22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola) • High-Level System Design (HLD): Distributed Systems at Scale • Rishabh Ki Kaksha

High-Level System Design (HLD): Distributed Systems at Scale › 22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola)

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Course Syllabus

Module 1: Distributed Systems Core & Scalability Fundamentals

01. Scalability Dimensions: Vertical Scaling vs Horizontal Scaling & Latency vs Throughput 02. CAP Theorem vs PACELC Theorem: Consistency, Availability & Partition Tolerance Tradeoffs 03. Consistency Models: Strong Consistency, Eventual Consistency, Read-Your-Writes & Monotonic Reads

Module 2: Networking, Load Balancing & Reverse Proxies

04. Layer 4 vs Layer 7 Load Balancing: Round Robin, Weighted, Least Connections & IP Hash 05. High-Availability Load Balancers: Keepalived, VRRP, Anycast DNS & Failover Clustering 06. API Gateway Pattern: Authentication, Rate Limiting, Request Routing & Circuit Breakers

Module 3: Database Scaling: Sharding, Replication & Distributed Transactions

07. Database Replication: Master-Slave vs Multi-Master & Synchronous vs Asynchronous Replication 08. Database Sharding: Horizontal Partitioning, Directory-Based Sharding & Rebalancing Hotspots 09. Distributed Transactions: Two-Phase Commit (2PC) vs Saga Pattern (Choreography vs Orchestration)

Module 4: Caching Architectures, CDN & Consistent Hashing

10. Caching Strategies: Cache-Aside, Read-Through, Write-Through, Write-Around & Write-Back 11. Cache Invalidation Nightmares: Cache Avalanche, Cache Stampede, Thundering Herd & Cache Penetration 12. Consistent Hashing with Virtual Nodes (Solving Cache Clustering & Dynamic Scaling) 13. Content Delivery Networks (CDN): Edge Caching, Dynamic Site Acceleration & Origin Shielding

Module 5: Message Brokers, Event-Driven Architecture & Stream Processing

14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups) 15. Exactly-Once Processing Semantics (EOS), Idempotency Keys & Deduplication Architecture 16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers

Module 6: FAANG Classic HLD Architectural Case Studies

17. System Design Case Study 1: URL Shortener (TinyURL / Bitly) at 100M Daily Requests 18. System Design Case Study 2: Distributed Rate Limiter & Web Application Firewall 19. System Design Case Study 3: Real-Time Chat & Notification System (WhatsApp / Slack) 20. System Design Case Study 4: Social Media News Feed (Twitter/Instagram) with Fan-out on Write/Read 21. System Design Case Study 5: Video Streaming Platform (YouTube / Netflix) Transcoding Architecture 22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola)

Interactive Lesson Studio

# 22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola)

> High-Level Design (HLD) Notes by Rishabh - Topic 22

---

### 1. The Core Scale & Real-Time Geospatial Problem 🚕

*   **Scale:** Millions of active Drivers broadcasting GPS coordinates every 4 seconds.
*   **Challenge:** Match incoming Rider with the optimal nearby Driver within 1 second!
*   *Why Traditional SQL Spatial Fails:* Millions of concurrent `UPDATE drivers SET lat=?, lng=?` database locks ko crash kar dete hain!

---

### 2. Uber H3 Hexagonal Hierarchical Spatial Indexing ⬡

Earth ko **Discrete Hexagonal Cells (Hexagons)** me divide kiya jata hai (Resolution 8 $\approx 460$ meters radius):

```
        / \     / \
       | H1|---| H2|
        \ /     \ /
         |  H3   |
        / \     / \
```

*   **Hexagon Advantage over Square Grids:** Hexagon ke saare 6 neighbors ka distance center se **exact identical (uniform)** hota hai!
*   **Lookup:** Rider ke location ka H3 Index nikalo $\rightarrow$ Direct memory hash table se us hexagon aur uske neighboring 6 hexagons ke drivers fetch karo in **$O(1)$!**

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### 3. Real-Time Geospatial Architecture Diagram

```
[ Driver App (GPS Every 4s) ] ===(WebSocket/gRPC)===> [ Location Ingestion Gateway ]
                                                             ⬇
                                           [ In-Memory Geospatial Ring Buffer (Redis/Ring) ]
                                                             ⬇
[ Rider Requests Cab ] ➔ [ Dispatch Engine ] ➔ Calculate H3 Hex Index ➔ Query Ring Buffer
                                                             ⬇
                                [ Dynamic Surge Pricing & Route Match (ETA Engine) ]
```

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### 4. Master Comparison Matrix

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### 5. Interview Ke 3 Sawal Is Topic Se

1. **Surge Pricing multiplier real-time me kaise compute hota hai?** -> Har H3 hexagon cell ke andar Demand (Rider search requests) vs Supply (Available idle drivers) ka ratio calculate karke exponential pricing function apply karna.
2. **Driver location updates ke liye WebSocket HTTP polling se better kyu hai?** -> Header overhead reduce karna (4 bytes vs 800 bytes per packet) aur million drivers par battery/network bandwidth save karna.
3. **Dispatch engine trip matching me ETA (Estimated Time of Arrival) kaise include karta hai?** -> Sirf straight-line distance nahi, routing graph (OSRM / Google Distance Matrix) se road traffic status calculate karke driver assign karna.

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**Course Completed:** High-Level System Design (HLD) Master Track (All 22 Lessons Completed!) 🎉🚀🏗️🌐💎⚡

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