Imagine you're building a real-time visualization tool for understanding the distribution of delivery agents from multiple food delivery services (like Swiggy and Zomato) on a map. This tool will display a heatmap, where the intensity of the color represents the density of agents in a particular area. The system needs to efficiently ingest location updates from these agents, aggregate them spatially, and update the heatmap in near real-time. The challenge lies in designing a scalable and maintainable system that can handle a high volume of agent location updates, support multiple data sources (Swiggy, Zomato, etc.), and allow for flexible heatmap configurations. We need to think about how to represent agent locations, how to divide the map into manageable regions for aggregation, how to efficiently update the heatmap, and how to ensure thread safety. Furthermore, we should consider how to extend the system to support new data sources or different heatmap algorithms in the future. We are dealing with location data that is constantly changing. The solution needs to be performant and scalable.

Requirements