Is Consecutive Transactions with Increasing Amounts easy or hard?

Consecutive Transactions with Increasing Amounts is classified as Hard because it combines multiple SQL techniques: window functions, consecutive date detection, and conditional filtering of increasing values. Many candidates struggle with the row_number grouping trick used to identify streaks.

Consecutive Transactions with Increasing Amounts Python/Java solution

This problem is typically solved using SQL rather than Python or Java because it operates directly on relational tables. In application code, a similar solution would sort transactions by customer and date, then scan the list while tracking increasing amounts and consecutive-day streaks.

What is the best approach for Consecutive Transactions with Increasing Amounts?

The best approach uses SQL window functions such as ROW_NUMBER() and LAG(). First order transactions by customer and date, then compare each amount with the previous row using LAG(). A row_number-based grouping trick identifies consecutive date streaks, and groups with at least three rows form valid results. This runs in roughly O(n log n) due to sorting.

Is Consecutive Transactions with Increasing Amounts asked at Google/Amazon/Meta?

Problems involving transaction streaks and time‑series comparisons frequently appear in database interviews at companies like Amazon, Google, and Meta. Variants often require detecting consecutive events, increasing metrics, or activity streaks using SQL window functions.

What data structure is used in Consecutive Transactions with Increasing Amounts?

The problem relies on relational database operations rather than traditional data structures. SQL window functions such as LAG() and ROW_NUMBER() act like sequential comparisons over ordered rows, enabling detection of increasing sequences and consecutive date groups.

What is the time complexity of Consecutive Transactions with Increasing Amounts?

The optimal SQL solution runs in O(n log n) time because transactions must be sorted by customer and date before applying window functions. Window operations such as LAG() and ROW_NUMBER() then process rows linearly. Space complexity is O(n) for intermediate result sets during grouping.

2701. Consecutive Transactions with Increasing Amounts - Practice Online

Q: How to solve Consecutive Transactions with Increasing Amounts in O(n)?

Pure O(n) performance is difficult in SQL because ordering by date is required to evaluate consecutive transactions. The closest practical solution uses window functions after sorting. Using LAG() to compare amounts and ROW_NUMBER() to detect consecutive dates efficiently processes the ordered rows in a single pass.

Table: Transactions

+------------------+------+
| Column Name      | Type |
+------------------+------+
| transaction_id   | int  |
| customer_id      | int  |
| transaction_date | date |
| amount           | int  |
+------------------+------+
transaction_id is the primary key of this table. 
Each row contains information about transactions that includes unique (customer_id, transaction_date) along with the corresponding customer_id and amount.

Write an SQL query to find the customers who have made consecutive transactions with increasing amount for at least three consecutive days. Include the customer_id, start date of the consecutive transactions period and the end date of the consecutive transactions period. There can be multiple consecutive transactions by a customer.

Return the result table ordered by customer_id, consecutive_start, consecutive_end in ascending order.

The query result format is in the following example.

Example 1:

Input: 
Transactions table:
+----------------+-------------+------------------+--------+
| 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    |
| 10             | 105         | 2023-05-04       | 300    |
| 11             | 105         | 2023-05-12       | 250    |
| 12             | 105         | 2023-05-13       | 260    |
| 13             | 105         | 2023-05-14       | 270    |
+----------------+-------------+------------------+--------+
Output: 
+-------------+-------------------+-----------------+
| customer_id | consecutive_start | consecutive_end | 
+-------------+-------------------+-----------------+
| 101         |  2023-05-01       | 2023-05-03      | 
| 105         |  2023-05-01       | 2023-05-04      |
| 105         |  2023-05-12       | 2023-05-14      | 
+-------------+-------------------+-----------------+
Explanation: 
- customer_id 101 has made consecutive transactions with increasing amounts from May 1st, 2023, to May 3rd, 2023
- customer_id 102 does not have any consecutive transactions for at least 3 days. 
- customer_id 105 has two sets of consecutive transactions: from May 1st, 2023, to May 4th, 2023, and from May 12th, 2023, to May 14th, 2023. 
customer_id is sorted in ascending order.