Verify metadata filter results against a simple reference evaluator
The database query for arrays and null values differs subtly from the API contract, making support reports inconsistent.
- Focused work estimate
- 4h + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Differential testing · SQL correctness
Estimated field mix
- Quality engineering50%
- Database engineering50%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional records portal stores every property in one JSON column. Queries disagree about missing values, and malformed records make ordinary filters fail.
Setup prerequisites
- Create synthetic document metadata with malformed and legacy versions.
- Use migrations and a local query API.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
- AMETA-101 · Separate stable document identity fields from flexible attributes
- AMETA-102 · Define versioned metadata schemas with explicit size and depth limits
- AMETA-103 · Choose distinct semantics for absent, null and empty metadata values
- AMETA-104 · Extract stable document fields through an additive migration
- AMETA-106 · Keep document metadata updates under optimistic revision control
- AMETA-107 · Build parameterized metadata filters with an allowlisted operator set
Acceptance criteria
- Author a bounded corpus covering all declared states.
- Compare SQL result identities with a simple reference evaluator.
- Report missing and extra identities separately.
Implementation constraints
- Use identical synthetic inputs and keep the reference implementation straightforward.
Verification to include
- Compare supported filters across the complete corpus.
- Mutate one SQL null condition and verify the differential check finds the mismatch.
Deliverables
- Differential query harness
Rollout and recovery
Require the harness for filter changes; keep the prior query compiler available for rollback.
Value of the work
For the engineer: Practice relational/JSON boundaries, versioned validation and query semantics.
For the team: Review a data model that stays inspectable while allowing controlled variation.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.