# -*- coding: utf-8 -*-
"""
Analytics Management Commands Package
====================================

Django management commands for the Adtlas Analytics module.
Provides command-line tools for analytics data processing,
maintenance, monitoring, and administrative operations.

Available Commands:

Data Processing Commands:
- process_analytics_data: Process and transform raw analytics data
- aggregate_metrics: Aggregate performance metrics by time periods
- generate_reports: Generate analytics reports in various formats
- cleanup_old_data: Remove old analytics data based on retention policies
- migrate_analytics_data: Migrate analytics data between systems
- validate_data_integrity: Validate analytics data integrity
- recalculate_metrics: Recalculate derived metrics and aggregations

Maintenance Commands:
- rebuild_analytics_cache: Rebuild and refresh analytics cache
- optimize_analytics_db: Optimize database indexes and performance
- backup_analytics_data: Create backups of analytics data
- restore_analytics_data: Restore analytics data from backups
- compress_old_data: Compress old analytics data for storage
- archive_data: Archive old data to long-term storage

Integration Commands:
- sync_sfr_data: Synchronize data with SFR analytics systems
- sync_bouygues_data: Synchronize data with Bouygues systems
- import_external_data: Import analytics data from external sources
- export_analytics_data: Export analytics data to external systems
- update_provider_configs: Update provider configuration settings
- test_integrations: Test external system integrations

Monitoring Commands:
- check_analytics_health: Perform comprehensive health checks
- monitor_performance: Monitor system performance metrics
- alert_thresholds: Check and alert on threshold violations
- generate_health_report: Generate detailed health reports
- check_data_quality: Validate data quality metrics
- monitor_api_usage: Monitor API usage and rate limits

Reporting Commands:
- daily_report: Generate daily analytics reports
- weekly_report: Generate weekly summary reports
- monthly_report: Generate monthly analytics reports
- custom_report: Generate custom reports with specified parameters
- dashboard_export: Export dashboard data
- performance_summary: Generate performance summary reports

Utility Commands:
- reset_analytics_cache: Reset all analytics cache entries
- update_geo_database: Update geographic IP database
- refresh_materialized_views: Refresh database materialized views
- reindex_search: Reindex search indexes for analytics
- clear_temp_files: Clear temporary analytics files
- update_configurations: Update system configurations

Command Features:
- Comprehensive logging and progress tracking
- Dry-run mode for safe testing
- Batch processing with configurable sizes
- Error handling and recovery mechanisms
- Performance monitoring and optimization
- Parallel processing support
- Configuration validation
- Progress reporting
- Email notifications
- Rollback capabilities

Common Options:
- --dry-run: Execute without making changes
- --verbose: Enable verbose output
- --batch-size: Set processing batch size
- --parallel: Enable parallel processing
- --config: Specify configuration file
- --log-level: Set logging level
- --notify: Enable notifications
- --force: Force execution without confirmations

Usage Examples:

# Process analytics data for a specific date
python manage.py process_analytics_data --date=2024-01-01 --batch-size=1000

# Generate daily reports with email notification
python manage.py daily_report --date=yesterday --notify --format=pdf

# Cleanup old data with dry-run first
python manage.py cleanup_old_data --days=90 --dry-run
python manage.py cleanup_old_data --days=90

# Sync provider data incrementally
python manage.py sync_sfr_data --incremental --parallel

# Health check with detailed output
python manage.py check_analytics_health --verbose --notify

# Backup analytics data
python manage.py backup_analytics_data --compress --encrypt

Scheduling:
Commands can be scheduled using:
- Cron jobs for Unix/Linux systems
- Task Scheduler for Windows systems
- Celery Beat for Django-integrated scheduling
- Kubernetes CronJobs for containerized deployments

Recommended Schedule:
- process_analytics_data: Every 15 minutes
- aggregate_metrics: Hourly
- daily_report: Daily at 2:00 AM
- weekly_report: Weekly on Monday at 3:00 AM
- cleanup_old_data: Weekly on Sunday at 1:00 AM
- check_analytics_health: Every 5 minutes
- backup_analytics_data: Daily at 3:00 AM
- sync_provider_data: Every 30 minutes

Configuration:
Commands use configuration from:
- Django settings (ANALYTICS_* settings)
- Environment variables
- Command-line arguments
- Configuration files (JSON/YAML)
- Database configuration tables

Logging:
All commands provide comprehensive logging:
- Command execution logs
- Performance metrics
- Error tracking
- Progress reporting
- Audit trails
- Debug information

Error Handling:
Commands include robust error handling:
- Graceful failure recovery
- Retry mechanisms
- Rollback capabilities
- Error notifications
- Detailed error reporting
- Safe mode operations

Security:
Commands implement security best practices:
- Input validation
- Access control
- Audit logging
- Secure data handling
- Encryption support
- Authentication checks

Performance:
Commands are optimized for performance:
- Batch processing
- Parallel execution
- Memory management
- Database optimization
- Caching strategies
- Resource monitoring

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

# Commands package initialization
# This file makes the commands directory a Python package
# and provides comprehensive documentation for all analytics management commands.

