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More gemini suggested improvements for speed

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This commit is contained in:
Matt
2025-02-10 16:16:01 -05:00
parent a9bccd4d01
commit f4f6215d03
3 changed files with 105 additions and 194 deletions
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69
inventory-server/scripts/metrics/product-metrics.js
View File

@@ -219,6 +219,28 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
JOIN products p ON pm.pid = p.pid
LEFT JOIN temp_sales_metrics sm ON pm.pid = sm.pid
LEFT JOIN temp_purchase_metrics lm ON pm.pid = lm.pid
LEFT JOIN (
SELECT
sf.pid,
AVG(CASE
WHEN o.quantity > 0
THEN ABS(sf.forecast_units - o.quantity) / o.quantity * 100
ELSE 100
END) as avg_forecast_error,
AVG(CASE
WHEN o.quantity > 0
THEN (sf.forecast_units - o.quantity) / o.quantity * 100
ELSE 0
END) as avg_forecast_bias,
MAX(sf.forecast_date) as last_forecast_date
FROM sales_forecasts sf
JOIN orders o ON sf.pid = o.pid
AND DATE(o.date) = sf.forecast_date
WHERE o.canceled = false
AND sf.forecast_date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
AND sf.pid IN (?)
GROUP BY sf.pid
) fa ON pm.pid = fa.pid
SET
pm.inventory_value = p.stock_quantity * p.cost_price,
pm.daily_sales_avg = COALESCE(sm.daily_sales_avg, 0),
@@ -229,51 +251,12 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
pm.first_sale_date = sm.first_sale_date,
pm.last_sale_date = sm.last_sale_date,
pm.avg_lead_time_days = COALESCE(lm.avg_lead_time_days, 30),
pm.days_of_inventory = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0
THEN FLOOR(p.stock_quantity / sm.daily_sales_avg)
ELSE NULL
END,
pm.weeks_of_inventory = CASE
WHEN COALESCE(sm.weekly_sales_avg, 0) > 0
THEN FLOOR(p.stock_quantity / sm.weekly_sales_avg)
ELSE NULL
END,
pm.stock_status = CASE
WHEN p.stock_quantity <= 0 THEN 'Out of Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 AND p.stock_quantity <= ? THEN 'Low Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 THEN 'In Stock'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ? THEN 'Critical'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ? THEN 'Reorder'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ? THEN 'Overstocked'
ELSE 'Healthy'
END,
pm.reorder_qty = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 THEN
GREATEST(
CEIL(sm.daily_sales_avg * COALESCE(lm.avg_lead_time_days, 30) * 1.96),
?
)
ELSE ?
END,
pm.overstocked_amt = CASE
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ?
THEN GREATEST(0, p.stock_quantity - CEIL(sm.daily_sales_avg * ?))
ELSE 0
END,
pm.forecast_accuracy = GREATEST(0, 100 - LEAST(fa.avg_forecast_error, 100)),
pm.forecast_bias = GREATEST(-100, LEAST(fa.avg_forecast_bias, 100)),
pm.last_forecast_date = fa.last_forecast_date,
pm.last_calculated_at = NOW()
WHERE p.pid IN (?)
`, [
defaultThresholds.low_stock_threshold,
defaultThresholds.critical_days,
defaultThresholds.reorder_days,
defaultThresholds.overstock_days,
defaultThresholds.low_stock_threshold,
defaultThresholds.low_stock_threshold,
defaultThresholds.overstock_days,
defaultThresholds.overstock_days,
batch.map(row => row.pid)
]);
`, [batch.map(row => row.pid), batch.map(row => row.pid)]);
lastPid = batch[batch.length - 1].pid;
myProcessedProducts += batch.length; // Increment the *module's* count

