248 lines
10 KiB
JavaScript
248 lines
10 KiB
JavaScript
const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
|
|
const { getConnection } = require('./utils/db');
|
|
|
|
async function calculateBrandMetrics(startTime, totalProducts, processedCount = 0, isCancelled = false) {
|
|
const connection = await getConnection();
|
|
let success = false;
|
|
const BATCH_SIZE = 5000;
|
|
|
|
try {
|
|
// Get last calculation timestamp
|
|
const [lastCalc] = await connection.query(`
|
|
SELECT last_calculation_timestamp
|
|
FROM calculate_status
|
|
WHERE module_name = 'brand_metrics'
|
|
`);
|
|
const lastCalculationTime = lastCalc[0]?.last_calculation_timestamp || '1970-01-01';
|
|
|
|
// Get total count of brands needing updates
|
|
const [brandCount] = await connection.query(`
|
|
SELECT COUNT(DISTINCT p.brand) as count
|
|
FROM products p
|
|
LEFT JOIN orders o ON p.pid = o.pid AND o.updated > ?
|
|
WHERE p.brand IS NOT NULL
|
|
AND (
|
|
p.updated > ?
|
|
OR o.id IS NOT NULL
|
|
)
|
|
`, [lastCalculationTime, lastCalculationTime]);
|
|
const totalBrands = brandCount[0].count;
|
|
|
|
if (totalBrands === 0) {
|
|
console.log('No brands need metric updates');
|
|
return {
|
|
processedProducts: 0,
|
|
processedOrders: 0,
|
|
processedPurchaseOrders: 0,
|
|
success: true
|
|
};
|
|
}
|
|
|
|
if (isCancelled) {
|
|
outputProgress({
|
|
status: 'cancelled',
|
|
operation: 'Brand metrics calculation cancelled',
|
|
current: processedCount,
|
|
total: totalBrands,
|
|
elapsed: formatElapsedTime(startTime),
|
|
remaining: null,
|
|
rate: calculateRate(startTime, processedCount),
|
|
percentage: ((processedCount / totalBrands) * 100).toFixed(1),
|
|
timing: {
|
|
start_time: new Date(startTime).toISOString(),
|
|
end_time: new Date().toISOString(),
|
|
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
|
|
}
|
|
});
|
|
return {
|
|
processedProducts: processedCount,
|
|
processedOrders: 0,
|
|
processedPurchaseOrders: 0,
|
|
success
|
|
};
|
|
}
|
|
|
|
outputProgress({
|
|
status: 'running',
|
|
operation: 'Starting brand metrics calculation',
|
|
current: processedCount,
|
|
total: totalBrands,
|
|
elapsed: formatElapsedTime(startTime),
|
|
remaining: estimateRemaining(startTime, processedCount, totalBrands),
|
|
rate: calculateRate(startTime, processedCount),
|
|
percentage: ((processedCount / totalBrands) * 100).toFixed(1),
|
|
timing: {
|
|
start_time: new Date(startTime).toISOString(),
|
|
end_time: new Date().toISOString(),
|
|
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
|
|
}
|
|
});
|
|
|
|
// Process in batches
|
|
let lastBrand = '';
|
|
while (true) {
|
|
if (isCancelled) break;
|
|
|
|
const [batch] = await connection.query(`
|
|
SELECT DISTINCT p.brand
|
|
FROM products p
|
|
WHERE p.brand IS NOT NULL
|
|
AND p.brand > ?
|
|
AND (
|
|
p.updated > ?
|
|
OR EXISTS (
|
|
SELECT 1 FROM orders o
|
|
WHERE o.pid = p.pid
|
|
AND o.updated > ?
|
|
)
|
|
)
|
|
ORDER BY p.brand
|
|
LIMIT ?
|
|
`, [lastBrand, lastCalculationTime, lastCalculationTime, BATCH_SIZE]);
|
|
|
|
if (batch.length === 0) break;
|
|
|
|
// Update brand metrics for this batch
|
|
await connection.query(`
|
|
INSERT INTO brand_metrics (
|
|
brand,
|
|
product_count,
|
|
active_products,
|
|
total_stock_units,
|
|
total_stock_cost,
|
|
total_stock_retail,
|
|
total_revenue,
|
|
avg_margin,
|
|
growth_rate,
|
|
last_calculated_at
|
|
)
|
|
WITH product_stats AS (
|
|
SELECT
|
|
p.brand,
|
|
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) as total_stock_units,
|
|
SUM(p.stock_quantity * p.cost_price) as total_stock_cost,
|
|
SUM(p.stock_quantity * p.price) as total_stock_retail,
|
|
SUM(pm.total_revenue) as total_revenue,
|
|
AVG(pm.avg_margin_percent) as avg_margin
|
|
FROM products p
|
|
LEFT JOIN product_metrics pm ON p.pid = pm.pid
|
|
WHERE p.brand IN (?)
