poc-v1/optimization_metadata.json

{
  "b6e1d99d9d5dd236": {
    "query_hash": "b6e1d99d9d5dd236",
    "original_query": "SELECT DISTINCT\n\nc.customer_id,\n\nc.first_name,\n\nc.last_name,\n\nc.email,\n\nCOUNT(o.order_id) as total_orders,\n\nSUM(CASE WHEN o.status = 'completed' THEN 1 ELSE 0 END) as completed_orders,\n\nSUM(CASE WHEN o.status = 'pending' THEN 1 ELSE 0 END) as pending_orders,\n\nAVG(o.total_amount) as avg_order_value,\n\nMAX(o.order_date) as last_order_date,\n\nCASE\n\nWHEN COUNT(o.order_id) > 50 THEN 'VIP'\n\nWHEN COUNT(o.order_id) > 20 THEN 'Premium'\n\nWHEN COUNT(o.order_id) > 5 THEN 'Regular'\n\nELSE 'New'\n\nEND as customer_tier\n\nFROM customers c\n\nLEFT JOIN orders o ON o.customer_id = c.customer_id\n\nWHERE c.customer_id IN (\n\nSELECT DISTINCT o19.customer_id\n\nFROM orders o19\n\nWHERE o19.order_date >= SYSDATE - 730\n\n)\n\nAND c.email LIKE '%@%'\n\nGROUP BY c.customer_id, c.first_name, c.last_name, c.email\n\nHAVING COUNT(o.order_id) > 0\n\nORDER BY COUNT(o.order_id) DESC",
    "explanation": "1. **Overall Purpose**\nThe query is designed to retrieve a list of customers, their order details, and a classification of their customer tier based on the number of orders they have placed. The query is intended to provide a detailed view of customer activity, including the total number of orders, the number of completed and pending orders, the average order value, the date of the last order, and a classification of the customer based on their order volume.\n\n2. **Involved Database Objects**\nThe query involves two tables: `customers` and `orders`. It also includes a subquery on the `orders` table.\n\n3. **Essential Operations**\n- The query retrieves the following columns: `c.customer_id`, `c.first_name`, `c.last_name`, `c.email`, `total_orders`, `completed_orders`, `pending_orders`, `avg_order_value`, `last_order_date`, and `customer_tier`.\n- A LEFT JOIN is performed between the `customers` table (aliased as `c`) and the `orders` table (aliased as `o`) on the condition that `o.customer_id = c.customer_id`.\n- The WHERE clause filters the customers based on two conditions: the customer's ID must be in the list of customer IDs who have placed an order in the last two years (730 days), and the customer's email must contain an '@' symbol.\n- The COUNT function is used to calculate the total number of orders per customer (`total_orders`), and the SUM function is used with a CASE statement to calculate the number of completed and pending orders.\n- The AVG function is used to calculate the average order value (`avg_order_value`), and the MAX function is used to determine the date of the last order (`last_order_date`).\n- A CASE statement is used to classify customers into tiers based on the number of orders they have placed.\n- The GROUP BY clause groups the results by `c.customer_id`, `c.first_name`, `c.last_name`, and `c.email`.\n- The HAVING clause filters out customers who have not placed any orders.\n- The ORDER BY clause sorts the results in descending order based on the total number of orders.\n\n4. **Performance Issues**\n- The query uses a leading wildcard in the LIKE condition (`c.email LIKE '%@%'`), which can prevent the use of indexes and slow down the query.\n- The subquery in the WHERE clause could potentially be a performance issue, depending on the size of the `orders` table and the number of distinct customer IDs. It might be more efficient to use a JOIN instead.\n- The query uses multiple COUNT functions in the SELECT and ORDER BY clauses, which could be a performance issue if the number of rows is large. It might be more efficient to calculate the count once and store it in a variable or a temporary table.",
    "database_type": "postgresql",
    "version": "1.1",
    "optimized_at": "2026-01-21T17:31:17.127080+00:00"
  },
  "60243f154ec1c6c5": {
    "query_hash": "60243f154ec1c6c5",
