Implement RecognitionTierService eligibility evaluation
epic-achievement-badges-services-task-011 — Implement RecognitionTierService.evaluateEligibility(mentorId, orgId) which queries current mentor stats via PeerMentorStatsAggregator and compares against tier thresholds loaded from BadgeConfigurationService. Return TierEligibility with the highest eligible tier and delta-to-next-tier values. Logic must handle organisations with custom tier thresholds and orgs with tiers disabled.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 4 - 323 tasks
Can start after Tier 3 completes
Implementation Notes
Define a TierEligibility value class with fields: eligibleTier (nullable TierLevel enum), tiersEnabled (bool), deltaToNextTier (Map
Keep evaluateEligibility a pure function that accepts stats and thresholds as parameters internally so it is trivially unit-testable without mocking the service collaborators. The public method simply resolves collaborators and delegates to the pure function.
Testing Requirements
Unit tests (flutter_test) covering: (1) correct highest-tier selection when mentor qualifies for multiple tiers, (2) correct delta calculation for each metric, (3) tiers-disabled org returns null tier + tiersEnabled=false, (4) custom org thresholds override global defaults, (5) MentorNotFoundException thrown for unknown mentorId, (6) OrgNotFoundException thrown for unknown orgId, (7) PeerMentorStatsAggregator called exactly once. Mock both PeerMentorStatsAggregator and BadgeConfigurationService. Target 90%+ branch coverage on evaluateEligibility business logic.
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.