Six-Minute Interval Order Rollup

Medium

A commerce reliability team monitors how order volume evolves across short, fixed-time windows. Table: Orders minute: minute index for a measurement row order_count: number of orders received in that minute Data guarantees: minute is unique. The table has exactly 6 * k rows for some positive integer k. Minutes cover a contiguous timeline from 1 through 6 * k. Task: Aggregate order_count into 6-minute intervals. Interval mapping: interval_no = 1 for minutes 1 to 6 interval_no = 2 for minutes 7 to 12 in general, minute m belongs to interval_no = floor((m + 5) / 6) Return one row per interval with: interval_no total_orders (sum of order_count in that interval) Output requirements: Return columns exactly in this order: interval_no, total_orders Sort by interval_no in ascending order Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Orders:
| minute | order_count |
|--------|-------------|
| 1      | 0           |
| 2      | 2           |
| 3      | 4           |
| 4      | 6           |
| 5      | 1           |
| 6      | 4           |
| 7      | 1           |
| 8      | 2           |
| 9      | 4           |
| 10     | 1           |
| 11     | 4           |
| 12     | 6           |

Output

[
  {"interval_no":1,"total_orders":17},
  {"interval_no":2,"total_orders":18}
]

Explanation

Minutes 1-6 form interval 1 (sum 17), and minutes 7-12 form interval 2 (sum 18).

Example 2

Input

Orders:
| minute | order_count |
|--------|-------------|
| 6      | 9           |
| 1      | 3           |
| 5      | 5           |
| 2      | 0           |
| 4      | 2           |
| 3      | 1           |

Output

[
  {"interval_no":1,"total_orders":20}
]

Explanation

Input row order is irrelevant. The six rows still belong to interval 1, and their sum is 20.

Example 3

Input

Orders:
| minute | order_count |
|--------|-------------|
| 1      | 10          |
| 2      | 0           |
| 3      | 0           |
| 4      | 0           |
| 5      | 0           |
| 6      | 0           |
| 7      | 1           |
| 8      | 1           |
| 9      | 1           |
| 10     | 1           |
| 11     | 1           |
| 12     | 1           |

Output

[
  {"interval_no":1,"total_orders":10},
  {"interval_no":2,"total_orders":6}
]

Explanation

Each block of six minutes is summed independently.

Constraints

6 <= number of rows in Orders <= 120000
Number of rows in Orders is a multiple of 6
1 <= minute <= 120000 and minute is unique
Minutes are contiguous: 1, 2, 3, ..., 6 * k
0 <= order_count <= 1000000
Return exactly columns: interval_no, total_orders
Sort output by interval_no ASC
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exact required columns

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Six-Minute Interval Order Rollup

Medium

A commerce reliability team monitors how order volume evolves across short, fixed-time windows. Table: Orders minute: minute index for a measurement row order_count: number of orders received in that minute Data guarantees: minute is unique. The table has exactly 6 * k rows for some positive integer k. Minutes cover a contiguous timeline from 1 through 6 * k. Task: Aggregate order_count into 6-minute intervals. Interval mapping: interval_no = 1 for minutes 1 to 6 interval_no = 2 for minutes 7 to 12 in general, minute m belongs to interval_no = floor((m + 5) / 6) Return one row per interval with: interval_no total_orders (sum of order_count in that interval) Output requirements: Return columns exactly in this order: interval_no, total_orders Sort by interval_no in ascending order Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Orders:
| minute | order_count |
|--------|-------------|
| 1      | 0           |
| 2      | 2           |
| 3      | 4           |
| 4      | 6           |
| 5      | 1           |
| 6      | 4           |
| 7      | 1           |
| 8      | 2           |
| 9      | 4           |
| 10     | 1           |
| 11     | 4           |
| 12     | 6           |

Output

[
  {"interval_no":1,"total_orders":17},
  {"interval_no":2,"total_orders":18}
]

Explanation

Minutes 1-6 form interval 1 (sum 17), and minutes 7-12 form interval 2 (sum 18).

Example 2

Input

Orders:
| minute | order_count |
|--------|-------------|
| 6      | 9           |
| 1      | 3           |
| 5      | 5           |
| 2      | 0           |
| 4      | 2           |
| 3      | 1           |

Output

[
  {"interval_no":1,"total_orders":20}
]

Explanation

Input row order is irrelevant. The six rows still belong to interval 1, and their sum is 20.

Example 3

Input

Orders:
| minute | order_count |
|--------|-------------|
| 1      | 10          |
| 2      | 0           |
| 3      | 0           |
| 4      | 0           |
| 5      | 0           |
| 6      | 0           |
| 7      | 1           |
| 8      | 1           |
| 9      | 1           |
| 10     | 1           |
| 11     | 1           |
| 12     | 1           |

Output

[
  {"interval_no":1,"total_orders":10},
  {"interval_no":2,"total_orders":6}
]

Explanation

Each block of six minutes is summed independently.

Constraints

6 <= number of rows in Orders <= 120000
Number of rows in Orders is a multiple of 6
1 <= minute <= 120000 and minute is unique
Minutes are contiguous: 1, 2, 3, ..., 6 * k
0 <= order_count <= 1000000
Return exactly columns: interval_no, total_orders
Sort output by interval_no ASC
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with exact required columns