Designing Google Maps | System Design

A web mapping tool called Google Maps offers a range of geographic data, such as street maps, satellite photos, streets’ aerial views, real-time traffic reports, and directions. There are several versions of it available, such as web, mobile, etc. In this article, we will see the system design of Google Maps

What is Google Maps?

Google Maps is a web mapping service developed by Google. It offers satellite imagery, aerial photography, street maps, 360° interactive panoramic views of streets (Street View), real-time traffic conditions, and route planning for traveling by foot, car, bicycle, or public transportation.

Users can access Google Maps through a web browser on a computer or through the Google Maps mobile app on smartphones and tablets.

Google

Requirements for Google Maps System Design

Functional Requirements for Google Maps System Design

• Map Display:
  • The system should display maps with different levels of zoom.
  • Provide support for different map views such as satellite, terrain, and street view.
• Location Search:
  • Allow users to search for locations by entering addresses, landmarks, or geographical coordinates.
• Provide and recommend the fastest route: 
  • Providing the source and destination place, the system should provide/recommend the optimal route by distance and time and also depending on the type of transportation
• Traffic and weather Information:
  • Display real-time traffic conditions on the map.
  • Provide traffic predictions and suggest optimal routes based on current traffic jams.
  • Weather updates (storms etc)
• Near me Model:
  • We search near me example, grocery, gym, saloon etc.
  • Place people have visited more with more rating it comes on top, it show the opening time, services they provide, etc.
  • And let suppose we want to visit at that place then it shows the routes.

Non Functional Requirements for Google Maps System Design

• Availability: There should be very little downtime and high availability for the system.
• Scalability: A large number of concurrent users and requests should be supported by the system.
• Accuracy: Information from the system ought to be current and accurate.
• Security: User data should be protected and the system should be secure.
• ETA accuracy can be impacted by: weather, road quality, accident, road block there is no math formula to detect it.

Capacity estimation for Google Maps System Design

Data Storage:

• Map Data:
  • Estimated at 1 Petabyte (PB).
  • 1 PB = 1,000 Terabytes (TB) = 1,000,000 Gigabytes (GB)
• Traffic Data:
  • Hundreds of TB daily.
  • Assuming 500 TB/day, 500 TB * 365 days = 182,500 TB annually.
• POI Data:
  • Estimated over 100 TB.

Server Resources:

• Processing Power:
  • Handling billions of user requests and real-time updates requires massive computational power.
  • Precise server counts and processing capabilities are not publicly available.
• Memory and Storage:
  • Each server needs sufficient memory and local storage to handle tasks efficiently.
  • Server specifications vary based on their purpose (rendering, routing, search).

Network Bandwidth:

• User Requests:
  • Billions of requests daily for map tiles, navigation, and POI searches.
  • Assuming an average of 10 TB of data transferred per request, 10 TB * 1 billion requests = 10,000,000 TB or 10,000 PB of daily bandwidth.

Overall Capacity:

• Data Storage: Potentially several Exabytes (EB).
  • 1 EB = 1,000 PB
• Servers: Millions globally.
• Network Bandwidth: Massive, spanning multiple continents.

Why we use Quad Tree in Google Maps?

Google Maps doesn’t actually use a pure quad tree for its overall data structure. However, it utilizes concepts similar to those used in a quad tree within certain functionalities. Here’s why:

Data Types in Google Maps:

• Google Maps deals with two main types of data:
  • Static data: Map imagery, street layouts, landmarks.
  • Dynamic data: Traffic flow, location of users, POIs with recent updates.

Quad Tree’s Advantages:

• Spatial Efficiency: A quad tree excels at partitioning 2D space into smaller grids, focusing on areas with more data points. This reduces unnecessary searches in sparsely populated areas.
• Fast Lookups: By recursively dividing space, quad trees enable efficient lookups for points within specific regions. This benefits functionalities like finding nearby POIs or calculating routes.

Google Maps Implementation:

• Google Maps implements similar techniques but not a strict quad tree for several reasons:
  • Dynamic Data Variations: Traffic and user locations constantly change, requiring a more flexible structure than a static quad tree.
  • Varying Data Density: Different regions have varying densities of roads, POIs, and traffic data. A quad tree’s rigid grid may not efficiently handle such discrepancies.

