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eea57528ab
| Author | SHA1 | Date | |
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| eea57528ab | |||
| 3d2d1b3946 |
@@ -138,11 +138,11 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
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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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SUM(p.stock_quantity) as total_stock_units,
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SUM(p.stock_quantity * p.cost_price) as total_stock_cost,
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SUM(p.stock_quantity * p.price) as total_stock_retail,
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SUM(pm.total_revenue) as total_revenue,
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AVG(pm.avg_margin_percent) as avg_margin
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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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@@ -158,32 +158,42 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
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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 index usage
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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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SUM(CASE
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WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
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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) as current_period_sales,
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SUM(CASE
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WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) AND DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
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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) as previous_period_sales
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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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AND o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY)
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AND o.updated > ?
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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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`, [lastCalculationTime, batch.map(row => row.brand)]);
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`, [batch.map(row => row.brand), lastCalculationTime]);
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// Update metrics using temp tables
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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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@@ -199,19 +209,19 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
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)
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SELECT
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ps.brand,
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COALESCE(ps.product_count, 0) as product_count,
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COALESCE(ps.active_products, 0) as active_products,
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COALESCE(ps.total_stock_units, 0) as total_stock_units,
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COALESCE(ps.total_stock_cost, 0) as total_stock_cost,
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COALESCE(ps.total_stock_retail, 0) as total_stock_retail,
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COALESCE(ps.total_revenue, 0) as total_revenue,
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COALESCE(ps.avg_margin, 0) as avg_margin,
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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 LEAST(999.99, GREATEST(-100,
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((COALESCE(ss.current_period_sales, 0) / ss.previous_period_sales) - 1) * 100
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))
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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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@@ -133,52 +133,71 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
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) ENGINE=MEMORY
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`);
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// Populate product stats
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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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c.cat_id,
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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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SUM(p.stock_quantity * p.cost_price) as total_value,
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AVG(pm.avg_margin_percent) as avg_margin,
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AVG(pm.turnover_rate) as turnover_rate
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COALESCE(SUM(p.stock_quantity * p.cost_price), 0) as total_value,
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COALESCE(AVG(NULLIF(pm.avg_margin_percent, 0)), 0) as avg_margin,
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COALESCE(AVG(NULLIF(pm.turnover_rate, 0)), 0) as turnover_rate
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FROM categories c
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FORCE INDEX (PRIMARY)
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JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
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INNER JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
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LEFT JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
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LEFT JOIN product_metrics pm FORCE INDEX (PRIMARY) ON p.pid = pm.pid
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WHERE c.cat_id 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 c.cat_id
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`, [batch.map(row => row.cat_id)]);
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`, [batch.map(row => row.cat_id), lastCalculationTime, lastCalculationTime]);
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// Populate sales stats
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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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c.cat_id,
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COALESCE(SUM(CASE
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WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
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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), 0) as recent_revenue,
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COALESCE(SUM(CASE
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WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) AND DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
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END
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), 0) as recent_revenue,
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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), 0) as previous_revenue
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END
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), 0) as previous_revenue
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FROM categories c
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FORCE INDEX (PRIMARY)
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JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
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LEFT JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
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LEFT JOIN orders o FORCE INDEX (idx_orders_metrics) ON p.pid = o.pid
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AND o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY)
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INNER JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
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INNER JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
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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 c.cat_id 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 c.cat_id
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`, [batch.map(row => row.cat_id)]);
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`, [batch.map(row => row.cat_id), lastCalculationTime]);
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// Update metrics using temp tables
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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 category_metrics (
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category_id,
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@@ -193,17 +212,17 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
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)
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SELECT
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c.cat_id,
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COALESCE(ps.product_count, 0) as product_count,
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COALESCE(ps.active_products, 0) as active_products,
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COALESCE(ps.total_value, 0) as total_value,
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COALESCE(ps.avg_margin, 0) as avg_margin,
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COALESCE(ps.turnover_rate, 0) as turnover_rate,
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ps.product_count,
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ps.active_products,
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ps.total_value,
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ps.avg_margin,
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ps.turnover_rate,
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CASE
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WHEN ss.previous_revenue = 0 AND ss.recent_revenue > 0 THEN 100
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WHEN ss.previous_revenue = 0 THEN 0
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ELSE LEAST(999.99, GREATEST(-100,
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((ss.recent_revenue / NULLIF(ss.previous_revenue, 0) - 1) * 100)
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))
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WHEN COALESCE(ss.previous_revenue, 0) = 0 AND COALESCE(ss.recent_revenue, 0) > 0 THEN 100
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WHEN COALESCE(ss.previous_revenue, 0) = 0 THEN 0
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ELSE ROUND(LEAST(999.99, GREATEST(-100,
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((ss.recent_revenue / NULLIF(ss.previous_revenue, 0)) - 1) * 100
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)), 2)
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END as growth_rate,
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c.status,
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NOW() as last_calculated_at
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