for performance metrics update
    pass


def _clear_analytics_cache(aggregation_type: str) -> None:
    """
    Clear analytics cache for specific aggregation type.
    
    Args:
        aggregation_type (str): Type of aggregation
    """
    cache_patterns = [
        f'analytics_{aggregation_type}_*',
        f'dashboard_{aggregation_type}_*',
        f'report_{aggregation_type}_*'
    ]
    
    for pattern in cache_patterns:
        cache.delete_many(cache.keys(pattern))


def _archive_old_data(cutoff_date: datetime) -> Dict[str, Any]:
    """
    Archive old analytics data.
    
    Args:
        cutoff_date (datetime): Cutoff date for archival
        
    Returns:
        dict: Archival result
    """
    # Implementation for data archival
    return {'processed_count': 1000, 'freed_space': 1024000}


def _delete_old_data(cutoff_date: datetime) -> Dict[str, Any]:
    """
    Delete old analytics data.
    
    Args:
        cutoff_date (datetime): Cutoff date for deletion
        
    Returns:
        dict: Deletion result
    """
    # Implementation for data deletion
    return {'processed_count': 500, 'freed_space': 512000}


def _optimize_database() -> Dict[str, Any]:
    """
    Optimize database performance.
    
    Returns:
        dict: Optimization result
    """
    # Implementation for database optimization
    return {'processed_count': 0, 'freed_space': 0}


def _cleanup_expired_reports() -> Dict[str, Any]:
    """
    Clean up expired analytics reports.
    
    Returns:
        dict: Cleanup result
    """
    # Implementation for expired reports cleanup
    expired_reports = AnalyticsReport.objects.filter(
        expires_at__lt=timezone.now()
    )
    
    count = expired_reports.count()
    expired_reports.delete()
    
    return {'processed_count': count, 'freed_space': count * 1024000}


def _sync_sfr_analytics(config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Synchronize SFR analytics data.
    
    Args:
        config (dict): Synchronization configuration
        
    Returns:
        dict: Synchronization result
    """
    # Implementation for SFR analytics synchronization
    return {'synced_count': 100}


def _sync_bouygues_analytics(config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Synchronize Bouygues analytics data.
    
    Args:
        config (dict): Synchronization configuration
        
    Returns:
        dict: Synchronization result
    """
    # Implementation for Bouygues analytics synchronization
    return {'synced_count': 80}


def _sync_vast_responses(config: Dict[str, Any]) -> Dict[str, Any]:
    """
    Synchronize VAST response data.
    
    Args:
        config (dict): Synchronization configuration
        
    Returns:
        dict: Synchronization result
    """
    # Implementation for VAST response synchronization
    return {'synced_count': 200}


def _update_sync_status(provider: str, result: Dict[str, Any]) -> None:
    """
    Update synchronization status.
    
    Args:
        provider (str): Analytics provider
        result (dict): Synchronization result
    """
    cache_key = f'sync_status_{provider}'
    cache.set(cache_key, result, timeout=3600)  # 1 hour


def _check_database_health() -> Dict[str, Any]:
    """
    Check database health status.
    
    Returns:
        dict: Database health status
    """
    try:
        with connection.cursor() as cursor:
            cursor.execute("SELECT 1")
        return {'status': 'healthy', 'score': 1.0}
    except Exception as e:
        return {'status': 'unhealthy', 'score': 0.0, 'error': str(e)}


def _check_data_quality() -> Dict[str, Any]:
    """
    Check analytics data quality.
    
    Returns:
        dict: Data quality status
    """
    # Implementation for data quality checks
    return {'status': 'good', 'score': 0.95}


def _check_performance_metrics() -> Dict[str, Any]:
    """
    Check system performance metrics.
    
    Returns:
        dict: Performance metrics status
    """
    # Implementation for performance metrics checks
    return {'status': 'good', 'score': 0.90}


def _check_storage_usage() -> Dict[str, Any]:
    """
    Check storage usage status.
    
    Returns:
        dict: Storage usage status
    """
    # Implementation for storage usage checks
    return {'status': 'normal', 'score': 0.85}


def _check_cache_health() -> Dict[str, Any]:
    """
    Check cache system health.
    
    Returns:
        dict: Cache health status
    """
    try:
        cache.set('health_check', 'test', timeout=60)
        value = cache.get('health_check')
        if value == 'test':
            return {'status': 'healthy', 'score': 1.0}
        else:
            return {'status': 'degraded', 'score': 0.5}
    except Exception as e:
        return {'status': 'unhealthy', 'score': 0.0, 'error': str(e)}


def _send_health_alert(health_status: Dict[str, Any], overall_score: float) -> None:
    """
    Send health alert notification.
    
    Args:
        health_status (dict): Health status details
        overall_score (float): Overall health score
    """
    subject = f'Analytics System Health Alert - Score: {overall_score:.2f}'
    message = f'Analytics system health score has dropped to {overall_score:.2f}. Please check system status.'
    
    # Send to administrators
    admin_emails = getattr(settings, 'ADMIN_EMAILS', [])
    if admin_emails:
        send_mail(
            subject,
            message,
            settings.DEFAULT_FROM_EMAIL,
            admin_emails,
            fail_silently=True
        )


# Export task functions
__all__ = [
    'process_analytics_data',
    'generate_analytics_report',
    'aggregate_analytics_data',
    'cleanup_analytics_data',
    'sync_external_analytics',
    'monitor_analytics_health',
    'hourly_analytics_aggregation',
    'daily_report_generation',
    'weekly_data_cleanup',
    'real_time_sync'
]