Implement tier assignment and revocation with award period
epic-achievement-badges-services-task-012 — Implement RecognitionTierService.assignTier(mentorId, tier, orgId) and revokeTier(mentorId, orgId). Assignment writes a TierAssignment record with award_period_start, award_period_end (derived from org's reporting period), and assigned_at. Revocation marks the record inactive but preserves history. Both operations are idempotent — assigning the same tier twice is a no-op.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 5 - 253 tasks
Can start after Tier 4 completes
Implementation Notes
Use a Supabase RPC function for the assignment upsert to guarantee atomicity — avoid SELECT + INSERT in Dart code which is subject to TOCTOU races. The RPC should implement INSERT ... ON CONFLICT (mentor_id, org_id, award_period_start, tier) DO NOTHING and return the existing or newly created row. Define OrgReportingPeriod as a value class with start/end computed from org config (e.g., calendar year Jan 1 – Dec 31, or fiscal year).
Store reporting period boundaries as ISO 8601 date strings in UTC. For revocation, use a Supabase UPDATE with a WHERE is_active=true filter — if 0 rows updated, treat as no-op. Soft delete pattern: never DELETE from tier_assignments; always preserve history for audit and gamification queries.
Testing Requirements
Unit tests (flutter_test) with mocked Supabase client covering: (1) successful assignment returns correct TierAssignment with populated period dates, (2) duplicate assignment is a no-op and returns existing record, (3) assigning a new tier supersedes the previous active assignment, (4) successful revocation sets is_active=false, (5) revocation on mentor with no active assignment completes without error, (6) transaction rollback is triggered on Supabase error during assignment. Integration test: verify unique constraint prevents duplicate active assignments at the database level.
peer-mentor-stats-aggregator must compute streaks and threshold counts across potentially hundreds of activity records per peer mentor. Naive queries (full table scans or N+1 patterns) will cause slow badge evaluation, especially when triggered on every activity save for all active peer mentors.
Mitigation & Contingency
Mitigation: Design aggregation queries using Supabase RPCs with window functions or materialised views from the start. Add database indexes on (peer_mentor_id, activity_date, activity_type) before writing any service code. Profile all aggregation queries against a dataset of 500+ activities during development.
Contingency: If query performance is insufficient at launch, implement incremental stat caching: maintain a peer_mentor_stats snapshot table updated on each activity insert via a database trigger, so the aggregator reads from pre-computed values rather than scanning raw activity rows.
badge-award-service must be idempotent, but if two concurrent edge function invocations evaluate the same peer mentor simultaneously (e.g., from a rapid double-save), both could pass the uniqueness check before either commits, resulting in duplicate badge records.
Mitigation & Contingency
Mitigation: Rely on the database-level uniqueness constraint (peer_mentor_id, badge_definition_id) as the final guard. In the service layer, use an upsert with ON CONFLICT DO NOTHING and return the existing record. Add a Postgres advisory lock or serialisable transaction for the award sequence during the edge function integration epic.
Contingency: If duplicate records are discovered in production, run a deduplication migration to remove extras (keeping earliest earned_at) and add a unique index if not already present. Alert engineering via Supabase database webhook on constraint violations.
The badge-configuration-service must validate org admin-supplied criteria JSON on save, but the full range of valid criteria types (threshold, streak, training-completion, tier-based) may not be fully enumerated during development, leading to either over-permissive or over-restrictive validation that frustrates admins.
Mitigation & Contingency
Mitigation: Define a versioned Dart sealed class hierarchy for CriteriaType before writing the validation logic. Review the hierarchy with product against all known badge types across NHF, Blindeforbundet, and HLF before implementation. Build the validator against the sealed class so new criteria types require an explicit code addition.
Contingency: If admins encounter validation rejections for legitimate criteria, expose a 'criteria_raw' escape hatch (JSON passthrough, admin-only) with a product warning, and schedule a sprint to formalise the new criteria type properly.