Example implementation:

Code for map-like scenario

# Create a quad tree with initial bounds
map_tree = QuadTreeNode(bounds=(0, 0, 100, 100))

# Add points representing landmarks or POIs
map_tree.insert((25, 35))
map_tree.insert((70, 80))
# ... (add more points)

# Find points within a search region
search_region = (50, 50, 75, 75)
points_in_region = map_tree.find_points_in_region(search_region)
print(points_in_region)

Low -Level Design for Google Maps System Design

Google Maps’ low-level design with illustrative images:

1. Data Structures:

• Map Data:
  • Stored in distributed file systems like Google File System (GFS) and Bigtable.
  • Map tiles organized geographically to facilitate efficient retrieval.
  • Image compression techniques utilized to optimize storage and bandwidth.

• Traffic Data:
  • Real-time traffic flow stored in stream processing frameworks like Apache Flink.
  • Historical data archived in data warehouses like Google BigQuery.
  • Data aggregated and updated constantly to provide dynamic traffic information.

• Point of Interest (POI) Data:
  • Stored in geospatial databases like Google Maps Platform’s Geocoding API.
  • Structured with details like name, category, location, and user reviews.
  • Continuously updated to ensure data accuracy and freshness.

2. Data Processing and Retrieval:

• Map Tile Serving:
  • Content Delivery Networks (CDNs) geographically distributed to deliver map tiles quickly based on user location.
  • Caching mechanisms implemented to reduce server load and improve response times.

• Traffic Flow Calculations:
  • Algorithms like Dijkstra’s shortest path algorithm used to calculate dynamic routes based on real-time traffic conditions.
  • Edge weights in the pathfinding graph constantly updated with traffic data.

• POI Search and Querying:
  • Spatial indexing techniques like R-trees and k-nearest neighbors (kNN) used for efficient POI search based on user queries.
  • Fuzzy matching algorithms handle typos and partial input for improved search results.

3. User Interface and Rendering:

• Interactive Map View:
  • WebGL library utilized for responsive and smooth map rendering on various devices.
  • Layers implemented for displaying map tiles, traffic overlays, and POIs separately.

• Turn-by-Turn Navigation:
  • Precise road network data and GPS location used to provide accurate turn-by-turn instructions.
  • Voice prompts and visual guidance ensure smooth navigation on various routes.

• User Interactions and Customization:
  • APIs designed for integrating features like route saving, location sharing, and personalized map styles.
  • Touch interactions and gestures optimized for mobile device usage.

4. Communication and Protocols:

• Map Tile Requests:
  • Protocol Buffers (protobuf) used for efficient map tile request and response messages.
  • Binary format reduces message size and transmission time.

• Real-time Traffic Updates:
  • Streaming protocols like MQTT or WebSockets employed for real-time traffic information exchange between servers and client devices.
  • Push notifications used to alert users about sudden traffic changes.

• API Communication:
  • RESTful APIs used for user interaction and integration with third-party applications.
  • OAuth authentication ensures secure access and data privacy.

Note:

This is a high-level overview of Google Maps’ low-level design. The actual implementation involves complex algorithms, distributed systems, and constant innovation to ensure its functionality and scalability.

High-Level Design of Google Maps

The high-level design’s components are described in detail in the low-level design, with an emphasis on their functions and interactions.

Example :- For a particular element, such as the Maps Rendering Service, for instance

• Geo-coordinates and zoom level are input.
• Overlay rendering and tile generation are processed.
• Produced: Detailed Map Tiles

There are several components that make up Google Maps’ low-level design (LLD), including:

• Rendering of maps: This part is in charge of producing maps of any place on the planet. It makes use of a range of data sources, including terrain data, satellite imagery, and street-level imagery.
• Routing: This part is in charge of giving users directions to their destination while accounting for other variables like road closures and traffic patterns. To determine the fastest and shortest routes, it employs a number of different algorithms.
• Place search: This feature is in charge of assisting users in locating establishments, eateries, landmarks, and other locations of interest. It draws from a wide range of data sources, including social media posts, restaurant reviews, and business listings.
• Real-time data processing: This part is in charge of handling data that is received in real-time from various sources, including social media, weather stations, and traffic sensors. It updates the maps and gives users the most recent information using this data.

