DSA in Python Course – Kth Largest Element in a Stream | Min Heap Design | Medium | Part 189

📘 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

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