|
|
AND (
|
|
p.updated > ?
|
|
OR EXISTS (
|
|
SELECT 1 FROM orders o
|
|
WHERE o.pid = p.pid
|
|
AND o.updated > ?
|
|
)
|
|
)
|
|
GROUP BY p.brand
|
|
),
|
|
sales_periods AS (
|
|
SELECT
|
|
p.brand,
|
|
SUM(CASE
|
|
WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
|
|
THEN o.quantity * o.price
|
|
ELSE 0
|
|
END) as current_period_sales,
|
|
SUM(CASE
|
|
WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY) AND DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
|
|
THEN o.quantity * o.price
|
|
ELSE 0
|
|
END) as previous_period_sales
|
|
FROM products p
|
|
INNER JOIN orders o ON p.pid = o.pid
|
|
AND o.canceled = false
|
|
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 60 DAY)
|
|
AND o.updated > ?
|
|
WHERE p.brand IN (?)
|
|
GROUP BY p.brand
|
|
)
|
|
SELECT
|
|
ps.brand,
|
|
COALESCE(ps.product_count, 0) as product_count,
|
|
COALESCE(ps.active_products, 0) as active_products,
|
|
COALESCE(ps.total_stock_units, 0) as total_stock_units,
|
|
COALESCE(ps.total_stock_cost, 0) as total_stock_cost,
|
|
COALESCE(ps.total_stock_retail, 0) as total_stock_retail,
|
|
COALESCE(ps.total_revenue, 0) as total_revenue,
|
|
COALESCE(ps.avg_margin, 0) as avg_margin,
|
|
CASE
|
|
WHEN COALESCE(sp.previous_period_sales, 0) = 0 AND COALESCE(sp.current_period_sales, 0) > 0 THEN 100
|
|
WHEN COALESCE(sp.previous_period_sales, 0) = 0 THEN 0
|
|
ELSE LEAST(999.99, GREATEST(-100,
|
|
((COALESCE(sp.current_period_sales, 0) / sp.previous_period_sales) - 1) * 100
|
|
))
|
|
END as growth_rate,
|
|
NOW() as last_calculated_at
|
|
FROM product_stats ps
|
|
LEFT JOIN sales_periods sp ON ps.brand = sp.brand
|
|
ON DUPLICATE KEY UPDATE
|
|
product_count = VALUES(product_count),
|
|
active_products = VALUES(active_products),
|
|
total_stock_units = VALUES(total_stock_units),
|
|
total_stock_cost = VALUES(total_stock_cost),
|
|
total_stock_retail = VALUES(total_stock_retail),
|
|
total_revenue = VALUES(total_revenue),
|
|
avg_margin = VALUES(avg_margin),
|
|
growth_rate = VALUES(growth_rate),
|
|
last_calculated_at = NOW()
|
|
`, [
|
|
batch.map(row => row.brand), // For first IN clause
|
|
lastCalculationTime, // For p.updated > ?
|
|
lastCalculationTime, // For o.updated > ? in EXISTS
|
|
lastCalculationTime, // For o.updated > ? in sales_periods
|
|
batch.map(row => row.brand) // For second IN clause
|
|
]);
|
|
|
|
lastBrand = batch[batch.length - 1].brand;
|
|
processedCount += batch.length;
|
|
|
|
outputProgress({
|
|
status: 'running',
|
|
operation: 'Processing brand metrics batch',
|
|
current: processedCount,
|
|
total: totalBrands,
|
|
elapsed: formatElapsedTime(startTime),
|
|
remaining: estimateRemaining(startTime, processedCount, totalBrands),
|
|
rate: calculateRate(startTime, processedCount),
|
|
percentage: ((processedCount / totalBrands) * 100).toFixed(1),
|
|
timing: {
|
|
start_time: new Date(startTime).toISOString(),
|
|
end_time: new Date().toISOString(),
|
|
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
|
|
}
|
|
});
|
|
}
|
|
|
|
// If we get here, everything completed successfully
|
|
success = true;
|
|
|
|
// Update calculate_status
|
|
await connection.query(`
|
|
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
|
|
VALUES ('brand_metrics', NOW())
|
|
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
|
|
`);
|
|
|
|
return {
|
|
processedProducts: processedCount,
|
|
processedOrders: 0,
|
|
processedPurchaseOrders: 0,
|
|
success
|
|
};
|
|
|
|
} catch (error) {
|
|
success = false;
|
|
logError(error, 'Error calculating brand metrics');
|
|
throw error;
|
|
} finally {
|
|
if (connection) {
|
|
connection.release();
|
|
}
|
|
}
|
|
}
|
|
|
|
module.exports = calculateBrandMetrics;
|