fix: Remove filter cache

[dblythy]

New Pull Request Checklist

• I am not disclosing a vulnerability.
• I am creating this PR in reference to an issue.

Issue Description

Closes: #2711

Approach

TODOs before merging

• Add tests
• Add changes to documentation (guides, repository pages, in-code descriptions)

Summary by CodeRabbit

• Refactor
  • Improved preference management by removing in-memory caching. Preferences are now always read directly from local storage, ensuring the most up-to-date information is used. No changes to the way you interact with preferences.

[parse-github-assistant]

I will reformat the title to use the proper commit message syntax.

[parse-github-assistant]

🚀 Thanks for opening this pull request! We appreciate your effort in improving the project. Please let us know once your pull request is ready for review.

[parseplatformorg]

🎉 Snyk checks have passed. No issues have been found so far.

✅ security/snyk check is complete. No issues have been found. (View Details)

[uffizzi-cloud]

Uffizzi Ephemeral Environment deployment-63188

⌚ Updated Apr 27, 2025, 08:10 UTC

☁️ https://app.uffizzi.com/github.com/parse-community/parse-dashboard/pull/2769

📄 View Application Logs etc.

What is Uffizzi? Learn more

[coderabbitai]

Walkthrough

The update removes the in-memory caching mechanism for class preferences in the src/lib/ClassPreferences.js module. Now, all preference reads and writes interact directly with localStorage without using an intermediate cache. The functions for updating, retrieving, and getting all preferences retain their original signatures and error handling, but any logic related to the cache object has been deleted.

Changes

File(s)	Change Summary
src/lib/ClassPreferences.js	Removed in-memory caching of preferences; all reads/writes now go directly to localStorage.

Assessment against linked issues

Objective	Addressed	Explanation
Prevent saving a filter from overwriting unloaded filters in other dashboard instances (#2711)	❌	The change removes caching but does not implement merging or additive saving logic to prevent overwrites.

✨ Finishing Touches

• 📝 Generate Docstrings

---

🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

• Review comments: Directly reply to a review comment made by CodeRabbit. Example:
  • I pushed a fix in commit <commit_id>, please review it.
  • Generate unit testing code for this file.
  • Open a follow-up GitHub issue for this discussion.
• Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
  • @coderabbitai generate unit testing code for this file.
  • @coderabbitai modularize this function.
• PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
  • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
  • @coderabbitai read src/utils.ts and generate unit testing code.
  • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
  • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

• @coderabbitai pause to pause the reviews on a PR.
• @coderabbitai resume to resume the paused reviews.
• @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
• @coderabbitai full review to do a full review from scratch and review all the files again.
• @coderabbitai summary to regenerate the summary of the PR.
• @coderabbitai generate docstrings to generate docstrings for this PR.
• @coderabbitai generate sequence diagram to generate a sequence diagram of the changes in this PR.
• @coderabbitai resolve resolve all the CodeRabbit review comments.
• @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
• @coderabbitai help to get help.

Other keywords and placeholders

• Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
• Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
• Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

• You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
• Please see the configuration documentation for more information.
• If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

• Visit our Documentation for detailed information on how to use CodeRabbit.
• Join our Discord Community to get help, request features, and share feedback.
• Follow us on X/Twitter for updates and announcements.

[coderabbitai]

Actionable comments posted: 1

📜 Review details

Configuration used: CodeRabbit UI
Review profile: ASSERTIVE
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between bcce5c5 and 530645e.

📒 Files selected for processing (1)

• src/lib/ClassPreferences.js (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (1)

• GitHub Check: Docker linux/amd64

[coderabbitai]

🧹 Nitpick (assertive)

Removing cache simplifies the code but may slightly impact performance

The code now directly returns the parsed JSON from localStorage without using an in-memory cache. This simplification aligns with the PR objective to remove the filter cache, which makes the code more predictable and reduces memory usage.

While this change simplifies the code, it might slightly impact performance as each preference read now requires parsing JSON from localStorage. For most use cases, this performance difference would be negligible, but if preference fetching becomes a performance bottleneck in the future, consider implementing a more sophisticated caching strategy with proper invalidation.