293 lines
12 KiB
JavaScript
293 lines
12 KiB
JavaScript
const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
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const { getConnection } = require('./utils/db');
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async function calculateBrandMetrics(startTime, totalProducts, processedCount = 0, isCancelled = false) {
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const connection = await getConnection();
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let success = false;
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const BATCH_SIZE = 5000;
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try {
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// Get last calculation timestamp
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const [lastCalc] = await connection.query(`
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SELECT last_calculation_timestamp
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FROM calculate_status
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WHERE module_name = 'brand_metrics'
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`);
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const lastCalculationTime = lastCalc[0]?.last_calculation_timestamp || '1970-01-01';
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// Get total count of brands needing updates
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const [brandCount] = await connection.query(`
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SELECT COUNT(DISTINCT p.brand) as count
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FROM products p
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LEFT JOIN orders o ON p.pid = o.pid AND o.updated > ?
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WHERE p.brand IS NOT NULL
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AND (
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p.updated > ?
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OR o.id IS NOT NULL
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)
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`, [lastCalculationTime, lastCalculationTime]);
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const totalBrands = brandCount[0].count;
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if (totalBrands === 0) {
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console.log('No brands need metric updates');
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return {
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processedProducts: 0,
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processedOrders: 0,
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processedPurchaseOrders: 0,
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success: true
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};
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}
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if (isCancelled) {
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outputProgress({
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status: 'cancelled',
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operation: 'Brand metrics calculation cancelled',
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current: processedCount,
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total: totalBrands,
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elapsed: formatElapsedTime(startTime),
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remaining: null,
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rate: calculateRate(startTime, processedCount),
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percentage: ((processedCount / totalBrands) * 100).toFixed(1),
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timing: {
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start_time: new Date(startTime).toISOString(),
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end_time: new Date().toISOString(),
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elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
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}
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});
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return {
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processedProducts: processedCount,
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processedOrders: 0,
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processedPurchaseOrders: 0,
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success
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};
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}
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outputProgress({
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status: 'running',
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operation: 'Starting brand metrics calculation',
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current: processedCount,
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total: totalBrands,
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elapsed: formatElapsedTime(startTime),
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remaining: estimateRemaining(startTime, processedCount, totalBrands),
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rate: calculateRate(startTime, processedCount),
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percentage: ((processedCount / totalBrands) * 100).toFixed(1),
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timing: {
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start_time: new Date(startTime).toISOString(),
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end_time: new Date().toISOString(),
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elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
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}
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});
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// Process in batches
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let lastBrand = '';
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while (true) {
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if (isCancelled) break;
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const [batch] = await connection.query(`
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SELECT DISTINCT p.brand
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FROM products p
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FORCE INDEX (idx_brand)
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LEFT JOIN orders o FORCE INDEX (idx_orders_metrics) ON p.pid = o.pid AND o.updated > ?
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WHERE p.brand IS NOT NULL
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AND p.brand > ?
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AND (
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p.updated > ?
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OR o.id IS NOT NULL
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)
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ORDER BY p.brand
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LIMIT ?
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`, [lastCalculationTime, lastBrand, lastCalculationTime, BATCH_SIZE]);
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if (batch.length === 0) break;
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// Create temporary tables for better performance
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await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_product_stats');
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await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_sales_stats');
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await connection.query(`
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CREATE TEMPORARY TABLE temp_product_stats (
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brand VARCHAR(100) NOT NULL,
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product_count INT,
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active_products INT,
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total_stock_units INT,
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total_stock_cost DECIMAL(15,2),
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total_stock_retail DECIMAL(15,2),
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total_revenue DECIMAL(15,2),
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avg_margin DECIMAL(5,2),
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PRIMARY KEY (brand),
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INDEX (total_revenue),
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INDEX (product_count)
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) ENGINE=MEMORY
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`);
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await connection.query(`
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CREATE TEMPORARY TABLE temp_sales_stats (
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brand VARCHAR(100) NOT NULL,
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current_period_sales DECIMAL(15,2),
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previous_period_sales DECIMAL(15,2),
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PRIMARY KEY (brand),
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INDEX (current_period_sales),
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INDEX (previous_period_sales)
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) ENGINE=MEMORY
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`);
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// Populate product stats with optimized index usage
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await connection.query(`
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INSERT INTO temp_product_stats
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SELECT
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p.brand,
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COUNT(DISTINCT p.pid) as product_count,
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COUNT(DISTINCT CASE WHEN p.visible = true THEN p.pid END) as active_products,
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COALESCE(SUM(p.stock_quantity), 0) as total_stock_units,
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COALESCE(SUM(p.stock_quantity * p.cost_price), 0) as total_stock_cost,
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COALESCE(SUM(p.stock_quantity * p.price), 0) as total_stock_retail,
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COALESCE(SUM(pm.total_revenue), 0) as total_revenue,
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COALESCE(AVG(NULLIF(pm.avg_margin_percent, 0)), 0) as avg_margin
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FROM products p
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FORCE INDEX (idx_brand)
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LEFT JOIN product_metrics pm FORCE INDEX (PRIMARY) ON p.pid = pm.pid
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WHERE p.brand IN (?)
