00:00:00

Description

Editorial

Top Seller Total Revenue Report

EASY10 pts

An e-commerce analytics platform tracks catalog metadata in Product and transactional records in Sales.

Product columns:

product_id: unique product identifier
product_name: product display name
unit_price: catalog list price

Sales columns:

seller_id: seller identifier
product_id: sold product identifier
buyer_id: buyer identifier
sale_date: sale event date
quantity: item quantity for the sale row
price: total revenue captured by that sale row

Task:

Compute each seller's total realized revenue using SUM(price).
Return all seller_id values whose total revenue equals the global maximum.
If multiple sellers tie for first place, include them all.

Output requirements:

Return exactly one column: seller_id
Sort by seller_id in ascending order

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 1          | S8           | 1000       |
| 2          | G4           | 800        |
| 3          | iPhone       | 1400       |

Sales:
| seller_id | product_id | buyer_id | sale_date  | quantity | price |
|-----------|------------|----------|------------|----------|-------|
| 1         | 1          | 1        | 2019-01-21 | 2        | 2000  |
| 1         | 2          | 2        | 2019-02-17 | 1        | 800   |
| 2         | 2          | 3        | 2019-06-02 | 1        | 800   |
| 3         | 3          | 4        | 2019-05-13 | 2        | 2800  |

Output

[
  {"seller_id":1},
  {"seller_id":3}
]

Explanation

Seller 1 and seller 3 each have total revenue 2800, which is the maximum.

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 10         | Cable        | 25         |
| 11         | Hub          | 65         |

Sales:
| seller_id | product_id | buyer_id | sale_date  | quantity | price |
|-----------|------------|----------|------------|----------|-------|
| 8         | 10         | 100      | 2021-03-01 | 4        | 100   |
| 9         | 11         | 101      | 2021-03-02 | 1        | 65    |
| 8         | 11         | 102      | 2021-03-04 | 2        | 130   |

Output

[
  {"seller_id":8}
]

Explanation

Seller 8 accumulates 230 revenue and is the sole top seller.

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 3          | Sensor       | 50         |
| 4          | Mount        | 20         |

Sales:
| seller_id | product_id | buyer_id | sale_date | quantity | price |
|-----------|------------|----------|-----------|----------|-------|
| (no rows) |            |          |           |          |       |

Output

[]

Explanation

With no sales rows, there is no top seller and the result is empty.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in Product <= 10,000
0 <= number of rows in Sales <= 10,000
1 <= product_id <= 1,000,000,000
1 <= seller_id, buyer_id <= 1,000,000,000
0 <= quantity <= 1,000,000,000
0 <= unit_price, price <= 1,000,000,000
Product.product_id is unique
Sales.product_id references Product.product_id
Sales may contain repeated rows
Compute seller totals using SUM(price)
Return exactly one column named seller_id
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column seller_id

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

sales =

[{"seller_id":1,"product_id":1,"buyer_id":1,"sale_date":"2019-01-21","quantity":2,"price":2000},{"seller_id":1,"product_id":2,"buyer_id":2,"sale_date":"2019-02-17","quantity":1,"price":800},{"seller_id":2,"product_id":2,"buyer_id":3,"sale_date":"2019-06-02","quantity":1,"price":800},{"seller_id":3,"product_id":3,"buyer_id":4,"sale_date":"2019-05-13","quantity":2,"price":2800}]

product =

[{"product_id":1,"product_name":"S8","unit_price":1000},{"product_id":2,"product_name":"G4","unit_price":800},{"product_id":3,"product_name":"iPhone","unit_price":1400}]

Loading problem...

101

00:00:00

Description

Editorial

Top Seller Total Revenue Report

EASY10 pts

An e-commerce analytics platform tracks catalog metadata in Product and transactional records in Sales.

Product columns:

product_id: unique product identifier
product_name: product display name
unit_price: catalog list price

Sales columns:

seller_id: seller identifier
product_id: sold product identifier
buyer_id: buyer identifier
sale_date: sale event date
quantity: item quantity for the sale row
price: total revenue captured by that sale row

Task:

Compute each seller's total realized revenue using SUM(price).
Return all seller_id values whose total revenue equals the global maximum.
If multiple sellers tie for first place, include them all.

Output requirements:

Return exactly one column: seller_id
Sort by seller_id in ascending order

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 1          | S8           | 1000       |
| 2          | G4           | 800        |
| 3          | iPhone       | 1400       |

Sales:
| seller_id | product_id | buyer_id | sale_date  | quantity | price |
|-----------|------------|----------|------------|----------|-------|
| 1         | 1          | 1        | 2019-01-21 | 2        | 2000  |
| 1         | 2          | 2        | 2019-02-17 | 1        | 800   |
| 2         | 2          | 3        | 2019-06-02 | 1        | 800   |
| 3         | 3          | 4        | 2019-05-13 | 2        | 2800  |

Output

[
  {"seller_id":1},
  {"seller_id":3}
]

Explanation

Seller 1 and seller 3 each have total revenue 2800, which is the maximum.

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 10         | Cable        | 25         |
| 11         | Hub          | 65         |

Sales:
| seller_id | product_id | buyer_id | sale_date  | quantity | price |
|-----------|------------|----------|------------|----------|-------|
| 8         | 10         | 100      | 2021-03-01 | 4        | 100   |
| 9         | 11         | 101      | 2021-03-02 | 1        | 65    |
| 8         | 11         | 102      | 2021-03-04 | 2        | 130   |

Output

[
  {"seller_id":8}
]

Explanation

Seller 8 accumulates 230 revenue and is the sole top seller.

Example

Input

Product:
| product_id | product_name | unit_price |
|------------|--------------|------------|
| 3          | Sensor       | 50         |
| 4          | Mount        | 20         |

Sales:
| seller_id | product_id | buyer_id | sale_date | quantity | price |
|-----------|------------|----------|-----------|----------|-------|
| (no rows) |            |          |           |          |       |

Output

[]

Explanation

With no sales rows, there is no top seller and the result is empty.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in Product <= 10,000
0 <= number of rows in Sales <= 10,000
1 <= product_id <= 1,000,000,000
1 <= seller_id, buyer_id <= 1,000,000,000
0 <= quantity <= 1,000,000,000
0 <= unit_price, price <= 1,000,000,000
Product.product_id is unique
Sales.product_id references Product.product_id
Sales may contain repeated rows
Compute seller totals using SUM(price)
Return exactly one column named seller_id
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column seller_id

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

sales =

product =

[{"product_id":1,"product_name":"S8","unit_price":1000},{"product_id":2,"product_name":"G4","unit_price":800},{"product_id":3,"product_name":"iPhone","unit_price":1400}]

Top Seller Total Revenue Report

Hints

Top Seller Total Revenue Report

Hints