How to Solve Number of Recent Calls

This problem tests your ability to design a system to track recent events efficiently. You need to maintain the count of events within a given time window. By using a queue, we can achieve optimal time complexity for this problem.

Problem Statement

You are tasked with designing a class called RecentCounter that tracks the number of requests made within a specific time frame. The class should have a method ping(t) that records the current time t and returns the number of pings that have been called within the last 3000 milliseconds (inclusive).

You are guaranteed that each call to ping uses a strictly larger value of t than the previous one. Your solution should efficiently handle up to 10,000 ping calls, considering both time and space complexity.

Examples

Example 1

Input: See original problem statement.

Output: See original problem statement.

Input ["RecentCounter", "ping", "ping", "ping", "ping"] [[], [1], [100], [3001], [3002]] Output [null, 1, 2, 3, 3]

Explanation RecentCounter recentCounter = new RecentCounter(); recentCounter.ping(1); // requests = [1], range is [-2999,1], return 1 recentCounter.ping(100); // requests = [1, 100], range is [-2900,100], return 2 recentCounter.ping(3001); // requests = [1, 100, 3001], range is [1,3001], return 3 recentCounter.ping(3002); // requests = [1, 100, 3001, 3002], range is [2,3002], return 3

Constraints

• 1 <= t <= 109
• Each test case will call ping with strictly increasing values of t.
• At most 104 calls will be made to ping.

Solution Approach

Use of Queue for Recent State

The optimal solution involves using a queue to store the ping times. Each time a new ping is recorded, the queue maintains the timestamps of the requests. The range of timestamps that fall within the last 3000 milliseconds is maintained by removing outdated requests from the front of the queue, ensuring the queue size always reflects the number of recent requests.

Efficiently Managing Time Window

The main challenge is efficiently managing the time window of 3000 milliseconds. By removing older pings that fall outside this time window, the queue will only store valid pings, and the number of pings in the queue can be returned in constant time.

Time Complexity Considerations

Each call to ping(t) has an amortized time complexity of O(1), as we only need to append to the queue and remove any outdated pings at the front. This allows the algorithm to handle the maximum input size efficiently.

Complexity Analysis

The time complexity for each ping is O(1) because both enqueueing and dequeuing operations are constant time. The space complexity is O(n) where n is the number of pings in the time window. Given that the maximum number of pings is 10,000, the space used is manageable.

What Interviewers Usually Probe

• The candidate understands how to implement a queue-driven solution for managing time-based state.
• The candidate focuses on time and space optimization by leveraging a queue.
• The candidate demonstrates efficiency in handling large numbers of requests within a given time frame.

Common Pitfalls or Variants

Common pitfalls

• Not handling edge cases where the time window is close to or exceeds 3000 milliseconds.
• Failing to manage the queue size and removing outdated pings, leading to incorrect results.
• Not considering the time complexity of the solution and implementing a brute force method that cannot handle large inputs.

Follow-up variants

• Consider handling requests for different time windows (e.g., 1000ms or 5000ms).
• Implement a version where the queue size is limited to a fixed number of pings.
• Introduce a new requirement where pings need to be categorized or processed differently based on time of arrival.

How GhostInterview Helps

• GhostInterview provides step-by-step guidance to implement a queue-based solution for this problem.
• GhostInterview helps identify and avoid common pitfalls related to time and space complexity.
• GhostInterview allows you to review and test your approach against multiple variants of the problem for broader practice.

Topic Pages

• Design
• Queue
• Data Stream

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FAQ

How do I efficiently track recent calls for the Number of Recent Calls problem?

Using a queue is an efficient way to track recent calls, as it allows constant-time access to the count of recent requests within a specified time window.

What is the time complexity of the solution for the Number of Recent Calls problem?

The time complexity of each ping operation is O(1) on average, due to the efficient use of a queue to manage the time window of requests.

Can the queue-based solution handle the maximum input size?

Yes, the queue-based approach is designed to efficiently handle up to 10,000 pings, with the space complexity being proportional to the number of pings within the time window.

How does GhostInterview help with solving the Number of Recent Calls problem?

GhostInterview provides a structured approach to solving the problem, helping you understand the queue-driven state processing and avoid common mistakes in time and space management.

What is the primary pattern for solving the Number of Recent Calls problem?

The primary pattern involves queue-driven state processing, where a queue is used to efficiently track and count recent requests within the time window.