System requirements

Functional Requirements

User Management:
- Allow users to sign up, log in, and manage profiles.
- Maintain user statistics such as win/loss records, ELO ratings, and games played.
Game Matching:
- Pair players for matches based on their skill level (ELO rating).
- Support manual pairing (e.g., invite specific players to a game).
- Allow users to play as a guest without requiring signup.
Real-Time Gameplay:
- Enable users to play chess games with real-time move updates.
- Display the chessboard with the current game state for both players.
- Provide a chat feature for communication between players during a match.
Move Validation:
- Enforce chess rules for all moves.
- Detect and handle invalid moves.
- Recognize game-ending scenarios (checkmate, stalemate, resignation, or timeout).
Game History:
- Save and allow retrieval of past games for replay and analysis.
- Provide export functionality in standard formats like PGN (Portable Game Notation).
Spectator Mode:
- Allow non-players to watch ongoing matches.
Scaling for Simultaneous Games:
- Support thousands of concurrent games with real-time performance.

Non-Functional Requirements

Scalability:
- Handle growth in the number of users and concurrent games.
- Efficiently manage resources for real-time updates.
Low Latency:
- Ensure minimal delay for real-time move updates, typically under 100 ms.
High Availability:
- Ensure the service is available 99.9% of the time.
Data Consistency:
- Maintain consistent game states across all participants and spectators.
Security:
- Protect user data with secure authentication (e.g., OAuth2).
- Prevent cheating by ensuring move validation and leveraging anti-cheat mechanisms.
Extensibility:
- Design the system to support future features like tournaments, AI opponents, or variant chess formats.
Global Accessibility:
- Provide multi-language support and ensure performance across regions.

Capacity estimation

Number of Active Users:
- Assume the system supports 1 million registered users.
- At peak, 10% of users (100,000) are online simultaneously.
Simultaneous Games:
- Assume, on average, 50% of active users are engaged in a game (one game involves two players).
- Estimated simultaneous games: 100,0002=50,000\frac{100,000}{2} = 50,0002100,000=50,000.
Move Frequency:
- On average, a player makes a move every 30 seconds.
- For 50,000 games, the system processes 100,000 moves per minute, or 1,666 moves per second.
Spectators:
- Assume 10% of active users are spectators watching games.
- With an average of 2 spectators per game, this involves 10,000 active WebSocket connections for spectators.
Data Storage:
- Each game generates ~5 KB of data (PGN format and metadata).
- For 50,000 simultaneous games, storage required for active games: 50,000×5 KB=250 MB50,000 \times 5 \text{ KB} = 250 \text{ MB}50,000×5 KB=250 MB.
- Assuming 10 million games/year, annual storage needs: 10,000,000×5 KB=50 GB10,000,000 \times 5 \text{ KB} = 50 \text{ GB}10,000,000×5 KB=50 GB.
Chat Messages:
- Assume each game generates 10 messages per minute.
- For 50,000 games, the system handles 500,000 messages per minute, or 8,333 messages per second.

Component-Wise Breakdown

1. API Gateway

Handles 2,500 requests per second (1,666 move updates + 833 other requests like logins, game history retrieval, etc.).
Needs to support ~150 MB/sec throughput for real-time game data.

2. WebSocket Connections

Connections:
- 100,000100,000100,000 active players + 10,00010,00010,000 spectators = 110,000110,000110,000 WebSocket connections.
Throughput:
- Each game state update is ~1 KB. With 1,666 moves/sec, the system requires 1,666×1 KB=1.6 MB/sec1,666 \times 1 \text{ KB} = 1.6 \text{ MB/sec}1,666×1 KB=1.6 MB/sec for moves alone.

3. Game Session Service

Needs to handle 50,000 concurrent game states in memory.
Performs 1,666 move validations per second, requiring low-latency operations (5-10 ms per validation).

4. Distributed Cache (Redis)

Stores transient data (e.g., game states, move counters):
- 50,00050,00050,000 games ×2 KB/game state=100 MB\times 2 \text{ KB/game state} = 100 \text{ MB} ×2 KB/game state=100 MB.
Handles 2,500 operations/sec2,500 \text{ operations/sec}2,500 operations/sec for reads and writes.

