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Our content import logic relies on legacy code, which is neither scalable nor maintainable. This spike aims to identify the necessary changes to enhance the current implementation, including considering a full refactoring if required. Specifically, we aim to improve:
1. Error Handling:
Implement comprehensive validation mechanisms.
Ensure clear, actionable, and user-friendly error messages to assist with troubleshooting.
Enhance reliability and readability for end users.
2. Custom Error Handling Support:
Introduce the capability to handle errors flexibly by accepting a parameter to specify whether the import process should stop or continue upon encountering an error.
3. Performance:
Evaluate the ability to handle large datasets efficiently without significant performance degradation.
Main Objective:
Provide clear recommendations for improving the import logic, along with a breakdown of tasks and implications for both partial and full refactoring options. The proposed changes must:
Be compatible with the existing Import Content feature (UI) to ensure seamless integration.
Consider future integration with the CLI, ensuring the design supports use cases for both UI and CLI implementations.
Once, this research concludes, we should be able to support the improvements suggested here (see items 2, 3 and 4)
Proposed Objective
Core Features
Proposed Priority
Priority 3 - Average
Acceptance Criteria
Deliver a detailed list of tasks required for the proposed changes, distinguishing between full refactoring and incremental improvements.
Include a high-level recommendation for the approach to take (e.g., full refactor or targeted fixes) with an explanation of the trade-offs and implications.
Outline potential risks and expected benefits for the chosen approach.
Validate that the proposed solutions are compatible with the existing Import Content feature (UI).
Ensure the recommended approach aligns with potential CLI integration requirements.
Expected Deliverables
A document or comment on this card outlining:
Key findings from the analysis.
Proposed changes and their justifications.
Task breakdown with prioritization.
A clear recommendation for the next steps based on feasibility, impact, and long-term maintainability.
External Links... Slack Conversations, Support Tickets, Figma Designs, etc.
No response
Assumptions & Initiation Needs
No response
Quality Assurance Notes & Workarounds
No response
Sub-Tasks & Estimates
No response
The text was updated successfully, but these errors were encountered:
Parent Issue
No response
Task
Our content import logic relies on legacy code, which is neither scalable nor maintainable. This spike aims to identify the necessary changes to enhance the current implementation, including considering a full refactoring if required. Specifically, we aim to improve:
1. Error Handling:
2. Custom Error Handling Support:
Introduce the capability to handle errors flexibly by accepting a parameter to specify whether the import process should stop or continue upon encountering an error.
3. Performance:
Evaluate the ability to handle large datasets efficiently without significant performance degradation.
Main Objective:
Provide clear recommendations for improving the import logic, along with a breakdown of tasks and implications for both partial and full refactoring options. The proposed changes must:
Once, this research concludes, we should be able to support the improvements suggested here (see items 2, 3 and 4)
Proposed Objective
Core Features
Proposed Priority
Priority 3 - Average
Acceptance Criteria
Expected Deliverables
A document or comment on this card outlining:
A clear recommendation for the next steps based on feasibility, impact, and long-term maintainability.
External Links... Slack Conversations, Support Tickets, Figma Designs, etc.
No response
Assumptions & Initiation Needs
No response
Quality Assurance Notes & Workarounds
No response
Sub-Tasks & Estimates
No response
The text was updated successfully, but these errors were encountered: