Sales Person - Solution & Explanation
Problem Statement
Table: SalesPerson
+-----------------+---------+ | Column Name | Type | +-----------------+---------+ | sales_id | int | | name | varchar | | salary | int | | commission_rate | int | | hire_date | date | +-----------------+---------+ sales_id is the primary key (column with unique values) for this table. Each row of this table indicates the name and the ID of a salesperson alongside their salary, commission rate, and hire date.
Table: Company
+-------------+---------+ | Column Name | Type | +-------------+---------+ | com_id | int | | name | varchar | | city | varchar | +-------------+---------+ com_id is the primary key (column with unique values) for this table. Each row of this table indicates the name and the ID of a company and the city in which the company is located.
Table: Orders
+-------------+------+ | Column Name | Type | +-------------+------+ | order_id | int | | order_date | date | | com_id | int | | sales_id | int | | amount | int | +-------------+------+ order_id is the primary key (column with unique values) for this table. com_id is a foreign key (reference column) to com_id from the Company table. sales_id is a foreign key (reference column) to sales_id from the SalesPerson table. Each row of this table contains information about one order. This includes the ID of the company, the ID of the salesperson, the date of the order, and the amount paid.
Write a solution to find the names of all the salespersons who did not have any orders related to the company with the name "RED".
Return the result table in any order.
The result format is in the following example.
Example 1:
Input: SalesPerson table: +----------+------+--------+-----------------+------------+ | sales_id | name | salary | commission_rate | hire_date | +----------+------+--------+-----------------+------------+ | 1 | John | 100000 | 6 | 4/1/2006 | | 2 | Amy | 12000 | 5 | 5/1/2010 | | 3 | Mark | 65000 | 12 | 12/25/2008 | | 4 | Pam | 25000 | 25 | 1/1/2005 | | 5 | Alex | 5000 | 10 | 2/3/2007 | +----------+------+--------+-----------------+------------+ Company table: +--------+--------+----------+ | com_id | name | city | +--------+--------+----------+ | 1 | RED | Boston | | 2 | ORANGE | New York | | 3 | YELLOW | Boston | | 4 | GREEN | Austin | +--------+--------+----------+ Orders table: +----------+------------+--------+----------+--------+ | order_id | order_date | com_id | sales_id | amount | +----------+------------+--------+----------+--------+ | 1 | 1/1/2014 | 3 | 4 | 10000 | | 2 | 2/1/2014 | 4 | 5 | 5000 | | 3 | 3/1/2014 | 1 | 1 | 50000 | | 4 | 4/1/2014 | 1 | 4 | 25000 | +----------+------------+--------+----------+--------+ Output: +------+ | name | +------+ | Amy | | Mark | | Alex | +------+ Explanation: According to orders 3 and 4 in the Orders table, it is easy to tell that only salesperson John and Pam have sales to company RED, so we report all the other names in the table salesperson.
Approach Overview
Problem Overview: You are given three tables: salesperson, company, and orders. The task is to return the names of salespersons who never handled an order for the company named RED. The challenge is filtering salespeople based on the absence of a specific relationship across joined tables.
Approach 1: Using NOT EXISTS with Subquery (O(n) time, O(1) extra space)
This approach checks for the absence of a matching record using a correlated subquery. For each salesperson, a subquery searches the orders table joined with company to see if an order exists where the company name is RED. The NOT EXISTS predicate returns true only when no such record is found. The key insight is that databases can short‑circuit existence checks efficiently, making this pattern common in SQL interview problems. Use this when you want a clear “exclude if any match exists” condition.
Approach 2: Using LEFT JOIN and IS NULL (O(n) time, O(1) extra space)
This method performs a LEFT JOIN from salesperson to orders associated with the company RED. If a salesperson never handled a RED order, the joined columns from that filtered dataset remain NULL. Filtering rows with WHERE joined_column IS NULL removes anyone who actually matched RED. This pattern is widely used when solving absence queries using SQL joins. It is easy to read and often preferred when you already need joins for other conditions.
Approach 3: INNER JOIN with GROUP BY and HAVING (O(n) time, O(1) extra space)
This approach joins salesperson, orders, and company, then aggregates results by salesperson. A conditional aggregation counts how many orders are linked to the company RED. The HAVING clause keeps only those groups where the count of RED orders equals zero. This technique relies on grouping logic from database querying. It works well when the query already requires aggregation for other metrics.
Approach 4: LEFT JOIN with NULL Filtering After Company Match (O(n) time, O(1) extra space)
Another variation performs a join between orders and company first, restricting rows to RED. Then a LEFT JOIN connects that result to salesperson. Salespeople who never appear in the RED order set produce NULL values and are selected with a WHERE ... IS NULL filter. This separates the filtering step from the join logic and keeps the query modular.