5. Database

User Data:
- For 1 million users, with each profile requiring 1 KB (metadata, stats): 1 MB×1,000=1 GB1 \text{ MB} \times 1,000 = 1 \text{ GB}1 MB×1,000=1 GB.
Game History:
- Annual storage: 50 GB50 \text{ GB}50 GB.
- Needs to support ~10 read/write operations per second for querying and storing completed games.

6. Matchmaking Service

Matchmaking operates asynchronously and processes ~500 requests/sec at peak.

7. Monitoring and Analytics

Logs for 2,500 requests/sec, generating ~2 GB/day of log data.
Requires scalable log storage and real-time dashboards.

Scalability Requirements

Compute Nodes:
- Game Session Service:
  - Each node can handle ~5,000 game states in memory.
  - For 50,000 games, at least 10 nodes are required.
- API Gateway:
  - Assume each gateway node handles ~1,000 RPS.
  - For 2,500 RPS, 3 nodes are required, plus 1 for redundancy.
Database:
- A relational database with read replicas to handle simultaneous queries.
- Primary node handles writes (~100 RPS), replicas handle reads (~500 RPS).
WebSocket Load Balancer:
- Distribute 110,000 WebSocket connections across 10 WebSocket servers, each supporting ~10,000 connections.
Network Bandwidth:
- Real-Time Updates:
  - 1.6 MB/sec (moves) + ~5 MB/sec (chat messages) = ~6.6 MB/sec.
- Requires 10-15 MB/sec network bandwidth for peak usage.

API design

1. User Management APIs

These APIs handle user registration, authentication, and profile management.

POST /api/users/signup
- Purpose: Registers a new user.
- Inputs: Username, email, password.
- Outputs: Success/failure message, user ID.
POST /api/users/login
- Purpose: Authenticates users and provides a token.
- Inputs: Email, password.
- Outputs: JWT token, user details.
GET /api/users/profile
- Purpose: Retrieves user profile and statistics.
- Inputs: User ID (from token).
- Outputs: Profile data (ELO, games played, win/loss stats).
PUT /api/users/profile
- Purpose: Updates user profile information.
- Inputs: Profile data (e.g., avatar, bio).
- Outputs: Success/failure message.

2. Matchmaking APIs

These APIs manage player pairing and game creation.

POST /api/matchmaking/find
- Purpose: Finds a match for a player based on ELO.
- Inputs: User ID, optional preferences (e.g., time control).
- Outputs: Matched game ID.
POST /api/matchmaking/invite
- Purpose: Sends a private game invitation.
- Inputs: Inviter ID, invitee ID.
- Outputs: Invitation status.
POST /api/matchmaking/cancel
- Purpose: Cancels an ongoing matchmaking request.
- Inputs: User ID.
- Outputs: Success/failure message.

3. Game Session APIs

These APIs manage ongoing games, move validation, and game state updates.

POST /api/games/start
- Purpose: Starts a new game session.
- Inputs: Player IDs, time control, game mode.
- Outputs: Game ID, initial board state.
POST /api/games/move
- Purpose: Submits a move and updates the game state.
- Inputs: Game ID, move (e.g., "e2e4").
- Outputs: Updated board state, move validity.
GET /api/games/state
- Purpose: Fetches the current state of a game.
- Inputs: Game ID.
- Outputs: Board state, active player, remaining time.
POST /api/games/end
- Purpose: Ends a game (e.g., resignation or timeout).
- Inputs: Game ID, result (e.g., checkmate, resignation).
- Outputs: Game summary.

4. Game History APIs

These APIs handle storage and retrieval of completed games.

GET /api/history/list
- Purpose: Retrieves a list of past games for a user.
- Inputs: User ID, optional filters (e.g., date range).
- Outputs: List of game summaries.
GET /api/history/details
- Purpose: Fetches detailed data for a specific game.
- Inputs: Game ID.
- Outputs: Move history (PGN format), result.
POST /api/history/export
- Purpose: Exports a game’s history in PGN format.
- Inputs: Game ID.
- Outputs: PGN file.

5. Spectator APIs

These APIs allow non-players to watch live games.

