## 📸 File History Snapshot (2025-12-05T07:40:57.338Z)
## ◂ USER
  Can you tell me the percentage change between 2024-11 and 2024-12 deliveries? And then explain the SQL query you used in detail
## ◆ ASSISTANT
### ▸ Tool: Bash
  (toolu_015pKWNs7xUbTgkheDhwn6fn)
    **command:** mariadb practice -e "
    SELECT 
        DATE_FORMAT(report_date, '%Y-%m') AS month,
        SUM(total_shipments) AS total_deliveries
    FROM daily_delivery_metrics
    WHERE report_date >= '2024-11-01' AND report_date...
    **description:** Get delivery counts for Nov and Dec 2024
  Tokens: in=2 out=177 cache=17760
### ✓ Result
  (toolu_015pKWNs7xUbTgkheDhwn6fn)
    month	total_deliveries
    2024-11	2218
    2024-12	2394
## ◆ ASSISTANT
  The percentage change between November 2024 and December 2024 deliveries is:
  
  **+7.94%** (from 2,218 to 2,394 deliveries)
  
  Calculation: `((2394 - 2218) / 2218) × 100 = 7.94%`
  
  ---
  
  ## SQL Query Explanation
  
  ```sql
  SELECT 
      DATE_FORMAT(report_date, '%Y-%m') AS month,
      SUM(total_shipments) AS total_deliveries
  FROM daily_delivery_metrics
  WHERE report_date >= '2024-11-01' AND report_date < '2025-01-01'
  GROUP BY DATE_FORMAT(report_date, '%Y-%m')
  ORDER BY month;
  ```
  
  ### Breakdown:
  
  1. **`DATE_FORMAT(report_date, '%Y-%m') AS month`**
     - Extracts the year and month from each `report_date` (e.g., `2024-11-15` becomes `2024-11`)
     - `%Y` = 4-digit year, `%m` = 2-digit month
     - Aliased as `month` for readability
  
  2. **`SUM(total_shipments) AS total_deliveries`**
     - Aggregates all shipments within each month
     - The table stores daily metrics per warehouse, so summing gives us the monthly total across all warehouses
  
  3. **`FROM daily_delivery_metrics`**
     - Uses the pre-aggregated daily metrics table (rather than counting raw shipment records)
  
  4. **`WHERE report_date >= '2024-11-01' AND report_date < '2025-01-01'`**
     - Filters to only include dates in November and December 2024
     - Uses a range condition (inclusive start, exclusive end) which is a best practice for date filtering
  
  5. **`GROUP BY DATE_FORMAT(report_date, '%Y-%m')`**
     - Groups all daily records into their respective months so `SUM()` calculates per-month totals
  
  6. **`ORDER BY month`**
     - Sorts chronologically so November appears before December
  Tokens: in=0 out=509 cache=22763
