Build parameterized metadata filters with an allowlisted operator set
A query endpoint interpolates client-provided JSON paths and operators into SQL.
- Focused work estimate
- 5h + prerequisites
- Priority in the scenario
- High
- Engineering practice
- SQL safety · Query compilation
Estimated field mix
- Security50%
- 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
Acceptance criteria
- Allow only documented fields and operators.
- Bind user values as parameters and tenant scope as a required predicate.
- Reject unsupported path syntax before query execution.
Implementation constraints
- Do not expose arbitrary SQL or JSON-path execution to clients.
Verification to include
- Filter known numeric and enum metadata under tenant scope.
- Submit malicious path/operator text and verify no query execution or cross-tenant results.
Deliverables
- Safe filter compiler and injection regressions
Rollout and recovery
Enable only reviewed filter combinations; disable newly added operators independently if checks fail.
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.