Detailed Component Design

Deep dive into 2-3 key components. Explain how they work, how they scale, discuss tradeoffs, capacity, and any relevant algorithms or data structures.

NOsql desing -

comments has a idempotent key. Separate tables has a GSI indexes with the comment_id of the parent which means it can load just data it needs very easily to avoid a full tree scan.

on demand loading based on depth_level: number and comment_id = uuid of parent comment.

easily extendable just add a depth table 6 if we want a new depth

Root-Level Comments Table: Store attributes like comment ID, user ID, timestamp, and any relevant metadata. This will allow for quick access to the main comments.
Replies Tables: Create separate tables for depth 1 ... depth 5 replies, linking them back to their parent comments using foreign keys. This will help maintain a clear hierarchy and facilitate efficient queries.
Indexing: Utilize global secondary indexes (GSIs) to optimize read performance based on common query patterns, such as retrieving all replies for a specific comment.
cursor based retrieval: Users can load data on demand so we have a cursor based retrieval for the data.
Partitioning in DynamoDB is handled automatically
Comment Tree Retrieval - very easily managed by the separate tables each level of tables has its own table in dynamoDB
its possible to delete comment we have a separate worker to delete all comments related to this comment. So our main orchestrator will create an event to delete all related comments to they dont end up orphaned.

Cache - all coments are editable - if somebody reads a cache we create a cache record for the most frequently accessed comments. TTL could be 1hour if somebody edit the comment we have eventual consistency we first in the main service read the comment edit it and then go to cache read the record and edit it also there.

Notification service - has its workers subscribed to queue main service is sending the notification data here. It sends commend_id and our notification service get the data of all users it should notify and creates a new messages per notification for a notification_dispatcher worker that then sends the notification to users one by one.

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