LeetCode #601 — HARD

Human Traffic of Stadium

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

The Problem

Problem Statement

Table: Stadium

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| id            | int     |
| visit_date    | date    |
| people        | int     |
+---------------+---------+
visit_date is the column with unique values for this table.
Each row of this table contains the visit date and visit id to the stadium with the number of people during the visit.
As the id increases, the date increases as well.

Write a solution to display the records with three or more rows with consecutive id's, and the number of people is greater than or equal to 100 for each.

Return the result table ordered by visit_date in ascending order.

The result format is in the following example.

Example 1:

Input: 
Stadium table:
+------+------------+-----------+
| id   | visit_date | people    |
+------+------------+-----------+
| 1    | 2017-01-01 | 10        |
| 2    | 2017-01-02 | 109       |
| 3    | 2017-01-03 | 150       |
| 4    | 2017-01-04 | 99        |
| 5    | 2017-01-05 | 145       |
| 6    | 2017-01-06 | 1455      |
| 7    | 2017-01-07 | 199       |
| 8    | 2017-01-09 | 188       |
+------+------------+-----------+
Output: 
+------+------------+-----------+
| id   | visit_date | people    |
+------+------------+-----------+
| 5    | 2017-01-05 | 145       |
| 6    | 2017-01-06 | 1455      |
| 7    | 2017-01-07 | 199       |
| 8    | 2017-01-09 | 188       |
+------+------------+-----------+
Explanation: 
The four rows with ids 5, 6, 7, and 8 have consecutive ids and each of them has >= 100 people attended. Note that row 8 was included even though the visit_date was not the next day after row 7.
The rows with ids 2 and 3 are not included because we need at least three consecutive ids.

Step 01

Brute Force Baseline

Problem summary: Table: Stadium +---------------+---------+ | Column Name | Type | +---------------+---------+ | id | int | | visit_date | date | | people | int | +---------------+---------+ visit_date is the column with unique values for this table. Each row of this table contains the visit date and visit id to the stadium with the number of people during the visit. As the id increases, the date increases as well. Write a solution to display the records with three or more rows with consecutive id's, and the number of people is greater than or equal to 100 for each. Return the result table ordered by visit_date in ascending order. The result format is in the following example.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

{"headers": {"Stadium": ["id", "visit_date", "people"]}, "rows": {"Stadium": [[1, "2017-01-01", 10], [2, "2017-01-02", 109], [3, "2017-01-03", 150], [4, "2017-01-04", 99], [5, "2017-01-05", 145], [6, "2017-01-06", 1455], [7, "2017-01-07", 199], [8, "2017-01-09", 188]]}}

Step 02

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Largest constraint values

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #601: Human Traffic of Stadium
// Auto-generated Java example from rust.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (rust):
// // Accepted solution for LeetCode #601: Human Traffic of Stadium
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #601: Human Traffic of Stadium
// # Write your MySQL query statement below
// WITH
//     S AS (
//         SELECT
//             *,
//             id - (ROW_NUMBER() OVER (ORDER BY id)) AS rk
//         FROM Stadium
//         WHERE people >= 100
//     ),
//     T AS (SELECT *, COUNT(1) OVER (PARTITION BY rk) AS cnt FROM S)
// SELECT id, visit_date, people
// FROM T
// WHERE cnt >= 3
// ORDER BY 1;
// "#
// }

// Accepted solution for LeetCode #601: Human Traffic of Stadium
// Auto-generated Go example from rust.
func exampleSolution() {
}
// Reference (rust):
// // Accepted solution for LeetCode #601: Human Traffic of Stadium
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #601: Human Traffic of Stadium
// # Write your MySQL query statement below
// WITH
//     S AS (
//         SELECT
//             *,
//             id - (ROW_NUMBER() OVER (ORDER BY id)) AS rk
//         FROM Stadium
//         WHERE people >= 100
//     ),
//     T AS (SELECT *, COUNT(1) OVER (PARTITION BY rk) AS cnt FROM S)
// SELECT id, visit_date, people
// FROM T
// WHERE cnt >= 3
// ORDER BY 1;
// "#
// }

# Accepted solution for LeetCode #601: Human Traffic of Stadium
# Auto-generated Python example from rust.
def example_solution() -> None:
    return
# Reference (rust):
# // Accepted solution for LeetCode #601: Human Traffic of Stadium
# pub fn sql_example() -> &'static str {
#     r#"
# -- Accepted solution for LeetCode #601: Human Traffic of Stadium
# # Write your MySQL query statement below
# WITH
#     S AS (
#         SELECT
#             *,
#             id - (ROW_NUMBER() OVER (ORDER BY id)) AS rk
#         FROM Stadium
#         WHERE people >= 100
#     ),
#     T AS (SELECT *, COUNT(1) OVER (PARTITION BY rk) AS cnt FROM S)
# SELECT id, visit_date, people
# FROM T
# WHERE cnt >= 3
# ORDER BY 1;
# "#
# }

// Accepted solution for LeetCode #601: Human Traffic of Stadium
pub fn sql_example() -> &'static str {
    r#"
-- Accepted solution for LeetCode #601: Human Traffic of Stadium
# Write your MySQL query statement below
WITH
    S AS (
        SELECT
            *,
            id - (ROW_NUMBER() OVER (ORDER BY id)) AS rk
        FROM Stadium
        WHERE people >= 100
    ),
    T AS (SELECT *, COUNT(1) OVER (PARTITION BY rk) AS cnt FROM S)
SELECT id, visit_date, people
FROM T
WHERE cnt >= 3
ORDER BY 1;
"#
}

// Accepted solution for LeetCode #601: Human Traffic of Stadium
// Auto-generated TypeScript example from rust.
function exampleSolution(): void {
}
// Reference (rust):
// // Accepted solution for LeetCode #601: Human Traffic of Stadium
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #601: Human Traffic of Stadium
// # Write your MySQL query statement below
// WITH
//     S AS (
//         SELECT
//             *,
//             id - (ROW_NUMBER() OVER (ORDER BY id)) AS rk
//         FROM Stadium
//         WHERE people >= 100
//     ),
//     T AS (SELECT *, COUNT(1) OVER (PARTITION BY rk) AS cnt FROM S)
// SELECT id, visit_date, people
// FROM T
// WHERE cnt >= 3
// ORDER BY 1;
// "#
// }

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.