My Solution for Design a Real Time Sports Scoring System

by nectar4678

System requirements


Functional:

Real-time Updates

  • The system must provide real-time updates for scores, player statistics, and game events.

Scoreboard Interface

  • Develop a user-friendly scoreboard interface displaying live scores, team names, and game status.

Player Statistics

  • Track and display real-time player statistics, including scores, assists, fouls, etc.

Game Timeline

  • Provide a timeline of game events (e.g., goals, fouls, time-outs) in real-time.

Event Detection

  • Automatically detect and update game events based on live data feeds.

Data Streaming

  • Support continuous data streaming to ensure timely updates.

User Interaction

  • Allow users to interact with the system by accessing different statistics and player details in real-time.

Notification System

  • Implement notifications for major game events (e.g., goals, end of period).



Non-Functional:

Scalability

  • The system must handle thousands to millions of concurrent users, especially during popular sports events.

Performance

  • Ensure low latency for real-time updates and interactions.

Reliability

  • Provide a highly reliable service with minimal downtime.

Usability

  • Design interfaces that are intuitive and easy to use for all user types.

Security

  • Implement security measures to protect user data and prevent unauthorized access.

Compatibility

  • Ensure compatibility across different devices and browsers.

Maintainability

  • Design the system to be maintainable and allow for easy updates and improvements.


Capacity estimation

Assumptions

Daily Active Users (DAU)

  • Peak DAU during major sporting events: 1,000,000 users
  • Average DAU during regular events: 100,000 users

Requests per User

  • Each user makes an average of 10 requests per minute (browsing scores, stats, updates).
  • Peak user activity could see up to 20 requests per minute per user.

Data Throughput

  • Average data payload per request: 2 KB
  • Event updates from data providers: 1 update per second per game
  • Each update payload: 5 KB

Concurrent Games

  • Maximum number of concurrent games during peak times: 50 games


Calculations

Requests per Second (RPS)

  • Peak RPS: 1,000,000 users * 20 requests/minute / 60 = 333,333 RPS
  • Average RPS: 100,000 users * 10 requests/minute / 60 = 16,667 RPS

Data Throughput

  • Peak throughput from users: 333,333 RPS * 2 KB/request = 666,666 KB/s (approx. 666.67 MB/s)
  • Average throughput from users: 16,667 RPS * 2 KB/request = 33,334 KB/s (approx. 33.34 MB/s)
  • Peak throughput from event updates: 50 games * 1 update/s * 5 KB/update = 250 KB/s

Total Peak Data Throughput

  • Total peak throughput: 666.67 MB/s (from users) + 250 KB/s (from event updates) ≈ 667 MB/s


Server and Database Capacity

Web Servers

  • To handle 333,333 RPS, we assume each server can handle 5,000 RPS.
  • Required web servers: 333,333 RPS / 5,000 RPS/server ≈ 67 servers

Database Servers

  • Assuming a sharded database approach, each shard handles 10,000 RPS.
  • Required database shards: 333,333 RPS / 10,000 RPS/shard ≈ 34 shards

Caching

  • Implement caching for frequently accessed data to reduce load on databases.
  • Estimated cache hit ratio: 80%



API design

Authentication API

Endpoint: /api/v1/auth Method: POST Request: {   "username": "[email protected]",   "password": "password123" } Response: {   "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." }


Get Live Scores

Endpoint: /api/v1/scores/live Method: GET Request: (Query Parameters) sport=football league=premier-league Response: {   "games": [     {       "game_id": "12345",       "team1": "Team A",       "team2": "Team B",       "score1": 2,       "score2": 3,       "status": "in-progress",       "time": "75:00"     }   ] }


Get Player Statistics

Endpoint: /api/v1/players/{player_id}/stats Method: GET Response: {   "player_id": "67890",   "name": "Player One",   "team": "Team A",   "goals": 5,   "assists": 3,   "yellow_cards": 2,   "red_cards": 0 }


Get Game Timeline

Endpoint: /api/v1/games/{game_id}/timeline Method: GET {   "game_id": "12345",   "events": [     {       "time": "10:00",       "type": "goal",       "description": "Player One scored a goal"     },     {       "time": "45:00",       "type": "yellow_card",       "description": "Player Two received a yellow card"     }   ] }


Submit Event Update

Endpoint: /api/v1/events Method: POST Response: {   "status": "success",   "event_id": "event_98765" }


