BE-17: Data Storage Operations Research

[tecnodeveloper]

Description:
Research how all chatbot data is stored and managed in MongoDB. Understand how users, sessions, messages, feedback, and analytics data are structured. Define clean storage strategy so system is scalable, fast, and easy to query.

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User Story

Given chatbot generates continuous data
When user interacts with system
Then all data should be stored properly for retrieval and analytics

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Tasks

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Understand Data Types

1. Identify All Data Generated

   • User data (signup/login)
   • Chat sessions
   • Messages (user + bot)
   • Feedback (ratings)
   • Analytics logs

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MongoDB Fundamentals

2. Study Database Structure

   • Collections vs documents
   • Embedded vs referenced data
   • Indexing basics

3. Understand Query Patterns

   • Fetch by user_id
   • Fetch by session_id
   • Time-based queries

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Core Collections Design

4. Users Collection

   • name
   • email
   • password (hashed)
   • auth_provider (local/google)

5. Sessions Collection

   • session_id
   • user_id
   • created_at
   • updated_at

6. Messages Collection (or embedded)

   • session_id
   • role (user/assistant)
   • content
   • timestamp

7. Feedback Collection

   • message_id
   • rating (1–5)
   • correctness
   • length feedback

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Storage Strategy Design

8. Decide Structure Type

   • Embedded messages inside sessions OR
   • Separate messages collection

9. Compare Approaches

   • Embedded → faster reads
   • Separate → scalable writes

10. Final Decision

• Choose best structure for chatbot scale

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Indexing Strategy

11. Create Indexes

• user_id index
• session_id index
• timestamp index

12. Optimize Queries

• Fast session retrieval
• Fast message loading

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Data Flow Design

13. Write Flow

• User sends message
• Save message
• Save response
• Update session

14. Read Flow

• Load session
• Fetch messages
• Send to frontend

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Analytics Storage

15. Track Metrics

• Response time
• Accuracy rating
• Session length
• Topic data

16. Store Logs

• Separate analytics collection
• Timestamp-based logs

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Scalability Planning

17. Large Data Handling

• Pagination for messages
• Limit history size
• Archive old sessions

18. Performance Optimization

• Avoid heavy nested documents
• Use selective queries

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Security Considerations

19. Data Protection

• Secure user data
• Hash passwords
• Avoid exposing sensitive fields

20. Access Control

• User can only access own data
• Session-level protection

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Backup & Recovery

21. Backup Strategy

• Daily backups
• MongoDB export strategy

22. Recovery Plan

• Restore collections
• Prevent data loss

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Postman Testing 🧪

23. Setup Postman

• Test data creation APIs
• Test retrieval APIs

24. Validate Storage

• Create session
• Send messages
• Check DB updates

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Acceptance Criteria

• All data structures defined
• MongoDB schema finalized
• Indexing strategy planned
• Storage flow defined
• Scalable design ensured

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Testing Steps

1. Simulate chat flow
2. Check MongoDB collections
3. Validate query performance
4. Test large message history
5. Verify data consistency

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Definition of Done

• Data storage architecture finalized
• MongoDB structure optimized
• Scalable design ready