Design Google Maps

What is Google Maps?

Google Maps is a global mapping service that provides navigation, search, and real-time traffic information. It's similar to Apple Maps, Waze, or HERE Maps. The service provides location-based services, route planning, and real-time traffic updates.

Geospatial data processing and real-time traffic updates at global scale is what makes systems like Google Maps unique. By understanding Google Maps, you can tackle interview questions for similar mapping platforms, since the core design challenges—geospatial indexing, route optimization, traffic processing, and global scalability—remain the same.

Functional Requirements

Core (Interview Focussed)

• Map Rendering: Display maps with roads, landmarks, and points of interest.
• Location Search: Search for places, addresses, and businesses.
• Route Planning: Calculate optimal routes between two points.
• Real-time Traffic: Provide current traffic conditions and updates.

Out of Scope

• User authentication and accounts
• Street View imagery
• Business listings and reviews
• Offline map downloads
• Mobile app specific features

Non-Functional Requirements

Core (Interview Focussed)

• Low latency: Sub-second response time for map requests.
• High availability: 99.9% uptime for mapping services.
• Scalability: Handle millions of users worldwide.
• Accuracy: Provide accurate location and routing information.

Out of Scope

• Data retention policies
• Compliance and privacy regulations

💡 Interview Tip: Focus on low latency, high availability, and scalability. Interviewers care most about geospatial indexing, route optimization, and traffic processing.

Core Entities

💡 Interview Tip: Focus on Location, Road, and Traffic as they drive geospatial indexing, route optimization, and real-time updates.

Core APIs

Map Services

• GET /maps/tile/{z}/{x}/{y} – Get map tile for specific coordinates
• GET /maps/search?query=&location=&radius= – Search for places
• GET /maps/places/{place_id} – Get details of a specific place

Navigation

• POST /routes/calculate { origin, destination, waypoints[], preferences } – Calculate route
• GET /routes/{route_id} – Get route details
• GET /routes/{route_id}/traffic – Get traffic information for route

Traffic

• GET /traffic/current?location=&radius= – Get current traffic conditions
• POST /traffic/report { location, speed, timestamp } – Report traffic data
• GET /traffic/historical?location=&date= – Get historical traffic data

High-Level Design

Key Components

• Map Service: Handle map rendering and tile generation
• Search Service: Process location and place searches
• Routing Service: Calculate optimal routes between points
• Traffic Service: Process and provide real-time traffic data
• Geospatial Database: Store location and road data
• CDN: Cache map tiles and static content

Mapping Core Functional Requirements to Components

Detailed Design

Map Service

Purpose: Generate and serve map tiles for different zoom levels and regions.

Key Design Decisions:

• Tile System: Use standard tile coordinates (z/x/y) for map rendering
• Pre-rendering: Pre-generate tiles for popular areas
• Dynamic Rendering: Generate tiles on-demand for less popular areas
• Caching: Cache tiles at multiple levels (CDN, application, database)

Algorithm: Map tile generation

1. Receive tile request with coordinates (z/x/y)
2. Check cache for existing tile
3. If not cached:
   - Query geospatial database for area data
   - Render tile with roads, landmarks, POIs
   - Cache rendered tile
4. Return tile to client
5. Update cache statistics

Search Service

Purpose: Process location and place searches with geospatial relevance.

Key Design Decisions:

• Geospatial Indexing: Use R-tree or similar for spatial queries
• Search Ranking: Rank results by relevance and distance
• Fuzzy Matching: Handle typos and partial matches
• Caching: Cache frequent search results

Algorithm: Location search

1. Parse search query and extract location context
2. Query geospatial database for matching places
3. Apply ranking algorithm:
   - Distance from user location
   - Relevance score
   - Popularity/rating
4. Filter results by category if specified
5. Return ranked results
6. Cache results for future queries

Routing Service

Purpose: Calculate optimal routes between two or more points.

