设计一个在线国际象棋服务

System requirements

Functional:

1. User Sign-up and Authentication:
2. Allow users to securely create accounts with unique credentials.
3. Implement authentication mechanisms such as email verification or OAuth for user identity verification.
4. Game Matching:
5. Pair users for matches based on skill level, rating, or randomly for casual games.
6. Ensure fair and balanced matchups to provide an enjoyable gaming experience for players of all skill levels.
7. Real-time Move Updates:
8. Provide real-time updates to players when their opponent makes a move.
9. Update the game board interface accordingly to reflect the latest move.
10. Move Validation according to Chess Rules:
11. Implement a method to validate moves according to standard chess rules.
12. Ensure that players cannot make illegal moves during a game to maintain game integrity.
13. Game Room/Session Management:
14. Create and manage game rooms or sessions for each chess match.
15. Allow players to join, leave, or spectate ongoing games seamlessly.
16. Game Completion and Result Recording:
17. Determine game outcomes (win, lose, draw) based on standard chess rules.
18. Record game results and update player statistics accordingly to track performance.

Non-Functional:

1. Scalability:
2. The system should be able to scale to handle millions of users and thousands of concurrent games.
3. Ensure that the architecture is horizontally scalable to accommodate increasing load and user growth.
4. Real-time Performance:
5. Real-time move updates should be delivered with low latency to provide a responsive gaming experience.
6. Minimize network latency and optimize server-side processing to ensure smooth gameplay.
7. Security:
8. Implement robust security measures to protect user accounts and game data.
9. Use encryption for data transmission and storage, and implement proper authentication and authorization mechanisms to prevent unauthorized access.
10. Reliability and Availability:
11. Ensure high availability of the service to handle peak traffic periods and minimize downtime.
12. Implement redundancy and failover mechanisms to mitigate the impact of server failures and ensure continuous service availability.
13. Data Integrity:
14. Maintain data integrity by ensuring that game states and player statistics are accurately recorded and updated.
15. Implement data validation and error handling mechanisms to prevent data corruption or loss.
16. Performance Optimization:
17. Optimize server-side processing and database queries to minimize response times and maximize throughput.
18. Use caching mechanisms to reduce database load and improve overall system performance.
19. Cross-platform Compatibility:
20. Ensure that the system is compatible with a wide range of devices and platforms, including desktops, laptops, tablets, and mobile phones.
21. Provide a responsive and user-friendly interface that works seamlessly across different screen sizes and resolutions.

Capacity estimation

To estimate the scale of the online chess service, we need to consider factors such as the number of users, daily active users (DAU), number of games played per day, and average moves per game. Based on the provided assumptions of 10 million users, 1 million DAU, 2 million games played per day, and an average of 30 moves per game, we can perform the following calculations:

• Number of Users: Total Users: 10 million
• Daily Active Users (DAU): 1 million
• Number of Games Played per Day: 2 million
• Average Moves per Game: 30

Now, let's calculate the scale of the system based on these numbers:

Total Games Played per Day:

Total Games Played per Day=DAU × Average Games per User per Day

Total Games Played per Day=1,000,000×2=2,000,000

Total Moves per Day:

Total Moves per Day=Total Games Played per Day × Average Moves per Game

Total Moves per Day=2,000,000 × 30=60,000,000

To estimate the size of the database required for saving moves, game outcomes, and user data, we need to consider the record size for each type of data and the total number of records generated per day. Let's break down the estimation for each type of data:

User Data:

User data typically includes information such as username, email, password hash, profile picture, and

other profile attributes.

Assuming an average record size of 1 KB per user, considering the additional overhead for indexing and metadata.

Total Size for User Data: Total Users×Average Record Size per User

=10,000,000×1 KB =10 GB

Moves and Game Outcomes:

Each move record includes information such as game ID, player ID, move details (e.g., starting and ending positions of the piece), timestamp, and move validation status.

Game outcome records include game ID, player IDs, outcome (win, lose, draw), timestamp, and other metadata.

Assuming an average record size of 1 KB per move or game outcome record.

Total Size for Moves and Game Outcomes:

Total Moves per Day×Average Record Size per Move

=60,000,000×1 KB

=60 GB

Adding the sizes for user data and moves/game outcomes, we get an estimated total size of:

Total Database Size=Size for User Data + Size for Moves and Game Outcomes

=10 GB+60 GB

=70 GB

So, the estimated size of the database required for saving moves, game outcomes, and user data is approximately 70 GB.

API design

For the online chess service, we need to define a set of APIs to support various functionalities such as user management, game operations, move validation, and real-time updates. Below are the expected APIs from the system:

• User Management APIs:
• Sign-up: Allows users to create new accounts securely.
• Login: Authenticates users and generates access tokens for authorization.
• Logout: Logs out the currently authenticated user and invalidates the access token.
• Profile Information: Retrieves user profile information such as username, email, and avatar.
• Update Profile: Allows users to update their profile information.
• Game Operations APIs:
• Create Game: Initiates a new chess game between two players.
• Join Game: Allows a player to join an existing game.
• Leave Game: Allows a player to leave a game in progress.
• Spectate Game: Allows users to spectate ongoing games as observers.
• Forfeit Game: Allows a player to forfeit or resign from a game.
• Move Validation APIs:
• Validate Move: Validates a move made by a player according to chess rules.
• Undo Move: Allows players to undo their last move (if supported).
• Real-time Updates APIs:
• Subscribe to Game Updates: Subscribes a player to receive real-time updates for a specific game.
• Unsubscribe from Game Updates: Unsubscribes a player from receiving updates for a specific game.
• Game State Retrieval APIs:
• Get Game State: Retrieves the current state of a game, including the position of pieces on the board and move history.
• Get Game History: Retrieves the entire move history of a game.
• Leaderboard APIs:
• Get Leaderboard: Retrieves the top-ranked players based on their performance and rating.
• Miscellaneous APIs:
• Search Users: Allows users to search for other users by username or other criteria.
• Reset Password: Initiates the password reset process for a user account.

