const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
const { getConnection } = require('./utils/db');

async function calculateBrandMetrics(startTime, totalProducts, processedCount = 0, isCancelled = false) {
    const connection = await getConnection();
    let success = false;
    const BATCH_SIZE = 5000;
    
    try {
        // Get last calculation timestamp
        const [lastCalc] = await connection.query(`
            SELECT last_calculation_timestamp 
            FROM calculate_status 
            WHERE module_name = 'brand_metrics'
        `);
        const lastCalculationTime = lastCalc[0]?.last_calculation_timestamp || '1970-01-01';

        // Get total count of brands needing updates
        const [brandCount] = await connection.query(`
            SELECT COUNT(DISTINCT p.brand) as count 
            FROM products p
            LEFT JOIN orders o ON p.pid = o.pid AND o.updated > ?
            WHERE p.brand IS NOT NULL
              AND (
                  p.updated > ?
                  OR o.id IS NOT NULL
              )
        `, [lastCalculationTime, lastCalculationTime]);
        const totalBrands = brandCount[0].count;

        if (totalBrands === 0) {
            console.log('No brands need metric updates');
            return {
                processedProducts: 0,
                processedOrders: 0,
                processedPurchaseOrders: 0,
                success: true
            };
        }

        if (isCancelled) {
            outputProgress({
                status: 'cancelled',
                operation: 'Brand metrics calculation cancelled',
                current: processedCount,
                total: totalBrands,
                elapsed: formatElapsedTime(startTime),
                remaining: null,
                rate: calculateRate(startTime, processedCount),
                percentage: ((processedCount / totalBrands) * 100).toFixed(1),
                timing: {
                    start_time: new Date(startTime).toISOString(),
                    end_time: new Date().toISOString(),
                    elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
                }
            });
            return {
                processedProducts: processedCount,
                processedOrders: 0,
                processedPurchaseOrders: 0,
                success
            };
        }

        outputProgress({
            status: 'running',
            operation: 'Starting brand metrics calculation',
            current: processedCount,
            total: totalBrands,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalBrands),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalBrands) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        // Process in batches
        let lastBrand = '';
        while (true) {
            if (isCancelled) break;

            const [batch] = await connection.query(`
                SELECT DISTINCT p.brand 
                FROM products p
                WHERE p.brand IS NOT NULL
                  AND p.brand > ?
                  AND (
                      p.updated > ?
                      OR EXISTS (
                          SELECT 1 FROM orders o 
                          WHERE o.pid = p.pid 
                          AND o.updated > ?
                      )
                  )
                ORDER BY p.brand 
                LIMIT ?
            `, [lastBrand, lastCalculationTime, lastCalculationTime, BATCH_SIZE]);

            if (batch.length === 0) break;