Key Design Decisions:

• Graph Representation: Represent road network as weighted graph
• Algorithm Selection: Use A* or Dijkstra for route calculation
• Traffic Integration: Incorporate real-time traffic data
• Route Optimization: Consider multiple factors (time, distance, tolls)

Algorithm: Route calculation

1. Build road network graph from database
2. Apply traffic weights to road segments
3. Use A* algorithm to find optimal path:
   - Heuristic: straight-line distance to destination
   - Cost function: travel time + traffic delays
4. Generate waypoints along route
5. Calculate total duration and distance
6. Return route with turn-by-turn directions

Traffic Service

Purpose: Process and provide real-time traffic data.

Key Design Decisions:

• Data Sources: Aggregate traffic data from multiple sources
• Real-time Processing: Process traffic updates in real-time
• Historical Analysis: Use historical data for predictions
• Data Quality: Filter and validate traffic data

Algorithm: Traffic data processing

1. Receive traffic data from various sources
2. Validate and clean data:
   - Check for outliers
   - Verify location accuracy
   - Filter duplicate reports
3. Aggregate data by road segment
4. Calculate traffic metrics:
   - Average speed
   - Congestion level
   - Travel time
5. Update traffic database
6. Notify routing service of changes

Database Design

Locations Table

Indexes:

• idx_coordinates on (latitude, longitude) - Geospatial queries
• idx_type on (type) - Location type queries

Roads Table

Indexes:

• idx_start on (start_lat, start_lng) - Start point queries
• idx_end on (end_lat, end_lng) - End point queries

Traffic Table

Indexes:

• idx_road_timestamp on (road_id, timestamp) - Road traffic history
• idx_timestamp on (timestamp) - Time-based queries

Scalability Considerations

Horizontal Scaling

• Map Service: Scale tile generation with load balancers
• Search Service: Use consistent hashing for geographic partitioning
• Routing Service: Scale route calculation with distributed computing
• Traffic Service: Partition traffic data by geographic regions

Caching Strategy

• CDN: Cache map tiles globally
• Redis: Cache search results and route calculations
• Application Cache: Cache frequently accessed data

Performance Optimization

• Connection Pooling: Efficient database connections
• Batch Processing: Batch traffic updates for efficiency
• Async Processing: Non-blocking map tile generation
• Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

• Map Load Time: Average time to load map tiles
• Search Latency: Average search response time
• Route Calculation Time: Average route calculation time
• System Health: CPU, memory, and disk usage

Alerting

• High Latency: Alert when response time exceeds threshold
• Cache Miss Rate: Alert when cache hit rate drops
• Traffic Data Quality: Alert when traffic data quality degrades
• System Errors: Alert on service failures

Trade-offs and Considerations

Consistency vs. Availability

• Choice: Eventual consistency for traffic data, strong consistency for routes
• Reasoning: Traffic data can tolerate slight delays, routes need immediate accuracy

Storage vs. Performance

• Choice: Use compression and efficient storage for map data
• Reasoning: Balance between storage costs and query performance

Accuracy vs. Performance

• Choice: Use approximation algorithms for complex calculations
• Reasoning: Balance between calculation accuracy and response time

Common Interview Questions

Q: How would you handle global map data?

A: Use geographic partitioning, CDN distribution, and regional data centers to handle global map data efficiently.

Q: How do you ensure route accuracy?

A: Use high-quality road data, real-time traffic integration, and continuous data updates to ensure route accuracy.

Q: How would you scale this system globally?

A: Deploy regional services, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle traffic data quality?

A: Use multiple data sources, data validation, and machine learning to ensure traffic data quality.

Key Takeaways

1. Geospatial Indexing: Essential for efficient location-based queries
2. Route Optimization: Multiple algorithms provide flexibility for different routing scenarios
3. Real-time Processing: Traffic data requires real-time processing and updates
4. Global Scalability: Geographic partitioning and CDN distribution are crucial for global scale
5. Monitoring: Comprehensive monitoring ensures system reliability and performance