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Customer Placing the Largest Number of Orders

Table: Orders

+-----------------+----------+
| Column Name     | Type     |
+-----------------+----------+
| order_number    | int      |
| customer_number | int      |
+-----------------+----------+
order_number is the primary key (column with unique values) for this table.
This table contains information about the order ID and the customer ID.

 

Write a solution to find the customer_number for the customer who has placed the largest number of orders.

The test cases are generated so that exactly one customer will have placed more orders than any other customer.

The result format is in the following example.

 

Example 1:

Input: 
Orders table:
+--------------+-----------------+
| order_number | customer_number |
+--------------+-----------------+
| 1            | 1               |
| 2            | 2               |
| 3            | 3               |
| 4            | 3               |
+--------------+-----------------+
Output: 
+-----------------+
| customer_number |
+-----------------+
| 3               |
+-----------------+
Explanation: 
The customer with number 3 has two orders, which is greater than either customer 1 or 2 because each of them only has one order. 
So the result is customer_number 3.

 

Follow up: What if more than one customer has the largest number of orders, can you find all the customer_number in this case?

Solution Explanation for LeetCode 586: Customer Placing the Largest Number of Orders

This problem requires finding the customer who has placed the most orders. The provided solutions leverage SQL's aggregation and sorting capabilities.

Approach

Both solutions follow a similar strategy:

  1. Grouping: The GROUP BY customer_number clause groups the Orders table rows based on the customer number. This aggregates all orders for each customer.

  2. Counting: COUNT(1) (or COUNT(customer_number)) counts the number of orders within each group (i.e., for each customer).

  3. Sorting: ORDER BY COUNT(1) DESC sorts the groups in descending order based on the order count. The customer with the highest order count will be at the top.

  4. Selecting the Top Customer: LIMIT 1 (MySQL) or TOP 1 (SQL Server/T-SQL) selects only the first row from the sorted result, which represents the customer with the most orders.

Code and Complexity Analysis

MySQL:

SELECT
    customer_number
FROM orders
GROUP BY customer_number
ORDER BY COUNT(1) DESC
LIMIT 1;

SQL Server (T-SQL):

SELECT TOP 1
    customer_number
FROM
    orders
GROUP BY customer_number
ORDER BY COUNT(customer_number) DESC;

Complexity Analysis:

  • Time Complexity: The time complexity is dominated by the GROUP BY and ORDER BY operations. The time complexity of these operations depends on the database implementation, but it's generally O(N log N) in the worst case, where N is the number of rows in the Orders table. This is because sorting often uses algorithms with O(N log N) complexity. In practice, optimized database systems can often perform these operations much faster.

  • Space Complexity: The space complexity depends on the size of the intermediate result generated by the GROUP BY operation. In the worst case, it could be O(N) if every customer has a unique customer_number. However, database systems often optimize this space usage.

Follow-up: Multiple Customers with the Largest Number of Orders

The problem statement mentions a follow-up: handling cases where multiple customers have the same (largest) number of orders. To solve this, we would modify the queries to return all customers with the maximum order count. Here are the adjusted queries:

MySQL:

SELECT customer_number
FROM orders
GROUP BY customer_number
HAVING COUNT(*) = (SELECT MAX(order_count) FROM (SELECT COUNT(*) AS order_count FROM orders GROUP BY customer_number) AS OrderCounts);

SQL Server (T-SQL):

WITH CustomerOrderCounts AS (
    SELECT customer_number, COUNT(*) AS OrderCount
    FROM Orders
    GROUP BY customer_number
),
MaxOrderCount AS (
    SELECT MAX(OrderCount) AS MaxCount
    FROM CustomerOrderCounts
)
SELECT customer_number
FROM CustomerOrderCounts
WHERE OrderCount = (SELECT MaxCount FROM MaxOrderCount);

These modified queries use subqueries or CTEs (Common Table Expressions) to first determine the maximum order count and then select all customers with that count. The time complexity remains largely the same (O(N log N)), but the space complexity might slightly increase depending on the database's implementation of subqueries or CTEs.