Median to StatsFinder: OOD Mastery in 3 Rounds

Background

In technical interviews at top tech companies, interviewers often start with a classic algorithm question and, through a series of ingenious follow-ups, gradually lead it to a more complex, real-world-like Object-Oriented Design (OOD) problem. This tests not only your algorithmic fundamentals but also your code abstraction ability, modular thinking, and adaptability to requirement iteration.

Recently, a student experienced this upgrade from "solving" to "designing" in three consecutive interview rounds. With oavoservice's "Real-time Algorithm Optimization + OOD Modeling Guidance + Concurrency Follow-up Prediction", he perfectly handled every technical challenge and demonstrated impressive design taste.

Round 1: Classic Algorithm - Median of a Data Stream

📜 Essence of the Problem

Design a data structure that supports addNum(num) to add a number and findMedian() to find the median of all current numbers.

oavoservice's Mindset Injection

This is a classic hard problem on LeetCode (295). The optimal solution is Two Heaps.

• Core Data Structure:
  • A Max-Heap small_half: Stores the smaller half of the data stream.
  • A Min-Heap large_half: Stores the larger half of the data stream.
• Core Logic (Balancing): Maintain equal size (or off by one). Rebalance after each addNum.
• Python Details: Reminder that Python's heapq is a min-heap, so store negatives for the max-heap.

Extreme Follow-ups

1. Add removeNum(num): Heaps don't support efficient random removal. We prompted Lazy Removal using a hash map to track deleted numbers.
2. Concurrency: What if multiple threads call methods simultaneously? We guided the student to mention using a Global Lock (Mutex) to protect the shared state (heaps and hash map).

Round 2: Algorithm "Design" - Implement a Stats Finder

📜 The Leap in Complexity

Refactor the previous solution into a more generic StatsFinder class supporting:

• addNum(num)
• getMax()
• getMin()
• getMedian() (Reuse)
• withdrawLatest() (Undo last add)

oavoservice's Mindset Injection

The trap is designing independent logic for each feature. The test is extracting reusable components and elegant OOD.

We guided the student to center the design on maintaining the ordered state of the data stream. The Two Heaps structure from Round 1 is perfect for this.

• OOD Design Idea:
  • StatsFinder core members: small_half (Max-Heap) and large_half (Min-Heap).
  • addNum & getMedian: Reuse Round 1 logic.
  • getMax(): Answer is at the top of large_half.
  • getMin(): Answer is at the top of small_half.
  • withdrawLatest(): Needs an extra Stack to record history for undo operations.

Round 3: Behavioral Round

Led by a Recruiter. We prepared classic BQ answers (STAR method) and professional responses for logistics.

🎯 Summary: oavoservice Helps You Show "Architectural" Full-Stack Ability

In this interlinked interview process, oavoservice's value lies in:

• Deepening Classics: Digging out industrial-grade points like concurrency and lazy removal from standard algorithms.
• Elevating Code: Guiding you from writing a function to designing a class, then a scalable system.
• Connecting Dots: Showing the deep connection between Algorithms (Two Heaps) and OOD, demonstrating a comprehensive knowledge system.

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