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hardSystem Design

How would you design a 'nearby drivers' / proximity search?

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01

Understand the problem

Outline geo search.

geoproximity
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

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03

Study the solution

Step 1: Outline use cases and constraints

Gather requirements and scope the problem. Ask questions to clarify use cases and constraints. Discuss assumptions.

Use cases

We'll scope the problem to handle only the following use cases

  • User performs core action described in How would you design a 'ne

Solution ready — 2 min read

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04

Read the code

Distance filter (haversine)
Run Playground
import math

def haversine(lat1, lon1, lat2, lon2):     # great-circle distance in km
    R = 6371
    p1, p2 = math.radians(lat1), math.radians(lat2)
    dphi = math.radians(lat2 - lat1)
    dlmb = math.radians(lon2 - lon1)
    a = math.sin(dphi/2)**2 + math.cos(p1)*math.cos(p2)*math.sin(dlmb/2)**2
    return 2 * R * math.asin(math.sqrt(a))

rider = (37.778, -122.415)
# In production you'd first narrow to the rider's geohash cell + neighbors,
# then compute exact distance only for that small candidate set:
drivers = {"d1": (37.776, -122.417), "d2": (37.781, -122.412), "d3": (37.760, -122.450)}

nearby = sorted(
    ((name, round(haversine(rider[0], rider[1], lat, lon), 2)) for name, (lat, lon) in drivers.items()),
    key=lambda x: x[1],
)
print("within 1 km:", [d for d in nearby if d[1] <= 1.0])
05

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