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Try to speed up category calcs

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Matt
2025-02-10 11:21:20 -05:00
parent 3d2d1b3946
commit eea57528ab
1 changed files with 49 additions and 30 deletions
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79
inventory-server/scripts/metrics/category-metrics.js
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@@ -133,52 +133,71 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
) ENGINE=MEMORY
`);
// Populate product stats
// Populate product stats with optimized index usage
await connection.query(`
INSERT INTO temp_product_stats
SELECT
c.cat_id,
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 * p.cost_price) as total_value,
AVG(pm.avg_margin_percent) as avg_margin,
AVG(pm.turnover_rate) as turnover_rate
COALESCE(SUM(p.stock_quantity * p.cost_price), 0) as total_value,
COALESCE(AVG(NULLIF(pm.avg_margin_percent, 0)), 0) as avg_margin,
COALESCE(AVG(NULLIF(pm.turnover_rate, 0)), 0) as turnover_rate
FROM categories c
FORCE INDEX (PRIMARY)
JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
INNER JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
LEFT JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
LEFT JOIN product_metrics pm FORCE INDEX (PRIMARY) ON p.pid = pm.pid
WHERE c.cat_id IN (?)
AND (
p.updated > ?
OR EXISTS (
SELECT 1 FROM orders o FORCE INDEX (idx_orders_metrics)
WHERE o.pid = p.pid
AND o.updated > ?
)
)
GROUP BY c.cat_id
`, [batch.map(row => row.cat_id)]);
`, [batch.map(row => row.cat_id), lastCalculationTime, lastCalculationTime]);
// Populate sales stats
// Populate sales stats with optimized date handling
await connection.query(`
INSERT INTO temp_sales_stats
WITH date_ranges AS (
SELECT
DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as current_start,
CURRENT_DATE as current_end,
DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) as previous_start,
DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as previous_end
)
SELECT
c.cat_id,
COALESCE(SUM(CASE
WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
COALESCE(SUM(
CASE WHEN o.date >= dr.current_start
THEN o.quantity * o.price
ELSE 0
END), 0) as recent_revenue,
COALESCE(SUM(CASE
WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) AND DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
END
), 0) as recent_revenue,
COALESCE(SUM(
CASE WHEN o.date >= dr.previous_start AND o.date < dr.current_start
THEN o.quantity * o.price
ELSE 0
END), 0) as previous_revenue
END
), 0) as previous_revenue
FROM categories c
FORCE INDEX (PRIMARY)
JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
LEFT JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
LEFT JOIN orders o FORCE INDEX (idx_orders_metrics) ON p.pid = o.pid
AND o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY)
INNER JOIN product_categories pc FORCE INDEX (idx_category) ON c.cat_id = pc.cat_id
INNER JOIN products p FORCE INDEX (PRIMARY) ON pc.pid = p.pid
INNER JOIN orders o FORCE INDEX (idx_orders_metrics) ON p.pid = o.pid
CROSS JOIN date_ranges dr
WHERE c.cat_id IN (?)
AND o.canceled = false
AND o.date >= dr.previous_start
AND o.updated > ?
GROUP BY c.cat_id
`, [batch.map(row => row.cat_id)]);
`, [batch.map(row => row.cat_id), lastCalculationTime]);
// Update metrics using temp tables
// Update metrics using temp tables with optimized calculations
await connection.query(`
INSERT INTO category_metrics (
category_id,
@@ -193,17 +212,17 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
SELECT
c.cat_id,
COALESCE(ps.product_count, 0) as product_count,
COALESCE(ps.active_products, 0) as active_products,
COALESCE(ps.total_value, 0) as total_value,
COALESCE(ps.avg_margin, 0) as avg_margin,
COALESCE(ps.turnover_rate, 0) as turnover_rate,
ps.product_count,
ps.active_products,
ps.total_value,
ps.avg_margin,
ps.turnover_rate,
CASE
WHEN ss.previous_revenue = 0 AND ss.recent_revenue > 0 THEN 100
WHEN ss.previous_revenue = 0 THEN 0
ELSE LEAST(999.99, GREATEST(-100,
((ss.recent_revenue / NULLIF(ss.previous_revenue, 0) - 1) * 100)
))
WHEN COALESCE(ss.previous_revenue, 0) = 0 AND COALESCE(ss.recent_revenue, 0) > 0 THEN 100
WHEN COALESCE(ss.previous_revenue, 0) = 0 THEN 0
ELSE ROUND(LEAST(999.99, GREATEST(-100,
((ss.recent_revenue / NULLIF(ss.previous_revenue, 0)) - 1) * 100
)), 2)
END as growth_rate,
c.status,
NOW() as last_calculated_at