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Solve chat, grid paths, and car rentals | Google Interview Question

Q: Solve chat, grid paths, and car rentals

This multi-part question evaluates algorithmic problem-solving across frequency counting and selection for top-K queries, dynamic programming and space-optimized state transitions for constrained grid path counting, and interval scheduling/resource-allocation reasoning for minimum-vehicle assignment.

You are given three independent programming tasks. For each, describe and implement an efficient algorithm, and be prepared to analyze its time and space complexity.

Q1. Find top-K most active chat users

You are given a log of chat messages from a messaging application.

Input:

An array messages of length n , where each element messages[i] is the user_id (string or integer) of the sender of the i-th message.
An integer k (1 ≤ k ≤ number of distinct users).

Output:

Return a list of k user IDs corresponding to the users who have sent the most messages.

Details & requirements:

The "activity" of a user is defined as the number of messages they have sent (i.e., their frequency in messages ).
If there are fewer than k distinct users, return all distinct users.
If multiple users have the same frequency, you may break ties arbitrarily, or (for determinism) sort tied users by user_id in ascending order.
Aim for an algorithm that is more efficient than sorting all users by count when k is much smaller than the number of distinct users.

Design and implement a function, for example:

List<UserId> topKActiveUsers(List<UserId> messages, int k)

Also explain:

At least two different approaches (e.g., based on sorting, heaps, or selection algorithms).
The time and space complexity of each approach.

Q2. Count paths in a 2D grid with constrained moves

You have a 2D grid with m rows and n columns. Index rows from top to bottom as 0..m-1 and columns from left to right as 0..n-1.

The start cell is the bottom-left corner: (m-1, 0) .
The end cell is the bottom-right corner: (m-1, n-1) .

From any cell (r, c), you may move only to the next column to the right, in one of up to three ways (if within bounds):

Right-up: (r-1, c+1)
Right: (r, c+1)
Right-down: (r+1, c+1)

You cannot move left or stay in the same column. All cells are passable (no obstacles).

Task:

Given integers m and n , compute the total number of distinct paths from the start cell (m-1, 0) to the end cell (m-1, n-1) using only the allowed moves.

Requirements:

Implement a function such as long countPaths(int m, int n) .
The result may be large; assume it fits in a 64-bit integer, or, if you prefer, you may return the count modulo a large prime (e.g., 1,000,000,007) — specify which convention you are using.
First design a straightforward dynamic programming solution using O(m·n) time and O(m·n) space.
Then describe how to optimize the space usage (e.g., to O(m)) while keeping the same time complexity, and explain why the optimization is correct.

Q3. Minimum cars needed for rentals and assignment

You work at a car rental company. You are given a list of rental bookings, each with a pickup and return time.

Input:

An array of R rental records. Each record has:
- rental_id : a unique identifier (e.g., integer or string).
- pickup_time : an integer start time.
- return_time : an integer end time, with pickup_time < return_time .

Assume times are on a single timeline (e.g., minutes from some epoch). A car can be reassigned immediately after it is returned, i.e., if one rental ends at time t and another starts at time t, the same car can serve both.

Tasks:

Capacity : Compute the minimum number of cars required so that all rentals can be fulfilled without time conflicts on any single car.
Assignment : Produce one valid assignment of rentals to cars that uses exactly that minimum number of cars.

Output:

An integer C = minimal number of cars needed.
A mapping from each rental_id to a car_id in the range 1..C , such that no two rentals assigned to the same car_id overlap in time.

Requirements & clarifications:

Overlap definition: two rentals with intervals [s1, e1) and [s2, e2) overlap if their time intervals intersect with non-empty duration. Using half-open intervals, if e1 == s2 they do not overlap.
Aim for an algorithm that is efficient for up to, say, 10^5 rental records.
Discuss a naive approach and then an optimized approach (for example, using a min-heap/priority queue keyed by the earliest return time to track currently in-use cars).
Analyze the time and space complexity of your final solution.

