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Update calculate script to account for import changes

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This commit is contained in:
Matt
2025-01-27 10:41:18 -05:00
parent 44d9ae2aad
commit 5781b45f37
10 changed files with 508 additions and 297 deletions
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44
inventory-server/scripts/metrics/category-metrics.js
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@@ -29,9 +29,9 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
WITH category_sales AS (
SELECT
c.id as category_id,
COUNT(DISTINCT p.product_id) as product_count,
COUNT(DISTINCT CASE WHEN p.visible = true THEN p.product_id END) as active_products,
c.cat_id as category_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,
CASE
WHEN SUM(o.price * o.quantity) > 0
@@ -68,10 +68,10 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
END) as previous_year_period_sales,
c.status
FROM categories c
LEFT JOIN product_categories pc ON c.id = pc.category_id
LEFT JOIN products p ON pc.product_id = p.product_id
LEFT JOIN orders o ON p.product_id = o.product_id AND o.canceled = false
GROUP BY c.id, c.status
LEFT JOIN product_categories pc ON c.cat_id = pc.cat_id
LEFT JOIN products p ON pc.pid = p.pid
LEFT JOIN orders o ON p.pid = o.pid AND o.canceled = false
GROUP BY c.cat_id, c.status
)
SELECT
category_id,
@@ -120,11 +120,11 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
turnover_rate
)
SELECT
c.id as category_id,
c.cat_id as category_id,
YEAR(o.date) as year,
MONTH(o.date) as month,
COUNT(DISTINCT p.product_id) as product_count,
COUNT(DISTINCT CASE WHEN p.visible = true THEN p.product_id END) as active_products,
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,
SUM(o.price * o.quantity) as total_revenue,
CASE
@@ -138,11 +138,11 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
ELSE 0
END as turnover_rate
FROM categories c
LEFT JOIN product_categories pc ON c.id = pc.category_id
LEFT JOIN products p ON pc.product_id = p.product_id
LEFT JOIN orders o ON p.product_id = o.product_id AND o.canceled = false
LEFT JOIN product_categories pc ON c.cat_id = pc.cat_id
LEFT JOIN products p ON pc.pid = p.pid
LEFT JOIN orders o ON p.pid = o.pid AND o.canceled = false
WHERE o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
GROUP BY c.id, YEAR(o.date), MONTH(o.date)
GROUP BY c.cat_id, YEAR(o.date), MONTH(o.date)
ON DUPLICATE KEY UPDATE
product_count = VALUES(product_count),
active_products = VALUES(active_products),
@@ -152,7 +152,7 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
turnover_rate = VALUES(turnover_rate)
`);
// Calculate category sales metrics
// Calculate category sales metrics with NULL brand handling
await connection.query(`
INSERT INTO category_sales_metrics (
category_id,
@@ -184,22 +184,22 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
),
category_metrics AS (
SELECT
c.id as category_id,
p.brand,
c.cat_id as category_id,
COALESCE(p.brand, 'Unbranded') as brand,
dr.period_start,
dr.period_end,
COUNT(DISTINCT p.product_id) as num_products,
COUNT(DISTINCT p.pid) as num_products,
COALESCE(SUM(o.quantity), 0) / DATEDIFF(dr.period_end, dr.period_start) as avg_daily_sales,
COALESCE(SUM(o.quantity), 0) as total_sold,
COALESCE(AVG(o.price), 0) as avg_price
FROM categories c
JOIN product_categories pc ON c.id = pc.category_id
JOIN products p ON pc.product_id = p.product_id
JOIN product_categories pc ON c.cat_id = pc.cat_id
JOIN products p ON pc.pid = p.pid
CROSS JOIN date_ranges dr
LEFT JOIN orders o ON p.product_id = o.product_id
LEFT JOIN orders o ON p.pid = o.pid
AND o.date BETWEEN dr.period_start AND dr.period_end
AND o.canceled = false
GROUP BY c.id, p.brand, dr.period_start, dr.period_end
GROUP BY c.cat_id, COALESCE(p.brand, 'Unbranded'), dr.period_start, dr.period_end
)
SELECT
category_id,