GET /api/spectator/games
- Purpose: Retrieves a list of ongoing games available for spectating.
- Inputs: Optional filters (e.g., skill level, game type).
- Outputs: List of live games.
GET /api/spectator/state
- Purpose: Fetches the current state of a game for spectators.
- Inputs: Game ID.
- Outputs: Board state, active player.

6. Real-Time APIs

These APIs handle WebSocket connections for real-time updates.

WS /ws/connect
- Purpose: Establishes a WebSocket connection for a player or spectator.
- Inputs: Token (for authentication), role (player/spectator), game ID.
- Outputs: Real-time updates (e.g., moves, timers).
WS /ws/move
- Purpose: Sends a move to the server in real-time.
- Inputs: Game ID, move.
- Outputs: Move acknowledgment, updated state.
WS /ws/chat
- Purpose: Sends and receives chat messages.
- Inputs: Game ID, message.
- Outputs: Chat updates for all participants.

7. Admin APIs

These APIs allow administrators to manage the system.

GET /api/admin/users
- Purpose: Lists all users.
- Inputs: Admin token, optional filters.
- Outputs: List of users with details.
POST /api/admin/games/terminate
- Purpose: Terminates an ongoing game.
- Inputs: Game ID.
- Outputs: Success/failure message.
POST /api/admin/config
- Purpose: Updates matchmaking or gameplay settings.
- Inputs: Configuration parameters.
- Outputs: Success/failure message.

8. Notification APIs

These APIs manage user notifications about game events.

POST /api/notifications/send
- Purpose: Sends a notification to a user.
- Inputs: User ID, notification message.
- Outputs: Delivery status.
GET /api/notifications
- Purpose: Retrieves notifications for a user.
- Inputs: User ID.
- Outputs: List of notifications.

Database design

1. User Database

Schema Details:

Users Table:
- user_id (Primary Key): Unique identifier.
- username: String, unique.
- email: String, unique.
- password_hash: String, for securely storing passwords.
- elo_rating: Integer, default 1200.
- profile_data: JSON, optional metadata like avatar, bio.

Purpose:

Stores user account details, authentication data, and player-specific statistics.

Tech Used:

Relational Database (e.g., PostgreSQL or MySQL).

Tradeoff:

Pros: Strong consistency and support for complex queries (e.g., leaderboard generation).
Cons: Less performant for high write workloads compared to NoSQL databases.

2. Game Session Database

Schema Details:

ActiveGames Table:
- game_id (Primary Key): Unique identifier.
- player1_id: Foreign Key to Users.
- player2_id: Foreign Key to Users.
- current_state: JSON, representing the board state.
- last_move_time: Timestamp, to track time controls.
- status: Enum (active, completed, abandoned).

Purpose:

Tracks ongoing games, including the current state, player associations, and game status.

Tech Used:

In-Memory Store (e.g., Redis or Memcached).

Tradeoff:

Pros: Low-latency reads/writes for real-time game state updates.
Cons: Limited durability and requires backup mechanisms for persistence.

3. Game History Database

Schema Details:

CompletedGames Table:
- game_id (Primary Key): Unique identifier.
- player1_id: Foreign Key to Users.
- player2_id: Foreign Key to Users.
- pgn_data: Text, stores the game in PGN format.
- result: Enum (win, loss, draw).
- completion_time: Timestamp.

Purpose:

Stores completed games for retrieval, analysis, and replay functionality.

Tech Used:

NoSQL Database (e.g., MongoDB or DynamoDB).

Tradeoff:

Pros: Flexible schema for storing PGN data and efficient for high-volume write operations.
Cons: Lack of strong relational features makes complex queries challenging.

4. Matchmaking Database

Schema Details:

MatchmakingQueue Table:
- queue_id (Primary Key): Unique identifier.
- user_id: Foreign Key to Users.
- elo_range: Integer, allowable opponent ELO range.
- time_requested: Timestamp, to prioritize older entries.

Purpose:

Queues players for matchmaking and prioritizes them based on ELO and time in the queue.

Tech Used:

Relational Database (e.g., PostgreSQL).

Tradeoff:

Pros: Ensures strong consistency for matching players based on ELO and time priority.
Cons: Limited scalability for extremely high matchmaking requests compared to distributed solutions.

