Implement Bufdir Payload Validation and Error Reporting
epic-bufdir-report-export-core-backend-task-007 — Add a validation pass to the Bufdir format serializer that checks the produced payload against Bufdir submission rules: required fields present, numeric totals non-negative, date range valid, org number format correct, and total participant count plausible. Return a structured validation result with field-level errors so the edge function can surface actionable feedback in the preview response without producing a malformed export.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 4 - 323 tasks
Can start after Tier 3 completes
Implementation Notes
Structure the validator as a list of rule functions, each with signature `List
Define error codes as Dart constants (e.g. `const String kMissingOrgNumber = 'MISSING_ORG_NUMBER'`) so the Flutter client can match on codes programmatically for localised display. The plausibility threshold should be read from an environment variable (`BUFDIR_MAX_PARTICIPANTS_PER_MENTOR`) with a fallback default of 5000. Keep validation logic free of any I/O — pure functions only.
Testing Requirements
Unit tests (dart:test) for each validation rule in isolation: (1) valid payload passes all checks, (2) missing organisation_number returns error with correct field path and code, (3) malformed org number (8 digits, letters) returns error, (4) start date after end date returns error, (5) future dates return error, (6) period > 12 months returns error, (7) negative activity_count returns error, (8) participant_count > threshold returns warning not error, (9) all-zero totals returns EMPTY_REPORT warning, (10) multiple errors on one payload are all reported (not fail-fast). Integration test: pass the serializer output from task-006 tests through the validator and assert no errors for well-formed data. Target 100% branch coverage for each individual rule function.
Supabase Edge Functions have a default execution timeout. For large national-scope exports aggregating tens of thousands of activities across 1,400 chapters, the edge function may time out before completing, leaving coordinators with a failed export and no partial output.
Mitigation & Contingency
Mitigation: Optimise the aggregation SQL using pre-materialised aggregation views or RPC functions that run inside the database rather than iterating records in Deno. Profile query execution time against realistic production data volumes early. Request an elevated timeout limit from Supabase if needed. Implement progress checkpointing so the export can be resumed from the last completed aggregation batch.
Contingency: For organisations exceeding a configurable threshold (e.g. >5,000 activities), switch to an asynchronous export pattern: the edge function writes a 'pending' audit record and enqueues the job; the client polls for completion and is notified via Supabase Realtime when the file is ready.
Server-side PDF generation in a Deno Edge Function environment restricts library choices. Many popular PDF libraries require Node.js APIs not available in Deno, or produce large bundle sizes that exceed edge function limits. Choosing the wrong library could block the entire PDF generation path.
Mitigation & Contingency
Mitigation: Spike PDF library selection as the first task of this epic, evaluating at least two Deno-compatible options (e.g. pdf-lib, jsPDF with Deno compatibility shim). Test bundle size and basic rendering before committing to an implementation. Document the chosen library's constraints.
Contingency: If no suitable Deno-native PDF library is found, generate a well-structured HTML report from the edge function and use a headless Chromium service (e.g. Browserless, Gotenberg) for HTML-to-PDF conversion, or temporarily ship CSV-only export while the PDF path is resolved.
Peer mentors affiliated with multiple chapters (a documented NHF scenario) must not be double-counted in participant totals. Incorrect deduplication logic would overreport participation figures to Bufdir, which could be discovered during audit and damage organisational credibility.
Mitigation & Contingency
Mitigation: Define and document the deduplication contract explicitly before coding: deduplication is per-person per-period, not per-activity. Build dedicated unit tests with fixtures containing the exact multi-chapter membership patterns described in NHF's documentation. Have a NHF representative validate test fixture outputs against known-good manual counts.
Contingency: If deduplication logic produces results that cannot be verified against manual counts before launch, surface a deduplication warning in the export preview listing the affected peer mentor IDs, and require explicit coordinator acknowledgement before finalising the export.