Continuous Log ID Range Boundaries

Medium

A platform stores processed event identifiers in the table Logs(log_id). Column details: log_id: unique integer identifier of an event record The IDs are not guaranteed to be consecutive. Missing values represent gaps where no event exists. Task: Identify every maximal contiguous ID segment present in Logs. A segment is contiguous if each neighboring sorted ID differs by exactly 1. For each segment, return its first and last ID. Output requirements: Return exactly these columns: start_id, end_id Sort results by start_id in ascending order Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Input

Logs:
| log_id |
|--------|
| 1      |
| 2      |
| 3      |
| 7      |
| 8      |
| 10     |

Output

[
  {"start_id":1,"end_id":3},
  {"start_id":7,"end_id":8},
  {"start_id":10,"end_id":10}
]

Explanation

IDs 1-3 are continuous, 7-8 are continuous, and 10 is isolated as a single-length segment.

Input

Logs:
| log_id |
|--------|
| 104    |
| 102    |
| 103    |
| 200    |
| 100    |

Output

[
  {"start_id":100,"end_id":100},
  {"start_id":102,"end_id":104},
  {"start_id":200,"end_id":200}
]

Explanation

Order in source rows is irrelevant; ranges are formed after sorting log_id values.

Input

Logs:
| log_id |
|--------|
| 5000   |

Output

[
  {"start_id":5000,"end_id":5000}
]

Explanation

A single value forms one contiguous segment whose start and end are identical.

Constraints

1 <= number of rows in Logs <= 200000
1 <= log_id <= 1000000000
log_id values are unique
A contiguous segment contains sorted IDs where adjacent differences equal 1
Return exactly two columns: start_id, end_id
Output must be sorted by start_id ascending
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with exactly columns: start_id, end_id