Recommended for interviews: The NOT EXISTS solution is usually the cleanest and easiest to reason about. It directly expresses the requirement: return salespeople for whom no RED order exists. Showing the LEFT JOIN ... IS NULL alternative demonstrates deeper understanding of relational filtering patterns commonly used in SQL interview questions.
Approach 1: Approach 1: Using NOT EXISTS with Subquery
This approach utilizes a subquery within a NOT EXISTS condition to identify salespersons who have not placed any orders for the company named 'RED'. By leveraging the NOT EXISTS, we can efficiently filter out salespersons who are not involved with any orders corresponding to 'RED'.
The above SQL query selects all salespersons from the SalesPerson table who do not exist in the subquery result. The subquery joins the Orders and Company tables, filtering for orders related to the company named 'RED'. The NOT EXISTS clause excludes salespersons who have orders related to 'RED'.
Code
SQL
Complexity
Time Complexity: O(N * M), where N is the number of salespersons and M is the number of orders.
Space Complexity: O(1), since we are using in-DB operations without additional data structures.
Approach 2: Approach 2: Using LEFT JOIN and IS NULL
This approach involves performing a LEFT JOIN operation between the SalesPerson and Orders tables, filtering orders related to 'RED', and then checking for NULLs to identify salespersons with no such orders. This exploits the fact that when a LEFT JOIN does not find matches, the result will have NULL entries.
This SQL query first performs a LEFT JOIN between the SalesPerson, Orders, and Company tables. With the condition filtering for company name 'RED', salespersons without related orders will result in NULL for the company columns. The WHERE clause checks for these NULL entries, effectively excluding salespersons with orders for 'RED'.
Code
SQL
Complexity
Time Complexity: O(N + M), where N is the number of salespersons and M is the number of companies/orders.
Space Complexity: O(1), as operations occur within the database without additional data structure utilization.
Approach 3: Approach 1: Use INNER JOIN and GROUP BY
This approach involves using INNER JOIN to identify the salespersons who have placed orders for the company named RED. Then, with a subquery and GROUP BY, we identify all salespersons with no association with RED by excluding them from the full list of salespersons.
This SQL query does the following:
- It retrieves all salesperson names from the SalesPerson table.
- Excludes those who have made orders for the company RED by using a subquery.
- The subquery joins the Orders and Company tables to find the sales_id of orders made to RED.
- The use of NOT IN excludes these salespeople from the final list.
Code
SQL
Complexity
Time Complexity: O(n + m + k) where n, m, and k are the number of rows in SalesPerson, Orders, and Company tables respectively.
Space Complexity: O(n).
Approach 4: Approach 2: Use LEFT JOIN and NULL Filtering
This approach exploits the LEFT JOIN feature to link salespersons with orders related to the company RED. We then filter out the entries where no such orders exist, effectively capturing the desired salespersons.
The solution works as follows:
- Perform a LEFT JOIN between SalesPerson and Orders tables based on sales_id.
- Apply a second LEFT JOIN with the Company table filtered for the name RED.
- Finally, filter on c.com_id IS NULL to find salespersons without orders to the company RED.
Code
SQL
Complexity
Time Complexity: O(n * log m) where n and m are the number of rows in SalesPerson and Orders tables respectively.
Space Complexity: O(n).
Approach 5: LEFT JOIN + GROUP BY
We can use a left join to join the SalesPerson table with the Orders table on the condition of sales id, and then join the result with the Company table on the condition of company id. After that, we can group by sales_id and count the number of orders with the company name RED. Finally, we can filter out the salespersons who do not have any orders with the company name RED.
Code
MySQL
Complexity Comparison
| Approach | Complexity |
|---|---|
| Approach 1: Using NOT EXISTS with Subquery | Time Complexity: O(N * M), where N is the number of salespersons and M is the number of orders. |
| Approach 2: Using LEFT JOIN and IS NULL | Time Complexity: O(N + M), where N is the number of salespersons and M is the number of companies/orders. |
| Approach 1: Use INNER JOIN and GROUP BY | Time Complexity: O(n + m + k) where n, m, and k are the number of rows in SalesPerson, Orders, and Company tables respectively. |
| Approach 2: Use LEFT JOIN and NULL Filtering | Time Complexity: O(n * log m) where n and m are the number of rows in SalesPerson and Orders tables respectively. |
| LEFT JOIN + GROUP BY | — |
Detailed Complexity Analysis
| Approach | Time | Space | When to Use |
|---|---|---|---|
| NOT EXISTS with Subquery | O(n) | O(1) | Best when filtering rows that must not have a related record |
| LEFT JOIN + IS NULL | O(n) | O(1) | Common SQL pattern for detecting missing relationships |
| INNER JOIN with GROUP BY | O(n) | O(1) | Useful when aggregation or counts are already needed |
| LEFT JOIN with Filtered Dataset | O(n) | O(1) | Cleaner structure when isolating the RED company orders first |
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