# Package metadata
__version__ = '1.0.0'
__author__ = 'Adtlas Development Team'
__description__ = 'Management commands for Adtlas Analytics platform'

# Command registry for dynamic discovery
COMMAND_REGISTRY = {
    # Data Processing Commands
    'data_processing': {
        'process_analytics_data': {
            'description': 'Process and transform raw analytics data',
            'category': 'processing',
            'frequency': 'every_15_minutes',
            'priority': 'high',
            'dependencies': [],
            'estimated_duration': '5-15 minutes',
            'resource_usage': 'medium'
        },
        'aggregate_metrics': {
            'description': 'Aggregate performance metrics by time periods',
            'category': 'processing',
            'frequency': 'hourly',
            'priority': 'medium',
            'dependencies': ['process_analytics_data'],
            'estimated_duration': '10-30 minutes',
            'resource_usage': 'high'
        },
        'generate_reports': {
            'description': 'Generate analytics reports in various formats',
            'category': 'processing',
            'frequency': 'daily',
            'priority': 'medium',
            'dependencies': ['aggregate_metrics'],
            'estimated_duration': '15-45 minutes',
            'resource_usage': 'medium'
        },
        'cleanup_old_data': {
            'description': 'Remove old analytics data based on retention policies',
            'category': 'processing',
            'frequency': 'weekly',
            'priority': 'low',
            'dependencies': [],
            'estimated_duration': '30-120 minutes',
            'resource_usage': 'low'
        }
    },
    
    # Maintenance Commands
    'maintenance': {
        'rebuild_analytics_cache': {
            'description': 'Rebuild and refresh analytics cache',
            'category': 'maintenance',
            'frequency': 'as_needed',
            'priority': 'medium',
            'dependencies': [],
            'estimated_duration': '10-30 minutes',
            'resource_usage': 'medium'
        },
        'optimize_analytics_db': {
            'description': 'Optimize database indexes and performance',
            'category': 'maintenance',
            'frequency': 'weekly',
            'priority': 'low',
            'dependencies': [],
            'estimated_duration': '30-90 minutes',
            'resource_usage': 'high'
        },
        'backup_analytics_data': {
            'description': 'Create backups of analytics data',
            'category': 'maintenance',
            'frequency': 'daily',
            'priority': 'high',
            'dependencies': [],
            'estimated_duration': '20-60 minutes',
            'resource_usage': 'medium'
        }
    },
    
    # Integration Commands
    'integration': {
        'sync_sfr_data': {
            'description': 'Synchronize data with SFR analytics systems',
            'category': 'integration',
            'frequency': 'every_30_minutes',
            'priority': 'high',
            'dependencies': [],
            'estimated_duration': '5-15 minutes',
            'resource_usage': 'low'
        },
        'sync_bouygues_data': {
            'description': 'Synchronize data with Bouygues systems',
            'category': 'integration',
            'frequency': 'every_30_minutes',
            'priority': 'high',
            'dependencies': [],
            'estimated_duration': '5-15 minutes',
            'resource_usage': 'low'
        },
        'import_external_data': {
            'description': 'Import analytics data from external sources',
            'category': 'integration',
            'frequency': 'as_needed',
            'priority': 'medium',
            'dependencies': [],
            'estimated_duration': '10-60 minutes',
            'resource_usage': 'medium'
        }
    },
    
    # Monitoring Commands
    'monitoring': {
        'check_analytics_health': {
            'description': 'Perform comprehensive health checks',
            'category': 'monitoring',
            'frequency': 'every_5_minutes',
            'priority': 'critical',
            'dependencies': [],
            'estimated_duration': '1-3 minutes',
            'resource_usage': 'low'
        },
        'monitor_performance': {
            'description': 'Monitor system performance metrics',
            'category': 'monitoring',
            'frequency': 'every_10_minutes',
            'priority': 'high',
            'dependencies': [],
            'estimated_duration': '2-5 minutes',
            'resource_usage': 'low'
        },
        'alert_thresholds': {
            'description': 'Check and alert on threshold violations',
            'category': 'monitoring',
            'frequency': 'every_5_minutes',
            'priority': 'high',
            'dependencies': ['monitor_performance'],
            'estimated_duration': '1-2 minutes',
            'resource_usage': 'low'
        }
    }
}

# Command execution priorities
PRIORITY_LEVELS = {
    'critical': 1,
    'high': 2,
    'medium': 3,
    'low': 4
}

# Resource usage categories
RESOURCE_USAGE = {
    'low': {
        'cpu_percent': 10,
        'memory_mb': 100,
        'disk_io': 'minimal'
    },
    'medium': {
        'cpu_percent': 30,
        'memory_mb': 500,
        'disk_io': 'moderate'
    },
    'high': {
        'cpu_percent': 60,
        'memory_mb': 1000,
        'disk_io': 'intensive'
    }
}

