229 lines
9.4 KiB
JavaScript
229 lines
9.4 KiB
JavaScript
const { outputProgress } = require('../utils/progress');
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const { getConnection } = require('../utils/db');
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async function calculateCategoryMetrics(startTime, totalProducts, processedCount) {
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const connection = await getConnection();
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try {
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outputProgress({
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status: 'running',
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operation: 'Calculating category metrics',
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current: Math.floor(totalProducts * 0.85),
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total: totalProducts,
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elapsed: formatElapsedTime(startTime),
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remaining: estimateRemaining(startTime, Math.floor(totalProducts * 0.85), totalProducts),
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rate: calculateRate(startTime, Math.floor(totalProducts * 0.85)),
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percentage: '85'
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});
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// Calculate category performance metrics
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await connection.query(`
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INSERT INTO category_metrics (
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category_id,
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product_count,
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active_products,
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total_value,
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avg_margin,
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turnover_rate,
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growth_rate,
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status
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)
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WITH category_sales AS (
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SELECT
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c.id as category_id,
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COUNT(DISTINCT p.product_id) as product_count,
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COUNT(DISTINCT CASE WHEN p.visible = true THEN p.product_id END) as active_products,
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SUM(p.stock_quantity * p.cost_price) as total_value,
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CASE
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WHEN SUM(o.price * o.quantity) > 0
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THEN (SUM((o.price - p.cost_price) * o.quantity) * 100.0) / SUM(o.price * o.quantity)
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ELSE 0
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END as avg_margin,
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CASE
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WHEN AVG(GREATEST(p.stock_quantity, 0)) >= 0.01
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THEN LEAST(
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SUM(CASE
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WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 1 YEAR)
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THEN COALESCE(o.quantity, 0)
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ELSE 0
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END) /
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GREATEST(
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AVG(GREATEST(p.stock_quantity, 0)),
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1.0
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),
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999.99
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)
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ELSE 0
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END as turnover_rate,
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-- Current period (last 3 months)
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SUM(CASE
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WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
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THEN COALESCE(o.quantity * o.price, 0)
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ELSE 0
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END) as current_period_sales,
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-- Previous year same period
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SUM(CASE
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WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH) AND DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
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THEN COALESCE(o.quantity * o.price, 0)
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ELSE 0
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END) as previous_year_period_sales,
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c.status
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FROM categories c
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LEFT JOIN product_categories pc ON c.id = pc.category_id
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LEFT JOIN products p ON pc.product_id = p.product_id
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LEFT JOIN orders o ON p.product_id = o.product_id AND o.canceled = false
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GROUP BY c.id, c.status
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)
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SELECT
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category_id,
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product_count,
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active_products,
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total_value,
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COALESCE(avg_margin, 0) as avg_margin,
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COALESCE(turnover_rate, 0) as turnover_rate,
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-- Enhanced YoY growth rate calculation
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CASE
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WHEN previous_year_period_sales = 0 AND current_period_sales > 0 THEN 100.0
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WHEN previous_year_period_sales = 0 THEN 0.0
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ELSE LEAST(
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GREATEST(
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((current_period_sales - previous_year_period_sales) /
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NULLIF(previous_year_period_sales, 0)) * 100.0,
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-100.0
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),
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999.99
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)
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END as growth_rate,
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status
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FROM category_sales
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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_value = VALUES(total_value),
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avg_margin = VALUES(avg_margin),
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turnover_rate = VALUES(turnover_rate),
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growth_rate = VALUES(growth_rate),
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status = VALUES(status),
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last_calculated_at = CURRENT_TIMESTAMP
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`);
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// Calculate category time-based metrics
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await connection.query(`
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INSERT INTO category_time_metrics (
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category_id,
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year,
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month,
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product_count,
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active_products,
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total_value,
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total_revenue,
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avg_margin,
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turnover_rate
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)
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SELECT
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c.id as category_id,
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YEAR(o.date) as year,
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MONTH(o.date) as month,
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COUNT(DISTINCT p.product_id) as product_count,
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COUNT(DISTINCT CASE WHEN p.visible = true THEN p.product_id END) as active_products,
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SUM(p.stock_quantity * p.cost_price) as total_value,
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SUM(o.price * o.quantity) as total_revenue,
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CASE
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WHEN SUM(o.price * o.quantity) > 0
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THEN (SUM((o.price - p.cost_price) * o.quantity) * 100.0) / SUM(o.price * o.quantity)
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ELSE 0
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END as avg_margin,
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CASE
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WHEN AVG(p.stock_quantity) > 0
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THEN SUM(o.quantity) / AVG(p.stock_quantity)
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ELSE 0
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END as turnover_rate
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FROM categories c
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LEFT JOIN product_categories pc ON c.id = pc.category_id
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LEFT JOIN products p ON pc.product_id = p.product_id
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LEFT JOIN orders o ON p.product_id = o.product_id AND o.canceled = false
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WHERE o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
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GROUP BY c.id, YEAR(o.date), MONTH(o.date)
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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_value = VALUES(total_value),
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total_revenue = VALUES(total_revenue),
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avg_margin = VALUES(avg_margin),
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turnover_rate = VALUES(turnover_rate)
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`);
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// Calculate category sales metrics
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await connection.query(`
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INSERT INTO category_sales_metrics (
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category_id,
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brand,
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period_start,
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period_end,
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avg_daily_sales,
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total_sold,
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num_products,
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avg_price,
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last_calculated_at
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)
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WITH date_ranges AS (
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SELECT
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DATE_SUB(CURDATE(), INTERVAL 30 DAY) as period_start,
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CURDATE() as period_end
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UNION ALL
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SELECT
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DATE_SUB(CURDATE(), INTERVAL 90 DAY),
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CURDATE()
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UNION ALL
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SELECT
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DATE_SUB(CURDATE(), INTERVAL 180 DAY),
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CURDATE()
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UNION ALL
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SELECT
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DATE_SUB(CURDATE(), INTERVAL 365 DAY),
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CURDATE()
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),
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category_metrics AS (
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SELECT
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c.id as category_id,
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p.brand,
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dr.period_start,
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dr.period_end,
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COUNT(DISTINCT p.product_id) as num_products,
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COALESCE(SUM(o.quantity), 0) / DATEDIFF(dr.period_end, dr.period_start) as avg_daily_sales,
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COALESCE(SUM(o.quantity), 0) as total_sold,
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COALESCE(AVG(o.price), 0) as avg_price
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FROM categories c
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JOIN product_categories pc ON c.id = pc.category_id
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JOIN products p ON pc.product_id = p.product_id
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CROSS JOIN date_ranges dr
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LEFT JOIN orders o ON p.product_id = o.product_id
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AND o.date BETWEEN dr.period_start AND dr.period_end
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AND o.canceled = false
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GROUP BY c.id, p.brand, dr.period_start, dr.period_end
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)
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SELECT
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category_id,
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brand,
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period_start,
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period_end,
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avg_daily_sales,
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total_sold,
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num_products,
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avg_price,
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NOW() as last_calculated_at
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FROM category_metrics
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ON DUPLICATE KEY UPDATE
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avg_daily_sales = VALUES(avg_daily_sales),
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total_sold = VALUES(total_sold),
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num_products = VALUES(num_products),
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avg_price = VALUES(avg_price),
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last_calculated_at = NOW()
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`);
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return Math.floor(totalProducts * 0.9);
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} finally {
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connection.release();
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}
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}
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module.exports = calculateCategoryMetrics;
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