Customer Placing the Largest Number of Orders - Video Solutions

Problem Overview: The Orders table stores customer purchases with a customer_number. The task is to return the customer who placed the highest number of orders. If one customer appears most frequently, that customer’s ID should be returned.

Approach 1: SQL GROUP BY with MAX Aggregation (Time: O(n), Space: O(k))

This approach relies on SQL aggregation. First group all rows by customer_number using GROUP BY. Then compute the order count for each customer with COUNT(*). The customer with the maximum count can be retrieved using a subquery with MAX or by sorting the grouped results and selecting the top record.

The key insight: aggregation compresses repeated rows into frequency counts directly inside the database engine. Instead of scanning repeatedly, the database builds grouped buckets for each customer. The final step simply returns the customer whose count equals the maximum value.

This is the most natural solution for a database problem because relational systems are optimized for grouping and aggregation operations. Complexity is linear relative to the number of rows O(n), while space is O(k) where k is the number of unique customers.

Approach 2: Manual Aggregation with Data Structures (Time: O(n), Space: O(k))

If the data is processed outside the database (for example in Python or Java), you can simulate SQL aggregation using a hash map. Iterate through every order and maintain a frequency counter where the key is customer_number and the value is the number of orders placed by that customer.

During the iteration, update the count for each customer and track the maximum frequency seen so far. After processing all rows, return the customer associated with the highest count.

This approach mirrors SQL GROUP BY behavior but uses a hash-based frequency table. Each lookup and update is constant time on average, so the full scan runs in O(n) time. Memory usage grows with the number of distinct customers, resulting in O(k) space. This technique is common in hash table problems and is useful when processing query results in application code rather than directly in SQL.

Recommended for interviews: The SQL GROUP BY approach is the expected answer because the problem is designed around database querying. Interviewers want to see correct use of aggregation functions and filtering with MAX. Understanding the manual hash-map version still matters since it shows you understand how grouping works internally rather than relying only on SQL syntax.