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AND (
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p.updated > ?
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OR EXISTS (
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SELECT 1 FROM orders o FORCE INDEX (idx_orders_metrics)
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WHERE o.pid = p.pid
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AND o.updated > ?
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)
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)
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GROUP BY p.brand
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`, [batch.map(row => row.brand), lastCalculationTime, lastCalculationTime]);
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// Populate sales stats with optimized date handling
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await connection.query(`
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INSERT INTO temp_sales_stats
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WITH date_ranges AS (
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SELECT
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DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as current_start,
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CURRENT_DATE as current_end,
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DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) as previous_start,
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DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as previous_end
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)
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SELECT
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p.brand,
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COALESCE(SUM(
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CASE WHEN o.date >= dr.current_start
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THEN o.quantity * o.price
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ELSE 0
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END
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), 0) as current_period_sales,
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COALESCE(SUM(
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CASE WHEN o.date >= dr.previous_start AND o.date < dr.current_start
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THEN o.quantity * o.price
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ELSE 0
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END
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), 0) as previous_period_sales
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FROM products p
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FORCE INDEX (idx_brand)
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INNER JOIN orders o FORCE INDEX (idx_orders_metrics) ON p.pid = o.pid
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CROSS JOIN date_ranges dr
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WHERE p.brand IN (?)
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AND o.canceled = false
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AND o.date >= dr.previous_start
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AND o.updated > ?
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GROUP BY p.brand
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`, [batch.map(row => row.brand), lastCalculationTime]);
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// Update metrics using temp tables with optimized calculations
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await connection.query(`
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INSERT INTO brand_metrics (
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brand,
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product_count,
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active_products,
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total_stock_units,
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total_stock_cost,
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total_stock_retail,
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total_revenue,
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avg_margin,
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growth_rate,
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last_calculated_at
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)
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SELECT
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ps.brand,
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ps.product_count,
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ps.active_products,
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ps.total_stock_units,
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ps.total_stock_cost,
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ps.total_stock_retail,
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ps.total_revenue,
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ps.avg_margin,
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CASE
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WHEN COALESCE(ss.previous_period_sales, 0) = 0 AND COALESCE(ss.current_period_sales, 0) > 0 THEN 100
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WHEN COALESCE(ss.previous_period_sales, 0) = 0 THEN 0
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ELSE ROUND(LEAST(999.99, GREATEST(-100,
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((ss.current_period_sales / NULLIF(ss.previous_period_sales, 0)) - 1) * 100
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)), 2)
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END as growth_rate,
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NOW() as last_calculated_at
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FROM temp_product_stats ps
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LEFT JOIN temp_sales_stats ss ON ps.brand = ss.brand
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ON DUPLICATE KEY UPDATE
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product_count = VALUES(product_count),
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active_products = VALUES(active_products),
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total_stock_units = VALUES(total_stock_units),
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total_stock_cost = VALUES(total_stock_cost),
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total_stock_retail = VALUES(total_stock_retail),
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total_revenue = VALUES(total_revenue),
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avg_margin = VALUES(avg_margin),
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growth_rate = VALUES(growth_rate),
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last_calculated_at = NOW()
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`);
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// Clean up temp tables
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await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_product_stats');
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await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_sales_stats');
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lastBrand = batch[batch.length - 1].brand;
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processedCount += batch.length;
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outputProgress({
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status: 'running',
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operation: 'Processing brand metrics batch',
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current: processedCount,
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total: totalBrands,
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elapsed: formatElapsedTime(startTime),
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remaining: estimateRemaining(startTime, processedCount, totalBrands),
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rate: calculateRate(startTime, processedCount),
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percentage: ((processedCount / totalBrands) * 100).toFixed(1),
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timing: {
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start_time: new Date(startTime).toISOString(),
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end_time: new Date().toISOString(),
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elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
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}
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});
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}
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// If we get here, everything completed successfully
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success = true;
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// Update calculate_status
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await connection.query(`
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INSERT INTO calculate_status (module_name, last_calculation_timestamp)
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VALUES ('brand_metrics', NOW())
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ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
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`);
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return {
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processedProducts: processedCount,
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processedOrders: 0,
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processedPurchaseOrders: 0,
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success
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};
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} catch (error) {
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success = false;
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logError(error, 'Error calculating brand metrics');
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throw error;
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} finally {
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if (connection) {
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connection.release();
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}
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}
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}
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module.exports = calculateBrandMetrics;
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