5. Chat and Notification Database

Schema Details:

ChatMessages Table:
- message_id (Primary Key): Unique identifier.
- game_id: Foreign Key to ActiveGames.
- sender_id: Foreign Key to Users.
- message_content: Text.
- timestamp: Timestamp of the message.
Notifications Table:
- notification_id (Primary Key): Unique identifier.
- user_id: Foreign Key to Users.
- content: Text, notification message.
- read_status: Boolean.

Purpose:

Stores in-game chat messages and user notifications.

Tech Used:

NoSQL Database (e.g., MongoDB or Cassandra).

Tradeoff:

Pros: High scalability and support for fast, frequent writes.
Cons: Complex queries (e.g., search/filter messages) are less efficient compared to relational databases.

6. Analytics Database

Schema Details:

Metrics Table:
- metric_id (Primary Key): Unique identifier.
- event_type: Enum (move, match_created, match_completed).
- timestamp: Timestamp of the event.
- details: JSON, event-specific metadata.

Purpose:

Tracks metrics and events for monitoring, analytics, and reporting.

Tech Used:

Columnar Database (e.g., Amazon Redshift or Google BigQuery).

Tradeoff:

Pros: Optimized for read-heavy analytical queries and large-scale data aggregation.
Cons: Poor performance for real-time writes; better suited for batch processing.

7. Logging Database

Schema Details:

Logs Table:
- log_id (Primary Key): Unique identifier.
- log_level: Enum (info, warning, error).
- message: Text, log details.
- timestamp: Timestamp of the log.

Purpose:

Stores logs for debugging, auditing, and monitoring.

Tech Used:

Time-Series Database (e.g., Elasticsearch or InfluxDB).

Tradeoff:

Pros: Optimized for high-frequency log writes and querying specific time ranges.
Cons: Requires careful management of log retention to avoid storage bloat.

High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...

1. User Management Service

Overview:

Manages user accounts, authentication, and profile data.
Tracks user-specific statistics like ELO ratings, game history, and preferences.

Key Responsibilities:

Handle user registration, login, and secure authentication.
Maintain user profile information.
Provide APIs for updating and retrieving user data.

Technology Stack:

Relational Database (e.g., PostgreSQL/MySQL) for structured user data.
Authentication protocols like OAuth2 or JWT for security.

2. Matchmaking Service

Overview:

Pairs players for games based on skill level (ELO ratings) and preferences.
Supports manual pairing through friend invitations.

Key Responsibilities:

Match players using a skill-based algorithm.
Manage a matchmaking queue for real-time pairing.
Handle time control and match preferences.

Technology Stack:

Backend logic written in Go/Java/Python.
Relational or distributed databases (e.g., PostgreSQL/Redis) for queue management.

3. Game Session Service

Overview:

Manages active chess games, including move validation, state updates, and time control enforcement.
Ensures synchronization between players and spectators.

Key Responsibilities:

Initialize and track game instances.
Enforce chess rules using a move validation engine.
Manage timers and detect game-ending conditions (checkmate, stalemate, timeout).

Technology Stack:

In-memory data store (e.g., Redis) for low-latency game state management.
Chess engine (e.g., Stockfish or custom engine) for move validation.

4. Real-Time Communication Service

Overview:

Enables real-time communication for move updates and in-game chat.

Key Responsibilities:

Maintain WebSocket connections for live updates.
Stream game state changes to players and spectators.
Support in-game chat between players.

Technology Stack:

WebSocket server (e.g., Socket.IO, AWS AppSync).
Load balancer (e.g., NGINX) to distribute connections.

5. Game History Service

Overview:

Stores and retrieves completed games for replay and analysis.

Key Responsibilities:

Archive game data (e.g., PGN format) for future retrieval.
Provide APIs for replaying or exporting completed games.

Technology Stack:

NoSQL Database (e.g., MongoDB) for storing flexible game data.
RESTful APIs for retrieval and export.

6. Spectator Service

Overview:

Allows users to watch ongoing games in real-time.

Key Responsibilities:

Stream game state updates to spectators.
Support optional features like commentary or highlighting key moves.

Technology Stack:

Real-time streaming via WebSocket.
Backend integration with the Game Session Service.