# Default command options
DEFAULT_OPTIONS = {
    'dry_run': False,
    'verbose': False,
    'batch_size': 1000,
    'parallel': False,
    'log_level': 'INFO',
    'notify': False,
    'force': False,
    'timeout': 3600,  # 1 hour
    'retry_attempts': 3,
    'retry_delay': 60  # seconds
}

# Notification settings
NOTIFICATION_TYPES = {
    'email': {
        'enabled': True,
        'recipients': [],
        'template': 'analytics/command_notification.html'
    },
    'slack': {
        'enabled': False,
        'webhook_url': '',
        'channel': '#analytics'
    },
    'webhook': {
        'enabled': False,
        'url': '',
        'headers': {}
    }
}

# Command dependencies graph
DEPENDENCY_GRAPH = {
    'process_analytics_data': [],
    'aggregate_metrics': ['process_analytics_data'],
    'generate_reports': ['aggregate_metrics'],
    'alert_thresholds': ['monitor_performance'],
    'daily_report': ['aggregate_metrics'],
    'weekly_report': ['daily_report'],
    'monthly_report': ['weekly_report']
}


def get_command_info(command_name: str) -> dict:
    """
    Get detailed information about a specific command.
    
    Args:
        command_name: Name of the command
        
    Returns:
        Dictionary with command information
    """
    for category, commands in COMMAND_REGISTRY.items():
        if command_name in commands:
            return {
                'name': command_name,
                'category': category,
                **commands[command_name]
            }
    return {}


def get_commands_by_category(category: str) -> list:
    """
    Get all commands in a specific category.
    
    Args:
        category: Command category
        
    Returns:
        List of command names
    """
    return list(COMMAND_REGISTRY.get(category, {}).keys())


def get_commands_by_priority(priority: str) -> list:
    """
    Get all commands with a specific priority.
    
    Args:
        priority: Priority level
        
    Returns:
        List of command names
    """
    commands = []
    for category, category_commands in COMMAND_REGISTRY.items():
        for cmd_name, cmd_info in category_commands.items():
            if cmd_info.get('priority') == priority:
                commands.append(cmd_name)
    return commands


def get_command_dependencies(command_name: str) -> list:
    """
    Get dependencies for a specific command.
    
    Args:
        command_name: Name of the command
        
    Returns:
        List of dependency command names
    """
    return DEPENDENCY_GRAPH.get(command_name, [])


def get_execution_order(commands: list) -> list:
    """
    Get optimal execution order for a list of commands based on dependencies.
    
    Args:
        commands: List of command names
        
    Returns:
        List of commands in execution order
    """
    # Simple topological sort implementation
    ordered = []
    remaining = commands.copy()
    
    while remaining:
        # Find commands with no unresolved dependencies
        ready = []
        for cmd in remaining:
            deps = get_command_dependencies(cmd)
            if all(dep in ordered or dep not in commands for dep in deps):
                ready.append(cmd)
        
        if not ready:
            # Circular dependency or missing dependency
            ordered.extend(remaining)
            break
        
        # Sort by priority
        ready.sort(key=lambda x: PRIORITY_LEVELS.get(
            get_command_info(x).get('priority', 'low'), 4
        ))
        
        ordered.extend(ready)
        for cmd in ready:
            remaining.remove(cmd)
    
    return ordered


def validate_command_options(command_name: str, options: dict) -> dict:
    """
    Validate and normalize command options.
    
    Args:
        command_name: Name of the command
        options: Command options dictionary
        
    Returns:
        Validated and normalized options
    """
    # Start with defaults
    validated = DEFAULT_OPTIONS.copy()
    
    # Update with provided options
    validated.update(options)
    
    # Command-specific validation
    cmd_info = get_command_info(command_name)
    
    # Adjust batch size based on resource usage
    resource_usage = cmd_info.get('resource_usage', 'medium')
    if resource_usage == 'high' and validated['batch_size'] > 500:
        validated['batch_size'] = 500
    elif resource_usage == 'low' and validated['batch_size'] < 100:
        validated['batch_size'] = 100
    
    return validated


def get_resource_requirements(command_name: str) -> dict:
    """
    Get resource requirements for a command.
    
    Args:
        command_name: Name of the command
        
    Returns:
        Dictionary with resource requirements
    """
    cmd_info = get_command_info(command_name)
    usage_level = cmd_info.get('resource_usage', 'medium')
    return RESOURCE_USAGE.get(usage_level, RESOURCE_USAGE['medium'])


# Package exports
__all__ = [
    'COMMAND_REGISTRY',
    'PRIORITY_LEVELS',
    'RESOURCE_USAGE',
    'DEFAULT_OPTIONS',
    'NOTIFICATION_TYPES',
    'DEPENDENCY_GRAPH',
    'get_command_info',
    'get_commands_by_category',
    'get_commands_by_priority',
    'get_command_dependencies',
    'get_execution_order',
    'validate_command_options',
    'get_resource_requirements'
]