You are given three independent programming tasks. For each, describe and implement an efficient algorithm, and be prepared to analyze its time and space complexity.

Q1. Find top-K most active chat users

You are given a log of chat messages from a messaging application.

Input:

An array messages of length n , where each element messages[i] is the user_id (string or integer) of the sender of the i-th message.
An integer k (1 ≤ k ≤ number of distinct users).

Output:

Return a list of k user IDs corresponding to the users who have sent the most messages.

Details & requirements:

The "activity" of a user is defined as the number of messages they have sent (i.e., their frequency in messages ).
If there are fewer than k distinct users, return all distinct users.
If multiple users have the same frequency, you may break ties arbitrarily, or (for determinism) sort tied users by user_id in ascending order.
Aim for an algorithm that is more efficient than sorting all users by count when k is much smaller than the number of distinct users.

Design and implement a function, for example:

List<UserId> topKActiveUsers(List<UserId> messages, int k)

Also explain:

At least two different approaches (e.g., based on sorting, heaps, or selection algorithms).
The time and space complexity of each approach.

Q2. Count paths in a 2D grid with constrained moves

You have a 2D grid with m rows and n columns. Index rows from top to bottom as 0..m-1 and columns from left to right as 0..n-1.

The start cell is the bottom-left corner: (m-1, 0) .
The end cell is the bottom-right corner: (m-1, n-1) .

From any cell (r, c), you may move only to the next column to the right, in one of up to three ways (if within bounds):

Right-up: (r-1, c+1)
Right: (r, c+1)
Right-down: (r+1, c+1)

You cannot move left or stay in the same column. All cells are passable (no obstacles).

Task:

Given integers m and n , compute the total number of distinct paths from the start cell (m-1, 0) to the end cell (m-1, n-1) using only the allowed moves.

Requirements:

Implement a function such as long countPaths(int m, int n) .
The result may be large; assume it fits in a 64-bit integer, or, if you prefer, you may return the count modulo a large prime (e.g., 1,000,000,007) — specify which convention you are using.
First design a straightforward dynamic programming solution using O(m·n) time and O(m·n) space.
Then describe how to optimize the space usage (e.g., to O(m)) while keeping the same time complexity, and explain why the optimization is correct.

Q3. Minimum cars needed for rentals and assignment

You work at a car rental company. You are given a list of rental bookings, each with a pickup and return time.

Input:

An array of R rental records. Each record has:
- rental_id : a unique identifier (e.g., integer or string).
- pickup_time : an integer start time.
- return_time : an integer end time, with pickup_time < return_time .

Tasks:

Capacity : Compute the minimum number of cars required so that all rentals can be fulfilled without time conflicts on any single car.
Assignment : Produce one valid assignment of rentals to cars that uses exactly that minimum number of cars.

Output:

An integer C = minimal number of cars needed.
A mapping from each rental_id to a car_id in the range 1..C , such that no two rentals assigned to the same car_id overlap in time.

Requirements & clarifications:

Overlap definition: two rentals with intervals [s1, e1) and [s2, e2) overlap if their time intervals intersect with non-empty duration. Using half-open intervals, if e1 == s2 they do not overlap.
Aim for an algorithm that is efficient for up to, say, 10^5 rental records.
Discuss a naive approach and then an optimized approach (for example, using a min-heap/priority queue keyed by the earliest return time to track currently in-use cars).
Analyze the time and space complexity of your final solution.

Solve chat, grid paths, and car rentals

Quick Overview

Q1. Find top-K most active chat users

Q2. Count paths in a 2D grid with constrained moves

Q3. Minimum cars needed for rentals and assignment

Comments (0)

Solve chat, grid paths, and car rentals

Quick Overview

Q1. Find top-K most active chat users

Q2. Count paths in a 2D grid with constrained moves

Q3. Minimum cars needed for rentals and assignment

Comments (0)