7. Notification Service

Overview:

Sends notifications to users about game events, such as invitations or move reminders.

Key Responsibilities:

Deliver alerts for events like match invitations or nearing timeouts.
Notify users about achievements or updates in their profile.

Technology Stack:

Push notification services (e.g., Firebase Cloud Messaging, AWS SNS).
Message queues (e.g., RabbitMQ/Kafka) for asynchronous event processing.

8. Monitoring and Analytics Service

Overview:

Tracks system performance, user behavior, and game statistics.

Key Responsibilities:

Aggregate and visualize data for system health and usage patterns.
Generate reports on ELO trends, user activity, or system anomalies.

Technology Stack:

Monitoring tools (e.g., Prometheus, Grafana).
Analytics databases (e.g., Amazon Redshift, Google BigQuery).

9. Logging Service

Overview:

Captures logs from various services for debugging, auditing, and monitoring.

Key Responsibilities:

Store logs for game events, user actions, and system errors.
Enable search and filtering for troubleshooting.

Technology Stack:

Log aggregation tools (e.g., ELK Stack – Elasticsearch, Logstash, Kibana).
Time-series databases (e.g., InfluxDB) for tracking event trends.

10. Admin Dashboard

Overview:

Provides a user-friendly interface for managing and monitoring the system.

Key Responsibilities:

Allow administrators to manage users, games, and configurations.
Provide real-time insights into system health and usage.

Technology Stack:

Frontend frameworks (e.g., ReactJS, Angular).
Backend APIs (e.g., Node.js/Django/Flask).

11. Anti-Cheat Service

Overview:

Detects and mitigates cheating by analyzing player behavior and game patterns.

Key Responsibilities:

Monitor game data for abnormal patterns (e.g., perfect play, time anomalies).
Flag suspicious games for admin review.
Optionally ban cheaters or impose penalties.

Technology Stack:

Machine learning models for pattern detection.
Backend integration with game session and analytics services.

12. Load Balancer

Overview:

Distributes traffic across multiple service instances to prevent overload.

Key Responsibilities:

Balance API requests, WebSocket connections, and matchmaking queues.
Ensure high availability by redirecting traffic during node failures.

Technology Stack:

Load balancer (e.g., AWS Elastic Load Balancer, NGINX, HAProxy).

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

1. User Login Request

Objective: Authenticate a user and provide a session token.

Steps:

API Gateway:
- The request is received at /api/users/login.
- Basic validations (e.g., request size, required fields) are performed.
User Management Service:
- Validates the credentials against the User Database.
- If valid:
  - Generates a JWT token with user details.
  - Updates the user's last_login timestamp in the database.
- If invalid:
  - Responds with an error.
Response:
- The JWT token is sent back to the client.
- The client stores the token for subsequent authenticated requests.

2. Matchmaking Request

Objective: Pair a player with an opponent or place them in the matchmaking queue.

Steps:

API Gateway:
- The request is sent to /api/matchmaking/find with the player's preferences (e.g., time control, ELO range).
- The JWT token is verified to ensure the player is authenticated.
Matchmaking Service:
- Checks the Matchmaking Queue in the Matchmaking Database for an available opponent within the specified ELO range.
- If a match is found:
  - Removes both players from the queue.
  - Sends a request to the Game Session Service to create a new game.
- If no match is found:
  - Adds the player to the queue.
  - Responds with a "searching" status.
Game Session Service:
- Creates a new game instance with initial settings (e.g., board state, time controls).
- Notifies both players through the Real-Time Communication Service to establish WebSocket connections.
Response:
- If matched, the game details (game ID, opponent info) are sent to the client.
- If not, a message indicating the player is queued is sent.

3. Gameplay Request (Move Submission)

Objective: Validate a player’s move, update the game state, and notify the opponent.

