Backend Django Views and Analytics for Dashboard Home

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Backend Implementation for Dashboard Analytics Data

Overview

Implement Django views and context processors to provide analytics data for the admin dashboard home page template. The dashboard view will gather real-time and historical data to render the "at a glance" dashboard with all necessary analytics.

Required Implementation

1. Dashboard View and Context

View: AdminDashboardView in admin/views.py

• Render dashboard template with analytics context
• Gather all analytics data in a single view
• Implement caching for performance
• Handle date filtering and time zone considerations

2. Analytics Service Layer

Service: DashboardAnalyticsService in admin/services.py

• Centralize all analytics calculations
• Provide methods for different metric types
• Handle data aggregation and formatting
• Implement caching strategies

3. Analytics Data Methods

Today's Sales Analytics

def get_todays_sales_data(self):
    return {
        'total_revenue': self._calculate_daily_revenue(),
        'order_count': self._get_completed_orders_count(),
        'average_order_value': self._calculate_avg_order_value(),
        'hourly_breakdown': self._get_hourly_sales(),
        'vs_yesterday': self._compare_with_yesterday(),
        'vs_last_week': self._compare_with_last_week()
    }

Customer Analytics

def get_todays_customer_data(self):
    return {
        'total_customers': self._count_unique_customers(),
        'new_customers': self._count_new_customers(),
        'returning_customers': self._count_returning_customers(),
        'predicted_remaining': self._predict_remaining_customers(),
        'peak_hours': self._identify_peak_hours(),
        'hourly_customer_flow': self._get_hourly_customer_count()
    }

Operations Overview

def get_operations_overview(self):
    return {
        'order_status_distribution': self._get_order_status_stats(),
        'popular_items': self._get_popular_menu_items(),
        'table_turnover_rate': self._calculate_table_turnover(),
        'staff_performance': self._get_staff_metrics(),
        'inventory_alerts': self._get_low_stock_alerts(),
        'system_notifications': self._get_system_alerts()
    }

4. Prediction Algorithm Implementation

Customer Prediction Service:

• Historical pattern analysis using previous weeks' data
• Time-of-day and day-of-week patterns
• Moving average calculations
• Seasonal adjustments (lunch rush, dinner rush)

def predict_remaining_customers(self):
    current_hour = timezone.now().hour
    historical_data = self._get_historical_hourly_data()
    remaining_hours_pattern = self._analyze_remaining_hours_pattern()
    return self._calculate_prediction_with_confidence()

Django Template Context Structure

context = {
    'dashboard_date': timezone.now().date(),
    'last_updated': timezone.now(),
    'sales_data': {
        'total_revenue': 2500.00,
        'order_count': 125,
        'average_order_value': 20.00,
        'hourly_breakdown': [...],
        'trend_indicators': {...}
    },
    'customer_data': {
        'actual_count': 89,
        'predicted_remaining': 45,
        'new_vs_returning': {...},
        'hourly_flow': [...]
    },
    'operations_data': {
        'order_statuses': {...},
        'popular_items': [...],
        'alerts': [...],
        'performance_metrics': {...}
    },
    'chart_data': {
        'sales_trend': [...],
        'customer_flow': [...],
        'revenue_comparison': [...]
    }
}

Database Optimization

Required Model Extensions

• Add analytics-friendly methods to existing models (Order, Customer, MenuItem)
• Create database indexes for common analytics queries
• Implement model managers with analytics querysets

Caching Strategy

• View-level caching: Cache entire dashboard context for 5 minutes
• Method-level caching: Cache individual analytics calculations
• Template fragment caching: Cache expensive chart data rendering
• Use Django's cache framework with Redis backend

Database Indexes

# Add to existing models
class Order(models.Model):
    # ... existing fields
    class Meta:
        indexes = [
            models.Index(fields=['created_at', 'status']),
            models.Index(fields=['created_at', 'total_amount']),
            models.Index(fields=['customer', 'created_at']),
        ]

class Customer(models.Model):
    # ... existing fields
    class Meta:
        indexes = [
            models.Index(fields=['created_at']),
            models.Index(fields=['last_visit_date']),
        ]

Implementation Tasks

• Create AdminDashboardView class-based view
• Implement DashboardAnalyticsService service layer
• Add analytics methods to existing models
• Create customer prediction algorithm
• Implement caching layer with Redis
• Add database indexes for analytics queries
• Create dashboard URL routing
• Add timezone handling for accurate daily calculations
• Write comprehensive unit tests for analytics functions
• Performance testing and query optimization
• Add management commands for analytics data maintenance

URL Configuration

# admin/urls.py
urlpatterns = [
    path('dashboard/', AdminDashboardView.as_view(), name='admin_dashboard'),
    # ... other admin URLs
]

Dependencies

• Django's cache framework configured with Redis
• Existing Order, Customer, and MenuItem models
• Django timezone utilities for accurate date handling
• Template system for rendering dashboard

Performance Requirements

• Dashboard view should load within 1 second
• Analytics calculations cached for 5 minutes
• Database queries optimized with select_related/prefetch_related
• Minimal database hits through effective caching

Success Criteria

• ✅ Dashboard view renders with all analytics data
• ✅ Customer prediction accuracy >70%
• ✅ Page load time <1 second with caching
• ✅ 100% test coverage for analytics functions
• ✅ Caching reduces database queries by >80%
• ✅ All time zone handling is accurate
• ✅ Analytics data updates reflect real-time changes

Related Issues

• Blocks: Frontend Dashboard Home implementation
• Part of: Implement Custom Admin Interface for Restaurant Management (Implement Custom Admin Interface #29)