System requirements

Functional:

• User Authentication: This is essential for ensuring only registered users can post comments.
• Comment Creation & Replies: This allows users to participate in discussions.
• Nested Comments: This is the core functionality, enabling threaded conversations.
• Real-time Updates: This keeps users engaged and informed about new activity.
• Edit & Delete: Users need control over their own comments.
• Moderation: Ensures a safe and healthy discussion environment.
• User Notifications: Keeps users informed about relevant replies.
• Search: Helps users find specific information within a conversation.

Non-Functional:

• Scalability: The system should be able to grow with the user base and activity.
• Reliability: Minimizes disruptions and ensures data availability.
• Security: Protects user data and prevents unauthorized access.
• Usability: The interface should be intuitive and user-friendly.
• Responsive Design: Provides a good experience on all devices.
• Data Integrity: Ensures the accuracy and consistency of comment data.
• Performance: Fast loading times and efficient data retrieval are crucial.

API design

User Management:

• POST /users (Create User): Creates a new user account. (Requires minimal data like username, password)
• POST /login (Login): Authenticates a user and returns a token for subsequent requests. (Requires username/email and password)
• GET /users/me (Get User Info): Retrieves information about the currently logged-in user. (Requires valid access token)

Comment Management:

• POST /posts/{postId}/comments (Create Comment): Creates a new comment on a specific post. (Requires valid access token, post ID, and comment content)
• POST /comments/{commentId}/replies (Reply to Comment): Creates a reply to an existing comment. (Requires valid access token, comment ID, and reply content)
• GET /posts/{postId}/comments (Get Post Comments): Retrieves all comments for a specific post, including nested replies. (Optional parameters for pagination and filtering)
• GET /comments/{commentId} (Get Comment): Retrieves a specific comment with its replies. (Optional parameter to include nested replies)
• PUT /comments/{commentId} (Edit Comment): Allows users to edit their own comments. (Requires valid access token and comment content)
• DELETE /comments/{commentId} (Delete Comment): Allows users and admins to delete comments. (Requires valid access token and authorization for deletion)

Moderation:

• DELETE /admin/comments/{commentId} (Admin Delete Comment): Allows admins to delete inappropriate comments. (Requires admin access token)

Notifications:

• GET /users/me/notifications (Get User Notifications): Retrieves unread notifications for the logged-in user. (Requires valid access token)
• PUT /notifications/{notificationId} (Mark Notification as Read): Allows users to mark notifications as read. (Requires valid access token and notification ID)

Search:

• GET /posts/{postId}/comments/search (Search Comments): Allows users to search for comments within a specific post based on keywords. (Requires valid access token and search query)

Database Design for Nested Comments System

Here's a breakdown of potential database choices for your nested comments system, considering the CAP theorem:

1. Relational Database (SQL):

• Database Type: SQL (e.g., MySQL, PostgreSQL)
• Reasoning: Structured data like users, posts, and their relationships are well-suited for relational databases with efficient querying capabilities.
• CAP Theorem: Leans towards Consistency. SQL databases prioritize data consistency, ensuring all reads reflect the latest updates across all replicas.

2. Document Database (NoSQL):

• Database Type: NoSQL (e.g., MongoDB)
• Reasoning: Document databases offer flexibility for storing hierarchical comment data with varying structures and embedded replies.
• CAP Theorem: Can be Balanced or Availability Focused. Some NoSQL databases prioritize consistency like SQL, while others favor high availability for handling read requests even during updates. Choose a database based on your specific needs for consistency vs. availability.

Data Partitioning:

• Best Strategy: Partition by Post ID.
• Reasoning: This distributes comments for each post across different database partitions, improving performance when retrieving comments for a specific post.
• Partitioning Algorithm: Range Partitioning can be used. It divides the data based on the continuous range of Post IDs.

Sharding Strategy:

• Best Strategy: Horizontal Sharding by Post ID.
• Reasoning: Sharding distributes data across multiple database servers, improving scalability as the number of posts and comments grows. Sharding by Post ID ensures comments for each post are likely to be co-located on the same shard, minimizing data movement for post-specific queries.