            // Update brand metrics for this batch
            await connection.query(`
                INSERT INTO brand_metrics (
                    brand,
                    product_count,
                    active_products,
                    total_stock_units,
                    total_stock_cost,
                    total_stock_retail,
                    total_revenue,
                    avg_margin,
                    growth_rate,
                    last_calculated_at
                )
                WITH product_stats AS (
                    SELECT 
                        p.brand,
                        COUNT(DISTINCT p.pid) as product_count,
                        COUNT(DISTINCT CASE WHEN p.visible = true THEN p.pid END) as active_products,
                        SUM(p.stock_quantity) as total_stock_units,
                        SUM(p.stock_quantity * p.cost_price) as total_stock_cost,
                        SUM(p.stock_quantity * p.price) as total_stock_retail,
                        SUM(pm.total_revenue) as total_revenue,
                        AVG(pm.avg_margin_percent) as avg_margin
                    FROM products p
                    LEFT JOIN product_metrics pm ON p.pid = pm.pid
                    WHERE p.brand IN (?)
                        AND (
                            p.updated > ?
                            OR EXISTS (
                                SELECT 1 FROM orders o 
                                WHERE o.pid = p.pid 
                                AND o.updated > ?
                            )
                        )
                    GROUP BY p.brand
                ),
                sales_periods AS (
                    SELECT 
                        p.brand,
                        SUM(CASE 
                            WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) 
                            THEN o.quantity * o.price 
                            ELSE 0 
                        END) as current_period_sales,
                        SUM(CASE 
                            WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) AND DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
                            THEN o.quantity * o.price 
                            ELSE 0 
                        END) as previous_period_sales
                    FROM products p
                    INNER JOIN orders o ON p.pid = o.pid 
                        AND o.canceled = false
                        AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY)
                        AND o.updated > ?
                    WHERE p.brand IN (?)
                    GROUP BY p.brand
                )
                SELECT 
                    ps.brand,
                    COALESCE(ps.product_count, 0) as product_count,
                    COALESCE(ps.active_products, 0) as active_products,
                    COALESCE(ps.total_stock_units, 0) as total_stock_units,
                    COALESCE(ps.total_stock_cost, 0) as total_stock_cost,
                    COALESCE(ps.total_stock_retail, 0) as total_stock_retail,
                    COALESCE(ps.total_revenue, 0) as total_revenue,
                    COALESCE(ps.avg_margin, 0) as avg_margin,
                    CASE
                        WHEN COALESCE(sp.previous_period_sales, 0) = 0 AND COALESCE(sp.current_period_sales, 0) > 0 THEN 100
                        WHEN COALESCE(sp.previous_period_sales, 0) = 0 THEN 0
                        ELSE LEAST(999.99, GREATEST(-100, 
                            ((COALESCE(sp.current_period_sales, 0) / sp.previous_period_sales) - 1) * 100
                        ))
                    END as growth_rate,
                    NOW() as last_calculated_at
                FROM product_stats ps
                LEFT JOIN sales_periods sp ON ps.brand = sp.brand
                ON DUPLICATE KEY UPDATE
                    product_count = VALUES(product_count),
                    active_products = VALUES(active_products),
                    total_stock_units = VALUES(total_stock_units),
                    total_stock_cost = VALUES(total_stock_cost),
                    total_stock_retail = VALUES(total_stock_retail),
                    total_revenue = VALUES(total_revenue),
                    avg_margin = VALUES(avg_margin),
                    growth_rate = VALUES(growth_rate),
                    last_calculated_at = NOW()
            `, [
                batch.map(row => row.brand),  // For first IN clause
                lastCalculationTime,          // For p.updated > ?
                lastCalculationTime,          // For o.updated > ? in EXISTS
                lastCalculationTime,          // For o.updated > ? in sales_periods
                batch.map(row => row.brand)   // For second IN clause
            ]);

            lastBrand = batch[batch.length - 1].brand;
            processedCount += batch.length;

            outputProgress({
                status: 'running',
                operation: 'Processing brand metrics batch',
                current: processedCount,
                total: totalBrands,
                elapsed: formatElapsedTime(startTime),
                remaining: estimateRemaining(startTime, processedCount, totalBrands),
                rate: calculateRate(startTime, processedCount),
                percentage: ((processedCount / totalBrands) * 100).toFixed(1),
                timing: {
                    start_time: new Date(startTime).toISOString(),
                    end_time: new Date().toISOString(),
                    elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
                }
            });
        }

        // If we get here, everything completed successfully
        success = true;
        
        // Update calculate_status
        await connection.query(`
            INSERT INTO calculate_status (module_name, last_calculation_timestamp)
            VALUES ('brand_metrics', NOW())
            ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
        `);

        return {
            processedProducts: processedCount,
            processedOrders: 0,
            processedPurchaseOrders: 0,
            success
        };

    } catch (error) {
        success = false;
        logError(error, 'Error calculating brand metrics');
        throw error;
    } finally {
        if (connection) {
            connection.release();
        }
    }
}

module.exports = calculateBrandMetrics;