These APIs provide the necessary functionalities for users to interact with the online chess service, including managing their accounts, playing games, validating moves, receiving real-time updates, and accessing game-related information.

Database design

Choice of Database:

User and Game Data

• Database Type: SQL (Relational Database Management System)
• Example: MySQL, PostgreSQL
• Reasoning: All entities (User, Game, Move) can be stored in a single SQL database since they have relational dependencies. SQL databases offer ACID properties, support complex queries, and provide strong consistency, making them suitable for managing user data, game data, and move history in the online chess service.
• CAP Theorem Focus: CP (Consistency, Partition Tolerance)

Partitioning Strategy:

In the context of the online chess service, we can partition the data efficiently to improve performance, scalability, and manageability. Here's how we can approach partitioning for the entities in the SQL database:

• User Data:
• Partitioning by User ID: We can partition the User table based on the user ID. Each partition will contain user records with a specific range of user IDs. This approach ensures that user data is evenly distributed across partitions and allows for efficient retrieval of user information.
• No geographical partitioning is needed for user data unless there are specific requirements for data localization or regulatory compliance.
• Game Data:
• Partitioning by Game ID or Game Start Time: We can partition the Game table based on the game ID or the game start time. Partitioning by game ID ensures that game data is evenly distributed across partitions. Alternatively, partitioning by game start time can help in managing historical game data efficiently.
• No geographical partitioning is needed for game data unless there are specific requirements for data localization or regulatory compliance.
• Move Data:
• Partitioning by Game ID: Since move data is associated with specific games, we can partition the Move table based on the game ID. Each partition will contain move records for a specific game, ensuring that move data is localized and easily retrievable for each game.
• No geographical partitioning is needed for move data unless there are specific requirements for data localization or regulatory compliance.

Scaling the System:

For scaling the online chess service, we can employ the following scaling strategies:

1. Vertical Scaling: Increase the computing resources (CPU, RAM) of the server hosting the SQL database to handle increased load. Vertical scaling is suitable for handling moderate increases in traffic and can be done by upgrading the hardware of the database server.
2. Horizontal Scaling: Scale out by adding more database servers and distributing the workload across multiple nodes. We can use techniques such as sharding or replication to distribute data across multiple database instances. Horizontal scaling is more suitable for handling rapid growth and high traffic volumes.

• Sharding: Partition the data across multiple database instances based on a shard key (e.g., user ID, game ID). Each database instance manages a subset of the data, allowing for parallel processing and improved scalability.
• Replication: Maintain multiple copies of the database across different nodes to provide fault tolerance and redundancy. Replication ensures data availability and can improve read scalability by distributing read queries across replicas.

By combining vertical and horizontal scaling strategies and implementing efficient partitioning techniques, we can ensure that the online chess service can handle increased load, maintain performance, and provide a seamless gaming experience for users.

High-level design

• Frontend Application:
• User Interface: Provides a user-friendly interface for players to interact with the chess game.
• Game Interface: Displays the chess board, pieces, and game status.
• Move Input: Allows players to input moves using drag-and-drop or click interactions.
• Backend Server:
• Game Logic: Manages game rules, move validation, and game state transitions.
• Matchmaking: Matches players based on skill level or randomly for games.
• User Management: Handles user authentication, registration, and profile management.
• Database Communication: Interacts with the database to retrieve user data, game states, and move history.
• Real-time Communication Service:
• WebSocket Server: Facilitates real-time communication between clients (web browsers) and the backend server.
• Move Updates: Sends move updates to players in real-time to synchronize game states.
• Chat Functionality: Enables players to communicate with each other during games.
• Database:
• User Data: Stores user profiles, authentication details, and preferences.
• Game Data: Stores game states, move history, and match outcomes.
• Real-time Updates: Stores temporary data for real-time communication, such as WebSocket connections.
• Game Engine:
• Chess Engine: Implements the rules of chess, move generation, and move validation.
• AI Opponent (Optional): Provides computer-controlled opponents for single-player games.

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...

Detailed component design

Real-Time Communication Between Players

Incorporating data synchronization mechanisms into the real-time communication service is essential to ensure consistency across clients and facilitate timely updates during gameplay in the online chess service. Here's how we can explore the inclusion of these mechanisms:

1. WebSocket Communication:
2. Utilize WebSocket technology to establish persistent, bidirectional communication channels between the server and clients.
3. WebSocket connections enable real-time data transmission, allowing clients to receive immediate updates from the server without the need for frequent polling.
4. Server as Single Source of Truth:
5. Designate the server as the single source of truth for game state and player actions.
6. All client interactions, including move submissions, chat messages, and game events, should be relayed to the server for validation and dissemination to other clients.
7. Broadcasting Updates:
8. Implement broadcasting mechanisms on the server to distribute updates to all relevant clients in real-time.
9. When a player makes a move or sends a chat message, the server broadcasts these updates to all other players in the same game room, ensuring that everyone stays synchronized.
10. Timestamps and Sequencing:
11. Include timestamps or sequence numbers in messages sent between the server and clients to maintain the order of events and resolve any potential inconsistencies.
12. Clients can use timestamps or sequence numbers to reconcile updates received from the server and ensure that they are processed in the correct order.

Trade offs/Tech choices

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

Failure scenarios/bottlenecks

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

Future improvements

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