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This approach leverages the SQL capabilities of grouping and aggregation to solve the problem efficiently.
First, we perform a GROUP BY operation on the customer_number to count the number of orders per customer. Then, we use ORDER BY in descending order and LIMIT 1 to retrieve only the customer with the maximum count of orders.
Time Complexity: O(n), where n is the number of orders.
Space Complexity: O(1), as we are not using any additional space outside of the database's space.
1using System;
2using System.Data.SQLite;
3
4class Program {
5 static void Main() {
6 using (var connection = new SQLiteConnection("Data Source=:memory:")) {
7 connection.Open();
8 using (var command = new SQLiteCommand(connection)) {
9 // Assumes table and data exist in SQLite.
10 command.CommandText = @"
11 SELECT customer_number
12 FROM Orders
13 GROUP BY customer_number
14 ORDER BY COUNT(order_number) DESC
15 LIMIT 1;";
16 using (var reader = command.ExecuteReader()) {
17 if (reader.Read()) {
18 Console.WriteLine("Customer Number: " + reader.GetInt32(0));
19 }
20 }
21 }
22 }
23 }
24}This C# solution utilizes SQLiteConnection and SQLiteCommand to execute a GROUP BY SQL query to find the customer with the highest number of orders. The results are read using a SQLiteDataReader.
This approach involves manually iterating over the orders and using a data structure like a hash map (or dictionary) to count the number of orders per customer. Once counted, we can iterate over this collection to find the customer with the maximum orders.
Time Complexity: O(n), where n is the number of orders.
Space Complexity: O(m), where m is the number of unique customers.
1def find_customer_with_most_orders(orders):
2 order_count = {}
3 # Count orders per customer
This Python code manually counts customer orders using a dictionary to map customer numbers to their respective order counts. Finally, it identifies the customer with the maximum count.