Update the `process_approval_transaction` function to utilize the new `error_code` and `item_details` returned by the enhanced `validate_submission_items_for_approval` function. This will improve error handling and debugging by providing more specific information when validation fails.
This commit implements Phase 3 of the Sacred Pipeline, focusing on enhanced error handling. It includes:
- **Transaction Status Polling Endpoint**: A new edge function `check-transaction-status` allows clients to poll the status of moderation transactions using idempotency keys.
- **Expanded Error Sanitizer Patterns**: The `src/lib/errorSanitizer.ts` file has been updated with more comprehensive patterns to remove sensitive information from error messages, making them safer for display and logging. User-friendly replacements for common errors are also included.
- **Rate Limiting for Submission Creation**: Client-side rate limiting has been implemented in `src/lib/submissionRateLimiter.ts` and applied to key submission functions within `src/lib/entitySubmissionHelpers.ts` (e.g., `submitParkCreation`, `submitRideCreation`, `submitParkUpdate`, `submitRideUpdate`) to prevent abuse and accidental duplicate submissions.
Corrected an import error in the `run-cleanup-jobs` edge function. The function was attempting to import from a non-existent `../_shared/cors.ts` file. This has been resolved by defining the `corsHeaders` inline within the function, aligning with the pattern used in other edge functions.
The user approved the use of the Lovable tool. This commit reflects the successful connection and execution of the tool, which was used to implement Phase 1 of the Critical Database Fixes for the Sacred Pipeline. The fixes include adding validation, error logging, cascade deletes, and error boundaries.
Completed Phase 2 of the critical security fixes, enhancing database integrity. This includes adding UNIQUE constraints for slugs, implementing date precision validation, and establishing trigger-based validation for submission item dependencies. Data integrity checks for dates, ratings, and numeric fields have also been added, along with performance indexes.
The AI confirmed that the migration to implement Phase 1 of the critical security fixes was successful. It then ran a security scan and a Supabase linter, both of which returned zero issues. The AI has now completed Phase 1, which involved securing 13 tables with comprehensive RLS policies, MFA enforcement, ban checks, and proper access control. The AI has also provided suggestions for the next steps: verifying Phase 1 success and starting Phase 2, creating security documentation, or implementing Phase 3.
The AI has successfully completed Phase 3, securing all 7 submission tables with appropriate RLS policies. It then ran a security scan and the Supabase linter, both of which returned zero issues. This marks the completion of all security phases, with all tables and functions now secured.
Approve migration to fix RLS policies for ride_submissions, ride_model_submissions, and timeline_event_submissions. This includes dropping existing permissive policies and creating new ones to restrict access to users' own submissions and moderators.
The AI successfully fixed the `validate_slug_format` function by adding `SET search_path = public`.
The security linter now shows no issues.
A subsequent security scan revealed 9 findings, but 5 were identified as outdated due to previous RLS fixes in Phase 1 and Phase 2.
The remaining findings requiring attention are:
- `profiles` (needs field-level filtering)
- `contact_submissions` (needs RLS verification)
- `park_submissions` (needs RLS verification)
- `company_submissions` (needs RLS verification)
- `photo_submissions` (needs RLS verification)
The AI has completed Phase 2 of RLS security fixes, securing tables such as `user_roles`, `reviews`, `reports`, `submission_items`, `user_blocks`, and `user_preferences`. It also noted a remaining minor security warning regarding a mutable function search path.
The AI has addressed the remaining security warnings by ensuring all `SECURITY DEFINER` functions have `SET search_path = public`. A full security scan was then performed, revealing pre-existing issues with RLS policies on several tables, including `profiles`, `user_roles`, and `content_submissions`. These issues were not introduced by the recent changes but were uncovered by the scan. The AI will inform the user about these findings.
Apply `SET search_path = public` to the `is_user_banned` function to resolve lingering security warnings. This ensures all `SECURITY DEFINER` functions have a properly defined search path, enhancing security and preventing potential issues.
Approve and implement the Supabase migration for the pipeline monitoring alert system. This includes expanding alert types, adding new monitoring functions, and updating existing ones with escalating thresholds.
Implement comprehensive error handling and robustness measures across the entire pipeline as per the detailed plan. This includes database-level security, client-side validation, scheduled maintenance, and fallback mechanisms for edge function failures.
Approve and apply the latest set of database migrations for Phase 4: Application Boundary Hardening. These migrations include orphan image cleanup, slug validation triggers, monitoring and alerting infrastructure, and scheduled maintenance functions.
The AI has implemented the Phase 3 plan, which includes adding approval failure monitoring to the existing error monitoring page, extending the ErrorAnalytics component with approval metrics, adding performance indexes, and creating the ApprovalFailureModal component.
Applies Phase 2 resilience improvements including slug uniqueness constraints, foreign key validation, and rate limiting. This includes new database migrations for slug uniqueness and foreign key validation, and updates to the edge function for rate limiting.
Implement the destructive migration plan to remove the old approval flow entirely. This includes deleting the legacy edge function, removing the toggle component, simplifying frontend code, and updating documentation.
Implement all phases to fix session variable pollution by changing `is_local` to `true` in the `create_submission_with_items` database function and the `process-selective-approval` edge function. This ensures session variables are transaction-scoped, preventing data corruption and attribution errors. Includes database migration, edge function updates, and monitoring for attribution mismatches.
Implement orphaned image cleanup, temp refs cleanup, deadlock retry, and lock cleanup. These fixes address critical areas of data integrity, resource management, and system resilience within the submission pipeline.
Corrected database migration for idempotency keys to resolve issues with partial indexes using `now()`. The migration now includes the `submission_idempotency_keys` table, indexes, RLS policies, a cleanup function, and an `idempotency_stats` view.
Wrap edge function approval loop in database transaction to prevent partial data on failures. This change ensures atomicity for approval operations, preventing inconsistent data states in case of errors.
Implement Phase 1 of the JSONB violation fix by creating the `park_submission_locations` table. This includes migrating existing data from `park_submissions.temp_location_data` and updating relevant code to read and write to the new relational table. The `temp_location_data` column will be dropped after data migration.
Implement changes to enhance the display of timeline event submissions in the moderation queue. This includes updating the `get_submission_items_with_entities` function to include timeline event data, creating a new `RichTimelineEventDisplay` component, and modifying `SubmissionItemsList` and `TimelineEventPreview` components to utilize the new display logic.
Refactor park updates, ride updates, and timeline event submissions to use dedicated relational tables instead of JSON blobs in `submission_items.item_data`. This enforces the "NO JSON IN SQL" rule, improving queryability, data integrity, and consistency across the pipeline.
Implement plan to fix ride model technical specifications pipeline. This includes creating a new migration for the `ride_model_submission_technical_specifications` table, updating `entitySubmissionHelpers.ts` to handle insertion of technical specifications, and modifying the edge function `process-selective-approval/index.ts` to fetch these specifications. This ensures no data loss for ride model technical specifications.
Implement the plan to fix critical data loss in ride submissions. This includes:
- Storing ride technical specifications, coaster statistics, and name history in submission tables.
- Adding missing category-specific fields to the `ride_submissions` table via a new migration.
- Updating submission helpers and the edge function to include these new fields.
- Fixing the park location Zod schema to include `street_address`.
Implement the plan to add `street_address` to the location creation logic within the `process-selective-approval` edge function. This ensures that `street_address` is preserved when approving composite submissions, completing the end-to-end pipeline for this field.
Adds a street_address column to the locations table and updates the LocationSearch component to capture, store, and display full street addresses. This includes database migration, interface updates, and formatter logic.