High-level design

Here's a breakdown of the high-level components needed for the nested comments system:

1. API Gateway:

• Serves as the single entry point for all API requests.
• Routes requests to appropriate backend services based on functionality (e.g., user management, comments).
• Handles authentication and authorization checks for incoming requests.

2. User Service:

• Manages user accounts (registration, login, profile information).
• Validates user credentials and issues access tokens for authorized requests.

3. Post Service:

• Handles CRUD operations for posts (create, read, update, delete).
• Enforces authorization for post creation and modification.

4. Comment Service:

• Handles CRUD operations for comments (create, read, update, delete).
• Validates comment creation requests and enforces authorization.
• Implements logic for nesting comments under a parent comment.

5. Notification Service (Optional):

• Generates notifications for users when they receive replies or mentions in comments.
• Manages notification delivery and user interaction (marking as read, deleting).

6. Database:

• Stores user data, posts, comments (including nested replies), and notification information (if applicable).
• Uses chosen database technology (e.g., relational, NoSQL) with appropriate partitioning and sharding strategies for scalability.

7. Cache (Optional):

• Can be implemented using a key-value store (e.g., Redis) to cache frequently accessed data (e.g., top-level comments for a post).
• Improves performance by reducing database load for frequently retrieved information.

Deep Dive into the Comment Service

The Comment Service plays a central role in your nested comments system, handling all functionalities related to comment creation, retrieval, and management. Here's a detailed breakdown:

Responsibilities:

• Create Comments:
• Receives user requests to create new comments.
• Validates comment content and user authorization.
• Stores the comment data in the database, including associations with the user and post.
• Optionally triggers notifications for mentioned users in the comment.
• Retrieve Comments:
• Handles requests to fetch comments for a specific post.
• Retrieves comments from the database, considering filtering options and pagination.
• Handles nested comments by recursively fetching replies for each parent comment.
• Optionally retrieves associated user information for comment authors.
• Edit/Delete Comments:
• Handles user requests to edit or delete existing comments.
• Performs authorization checks to ensure the user has permission to modify the comment.
• Updates or deletes the comment data in the database.
• Optionally removes associated notifications if a comment is deleted.
• Moderation (Optional):
• Provides functionalities for admins to moderate comments (e.g., delete inappropriate comments).

Implementation Considerations:

• Data Model: The comment service should utilize a data model that efficiently represents comments and their relationships. This could involve nested objects or references to parent comments for hierarchical organization.
• Database Interactions: The service needs to interact with the database to store, retrieve, and update comment data. Consider using prepared statements to prevent SQL injection vulnerabilities.
• Caching: Caching frequently accessed data, like top-level comments for a post, can improve performance by reducing database load.
• Error Handling: Implement robust error handling to gracefully handle invalid requests, database errors, and other unexpected situations.
• Security: Sanitize user input to prevent XSS vulnerabilities and ensure comment content doesn't contain malicious code.

Scalability:

The comment service needs to be designed for scalability as the number of comments grows. Here are some approaches:

• Horizontal Sharding: Shard comment data by Post ID to distribute the load across multiple database servers.
• Asynchronous Processing: Implement asynchronous tasks for comment notifications or real-time updates to avoid blocking the main request flow.
• Database Optimization: Optimize database queries to efficiently retrieve nested comments and minimize latency.

By considering these details and tailoring them to your specific requirements, you can build a robust and efficient Comment Service that forms the backbone of your nested comments system.

Here's an example JSON document demonstrating nested comments in a Cosmos DB collection:

Upvotes and Downvotes for Comments

allowing users to upvote or downvote comments can be a valuable feature for your nested comments system. It can help surface the most valuable or insightful comments and promote user engagement. Here's how the Comment Service can handle this functionality:

Implementing Upvote/Downvote:

• Data Model:
• Extend the comment document in Cosmos DB to include fields for storing vote information:
• vode_count: Integer representing the total number of upvotes minus downvotes (net score).
• user_votes: Object or array storing user IDs and their vote type (upvote or downvote) for this comment.
• Upvote/Downvote Actions:
• The Comment Service should handle user requests to upvote or downvote a comment.
• Validate user authorization to ensure only registered users can vote.
• Update the vode_count field in the comment document based on the vote type (increment for upvote, decrement for downvote).
• Update the vode_count field to track the user's vote and prevent them from voting multiple times.
• Retrieving Vote Information:
• When retrieving comments, include the vode_count field to display the overall score.
• You can optionally choose to include a flag in the response indicating the current user's vote (upvoted/downvoted/not voted) based on their user ID and the user_votes information.