Three primary layers comprise Google Maps’ high-level design (HLD):

1. Presentation layer:
   • The presentation layer is in charge of rendering the maps and giving users the option to look up locations and directions.
2. Application layer:
   • This layer is in charge of managing real-time data and processing user requests as well as request routing.
3. Data layer:
   • This layer manages the databases’ data storage and retrieval.

System Design Diagram of Google Maps

A system such as Google Maps needs to be carefully designed with many different components and their interactions in mind. This is a high-level system design overview :

Core Components:

• Map Data Storage:
  • To store and manage the enormous amount of map data, which includes roads, landmarks, points of interest, and real-time traffic information, a massive distributed datastore is required. For effective data retrieval, spatially partitioned data structures such as quadtrees or geohashes can be utilized.
• Geolocation Services:
  • To provide location-based services and ascertain the current location of a user, precise geolocation capabilities are essential. To determine user coordinates, this entails integrating with data from cellular networks, Wi-Fi, and GPS systems.
• Routing Engine:
  • Taking into account a number of variables, including user preferences, traffic conditions, and road types, a robust routing engine determines the quickest or shortest path between two points. Graph algorithms such as A* or Dijkstra’s can be used to determine the best routes.
• Map Rendering Engine:
  • Based on the user’s location and zoom levels, an effective map rendering engine creates map tiles. In order to do this, map data must be retrieved, processed, and appropriately styled and labelled from the datastore.
• Search and Navigation:
  • Users can locate particular places, companies, or points of interest with the help of a strong search and navigation system. This entails giving precise navigational instructions and indexing map data for effective search.
• Real-time Traffic Updates:
  • In order to provide precise estimated travel times and recommend alternate routes, real-time traffic information is essential. This entails gathering traffic data and feeding it into the routing engine from multiple sources, including sensors, GPS units, and user input.

Database design of Google Maps System Design

A distributed database architecture is used by Google Maps to provide scalability and reliability. For real-time analytics, they might use Bigtable on Google Cloud, and for structured data, they might use Cloud SQL, a relational database which is already mention in System Design diagram.

Google Maps stores its data in multiple databases. A few of the important databases are:

• Map data database: This database houses the terrain information, satellite imagery, and street-level imagery.
• Routing database: Road network information, traffic patterns, and road closures are all stored in the routing database.
• Places database: This database contains information about places, such as social media posts, restaurant reviews, and business listings.
• Real-time data: Information from traffic sensors, weather stations, and social media platforms is stored in this database.

Scalability of Google Maps

One extremely scalable system is Google Maps. It employs a number of strategies, such as the following, to manage numerous concurrent users and requests:

• Load balancing :- Google Maps employs load balancers to disperse traffic among several servers. This makes it possible for the system to manage even the highest traffic volume.
• Replication :- Data is replicated by Google Maps among several servers. This contributes to maintaining high system availability and preventing data loss.
• Caching :- Google Maps caches information that is accessed often. This lessens the strain on the database servers and enhances performance.

Microservices and API used for Google Maps

The architecture of Google Maps is based on microservices, with each component implemented as a separate microservice. This enables us to swiftly roll out new features and scale each component independently.

Google Maps makes use of the following APIs:

• Maps API: With this API, programmers can incorporate Google Maps into their own apps.
• Directions API: Developers can determine the optimal path between two locations with the help of the Directions API.
• Places API: Developers can use the Places API to look up and retrieve details about establishments, eateries, and tourist destinations.
• Geocoding API: Developers can use the geocoding API to convert addresses to coordinates.
• Distance Matrix API: Developers can use the Distance Matrix API to determine the time and distance between various locations.

For various operations, the APIs use common HTTP methods like GET, POST, and PUT:

• GET: Get information, like directions or map tiles.
• POST: Send in data, like user location updates or newly contributed points of interest.
• PUT: Modify current information, like a company’s address or operating hours.

Components Details of Google Maps

• Map Server:
  • The map data must be stored and served by the map server. Usually, a distributed file system like Google Cloud Storage is used to store the map data. To efficiently serve the map data, the map server employs a number of strategies, including load balancing and caching.
• Routing Service:
  • The routing service is in charge of figuring out the most efficient path between two points. Numerous factors, including user preferences, road types, and traffic conditions, are taken into consideration by the routing service. The routing service employs a number of algorithms, including Dijkstra’s algorithm and A* search, to determine the optimal path.