00:00:00

Description

Editorial

Customers With Longest Consecutive Transaction Streak

HARD30 pts

A payments intelligence team wants to identify customers with the strongest day-by-day transaction consistency.

Table: Transactions

transaction_id: unique transaction identifier
customer_id: customer identifier
transaction_date: transaction date in YYYY-MM-DD format
amount: transaction value

Data guarantee:

For any customer, there is at most one transaction row per calendar date.

Streak definition:

A consecutive streak is a run of transaction dates where every next date is exactly one day after the previous date.
Each customer has a longest streak length (their maximum run length).

Task: Return all customer IDs whose longest streak length is the highest among all customers.

Output requirements:

Return exactly one column named customer_id.
Sort output by customer_id in ascending order.

Important note:

The amount column is metadata in this task and does not affect streak length.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 1              | 101         | 2023-05-01       | 100    |
| 2              | 101         | 2023-05-02       | 150    |
| 3              | 101         | 2023-05-03       | 200    |
| 4              | 102         | 2023-05-01       | 50     |
| 5              | 102         | 2023-05-03       | 100    |
| 6              | 102         | 2023-05-04       | 200    |
| 7              | 105         | 2023-05-01       | 100    |
| 8              | 105         | 2023-05-02       | 150    |
| 9              | 105         | 2023-05-03       | 200    |

Output

[
  {"customer_id":101},
  {"customer_id":105}
]

Explanation

Customers 101 and 105 each have a longest streak of 3 consecutive days. Customer 102 has a best streak of 2 days, so it is not selected.

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 10             | 201         | 2024-01-30       | 800    |
| 11             | 201         | 2024-01-31       | 900    |
| 12             | 201         | 2024-02-01       | 950    |
| 13             | 201         | 2024-02-02       | 1000   |
| 14             | 202         | 2024-01-10       | 10000  |
| 15             | 202         | 2024-01-12       | 5      |
| 16             | 202         | 2024-01-13       | 7      |
| 17             | 203         | 2023-12-31       | 40     |
| 18             | 203         | 2024-01-01       | 60     |
| 19             | 203         | 2024-01-02       | 80     |
| 20             | 203         | 2024-01-03       | 20     |

Output

[
  {"customer_id":201},
  {"customer_id":203}
]

Explanation

Customers 201 and 203 both achieve streak length 4. Amount sizes do not influence the streak metric.

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 30             | 300         | 2025-06-05       | 10     |
| 31             | 301         | 2025-06-20       | 20     |
| 32             | 302         | 2025-07-01       | 30     |

Output

[
  {"customer_id":300},
  {"customer_id":301},
  {"customer_id":302}
]

Explanation

Every customer has longest streak length 1, so all customers tie for the global maximum.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in Transactions <= 200000
transaction_id values are unique
1 <= transaction_id <= 1000000000
1 <= customer_id <= 1000000000
1 <= amount <= 100000
transaction_date is a valid date in YYYY-MM-DD format
(customer_id, transaction_date) is unique
Consecutive means adjacent transaction dates differ by exactly one day
Longest streak for a customer is their maximum consecutive-day run length
Return all customers whose longest streak equals the global maximum
Output must be sorted by customer_id ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named customer_id

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Transactions =

[{"transaction_id":1,"customer_id":101,"transaction_date":"2023-05-01","amount":79480},{"transaction_id":2,"customer_id":101,"transaction_date":"2023-05-02","amount":71751},{"transaction_id":3,"customer_id":101,"transaction_date":"2023-05-03","amount":13792},{"transaction_id":4,"customer_id":102,"transaction_date":"2023-05-01","amount":28157},{"transaction_id":5,"customer_id":102,"transaction_date":"2023-05-03","amount":22519},{"transaction_id":6,"customer_id":102,"transaction_date":"2023-05-04","amount":1041},{"transaction_id":7,"customer_id":105,"transaction_date":"2023-05-01","amount":79454},{"transaction_id":8,"customer_id":105,"transaction_date":"2023-05-02","amount":12645},{"transaction_id":9,"customer_id":105,"transaction_date":"2023-05-03","amount":84305}]

Loading problem...

101

00:00:00

Description

Editorial

Customers With Longest Consecutive Transaction Streak

HARD30 pts

A payments intelligence team wants to identify customers with the strongest day-by-day transaction consistency.

Table: Transactions

transaction_id: unique transaction identifier
customer_id: customer identifier
transaction_date: transaction date in YYYY-MM-DD format
amount: transaction value

Data guarantee:

For any customer, there is at most one transaction row per calendar date.

Streak definition:

A consecutive streak is a run of transaction dates where every next date is exactly one day after the previous date.
Each customer has a longest streak length (their maximum run length).

Task: Return all customer IDs whose longest streak length is the highest among all customers.

Output requirements:

Return exactly one column named customer_id.
Sort output by customer_id in ascending order.

Important note:

The amount column is metadata in this task and does not affect streak length.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 1              | 101         | 2023-05-01       | 100    |
| 2              | 101         | 2023-05-02       | 150    |
| 3              | 101         | 2023-05-03       | 200    |
| 4              | 102         | 2023-05-01       | 50     |
| 5              | 102         | 2023-05-03       | 100    |
| 6              | 102         | 2023-05-04       | 200    |
| 7              | 105         | 2023-05-01       | 100    |
| 8              | 105         | 2023-05-02       | 150    |
| 9              | 105         | 2023-05-03       | 200    |

Output

[
  {"customer_id":101},
  {"customer_id":105}
]

Explanation

Customers 101 and 105 each have a longest streak of 3 consecutive days. Customer 102 has a best streak of 2 days, so it is not selected.

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 10             | 201         | 2024-01-30       | 800    |
| 11             | 201         | 2024-01-31       | 900    |
| 12             | 201         | 2024-02-01       | 950    |
| 13             | 201         | 2024-02-02       | 1000   |
| 14             | 202         | 2024-01-10       | 10000  |
| 15             | 202         | 2024-01-12       | 5      |
| 16             | 202         | 2024-01-13       | 7      |
| 17             | 203         | 2023-12-31       | 40     |
| 18             | 203         | 2024-01-01       | 60     |
| 19             | 203         | 2024-01-02       | 80     |
| 20             | 203         | 2024-01-03       | 20     |

Output

[
  {"customer_id":201},
  {"customer_id":203}
]

Explanation

Customers 201 and 203 both achieve streak length 4. Amount sizes do not influence the streak metric.

Example

Input

Transactions:
| transaction_id | customer_id | transaction_date | amount |
|----------------|-------------|------------------|--------|
| 30             | 300         | 2025-06-05       | 10     |
| 31             | 301         | 2025-06-20       | 20     |
| 32             | 302         | 2025-07-01       | 30     |

Output

[
  {"customer_id":300},
  {"customer_id":301},
  {"customer_id":302}
]

Explanation

Every customer has longest streak length 1, so all customers tie for the global maximum.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in Transactions <= 200000
transaction_id values are unique
1 <= transaction_id <= 1000000000
1 <= customer_id <= 1000000000
1 <= amount <= 100000
transaction_date is a valid date in YYYY-MM-DD format
(customer_id, transaction_date) is unique
Consecutive means adjacent transaction dates differ by exactly one day
Longest streak for a customer is their maximum consecutive-day run length
Return all customers whose longest streak equals the global maximum
Output must be sorted by customer_id ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exactly one column named customer_id

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Transactions =

Customers With Longest Consecutive Transaction Streak

Hints

Customers With Longest Consecutive Transaction Streak

Hints