Add offline caching layer to Stats Repository
epic-activity-statistics-dashboard-data-foundation-task-009 — Extend the Stats Repository with a local Hive or shared_preferences cache so that the last-fetched StatsSnapshot and PeerMentorStatRow list are available when the device is offline. Cache key must include the TimeWindow enum value and chapter ID set hash. Stale-while-revalidate strategy: return cached data immediately, then silently re-fetch and notify if data changed. Cache TTL is 15 minutes. Expose a clearCache() method called by the Stats Cache Invalidator on activity insert.
Acceptance Criteria
Technical Requirements
Execution Context
Tier 5 - 253 tasks
Can start after Tier 4 completes
Implementation Notes
Use the Decorator pattern: `CachedStatsRepository implements StatsRepository` and takes an inner `StatsRepository` as a constructor parameter. Register via Riverpod as `cachedStatsRepositoryProvider` that overrides `statsRepositoryProvider`. Stale-while-revalidate is best implemented with a `StreamController.broadcast()` per cache key — the repository emits the cached value synchronously, then emits again after the network response if values differ. Use `jsonEncode`/`jsonDecode` for serialisation rather than Hive TypeAdapters to avoid code-gen complexity for this cache layer.
Cache key hashing: sort `chapterIds` alphabetically before hashing to ensure key stability regardless of list order. Eviction on startup: iterate all keys in the Hive box and delete entries where `cachedAt` is older than 1 hour — this prevents unbounded disk usage over time. The `connectivity_plus` package provides a stream of `ConnectivityResult`; check it before attempting network fetch and set `CacheSource.stale` on the returned result wrapper.
Testing Requirements
Unit tests using `mocktail`: (1) cache miss — delegates to inner repository and stores result; (2) cache hit within TTL — returns cached data and fires background fetch; (3) cache hit beyond TTL — treats as miss; (4) offline state — returns stale cache with `CacheSource.stale` flag; (5) clearCache() empties all stats entries; (6) background re-fetch emits update when data changes. Use a fake `HiveInterface` or in-memory map for Hive in unit tests. Integration test: cold-start app, fetch data, disable network, re-open stats screen — verify cached data is shown within 50 ms.
Materialized views over large activity tables may have refresh latency exceeding the 2-second SLA under high insert load, causing stale data to appear on the dashboard immediately after a peer mentor registers an activity.
Mitigation & Contingency
Mitigation: Design the materialized view refresh trigger to run asynchronously via a Supabase Edge Function rather than a synchronous trigger, and set a maximum staleness tolerance of 5 seconds documented in the feature spec. Add a CONCURRENTLY refresh strategy so reads are never blocked.
Contingency: If refresh latency cannot meet SLA, fall back to a regular (non-materialized) view for the dashboard and accept slightly higher query cost per request. Revisit materialized approach once Supabase pg_cron or background workers are available.
The aggregation counting rules for the dashboard may diverge from those used in the Bufdir export pipeline (e.g., which activity types count, how duplicate registrations are handled), creating a reconciliation burden for coordinators at reporting time.
Mitigation & Contingency
Mitigation: Run the BufDir Alignment Validator against a shared reference dataset before any view is merged to main. Encode the counting rules as a shared Supabase function called by both the stats views and the export query builder so there is a single source of truth.
Contingency: If divergence is discovered post-launch, ship a visible banner on the dashboard stating that numbers are indicative and may differ from the export until the reconciliation fix is deployed. Prioritize the fix as a P0 defect.
Multi-chapter coordinators (up to 5 chapters per NHF requirement) require RLS policies that filter on an array of chapter IDs, which is more complex than single-value RLS and could be misconfigured, leaking data across chapters or blocking legitimate access.
Mitigation & Contingency
Mitigation: Write integration tests that verify cross-chapter isolation for a coordinator assigned to chapters A and B cannot see data from chapter C. Use parameterized RLS policies with auth.uid()-based chapter lookup to avoid hardcoded values.
Contingency: If RLS misconfiguration is detected in testing, temporarily restrict coordinator queries to single-chapter scope (coordinator's primary chapter) and ship multi-chapter support as a fast-follow patch once RLS logic is verified.