Design UberPool matching with zero Manhattan detour

Q: Design UberPool matching with zero Manhattan detour

This question evaluates system design and algorithmic reasoning for constrained spatio-temporal matching and routing, covering Manhattan-distance geometry, matching algorithms, API and data model design, and scalability/consistency considerations.

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Question

Design a ride-sharing (UberPool-like) system focused on matching and routing.

You are operating on a grid where travel distance is Manhattan distance:
$d((x_1,y_1),(x_2,y_2)) = |x_1-x_2| + |y_1-y_2|$ .

A driver starts at S and wants to go to destination T. The driver is willing to pick up additional riders only if doing so does not increase the driver’s total travel distance, i.e., the final driven distance must remain exactly $d(S,T)$ .

Goal

Serve as many additional riders as possible while respecting the “zero extra Manhattan distance” constraint.

What to cover

Clarify product requirements and constraints.
Core APIs (request ride, accept match, updates).
High-level architecture and key components.
Data model and storage choices.
Matching strategy/algorithm under the zero-detour Manhattan constraint.
Scalability, latency, consistency, and failure handling.
Metrics and edge cases.

Design UberPool matching with zero Manhattan detour

Quick Overview

Goal

What to cover

Solution

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