Kth Largest Element In Stream
Detailed guide and Python implementation for the 'Kth Largest Element In Stream' problem.
1. Concept Overview
The 'Kth Largest Element In Stream' problem is a key challenge in the Heap / Priority Queue section.
This implementation focuses on easy-level logic in Python.
We prioritize technical accuracy and code readability in our provided solutions.
2. Real-World Applications
3. Visual Intuition
Visualizing the logic flow for Kth Largest Element In Stream.
4. Prerequisites
5. Step-by-Step Thinking
1. Understand the problem
Read the problem statement for Kth Largest Element In Stream carefully.
2. Formulate brute force
Draft a simple iterative solution.
3. Identify inefficiency
Look for redundant calculations.
4. Optimize search path
Use hashing or sorting to speed up the process.
5. Final Implementation
Clean up the code for production standards.
Problem Statement
Design a class to find the kth largest element in a stream. Note that it is the kth largest element in the sorted order, not the kth distinct element.
Implement the KthLargest class:
- KthLargest(k: int, nums: List[int]) Initializes the object with the integer k and the stream of integers nums.
- add(val: int) -> int Appends the integer val to the stream and returns the element representing the kth largest element.
Input is a list of operations and arguments. Implement a function kthLargest(operations: list, arguments: list) -> list that returns a list of results (None for constructor, int for add).
- •1 <= k <= 10^4
- •0 <= len(nums) <= 10^4
- •-10^4 <= nums[i], val <= 10^4
- •At most 10^4 calls will be made to add
Examples
operations = ["KthLargest", "add", "add", "add", "add", "add"], arguments = [[3, [4, 5, 8, 2]], [3], [5], [10], [9], [4]]
[None, 4, 5, 5, 8, 8]
KthLargest class is initialized with k=3 and nums=[4,5,8,2]. add(3) returns 4. add(5) returns 5. add(10) returns 5. add(9) returns 8. add(4) returns 8.
Need a Hint?
Edge Cases to Watch
- Empty input structures
- Single element inputs
- Large numerical bounds
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Interview Insights & Variations
Complexity Analysis Breakdown
Why Time: Directly evaluates all possibilities.
Why Space: Uses standard local memory.
Why Time: Optimized paths reduce total operations.
Why Space: May trade memory for speed.
Optimized Solution Python Code
Optimized Solution Python Code
import heapq
class KthLargestOpt:
def __init__(self, k, nums):
self.k, self.heap = k, nums
heapq.heapify(self.heap)
while len(self.heap) > k: heapq.heappop(self.heap)
def add(self, val):
heapq.heappush(self.heap, val)
if len(self.heap) > self.k: heapq.heappop(self.heap)
return self.heap[0]Brute Force Code (Spoiler Guarded)
Brute Force Code (Spoiler Guarded)
class KthLargestBrute:
def __init__(self, k, nums):
self.k, self.nums = k, nums
def add(self, val):
self.nums.append(val)
self.nums.sort()
return self.nums[-self.k]Algorithm Pattern Checklist
When dealing with Heap / Priority Queue data patterns.
Core Prerequisites
Revision Key Notes
Common Mistakes & Pitfalls
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