Introduction

In this blog post, we'll share the most commonly asked coding interview questions at Tinder. If you don't have months to study for your interviews, you can use AI tools like Chatmagic to generate solutions quickly and efficiently - helping you pass the interviews and get the job offer!

Problem #1: Kth Largest Element in a Stream

You are part of a university admissions office and need to keep track of the kth highest test score from applicants in real-time. This helps to determine cut-off marks for interviews and admissions dynamically as new applicants submit their scores. You are tasked to implement a class which, for a given integer k, maintains a stream of test scores and continuously returns the kth highest test score after a new score has been submitted. More specifically, we are looking for the kth highest score in the sorted list of all scores. Implement the KthLargest class: KthLargest(int k, int[] nums) Initializes the object with the integer k and the stream of test scores nums. int add(int val) Adds a new test score val to the stream and returns the element representing the kth largest element in the pool of test scores so far. Example 1: Input: ["KthLargest", "add", "add", "add", "add", "add"] [[3, [4, 5, 8, 2]], [3], [5], [10], [9], [4]] Output: [null, 4, 5, 5, 8, 8] Explanation: KthLargest kthLargest = new KthLargest(3, [4, 5, 8, 2]); kthLargest.add(3); // return 4 kthLargest.add(5); // return 5 kthLargest.add(10); // return 5 kthLargest.add(9); // return 8 kthLargest.add(4); // return 8 Example 2: Input: ["KthLargest", "add", "add", "add", "add"] [[4, [7, 7, 7, 7, 8, 3]], [2], [10], [9], [9]] Output: [null, 7, 7, 7, 8] Explanation: KthLargest kthLargest = new KthLargest(4, [7, 7, 7, 7, 8, 3]); kthLargest.add(2); // return 7 kthLargest.add(10); // return 7 kthLargest.add(9); // return 7 kthLargest.add(9); // return 8 Constraints: 0 <= nums.length <= 104 1 <= k <= nums.length + 1 -104 <= nums[i] <= 104 -104 <= val <= 104 At most 104 calls will be made to add.

Topics: Tree, Design, Binary Search Tree, Heap (Priority Queue), Binary Tree, Data Stream

Problem #2: Find Median from Data Stream

The median is the middle value in an ordered integer list. If the size of the list is even, there is no middle value, and the median is the mean of the two middle values. For example, for arr = [2,3,4], the median is 3. For example, for arr = [2,3], the median is (2 + 3) / 2 = 2.5. Implement the MedianFinder class: MedianFinder() initializes the MedianFinder object. void addNum(int num) adds the integer num from the data stream to the data structure. double findMedian() returns the median of all elements so far. Answers within 10-5 of the actual answer will be accepted. Example 1: Input ["MedianFinder", "addNum", "addNum", "findMedian", "addNum", "findMedian"] [[], [1], [2], [], [3], []] Output [null, null, null, 1.5, null, 2.0] Explanation MedianFinder medianFinder = new MedianFinder(); medianFinder.addNum(1); // arr = [1] medianFinder.addNum(2); // arr = [1, 2] medianFinder.findMedian(); // return 1.5 (i.e., (1 + 2) / 2) medianFinder.addNum(3); // arr[1, 2, 3] medianFinder.findMedian(); // return 2.0 Constraints: -105 <= num <= 105 There will be at least one element in the data structure before calling findMedian. At most 5 * 104 calls will be made to addNum and findMedian. Follow up: If all integer numbers from the stream are in the range [0, 100], how would you optimize your solution? If 99% of all integer numbers from the stream are in the range [0, 100], how would you optimize your solution?

Topics: Two Pointers, Design, Sorting, Heap (Priority Queue), Data Stream