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More gemini suggested improvements for speed

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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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95
inventory-server/scripts/metrics/sales-forecasts.js
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@@ -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;