Real-Time Geo Tracking with Interactive Grid
Built a scalable microservice using Kafka and Spring Boot for live location tracking. S3 cells create an interactive grid where users can produce and consume location updates within 1.5km radius cells via REST API.
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The problem
Real-time location tracking at scale requires efficient data distribution and query mechanisms. Traditional approaches struggle with high-frequency updates and localized queries. The challenge was to create a system where location updates are organized geographically, allowing instant discovery of who is nearby within specific regions, all accessible through simple REST APIs from any device.
The S3 cell-based architecture
The system divides geographical areas into S3 cells of approximately 1.5km radius. Each cell maps to a dedicated Kafka topic for location updates. When users come online, they publish to their cell's topic. This creates an interactive grid where presence is instantly visible and queryable per cell, enabling scalable real-time tracking across entire cities.
REST API for universal access
A Spring Boot REST API exposes endpoints for producing and consuming location updates. Mobile apps, web clients, or backend services can call these APIs to publish their location or query who's active in any cell. This design makes the system accessible from anywhere, enabling flexible integration with various platforms and use cases.
Live location streaming
Location updates flow through Kafka topics in real-time. As users move between cells, their presence updates instantly. The system handles high-frequency location events efficiently, providing immediate visibility into user distribution across the grid for applications like ride-sharing and delivery services.
Scalability and cell distribution
The cell-based architecture scales horizontally by distributing load across Kafka partitions. Each cell operates independently, handling location events for its region. This design supports thousands of concurrent users while maintaining low latency. The system adapts to varying user density, ensuring consistent performance across different areas of the city.
City-wide location tracking at scale
The microservice enables real-time location tracking across entire metropolitan areas. By organizing updates into geographical cells with dedicated Kafka topics, the system provides instant visibility into user presence while maintaining scalability and performance.
Interactive grid visualization
The grid displays live location updates as they happen. Users can see active drivers and participants in each cell, with markers updating in real-time as people move across the city. This creates an intuitive view of activity distribution.
Cell-based queries
Applications can query specific cells to find all active users within a 1.5km radius. This enables proximity features like finding nearby drivers, discovering users in your area, or matching people based on location without scanning the entire city.
Event streaming architecture
Kafka ensures reliable, ordered delivery of location events. The system can replay event history for analytics, maintain audit logs, and support multiple consumers. Each cell's topic acts as an independent event stream, enabling flexible integration patterns.
Project outcomes
The geo tracking microservice successfully enables real-time location-based applications at scale. The cell-based architecture with Kafka topics provides efficient data distribution while maintaining low latency. The Spring REST API makes the system accessible to any client platform, from mobile apps to web dashboards. This foundation supports diverse use cases including ride-sharing, delivery tracking, fleet management, and proximity-based social features.