Steps:

API Gateway:
- The request is sent to /api/games/move with the move details (e.g., "e2e4") and game ID.
- The JWT token is validated to identify the player.
Game Session Service:
- Fetches the current game state from the Distributed Cache (e.g., Redis).
- Passes the move to the Move Validation Engine, which:
  - Checks if the move is valid according to chess rules.
  - Detects if the move results in check, checkmate, or stalemate.
- If valid:
  - Updates the game state in the cache.
  - Resets the player's timer if time controls are enabled.
- If invalid:
  - Responds with an error message.
Real-Time Communication Service:
- Sends the updated game state to both players over WebSocket connections.
- Updates the game state for any spectators watching the match.
Response:
- If valid, the updated game state is returned to the client.
- If invalid, an error message indicating why the move was rejected is sent.

4. Game Completion Request

Objective: End a game due to checkmate, resignation, timeout, or agreement.

Steps:

API Gateway:
- The request is sent to /api/games/end with the game ID and result (e.g., resignation).
- The JWT token is validated.
Game Session Service:
- Fetches the game state from the Distributed Cache.
- Validates the game-ending condition.
- Marks the game as completed and removes it from active game tracking.
- Sends the final game state to the Game History Service.
Game History Service:
- Stores the completed game in the Game History Database in PGN format along with metadata (e.g., result, players).
- Updates player statistics (e.g., ELO ratings) in the User Database.
Notification Service:
- Sends notifications to both players summarizing the match results.
Response:
- Confirms the game has ended and provides the final state to the client.

5. Game History Retrieval Request

Objective: Retrieve a user’s past games for viewing or analysis.

Steps:

API Gateway:
- The request is sent to /api/history/list with optional filters (e.g., date range, opponent).
- The JWT token is validated.
Game History Service:
- Queries the Game History Database for the user’s completed games based on the filters.
- Fetches metadata for each game (e.g., date, result, opponent).
- Limits results for pagination.
Response:
- Returns a list of game summaries to the client.
- Each summary includes the game ID, opponent, result, and a link to view or export the game.

6. Spectator Mode Request

Objective: Allow a spectator to watch an ongoing game in real-time.

Steps:

API Gateway:
- The request is sent to /api/spectator/state with the game ID.
- If authenticated, the user’s JWT token is verified. If not, the request proceeds as a guest.
Spectator Service:
- Queries the Game Session Service for the current game state.
- Subscribes the spectator to the game’s WebSocket channel through the Real-Time Communication Service.
Real-Time Communication Service:
- Streams game state updates to the spectator as moves are made.
Response:
- Returns the initial game state to the spectator.
- Streams real-time updates via WebSocket.

7. Notification Delivery Request

Objective: Notify a user about a match invitation or game status.

Steps:

Notification Service:
- Retrieves pending notifications for the user from the Notifications Database.
- Formats the notifications for delivery.
Response:
- Sends notifications via WebSocket (if connected) or email/SMS.

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

1. User Management Service

End-to-End Working:

The User Management Service handles user registration, authentication, and profile management.

When a user signs up, the service validates the input data (e.g., username uniqueness) and securely hashes the password before storing it in the database.
During login, the service verifies the credentials and issues a JWT token upon successful authentication.
The service also allows users to update their profiles and tracks statistics like ELO rating, games played, and win/loss records.

Data Structures and Algorithms:

Hash Map (In-Memory Cache):
- Temporarily caches frequently accessed user data (e.g., profile details) to reduce database calls.
Hashing Algorithm:
- Secure password hashing with bcrypt or Argon2 to protect user credentials.
Relational Database:
- Schema includes Users table with fields like user_id, username, email, password_hash, and elo_rating.

Scaling for Peak Traffic:

Horizontal Scaling:
- Deploy multiple instances of the service behind a load balancer.
Caching:
- Use Redis to cache user profiles and tokens, reducing read pressure on the database.
Database Read Replicas:
- Employ read replicas for handling high read workloads during peak periods.

Edge Cases and Handling:

Duplicate Usernames or Emails:
- Enforced by database-level unique constraints and pre-validation in the API.
Token Expiry:
- Handle expired tokens by refreshing them via a /refresh API endpoint.
Password Recovery:
- Implement secure workflows like one-time reset links.

2. Matchmaking Service

End-to-End Working:

The Matchmaking Service pairs players for matches based on their ELO ratings and preferences (e.g., time control, game type).

It uses a matchmaking queue where players wait until a suitable opponent is found. Once matched, the service creates a new game instance and notifies both players.