    "original_query": "SELECT DISTINCT\n\nc.customer_id,\n\nc.first_name,\n\nc.last_name,\n\nc.email,\n\n(SELECT COUNT() FROM orders o1 WHERE o1.customer_id = c.customer_id) as total_orders,\n\n(SELECT COUNT() FROM orders o2 WHERE o2.customer_id = c.customer_id AND o2.status = 'completed') as completed_orders,\n\n(SELECT COUNT() FROM orders o3 WHERE o3.customer_id = c.customer_id AND o3.status = 'pending') as pending_orders,\n\n(SELECT AVG(o5.total_amount) FROM orders o5 WHERE o5.customer_id = c.customer_id) as avg_order_value,\n\n(SELECT MAX(o6.order_date) FROM orders o6 WHERE o6.customer_id = c.customer_id) as last_order_date,\n\nCASE\n\nWHEN (SELECT COUNT() FROM orders o8 WHERE o8.customer_id = c.customer_id) > 50 THEN 'VIP'\n\nWHEN (SELECT COUNT() FROM orders o9 WHERE o9.customer_id = c.customer_id) > 20 THEN 'Premium'\n\nWHEN (SELECT COUNT() FROM orders o10 WHERE o10.customer_id = c.customer_id) > 5 THEN 'Regular'\n\nELSE 'New'\n\nEND as customer_tier\n\nFROM customers c\n\nWHERE c.customer_id IN (\n\nSELECT DISTINCT o19.customer_id\n\nFROM orders o19\n\nWHERE o19.order_date >= SYSDATE - 730\n\n)\n\nAND EXISTS (\n\nSELECT 1 FROM orders o21 WHERE o21.customer_id = c.customer_id\n\n)\n\nAND c.email LIKE '%@%'\n\nORDER BY (SELECT COUNT(*) FROM orders o22 WHERE o22.customer_id = c.customer_id) DESC.",
    "explanation": "1. **Overall Purpose**\nThe query retrieves a list of customers and their order details from an Oracle database. It provides a summary of each customer's order history, including the total number of orders, the number of completed and pending orders, the average order value, the date of the last order, and a customer tier based on the total number of orders. The query is designed to only include customers who have placed an order in the last two years, have at least one order in the system, and have a valid email address.\n\n2. **Involved Database Objects**\nThe query involves two tables: `customers` and `orders`. There are no views, CTEs, stored procedures, temporary tables, or schema-qualified objects involved. The query uses several subqueries and a CASE statement.\n\n3. **Essential Operations**\n- Columns retrieved: `customer_id`, `first_name`, `last_name`, `email` from the `customers` table. The query also calculates and retrieves `total_orders`, `completed_orders`, `pending_orders`, `avg_order_value`, `last_order_date`, and `customer_tier`.\n- There are no joins in this query.\n- Filters and conditions: The query filters customers based on whether they have placed an order in the last two years (`o19.order_date >= SYSDATE - 730`), whether they have at least one order in the system (`EXISTS (SELECT 1 FROM orders o21 WHERE o21.customer_id = c.customer_id)`), and whether they have a valid email address (`c.email LIKE '%@%'`).\n- Aggregations: The query uses `COUNT()` to calculate the total, completed, and pending orders for each customer, `AVG()` to calculate the average order value, and `MAX()` to find the date of the last order.\n- Sorting: The query sorts the results by the total number of orders in descending order (`ORDER BY (SELECT COUNT(*) FROM orders o22 WHERE o22.customer_id = c.customer_id) DESC`).\n- The query uses the `DISTINCT` keyword to ensure that each customer appears only once in the results.\n- Oracle-specific features: The query uses the `SYSDATE` function to get the current date and time.\n\n4. **Performance Issues**\n- The query uses a leading wildcard in the `LIKE` operator (`c.email LIKE '%@%'`). This can prevent the use of an index and slow down the query.\n- The query uses multiple subqueries in the `SELECT` clause, which can be inefficient. These could potentially be replaced with JOINs or window functions for better performance.\n- The query uses a subquery in the `ORDER BY` clause, which can be inefficient. This could potentially be replaced with a calculated column in the main query.\n- The query uses a subquery in the `WHERE` clause to filter customers based on whether they have placed an order in the last two years. This could potentially be replaced with a JOIN for better performance.",
    "database_type": "oracle",
    "version": "1.1",
    "optimized_at": "2026-01-21T17:34:23.063780+00:00"
  }
}