208 lines
8.1 KiB
JavaScript
208 lines
8.1 KiB
JavaScript
const { outputProgress } = require('./utils/progress');
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const { getConnection } = require('./utils/db');
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async function calculateSalesForecasts(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 sales forecasts',
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current: Math.floor(totalProducts * 0.98),
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total: totalProducts,
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elapsed: formatElapsedTime(startTime),
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remaining: estimateRemaining(startTime, Math.floor(totalProducts * 0.98), totalProducts),
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rate: calculateRate(startTime, Math.floor(totalProducts * 0.98)),
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percentage: '98'
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});
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// First, create a temporary table for forecast dates
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await connection.query(`
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CREATE TEMPORARY TABLE IF NOT EXISTS temp_forecast_dates (
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forecast_date DATE,
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day_of_week INT,
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month INT,
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PRIMARY KEY (forecast_date)
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)
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`);
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await connection.query(`
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INSERT INTO temp_forecast_dates
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SELECT
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DATE_ADD(CURRENT_DATE, INTERVAL n DAY) as forecast_date,
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DAYOFWEEK(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as day_of_week,
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MONTH(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as month
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FROM (
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SELECT a.N + b.N * 10 as n
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FROM
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(SELECT 0 as N UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
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SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9) a,
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(SELECT 0 as N UNION SELECT 1 UNION SELECT 2) b
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ORDER BY n
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LIMIT 31
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) numbers
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`);
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// Create temporary table for daily sales stats
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await connection.query(`
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CREATE TEMPORARY TABLE IF NOT EXISTS temp_daily_sales AS
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SELECT
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o.pid,
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DAYOFWEEK(o.date) as day_of_week,
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SUM(o.quantity) as daily_quantity,
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SUM(o.price * o.quantity) as daily_revenue,
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COUNT(DISTINCT DATE(o.date)) as day_count
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FROM orders o
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WHERE o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
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GROUP BY o.pid, DAYOFWEEK(o.date)
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`);
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// Create temporary table for product stats
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await connection.query(`
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CREATE TEMPORARY TABLE IF NOT EXISTS temp_product_stats AS
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SELECT
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pid,
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AVG(daily_revenue) as overall_avg_revenue,
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SUM(day_count) as total_days
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FROM temp_daily_sales
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GROUP BY pid
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`);
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// Calculate product-level forecasts
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await connection.query(`
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INSERT INTO sales_forecasts (
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pid,
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forecast_date,
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forecast_units,
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forecast_revenue,
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confidence_level,
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last_calculated_at
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)
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SELECT
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ds.pid,
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fd.forecast_date,
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GREATEST(0,
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AVG(ds.daily_quantity) *
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(1 + COALESCE(sf.seasonality_factor, 0))
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) as forecast_units,
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GREATEST(0,
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COALESCE(
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CASE
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WHEN SUM(ds.day_count) >= 4 THEN AVG(ds.daily_revenue)
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ELSE ps.overall_avg_revenue
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END *
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(1 + COALESCE(sf.seasonality_factor, 0)) *
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(0.95 + (RAND() * 0.1)),
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0
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)
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) as forecast_revenue,
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CASE
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WHEN ps.total_days >= 60 THEN 90
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WHEN ps.total_days >= 30 THEN 80
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WHEN ps.total_days >= 14 THEN 70
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ELSE 60
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END as confidence_level,
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NOW() as last_calculated_at
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FROM temp_daily_sales ds
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JOIN temp_product_stats ps ON ds.pid = ps.pid
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CROSS JOIN temp_forecast_dates fd
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LEFT JOIN sales_seasonality sf ON fd.month = sf.month
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GROUP BY ds.pid, fd.forecast_date, ps.overall_avg_revenue, ps.total_days, sf.seasonality_factor
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HAVING AVG(ds.daily_quantity) > 0
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ON DUPLICATE KEY UPDATE
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forecast_units = VALUES(forecast_units),
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forecast_revenue = VALUES(forecast_revenue),
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confidence_level = VALUES(confidence_level),
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last_calculated_at = NOW()
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`);
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// Create temporary table for category stats
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await connection.query(`
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CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_sales AS
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SELECT
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pc.cat_id,
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DAYOFWEEK(o.date) as day_of_week,
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SUM(o.quantity) as daily_quantity,
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SUM(o.price * o.quantity) as daily_revenue,
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COUNT(DISTINCT DATE(o.date)) as day_count
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FROM orders o
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JOIN product_categories pc ON o.pid = pc.pid
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WHERE o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
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GROUP BY pc.cat_id, DAYOFWEEK(o.date)
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`);
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await connection.query(`
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CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_stats AS
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SELECT
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cat_id,
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AVG(daily_revenue) as overall_avg_revenue,
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SUM(day_count) as total_days
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FROM temp_category_sales
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GROUP BY cat_id
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`);
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// Calculate category-level forecasts
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await connection.query(`
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INSERT INTO category_forecasts (
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category_id,
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forecast_date,
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forecast_units,
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forecast_revenue,
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confidence_level,
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last_calculated_at
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)
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SELECT
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cs.cat_id as category_id,
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fd.forecast_date,
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GREATEST(0,
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AVG(cs.daily_quantity) *
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(1 + COALESCE(sf.seasonality_factor, 0))
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) as forecast_units,
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GREATEST(0,
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COALESCE(
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CASE
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WHEN SUM(cs.day_count) >= 4 THEN AVG(cs.daily_revenue)
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ELSE ct.overall_avg_revenue
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END *
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(1 + COALESCE(sf.seasonality_factor, 0)) *
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(0.95 + (RAND() * 0.1)),
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0
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)
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) as forecast_revenue,
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CASE
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WHEN ct.total_days >= 60 THEN 90
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WHEN ct.total_days >= 30 THEN 80
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WHEN ct.total_days >= 14 THEN 70
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ELSE 60
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END as confidence_level,
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NOW() as last_calculated_at
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FROM temp_category_sales cs
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JOIN temp_category_stats ct ON cs.cat_id = ct.cat_id
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CROSS JOIN temp_forecast_dates fd
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LEFT JOIN sales_seasonality sf ON fd.month = sf.month
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GROUP BY cs.cat_id, fd.forecast_date, ct.overall_avg_revenue, ct.total_days, sf.seasonality_factor
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HAVING AVG(cs.daily_quantity) > 0
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ON DUPLICATE KEY UPDATE
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forecast_units = VALUES(forecast_units),
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forecast_revenue = VALUES(forecast_revenue),
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confidence_level = VALUES(confidence_level),
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last_calculated_at = NOW()
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`);
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// Clean up temporary tables
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await connection.query(`
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DROP TEMPORARY TABLE IF EXISTS temp_forecast_dates;
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DROP TEMPORARY TABLE IF EXISTS temp_daily_sales;
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DROP TEMPORARY TABLE IF EXISTS temp_product_stats;
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DROP TEMPORARY TABLE IF EXISTS temp_category_sales;
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DROP TEMPORARY TABLE IF EXISTS temp_category_stats;
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`);
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return Math.floor(totalProducts * 1.0);
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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 = calculateSalesForecasts;
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