How Ride-Hailing Apps Efficiently Fetch Nearby Drivers | QuadTrees & Hash Tables Explained

🚗 *Efficient Driver Fetching in Ride-Hailing Apps: The Role of QuadTrees and Hash Tables* 🚗

Ever wondered how apps like Uber efficiently fetch nearby drivers without overloading their systems? In this video, we’ll break down the genius behind combining *QuadTrees* and *Hash Tables* to solve this complex challenge.

Here’s what you’ll learn:
✅ The limitations of frequent QuadTree updates.
✅ How hash tables store real-time driver locations with minimal overhead.
✅ The hybrid approach: updating the hash table every 4 seconds and the QuadTree every 15 seconds.
✅ How this system optimizes performance while maintaining real-time accuracy.

✨ *Why it matters:*
This hybrid approach balances efficiency, real-time updates, and scalability, ensuring ride-hailing apps deliver seamless experiences for both riders and drivers.



📌 Whether you're a system design enthusiast, a software engineer, or just curious about how these apps work, this video is for you!

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