Requirements

Functional Requirements:

• Allow users to post top-level comments and replies in a nested hierarchy.
• Enable users to edit or update their own comments and delete them; deletion may be restricted if the comment has replies.
• Provide real-time updates or notifications when someone replies to their comment or posts in a discussion they follow.
• Allow users to upvote or downvote comments to indicate quality or popularity; vote counts influence sorting.
• Enable sorting comments chronologically or by popularity.
• Enforce a maximum depth for nested replies.

Non-Functional Requirements:

• 1.Performance: The system must deliver low-latency reads (<50ms) as comments are highly read-heavy.
• 2.Scalability: Must handle massive read spikes and allow horizontal scaling.
• 3.Availability: The system should be highly available (99.99% uptime).
• 4.Reliability: No loss of comments or votes once confirmed.
• 5.Fault tolerance: Sub-system failures (like notifications) shouldn't break the core commenting ability.
• 6.Resilience: Graceful degradation during extreme traffic surges.

API Design

1.POST /v1/comments

* Headers: `Idempotency-Key` (UUID) to handle duplicate retries.

* Body: `{ "parent_id": "optional", "content": "text" }`

2.GET /v1/comments/{post_id}?sort_by=popularity&page=1

3.PUT /v1/comments/{comment_id}

4.DELETE /v1/comments/{comment_id}

5.POST /v1/comments/{comment_id}/vote

* Headers: `Idempotency-Key` (UUID).

* Body: `{ "type": "upvote|downvote" }`

High-Level Design

Describe the overall system architecture. Identify the main components needed to solve the problem end-to-end. Use the diagramming tool to create a block diagram.

Detailed Component Design

1.Scalability, Availability, and Performance

• Database: A Relational Database (like PostgreSQL) is ideal for storing hierarchical data using an adjacency list or a materialized path (e.g., LTree extension in Postgres) for lightning-fast depth queries and sorting. We scale this horizontally for reads by using a primary-replica architecture where all heavy GET requests hit read replicas.
• Caching layer: A distributed Redis cache sits in front of the database to serve comment threads. Given that comment systems generally see a 100:1 read-to-write ratio, this cache drastically improves performance and reduces the load on our primary database.

2.Reliability, Fault Tolerance, and Resilience

• Asynchronous processing: When a user comments or votes, the Comment Service executes the write to the database and simultaneously publishes an event to Kafka. The Notification Service consumes this asynchronously. If the Notification Service fails, Kafka retains the messages, ensuring resilience and reliability (no data loss) while the core system remains available.
• Circuit breakers: The API Gateway is equipped with circuit breaker patterns. If a backend service is struggling, it fails fast rather than exhausting connection pools, ensuring the rest of the ecosystem survives.

3.Handling Critical Edge Cases

1.The "Thundering Herd" Scenario

• Problem: When a cache key for a highly popular post's comments expires, thousands of concurrent read requests will bypass the cache and hit the database simultaneously, potentially causing an outage.
• Solution: We implement a Cache Stampede Prevention strategy using a Mutex lock via Redis SETNX. Only the first thread that acquires the lock is permitted to query the database and repopulate the cache. All other requests either wait briefly and retry the cache, or are served slightly stale data. We also add random "jitter" to cache TTLs so keys don't expire simultaneously.

2.Idempotent Case

• Problem: Network partitions or client-side glitches can cause a user to retry submitting a comment or a vote, resulting in duplicates.
• Solution: All state-mutating APIs require an Idempotency-Key in the HTTP header. The API Gateway or the service itself checks this key against a fast Key-Value store (like Redis) containing recently processed requests. If a match is found, the system immediately returns the cached success response without reprocessing the write, ensuring data integrity.

3.Rate Limit Case

• Problem: Malicious actors or bots might flood the system with comment spam or vote manipulation, degrading performance for everyone.
• Solution: We strictly enforce Rate Limiting at the API Gateway using the Token Bucket algorithm. We limit actions based on User ID or IP address (e.g., maximum of 5 comments per minute). Requests exceeding this threshold instantly receive an HTTP 429 Too Many Requests status, protecting downstream microservices and maintaining system stability.