[ML System Design Tech Case Study Pulse #5 : Top Question] Predict Real-time Store Status to Billions of Users Worldwide: How Google Maps Actually Work
Behind the tech with detailed explanation and flow chart....
Hi All,
Google Maps' store open prediction system is a sophisticated machine learning infrastructure designed to provide accurate real-time information about business operating hours to billions of users worldwide.
By synthesizing diverse data sources, implementing specialized models for different business types, and maintaining high availability at global scale, the system delivers accurate open status predictions that meaningfully improve user experience
Let's explore the key metrics and capabilities of this system:
Key Metrics:
Daily Active Users (DAU): 1+ billion
Business listings: 200+ million
Open status predictions generated daily: 50+ billion
Average prediction generation time: < 50ms
ML model inference time: < 10ms
Data points processed daily: 15+ trillion
Global data centers: 30+
Edge locations: 500+
System availability: 99.999%
Prediction accuracy rate: > 95%
Information queries about business hours: 25% of all location searches
Features considered per prediction: 5,000+
Model training datasets: Petabytes of historical data
ML models in production: 250+
Real-time signals processed: Millions per second
System redundancy: N+2 architecture
Average model update cycle: 12 hours
Complete Process Flow: How It Works
The entire open status prediction process operates as a comprehensive pipeline from user query to accurate store status display:
User queries a business on Google Maps:
The client-side application sends the search query
Location data is transmitted with appropriate permissions
The application establishes a secure connection with Google's servers
Query data is encrypted and sent to the Location Query Processing Service
How it works: When a user searches for a business on Google Maps, the MapCore SDK activates in the background. This SDK collects important contextual signals including precise geolocation (with user permission), time of query, device type, and search history patterns. It records subtle intent indicators such as previous location-based queries and dwell time on specific business types. The SDK uses a custom WebSocket protocol to maintain continuous data synchronization with Google's edge servers, even during intermittent connectivity, through an encrypted TLS 1.3 channel with certificate pinning.
Location Query Processing Service handles the search request:
Decrypts and validates the query data
Enriches query with contextual metadata
Generates unique query IDs for tracking
Routes the query to the Business Information Service
How it works: The Location Query Processing Service operates as a globally distributed system across Google's custom infrastructure. When a query arrives,
