Refactor observation data pipeline to serialize once after collection and
keep json.RawMessage throughout storage and API responses. This eliminates
double-serialization and makes DB round-trips lossless.
Add the persistence layer for the checker system:
- Storage interfaces (CheckPlanStorage, CheckerOptionsStorage,
CheckEvaluationStorage, ExecutionStorage, ObservationSnapshotStorage,
SchedulerStateStorage) in the usecase/checker package
- KV-based implementations for LevelDB/Oracle NoSQL/InMemory backends
- Integrate checker storage into the main Storage interface
- Add tidy methods for checker entities (plans, configurations,
evaluations, executions, snapshots, observation cache) and
secondary index cleanup
When TidyUsers removes an AuthUser with unverified email and no login
after 7 days, the corresponding User record was left orphaned in the
database. Now DeleteUser is called before dropping the AuthUser.
Add Err() method to storage.Iterator interface and implement it on all
backends (LevelDB, PostgreSQL, in-memory). Update kvtpl.KVIterator.Err()
to fall through to the underlying iterator's error. Check Err() after
every iteration loop across kvtpl and usecase layers to surface I/O
failures and rate-limit errors instead of returning truncated data.
- Moved usecases into internal/usecase
- Renamed internal/utils to internal/helpers for better naming semantics
- Clarified separation between domain model, usecases, adapters, and web/API
