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📘 Welcome to Part 189 of Code & Debug's DSA in Python Course! Building on your mastery of QuickSelect algorithm from the previous session, we now tackle a related but distinct problem: Kth Largest Element in a Stream from LeetCode. This time, we'll design a class that efficiently maintains the kth largest element in a continuously growing stream of numbers using a Min Heap of size k, making it perfect for real-time data processing scenarios. This problem demonstrates the power of heap-based design patterns and is frequently asked in system design and streaming data interviews at top tech companies! 👨🏫 What's covered in this video: ✅ Understanding the problem: Stream vs. Static Array ✅ Why Min Heap is perfect for this streaming scenario ✅ Complete KthLargest class implementation in Python ✅ Constructor initialization with existing numbers ✅ Add method for inserting new elements efficiently ✅ Maintaining heap size to exactly k elements ✅ Time Complexity: O(log k) per insertion analysis ✅ Space Complexity: O(k) for optimal memory usage ✅ Interview tips and edge cases handling By the end of this session, you'll master heap-based class design and understand how to handle streaming data efficiently using priority queues! 🔗 LeetCode Problem - Kth Largest Element in a Stream: https://leetcode.com/problems/kth-largest-element-in-a-stream/description/ 📄 Full Playlist Sheet (All Questions in Order): https://docs.google.com/spreadsheets/d/1AWE15Fy3wD2iqu2vjK_R7cCiuvSsjYQclcdZmHpF66o/edit?usp=sharing 🎓 Enroll in the FREE Python DSA Course: https://codeanddebug.in/course/master-dsa-with-leetcode 🚀 Advance Python DSA for FAANG (Zero to Hero Course): https://codeanddebug.in/course/zero-to-hero-python-dsa Stay focused and keep coding with Code & Debug. Like | Share | Subscribe | Hit the 🔔 #KthLargestStream #MinHeap #PriorityQueue #LeetCode #PythonDSA #StreamingData #HeapDesign #CodeAndDebug #Part189 #DataStructures #DSAforInterviews #LeetCodeMedium #ClassDesign
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