Spam Detection in Nested Comments System

Here are some approaches to handle potential spam comments in your nested comments system:

1. Preventative Measures:

• Captcha Verification: Implement Captcha challenges during comment creation to deter automated bots from posting spam. You can adjust the difficulty of the Captcha based on risk assessment.
• Rate Limiting: Limit the number of comments a user can post within a specific timeframe. This can prevent automated scripts from flooding the system with spam.
• Content Filtering: Implement basic content filtering rules to automatically flag comments containing known spam keywords or patterns.

2. User-driven Reporting:

• Report Button: Allow users to report comments they suspect to be spam. The reported comments can be reviewed by moderators or flagged for automatic filtering based on a certain number of reports.
• Downvote System (if implemented): While upvote/downvote is primarily for content ranking, a significant number of downvotes can also indicate potential spam.

3. Moderation:

• Human Moderation: Have a dedicated team of moderators who can review reported comments and take appropriate actions (deletion, user suspension).
• Automated Moderation (Optional): Utilize machine learning models trained on labeled spam comments to automatically filter or flag suspicious comments for further review.

Cosmos DB Integration:

• Store a "spam_flag" field in the comment document (boolean or enum) to indicate if a comment is flagged as spam.
• Utilize Cosmos DB's triggers to automatically flag comments based on predefined rules (e.g., containing blacklisted keywords).

Spam Detection in Nested Comments System

Here are some approaches to handle potential spam comments in your nested comments system:

1. Preventative Measures:

• Captcha Verification: Implement Captcha challenges during comment creation to deter automated bots from posting spam. You can adjust the difficulty of the Captcha based on risk assessment.
• Rate Limiting: Limit the number of comments a user can post within a specific timeframe. This can prevent automated scripts from flooding the system with spam.
• Content Filtering: Implement basic content filtering rules to automatically flag comments containing known spam keywords or patterns.

2. User-driven Reporting:

• Report Button: Allow users to report comments they suspect to be spam. The reported comments can be reviewed by moderators or flagged for automatic filtering based on a certain number of reports.
• Downvote System (if implemented): While upvote/downvote is primarily for content ranking, a significant number of downvotes can also indicate potential spam.

3. Moderation:

• Human Moderation: Have a dedicated team of moderators who can review reported comments and take appropriate actions (deletion, user suspension).
• Automated Moderation (Optional): Utilize machine learning models trained on labeled spam comments to automatically filter or flag suspicious comments for further review.

Cosmos DB Integration:

• Store a "spam_flag" field in the comment document (boolean or enum) to indicate if a comment is flagged as spam.
• Utilize Cosmos DB's triggers to automatically flag comments based on predefined rules (e.g., containing blacklisted keywords).

Implementing and Measuring Non-Functional Requirements

Here's a breakdown of how each non-functional requirement can be implemented and measured in your nested comments system:

Scalability:

• Implementation:
• Utilize horizontal sharding by Post ID to distribute data across multiple database servers.
• Implement caching (e.g., Redis) for frequently accessed data like top-level comments.
• Design the system with modular components that can scale independently.
• Measurement:
• Monitor resource utilization (CPU, memory) on database servers.
• Track response times for API requests under varying loads.
• Conduct performance testing with increasing numbers of concurrent users.

Reliability:

• Implementation:
• Use a highly available database technology with replication and failover mechanisms.
• Implement proper error handling and recovery routines in all system components.
• Regularly monitor system health and perform backups for disaster recovery.
• Measurement:
• Track uptime and downtime metrics (e.g., percentage of time system is available).
• Monitor the number and types of errors encountered in the system logs.
• Conduct periodic disaster recovery drills to assess recovery time objectives (RTO).

Performance:

• Implementation:
• Optimize database queries and data retrieval processes.
• Utilize caching for frequently accessed data to reduce database load.
• Implement efficient algorithms for handling nested comments.
• Measurement:
• Monitor API request response times under varying loads.
• Track page load times and user interaction delays.
• Conduct performance profiling to identify bottlenecks and optimize code.