How Uber Finds Cabs in 2 Seconds? 🚖 | System Design Explained #ola #uber #coding #education

Swiggy/Zomato live tracking ke baad, ab samajhte hain ki Uber aur Ola ki match-making algorithm kaise kaam karti hai! Agar aapko lagta hai ki Uber sabhi cabs ka distance calculate karta hai, toh aap galat hain! Let's decode this System Design

The SQL Problem:

Agar aap normal SQL query chalayenge: SELECT * FROM cabs WHERE distance less than 5km, toh database ko har active cab ka coordinates aapke coordinates se minus karke check karna padega. Ek city mein 50k+ active cabs pe ye query chalane se system completely crash ho jayega.

The Real Tech 1: Geohashing & H3 Grids
Uber traditional Lat/Long coordinates pe calculations nahi karta. Wo puri duniya ke map ko chote-chote blocks ya hexagons mein divide kar dete hain. Uber ne apna khud ka open-source grid system banaya hai jise Uber H3 (Hexagonal Hierarchical Spatial Index) kehte hain. Har hexagon ki ek unique ID hoti hai.

The Real Tech 2: QuadTrees & Spatial Indexing
Jab aap cab search karte hain, aapka system ye dekhta hai ki aap kis "Hexagon" ya "Geohash grid" mein hain. Phir wo sirf usi grid aur uske immediate neighbor grids mein available cabs ko search karta hai. Baki ki 49,000 cabs ko system touch bhi nahi karta! Isse computation power 99% bach jati hai.

The Real Tech 3: Redis Geospatial
Ye saari indexing slow hard drives pe nahi, balki directly RAM mein hoti hai using In-Memory data stores like Redis Geospatial. Isliye search results milliseconds mein aate hain.

Data Structures (DSA) ka isse behtar practical use-case nahi ho sakta! Aise hi hardcore tech architecture aasan bhasha mein samajhne ke liye Dev.Java ko Subscribe karein! ❤️💻

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