Data Structures and Algorithms:

Priority Queue:
- Maintains the matchmaking queue, prioritizing players by the time they entered the queue.
ELO Matching Algorithm:
- Uses a range-based search (e.g., ±50 ELO) to find opponents.
Relational Database:
- Stores player matchmaking requests, with fields for user_id, elo_range, time_requested, and status.

Scaling for Peak Traffic:

Distributed Queue:
- Use a distributed message queue (e.g., Kafka) to handle matchmaking requests across multiple service nodes.
Asynchronous Matching:
- Process matchmaking requests asynchronously to avoid blocking.
Sharding:
- Partition matchmaking queues by ELO ranges to distribute the load.

Edge Cases and Handling:

No Opponent Found:
- Periodically expand the ELO range and retry matching.
Queue Overload:
- Implement rate-limiting to throttle incoming matchmaking requests during spikes.

3. Game Session Service

End-to-End Working:

The Game Session Service manages ongoing games, including initializing new games, validating moves, and tracking timers.

Each game session starts with a default board state, and moves are validated in real-time.
Game-ending conditions (e.g., checkmate, stalemate) are detected, and the game state is updated accordingly.

Data Structures and Algorithms:

Chess Board Representation:
- Use a 2D array or FEN (Forsyth–Edwards Notation) string to represent the board state.
Move Validation Engine:
- Algorithms to validate moves include:
  - Piece-specific rules (e.g., bishop moves diagonally).
  - Game-ending checks (e.g., king in check, checkmate).
Timer Management:
- Use countdown timers in memory or distributed cache for tracking time controls.

Scaling for Peak Traffic:

In-Memory Store (Redis):
- Store active game states for low-latency access.
Horizontal Scaling:
- Spin up additional game session nodes with a consistent hashing mechanism to distribute game sessions evenly.
Event-Driven Architecture:
- Use message queues for processing game events (e.g., move updates, timeouts).

Edge Cases and Handling:

Invalid Moves:
- Provide detailed feedback (e.g., "Move places the king in check").
Session Loss:
- Use persistent storage for recovering game states in case of service crashes.
Time Expiry:
- Enforce time controls with clear notification mechanisms.

4. Real-Time Communication Service

End-to-End Working:

The Real-Time Communication Service ensures low-latency updates for moves, timers, and chat messages.

It establishes WebSocket connections for players and spectators, enabling real-time synchronization.

Data Structures and Algorithms:

Connection Pool:
- Maintain active WebSocket connections in a pool indexed by game ID and user ID.
Message Queue:
- Queue messages for efficient delivery to connected clients.

Scaling for Peak Traffic:

WebSocket Load Balancing:
- Use sticky sessions to route WebSocket connections consistently to the same server.
Horizontal Scaling:
- Deploy multiple WebSocket servers and distribute connections using a load balancer.
Backpressure Management:
- Implement buffering to handle bursty message traffic without overwhelming the system.

Edge Cases and Handling:

Connection Drops:
- Re-establish connections and resynchronize the game state.
Delayed Messages:
- Retry message delivery with exponential backoff.

5. Game History Service

End-to-End Working:

The Game History Service stores completed games and provides APIs for replay or analysis.

Upon game completion, the final state and move history are saved in PGN format.

Data Structures and Algorithms:

Game History Schema:
- Store metadata (players, result, duration) and PGN data in a NoSQL database.
Search Index:
- Create indices on player IDs and game dates for efficient querying.

Scaling for Peak Traffic:

NoSQL Database:
- Use a distributed database (e.g., MongoDB) to handle high write throughput.
Archiving:
- Periodically archive older games to a data lake or cold storage.

Edge Cases and Handling:

Incomplete Games:
- Mark games as abandoned if they end prematurely.
Corrupted PGN Data:
- Validate PGN data before saving.

6. Spectator Service

End-to-End Working:

The Spectator Service allows non-players to watch ongoing games in real-time.

It streams game state updates and highlights key moves or events.

Data Structures and Algorithms:

Subscription List:
- Maintain a list of spectators subscribed to each game.
Game Event Queue:
- Broadcast game updates to all subscribed spectators.

Scaling for Peak Traffic:

Content Delivery Network (CDN):
- Cache non-dynamic data (e.g., game highlights) for spectators.
Horizontal Scaling:
- Deploy additional servers to handle large numbers of simultaneous spectators.

