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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

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This is a hard difficulty System Design question, commonly asked during Onsite rounds at Uber.

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This question is commonly asked for Software Engineer candidates at Uber during technical interviews.

Design real-time driver heatmap system | Uber Interview Question

Quick Overview

This question evaluates a candidate's ability to design real-time distributed systems for geospatial aggregation, covering skills in spatial partitioning, high-throughput streaming ingestion, low-latency cell counting, WebSocket-based client updates, and top-K ranking within a fault-tolerant multi-region architecture.

System Design: Real-Time Driver Heatmap and Top-K Hottest Cells

Context

You are building a real-time heatmap for a ride-hailing platform to show driver density across a city. The city is partitioned into spatial grid cells (e.g., geohash/H3/S2). Driver apps send periodic location pings. The backend must aggregate these into live cell counts and stream updates to rider/ops clients over WebSockets. Additionally, the system must compute and publish the top-K hottest cells per region with low latency.

Assume city-scale load (hundreds of thousands of drivers, 1–5 pings/driver/min), sub-second to low-seconds end-to-end latency, and multi-region deployment.

Requirements

Partition city into fixed-size grid cells (e.g., geohash/H3/S2).
Ingest driver pings and compute active driver counts per cell in near real time.
Stream updates to clients over WebSockets (subscribe/unsubscribe flows).
Compute and publish top-K hottest cells per region with low latency.
Detail: data model, aggregation strategy (edge vs. server), update frequency, scalability, fault tolerance, and client subscription management.

Quick Overview

Context

Assume city-scale load (hundreds of thousands of drivers, 1–5 pings/driver/min), sub-second to low-seconds end-to-end latency, and multi-region deployment.

Requirements

Partition city into fixed-size grid cells (e.g., geohash/H3/S2).

Ingest driver pings and compute active driver counts per cell in near real time.

Stream updates to clients over WebSockets (subscribe/unsubscribe flows).

Compute and publish top-K hottest cells per region with low latency.

Detail: data model, aggregation strategy (edge vs. server), update frequency, scalability, fault tolerance, and client subscription management.

Design real-time driver heatmap system

Quick Overview

System Design: Real-Time Driver Heatmap and Top-K Hottest Cells

Context

Requirements

Solution

Comments (0)

Design real-time driver heatmap system

Quick Overview

System Design: Real-Time Driver Heatmap and Top-K Hottest Cells

Context

Requirements

Solution

Comments (0)