95
inventory-server/scripts/metrics/sales-forecasts.js
View File

@@ -101,12 +101,6 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
if (batch.length === 0) break;
// Create temporary tables for better performance
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_historical_sales');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_sales_stats');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_recent_trend');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_confidence_calc');
// Create optimized temporary tables with indexes
await connection.query(`
CREATE TEMPORARY TABLE temp_historical_sales (
@@ -128,25 +122,15 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
days_with_sales INT,
first_sale DATE,
last_sale DATE,
PRIMARY KEY (pid),
INDEX (days_with_sales),
INDEX (last_sale)
) ENGINE=MEMORY
`);
await connection.query(`
CREATE TEMPORARY TABLE temp_recent_trend (
pid BIGINT NOT NULL,
recent_avg_units DECIMAL(10,2),
recent_avg_revenue DECIMAL(15,2),
PRIMARY KEY (pid)
) ENGINE=MEMORY
`);
await connection.query(`
CREATE TEMPORARY TABLE temp_confidence_calc (
CREATE TEMPORARY TABLE temp_recent_stats (
pid BIGINT NOT NULL,
confidence_level TINYINT,
recent_avg_units DECIMAL(10,2),
recent_avg_revenue DECIMAL(15,2),
PRIMARY KEY (pid)
) ENGINE=MEMORY
`);
@@ -167,7 +151,7 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
GROUP BY o.pid, DATE(o.date)
`, [batch.map(row => row.pid)]);
// Populate sales stats
// Combine sales stats and recent trend calculations
await connection.query(`
INSERT INTO temp_sales_stats
SELECT
@@ -182,23 +166,40 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
GROUP BY pid
`);
// Populate recent trend
// Calculate recent averages
await connection.query(`
INSERT INTO temp_recent_trend
INSERT INTO temp_recent_stats
SELECT
h.pid,
AVG(h.daily_quantity) as recent_avg_units,
AVG(h.daily_revenue) as recent_avg_revenue
FROM temp_historical_sales h
WHERE h.sale_date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
GROUP BY h.pid
pid,
AVG(daily_quantity) as recent_avg_units,
AVG(daily_revenue) as recent_avg_revenue
FROM temp_historical_sales
WHERE sale_date >= DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY)
GROUP BY pid
`);
// Calculate confidence levels
// Generate forecasts using temp tables - optimized version
await connection.query(`
INSERT INTO temp_confidence_calc
REPLACE INTO sales_forecasts
(pid, forecast_date, forecast_units, forecast_revenue, confidence_level, last_calculated_at)
SELECT
s.pid,
s.pid,
DATE_ADD(CURRENT_DATE, INTERVAL n.days DAY),
GREATEST(0, ROUND(
CASE
WHEN s.days_with_sales >= n.days
THEN COALESCE(r.recent_avg_units, s.avg_daily_units)
ELSE s.avg_daily_units * (s.days_with_sales / n.days)
END
)),
GREATEST(0, ROUND(
CASE
WHEN s.days_with_sales >= n.days
THEN COALESCE(r.recent_avg_revenue, s.avg_daily_revenue)
ELSE s.avg_daily_revenue * (s.days_with_sales / n.days)
END,
2
)),
LEAST(100, GREATEST(0, ROUND(
(s.days_with_sales / 180.0 * 50) + -- Up to 50 points for history length
(CASE
@@ -213,47 +214,21 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
WHEN DATEDIFF(CURRENT_DATE, s.last_sale) <= 30 THEN 10
ELSE 0
END) -- Up to 20 points for recency
))) as confidence_level
FROM temp_sales_stats s
`);
// Generate forecasts using temp tables
await connection.query(`
REPLACE INTO sales_forecasts
(pid, forecast_date, forecast_units, forecast_revenue, confidence_level, last_calculated_at)
SELECT
s.pid,
DATE_ADD(CURRENT_DATE, INTERVAL n.days DAY),
GREATEST(0, ROUND(
CASE
WHEN s.days_with_sales >= n.days THEN COALESCE(t.recent_avg_units, s.avg_daily_units)
ELSE s.avg_daily_units * (s.days_with_sales / n.days)
END
)),
GREATEST(0, ROUND(
CASE
WHEN s.days_with_sales >= n.days THEN COALESCE(t.recent_avg_revenue, s.avg_daily_revenue)
ELSE s.avg_daily_revenue * (s.days_with_sales / n.days)
END,
2
)),
c.confidence_level,
))),
NOW()
FROM temp_sales_stats s
LEFT JOIN temp_recent_stats r ON s.pid = r.pid
CROSS JOIN (
SELECT 30 as days
UNION SELECT 60
UNION SELECT 90
) n
LEFT JOIN temp_recent_trend t ON s.pid = t.pid
LEFT JOIN temp_confidence_calc c ON s.pid = c.pid;
`);
// Clean up temp tables
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_historical_sales');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_sales_stats');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_recent_trend');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_confidence_calc');
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_recent_stats');
lastPid = batch[batch.length - 1].pid;
myProcessedProducts += batch.length;