LeetCode #1934 — MEDIUM

Confirmation Rate

Move from brute-force thinking to an efficient approach using core interview patterns strategy.

The Problem

Problem Statement

Table: Signups

+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
+----------------+----------+
user_id is the column of unique values for this table.
Each row contains information about the signup time for the user with ID user_id.

Table: Confirmations

+----------------+----------+
| Column Name    | Type     |
+----------------+----------+
| user_id        | int      |
| time_stamp     | datetime |
| action         | ENUM     |
+----------------+----------+
(user_id, time_stamp) is the primary key (combination of columns with unique values) for this table.
user_id is a foreign key (reference column) to the Signups table.
action is an ENUM (category) of the type ('confirmed', 'timeout')
Each row of this table indicates that the user with ID user_id requested a confirmation message at time_stamp and that confirmation message was either confirmed ('confirmed') or expired without confirming ('timeout').

The confirmation rate of a user is the number of 'confirmed' messages divided by the total number of requested confirmation messages. The confirmation rate of a user that did not request any confirmation messages is 0. Round the confirmation rate to two decimal places.

Write a solution to find the confirmation rate of each user.

Return the result table in any order.

The result format is in the following example.

Example 1:

Input: 
Signups table:
+---------+---------------------+
| user_id | time_stamp          |
+---------+---------------------+
| 3       | 2020-03-21 10:16:13 |
| 7       | 2020-01-04 13:57:59 |
| 2       | 2020-07-29 23:09:44 |
| 6       | 2020-12-09 10:39:37 |
+---------+---------------------+
Confirmations table:
+---------+---------------------+-----------+
| user_id | time_stamp          | action    |
+---------+---------------------+-----------+
| 3       | 2021-01-06 03:30:46 | timeout   |
| 3       | 2021-07-14 14:00:00 | timeout   |
| 7       | 2021-06-12 11:57:29 | confirmed |
| 7       | 2021-06-13 12:58:28 | confirmed |
| 7       | 2021-06-14 13:59:27 | confirmed |
| 2       | 2021-01-22 00:00:00 | confirmed |
| 2       | 2021-02-28 23:59:59 | timeout   |
+---------+---------------------+-----------+
Output: 
+---------+-------------------+
| user_id | confirmation_rate |
+---------+-------------------+
| 6       | 0.00              |
| 3       | 0.00              |
| 7       | 1.00              |
| 2       | 0.50              |
+---------+-------------------+
Explanation: 
User 6 did not request any confirmation messages. The confirmation rate is 0.
User 3 made 2 requests and both timed out. The confirmation rate is 0.
User 7 made 3 requests and all were confirmed. The confirmation rate is 1.
User 2 made 2 requests where one was confirmed and the other timed out. The confirmation rate is 1 / 2 = 0.5.

Step 01

Brute Force Baseline

Problem summary: Table: Signups +----------------+----------+ | Column Name | Type | +----------------+----------+ | user_id | int | | time_stamp | datetime | +----------------+----------+ user_id is the column of unique values for this table. Each row contains information about the signup time for the user with ID user_id. Table: Confirmations +----------------+----------+ | Column Name | Type | +----------------+----------+ | user_id | int | | time_stamp | datetime | | action | ENUM | +----------------+----------+ (user_id, time_stamp) is the primary key (combination of columns with unique values) for this table. user_id is a foreign key (reference column) to the Signups table. action is an ENUM (category) of the type ('confirmed', 'timeout') Each row of this table indicates that the user with ID user_id requested a confirmation message at time_stamp and that confirmation message was either confirmed

Baseline thinking

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

Pattern signal: General problem-solving

Example 1

{"headers": {"Signups": ["user_id", "time_stamp"], "Confirmations": ["user_id", "time_stamp", "action"]}, "rows": {"Signups": [[3, "2020-03-21 10:16:13"], [7, "2020-01-04 13:57:59"], [2, "2020-07-29 23:09:44"], [6, "2020-12-09 10:39:37"]], "Confirmations": [[3, "2021-01-06 03:30:46", "timeout"], [3, "2021-07-14 14:00:00", "timeout"], [7, "2021-06-12 11:57:29", "confirmed"], [7, "2021-06-13 12:58:28", "confirmed"], [7, "2021-06-14 13:59:27", "confirmed"], [2, "2021-01-22 00:00:00", "confirmed"], [2, "2021-02-28 23:59:59", "timeout"]]}}

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

Upper-end input sizes

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 #1934: Confirmation Rate
// Auto-generated Java example from rust.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (rust):
// // Accepted solution for LeetCode #1934: Confirmation Rate
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #1934: Confirmation Rate
// # Write your MySQL query statement below
// SELECT
//     user_id,
//     ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
// FROM
//     SignUps
//     LEFT JOIN Confirmations USING (user_id)
// GROUP BY 1;
// "#
// }

// Accepted solution for LeetCode #1934: Confirmation Rate
// Auto-generated Go example from rust.
func exampleSolution() {
}
// Reference (rust):
// // Accepted solution for LeetCode #1934: Confirmation Rate
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #1934: Confirmation Rate
// # Write your MySQL query statement below
// SELECT
//     user_id,
//     ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
// FROM
//     SignUps
//     LEFT JOIN Confirmations USING (user_id)
// GROUP BY 1;
// "#
// }

# Accepted solution for LeetCode #1934: Confirmation Rate
# Auto-generated Python example from rust.
def example_solution() -> None:
    return
# Reference (rust):
# // Accepted solution for LeetCode #1934: Confirmation Rate
# pub fn sql_example() -> &'static str {
#     r#"
# -- Accepted solution for LeetCode #1934: Confirmation Rate
# # Write your MySQL query statement below
# SELECT
#     user_id,
#     ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
# FROM
#     SignUps
#     LEFT JOIN Confirmations USING (user_id)
# GROUP BY 1;
# "#
# }

// Accepted solution for LeetCode #1934: Confirmation Rate
pub fn sql_example() -> &'static str {
    r#"
-- Accepted solution for LeetCode #1934: Confirmation Rate
# Write your MySQL query statement below
SELECT
    user_id,
    ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
FROM
    SignUps
    LEFT JOIN Confirmations USING (user_id)
GROUP BY 1;
"#
}

// Accepted solution for LeetCode #1934: Confirmation Rate
// Auto-generated TypeScript example from rust.
function exampleSolution(): void {
}
// Reference (rust):
// // Accepted solution for LeetCode #1934: Confirmation Rate
// pub fn sql_example() -> &'static str {
//     r#"
// -- Accepted solution for LeetCode #1934: Confirmation Rate
// # Write your MySQL query statement below
// SELECT
//     user_id,
//     ROUND(IFNULL(SUM(action = 'confirmed') / COUNT(1), 0), 2) AS confirmation_rate
// FROM
//     SignUps
//     LEFT JOIN Confirmations USING (user_id)
// GROUP 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.