Get Notifications

Endpoint: /api/v1/notifications Method: GET Request: (Query Parameters) user_id=user123 Response: {   "notifications": [     {       "notification_id": "notif_001",       "message": "Team A scored a goal!"     }   ] }


Update User Preferences

Endpoint: /api/v1/users/{user_id}/preferences Method: PUT Request: {   "preferences": {     "favorite_team": "Team A",     "notification_settings": {       "goals": true,       "cards": false     }   } } Response: {   "status": "success" }



Database design



High-level design

API Gateway

  • Routes requests to appropriate microservices.
  • Handles authentication and authorization.

User Service

  • Manages user data and preferences.
  • Handles user authentication and authorization.

Game Service

  • Manages game data, including live scores, team information, and game status.

Player Service

  • Manages player data and statistics.

Event Service

  • Handles event detection and processing for live games.
  • Manages game timelines and events.

Notification Service

  • Manages user notifications for game events and updates.

Data Stream Processor

  • Processes incoming data streams from external sources (e.g., sports data providers).
  • Detects events and updates game states in real-time.

Database

  • Stores all persistent data including users, games, players, stats, and events.

Cache

  • Provides a caching layer for frequently accessed data to reduce database load and improve performance.

Front-End Application

  • User interface for displaying live scores, player statistics, and game timelines.
  • Interfaces include web and mobile applications.



Request flows

Fetching Live Scores

User requests live scores:

  • The user queries live scores for a specific sport via the front-end application.

API Gateway:

  • Routes the request to the Game Service.

Game Service:

  • Fetches the latest scores from the database or cache.

Response:

  • The scores are sent back to the user via the API Gateway.


Event Detection and Update

Data Stream Processor detects event:

  • A new event (e.g., goal) is detected from an external data feed.

Event Service:

  • Processes the event and updates the game state.

Game Service:

  • Updates the score and game status.
  • Stores the event in the database.

Notification Service:

  • Sends notifications to subscribed users.

Cache:

  • Updates the cache with the latest event and scores.


Fetching Player Statistics

User requests player statistics:

  • The user queries for specific player statistics via the front-end application.

API Gateway:

  • Routes the request to the Player Service.

Player Service:

  • Fetches the latest statistics from the database or cache.

Response:

  • The statistics are sent back to the user via the API Gateway.



Detailed component design

Game Service

Responsibilities

  • Manage game data, including live scores, team information, and game status.
  • Handle requests for fetching live scores and game details.
  • Update game states based on events received from the Event Service.

Architecture and Scalability

  • Microservices Architecture: Game Service operates as a microservice, enabling independent deployment and scaling.
  • Stateless Design: The service is stateless, with all state information stored in a distributed database and cache.
  • Horizontal Scaling: Instances of the Game Service can be scaled horizontally to handle increased load during peak times.

Key Algorithms and Data Structures

  • Caching Strategy: Uses an in-memory cache (e.g., Redis) to store frequently accessed game data, reducing load on the database.
  • Database Sharding: Implements sharding for the game data to distribute load across multiple database instances.




Event Service

Responsibilities

  • Detect and process game events (e.g., goals, fouls, timeouts).
  • Update game timelines and notify other components of changes.
  • Interface with the Data Stream Processor to receive real-time event data.

Architecture and Scalability

  • Event-Driven Architecture: Utilizes event-driven design to react to incoming data streams and update game states.
  • Message Queue: Implements a message queue (e.g., Kafka) to handle high-throughput event data.
  • Horizontal Scaling: Can scale horizontally by adding more instances to process events concurrently.

Key Algorithms and Data Structures

  • Event Processing: Uses a rules engine to classify and process different types of events.
  • Timeline Management: Maintains a time-ordered list of events for each game, stored in the database and cached for quick access.




Data Stream Processor

Responsibilities

  • Process incoming data streams from external sports data providers.
  • Detect real-time events and forward them to the Event Service.
  • Ensure data integrity and low-latency processing.

Architecture and Scalability

  • Stream Processing Framework: Uses a framework like Apache Flink or Spark Streaming for real-time data processing.
  • Scalable Data Ingestion: Can scale ingestion pipelines to handle varying data rates.
  • Fault Tolerance: Implements checkpointing and data replication to ensure fault tolerance and data integrity.

