# Design a nearby places search service: system design interview question

> Geographic search across uneven place density: correct boundaries, nearest results, and fresh data after place changes.

Source: https://sysround.com/questions/design-nearby-places-search-service
Updated: 2026-11-05


Difficulty: medium. Levels: Mid-level, Senior, Staff+.

## How the interviewer opens it

"Design a service that finds nearby places within a radius or a map viewport. It must support low-latency searches over millions of places."

## What to clarify first

- Scale and distribution
- Read and write mix
- Search shapes and distance
- Result order and size
- Place fields and filters
- Latency and supported failures
- Freshness and availability
- Change source and durability
- Record sizes
- Scope limits

## Scope of a strong answer

Core requirements:

- Find places within a radius or a map viewport
- Return the nearest places, with an optional category filter
- Apply place additions, changes, and deletions

Usually out of scope:

- Text search
- Routes and travel times
- Reviews and personal scores
- The source of place data

## What interviewers probe

### Search boundaries

Longitude wraps at the map edge. Distance and viewport rules must also remain correct near the poles.

### Search work in dense areas

One city query can cover hundreds of thousands of places. A small result limit does not guarantee little database work.

### Place changes

A coordinate change affects both the old and new search areas. Repeated or delayed changes can restore obsolete data.

## Common mistakes

- Use latitude and longitude differences as distance without a geographic distance calculation.
- Return every place from intersecting cells without an exact shape check.
- Search a fixed number of adjacent cells for every radius.
- Apply the result limit before the category and shape checks.
- Add geographic shards before a capacity estimate shows that one indexed database cannot meet demand.
- Accept old place changes without a version rule.

## The bar by level

- **Mid-level:** Defines both search APIs and a usable place model. Uses a spatial index, exact shape checks, and correct distance order. Estimates read load and storage.
- **Senior:** Tests query plans for dense areas and rare categories. Handles longitude boundaries, versioned changes, replica lag, and durable changes after node failure.
- **Staff+:** Proves that distance pruning preserves the nearest results. Defines a combined freshness budget and tests capacity after failures. Plans geographic partitions only when growth requires them.

Practice this question with an AI interviewer: https://sysround.com/practice/new?problem=nearby-places-search


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SysRound (https://sysround.com) is an AI practice tool for system design interviews. It has a voice or typed AI interviewer and a shared whiteboard. At the end, you get feedback on each skill and a model answer. The AI feedback can be wrong.