Edge Cases and Handling:

Spectator Overload:
- Limit the number of spectators per game or prioritize based on user roles (e.g., premium users).
Stale Data:
- Resynchronize spectators when they reconnect.

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

Redis for Game State Storage:

Trade-off: Chose Redis for low-latency in-memory storage at the expense of data durability.
Reason: Ensures real-time performance for active game states, with periodic backups to handle crash recovery.

Priority Queue for Matchmaking:

Trade-off: Used a priority queue for efficient matchmaking but sacrificed simplicity in queue management.
Reason: Allows dynamic prioritization (e.g., by ELO range or wait time), improving player experience.

NoSQL for Game History:

Trade-off: Opted for MongoDB (NoSQL) over relational databases, sacrificing complex querying capabilities.
Reason: Flexible schema and high write throughput handle large volumes of completed games efficiently.

WebSocket Communication:

Trade-off: WebSocket connections increase server resource usage compared to HTTP polling.
Reason: Enables real-time, bi-directional updates with minimal latency for gameplay and chat.

Distributed Matchmaking:

Trade-off: Introduced complexity with a distributed queue but improved scalability.
Reason: Ensures high availability and low wait times during peak loads.

Stateless API Gateway:

Trade-off: Stateless design requires external authentication (JWT) management.
Reason: Simplifies horizontal scaling and ensures high availability.

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

Redis Cache Failure:

Issue: Game state loss or delays in fetching game data.
Mitigation: Enable Redis replication and backups; fall back to a persistent database.

WebSocket Overload:

Issue: High concurrent connections may overwhelm servers.
Mitigation: Use load balancers and implement connection pooling with backpressure management.

Matchmaking Queue Overload:

Issue: Delayed pairing during peak traffic.
Mitigation: Shard queues by ELO range and prioritize high-wait-time players.

Move Validation Delays:

Issue: High concurrency causes lag in validating moves.
Mitigation: Optimize validation logic and scale validation nodes horizontally.

Database Bottlenecks:

Issue: Slow user queries or game history writes during spikes.
Mitigation: Use read replicas for user queries and batch writes for game history.

DDoS Attacks:

Issue: Malicious traffic overwhelms services.
Mitigation: Implement rate-limiting, IP filtering, and WAF (Web Application Firewall).

Stale Data in Spectator Mode:

Issue: Spectators see outdated game states after reconnections.
Mitigation: Resend the latest state upon reconnection.

Token Expiry Issues:

Issue: Expired JWTs disrupt user sessions.
Mitigation: Provide token refresh APIs with reasonable TTL (time-to-live).

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?

Enhanced Redis Reliability:

Improvement: Use Redis Cluster with multi-region replication.
Mitigation: Prevent single-point-of-failure for game state storage.

Dynamic WebSocket Scaling:

Improvement: Implement auto-scaling for WebSocket servers based on connection load.
Mitigation: Handle spikes in concurrent users efficiently.

Improved Matchmaking Performance:

Improvement: Introduce distributed message queues like Kafka for matchmaking.
Mitigation: Ensure high throughput and reduce queue bottlenecks during peak times.

Optimized Move Validation:

Improvement: Use pre-validated move caches for frequently played openings.
Mitigation: Reduce validation latency during gameplay.

Database Scalability:

Improvement: Partition databases (e.g., sharding game history by date).
Mitigation: Alleviate read/write pressure during high traffic.

Anti-DDoS Measures:

Improvement: Integrate a Web Application Firewall (WAF) and CDN-based traffic filtering.
Mitigation: Block malicious traffic and protect system resources.

Spectator Mode Resilience:

Improvement: Add a synchronization API for spectators to fetch the latest state on reconnections.
Mitigation: Ensure consistency in live game updates.

Advanced Monitoring and Alerting:

Improvement: Implement real-time anomaly detection for server health.
Mitigation: Proactively identify and resolve potential failures.

Solutions

utopia4715 - 05/24/2024

3

alchemy1135 - 05/18/2024

1

iridescent_luminous693 - 01/06/2025

0

utopia4715 - 05/24/2024

0