Introduction

The Nearby Places system design interview is a common question at Meta (Facebook). This problem involves designing a scalable and efficient location-based recommendation system that allows users to discover nearby businesses, restaurants, and points of interest.

1. Understanding the Problem Statement

What is Nearby Places?

• A feature that helps users find places of interest near their location.
• Supports filtering by category (e.g., restaurants, cafes, hotels).
• Provides rankings based on relevance, distance, and popularity.
• Integrates real-time updates (e.g., open hours, check-ins, reviews).

Functional Requirements

• Users should see nearby places within a specified radius.
• Places should be ranked based on relevance (distance, ratings, popularity).
• Users can filter by category (restaurants, gas stations, etc.).
• Real-time updates on places (e.g., user check-ins, business status).
• Ability to handle millions of concurrent users globally.

Non-Functional Requirements

• Low-latency search results.
• High availability and fault tolerance.
• Support for millions of locations and users worldwide.
• Scalability to accommodate increasing user base and data volume.

2. High-Level Architecture

The Nearby Places system consists of:

• Write Path: Handling new place additions, updates, and user check-ins.
• Read Path: Efficiently retrieving and ranking nearby locations.
• Storage: Managing large-scale geospatial data efficiently.

Architecture Diagram

[User] → [API Gateway] → [Load Balancer] → [Nearby Places Service] → [Geospatial Index]
                                  ↓
                             [Ranking System]
                                  ↓
                          [Cache & Database]
                                  ↓
                               [User]

3. Database & Storage

Place Data Storage

• Use NoSQL (Cassandra, DynamoDB) for storing place information.
• Store metadata (place ID, name, category, location, ratings, reviews).

Geospatial Indexing

• Use QuadTree, R-tree, or Geohash for fast spatial queries.
• Use PostGIS (PostgreSQL extension) or Elasticsearch for location-based searches.

Caching Layer

• Use Redis for frequently accessed places.
• Store recent queries to reduce database lookups.

4. Nearby Search Algorithm

Approach 1: Bounding Box Search

• Convert latitude/longitude to a grid-based system.
• Fetch all places within the user’s grid.
• Filter by distance and category.

Approach 2: Geohashing

• Convert locations into Geohash strings.
• Nearby locations have similar geohash prefixes.
• Efficient for distributed searches.

Approach 3: K-D Tree for Nearest Neighbor Search

• Efficient multi-dimensional indexing.
• Useful when filtering by multiple attributes (e.g., rating + distance).

5. Ranking & Personalization

Ranking determines which places appear at the top. Factors include:

• Proximity (closer places ranked higher).
• User Preferences (based on past searches, check-ins, and likes).
• Popularity (number of visits, reviews, and ratings).
• Business Type (high-priority categories, e.g., emergency services).
• Real-time Data (current foot traffic, promotions, events).

Machine Learning for Ranking

• Train models using user interactions to predict preference.
• Use collaborative filtering for personalized recommendations.

6. Handling Scale & Real-Time Updates

Scalability Strategies

• Sharding & Partitioning: Distribute geospatial data across servers.
• Load Balancers: Efficient request routing to optimize performance.

Real-Time Updates

• WebSockets: Push updates for live check-ins.
• Kafka/Event Streaming: Process place updates asynchronously.

7. Handling Failures & Monitoring

• Data Replication: Ensure high availability of place data.
• Circuit Breakers prevent system overload.
• Monitoring with Prometheus & Grafana for performance insights.

8. Interview Approach

Step 1: Clarify Requirements

• Ask about filtering options, ranking complexity, and real-time updates.

Step 2: Define High-Level Architecture

• Sketch the major components (API Gateway, Geospatial Index, Ranking System).

Step 3: Discuss Data Storage

• Choose databases for place information and geospatial indexing.

Step 4: Explain Nearby Search Algorithm

• Compare Bounding Box, Geohashing, and K-D Trees.

Step 5: Discuss Ranking & Scaling

• Show how ML and personalization improve results.

Step 6: Handle Edge Cases

• What happens if a business closes or moves?
• How do you deal with incorrect location data?

Conclusion

Designing Meta’s Nearby Places requires balancing geospatial queries, ranking, and real-time updates. By following a structured approach, you can confidently tackle this system design interview. 🚀

For more resources:

• https://engineering.fb.com/
• https://github.com/donnemartin/system-design-primer