Is Sellers With No Sales easy or hard?

Sellers With No Sales is considered an Easy SQL problem. It mainly tests understanding of LEFT JOIN semantics and filtering aggregated results using GROUP BY and HAVING.

Sellers With No Sales Python/Java solution

This problem is typically solved using SQL rather than Python or Java because it operates directly on relational tables. The core solution uses a LEFT JOIN with GROUP BY or a NOT EXISTS subquery to filter sellers without orders in 2020.

How to solve Sellers With No Sales in O(n + m)?

Join the Seller table with Orders using a LEFT JOIN so that sellers without orders are still included. Group by seller and count orders where the sale date falls in 2020. Use HAVING count = 0 to filter sellers with no qualifying sales.

What is the best approach for Sellers With No Sales?

The most common solution uses a LEFT JOIN between the Seller and Orders tables, followed by GROUP BY and a HAVING filter. Count only the orders that occur in 2020 and return sellers whose count is zero. This runs in O(n + m) time where n is the number of sellers and m is the number of orders.

Is Sellers With No Sales asked at Google/Amazon/Meta?

Database filtering and join problems like this appear frequently in SQL interview rounds at companies such as Amazon, Google, and Meta. Candidates are often tested on LEFT JOIN behavior, conditional aggregation, and filtering records that lack related rows.

What data structure is used in Sellers With No Sales?

The problem relies on relational database tables and SQL join operations rather than traditional data structures. Concepts like joins, grouping, and filtering are the primary tools used to derive the result set.

What is the time complexity of Sellers With No Sales?

Most SQL solutions run in O(n + m) time because the query scans the Seller table and the Orders table once before performing aggregation or existence checks. With proper indexing on seller_id and sale_date, the database optimizer can execute the join efficiently.

1607. Sellers With No Sales - Practice Online

Table: Customer

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| customer_id   | int     |
| customer_name | varchar |
+---------------+---------+
customer_id is the column with unique values for this table.
Each row of this table contains the information of each customer in the WebStore.

Table: Orders

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| order_id      | int     |
| sale_date     | date    |
| order_cost    | int     |
| customer_id   | int     |
| seller_id     | int     |
+---------------+---------+
order_id is the column with unique values for this table.
Each row of this table contains all orders made in the webstore.
sale_date is the date when the transaction was made between the customer (customer_id) and the seller (seller_id).

Table: Seller

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| seller_id     | int     |
| seller_name   | varchar |
+---------------+---------+
seller_id is the column with unique values for this table.
Each row of this table contains the information of each seller.

Write a solution to report the names of all sellers who did not make any sales in 2020.

Return the result table ordered by seller_name in ascending order.

The result format is in the following example.

Example 1:

Input: 
Customer table:
+--------------+---------------+
| customer_id  | customer_name |
+--------------+---------------+
| 101          | Alice         |
| 102          | Bob           |
| 103          | Charlie       |
+--------------+---------------+
Orders table:
+-------------+------------+--------------+-------------+-------------+
| order_id    | sale_date  | order_cost   | customer_id | seller_id   |
+-------------+------------+--------------+-------------+-------------+
| 1           | 2020-03-01 | 1500         | 101         | 1           |
| 2           | 2020-05-25 | 2400         | 102         | 2           |
| 3           | 2019-05-25 | 800          | 101         | 3           |
| 4           | 2020-09-13 | 1000         | 103         | 2           |
| 5           | 2019-02-11 | 700          | 101         | 2           |
+-------------+------------+--------------+-------------+-------------+
Seller table:
+-------------+-------------+
| seller_id   | seller_name |
+-------------+-------------+
| 1           | Daniel      |
| 2           | Elizabeth   |
| 3           | Frank       |
+-------------+-------------+
Output: 
+-------------+
| seller_name |
+-------------+
| Frank       |
+-------------+
Explanation: 
Daniel made 1 sale in March 2020.
Elizabeth made 2 sales in 2020 and 1 sale in 2019.
Frank made 1 sale in 2019 but no sales in 2020.