Key Algorithms and Data Structures

  • Event Detection: Uses pattern matching and stateful processing to detect events in the data stream.
  • Data Enrichment: Enriches raw data with additional context (e.g., player info, game state) before forwarding to Event Service.


Trade offs/Tech choices

1. Microservices vs. Monolithic Architecture

Choice: Microservices

Reason:

  • Scalability: Microservices allow individual components to scale independently based on demand, improving resource utilization.
  • Deployment: Independent deployment of services makes it easier to update and deploy without affecting the entire system.
  • Resilience: Failure in one service does not bring down the entire system, enhancing overall resilience.

Trade-Offs:

  • Complexity: Microservices introduce complexity in terms of communication, data consistency, and service management.
  • Latency: Increased network latency due to inter-service communication.
  • Deployment Overhead: Managing multiple services requires sophisticated orchestration and monitoring tools.

2. Stateless vs. Stateful Services

Choice: Stateless Services

Reason:

  • Scalability: Stateless services can be easily scaled horizontally as they do not rely on server-side sessions.
  • Resilience: Easier to recover from failures since no state is maintained between requests.

Trade-Offs:

  • State Management: Client-side or external storage (like a database or cache) is needed for state management, potentially increasing complexity.

3. SQL vs. NoSQL Databases

Choice: Both SQL and NoSQL

Reason:

  • SQL: Relational databases are suitable for structured data with complex relationships, ensuring ACID compliance (e.g., user data, player stats).
  • NoSQL: NoSQL databases are better for high-throughput and flexible schema requirements, suitable for event data and caching (e.g., MongoDB, Redis).

Trade-Offs:

  • Consistency vs. Availability: NoSQL databases often sacrifice consistency for availability and partition tolerance (CAP theorem).
  • Complexity: Using both SQL and NoSQL databases can introduce additional complexity in terms of data synchronization and management.



Failure scenarios/bottlenecks

API Gateway Failure

Scenario:

  • The API Gateway becomes a single point of failure, causing the entire system to become inaccessible.

Mitigation:

  • Load Balancing: Use multiple instances of the API Gateway behind a load balancer.
  • Auto-Scaling: Implement auto-scaling to handle increased load and failover.
  • Health Checks: Regularly monitor the health of API Gateway instances and perform automated failovers.


Database Overload

Scenario:

  • High read/write operations overwhelm the database, leading to slow response times or crashes.

Mitigation:

  • Sharding: Distribute the database load across multiple shards.
  • Replication: Use database replication to enhance read performance and fault tolerance.
  • Caching: Implement caching for frequently accessed data to reduce database load.


Cache Inconsistency

Scenario:

  • Cached data becomes stale or inconsistent with the underlying database, leading to incorrect information being served.

Mitigation:

  • Cache Expiration: Set appropriate TTL (Time-To-Live) for cached data.
  • Cache Invalidation: Implement strategies for cache invalidation when the underlying data changes.
  • Write-Through Cache: Update the cache at the same time as the database to ensure consistency.



Future improvements

1. Advanced Analytics and Insights

Description:

  • Implement advanced analytics to provide deeper insights into player and team performance.
  • Use machine learning algorithms to predict outcomes and provide recommendations.

Benefits:

  • Enhances user engagement with predictive insights.
  • Provides valuable data for teams and analysts.

Implementation Steps:

  • Integrate a data warehouse to store historical data.
  • Develop machine learning models for predictions and recommendations.
  • Create an analytics dashboard for users and administrators.


2. Multi-Language Support

Description:

  • Add support for multiple languages to cater to a global audience.
  • Provide localized content and interfaces.

Benefits:

  • Expands user base by reaching non-English speaking audiences.
  • Improves user experience with localized content.

Implementation Steps:

  • Identify target languages and regions.
  • Translate user interface elements and content.
  • Implement language selection and localization features.


3. Progressive Web App (PWA) Support

Description:

  • Develop a Progressive Web App to provide a seamless experience across web and mobile platforms.
  • Leverage PWA features like offline access, push notifications, and home screen installation.

Benefits:

  • Provides a native app-like experience without the need for separate mobile apps.
  • Enhances user engagement with offline capabilities and push notifications.

Implementation Steps:

  • Convert existing web application into a PWA.
  • Implement service workers for offline access and caching.
  • Enable push notifications and home screen installation.