Check whether keys, constraints and relationships actually enforce the application's stated data rules.
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Version 1 · 3 variables · 3 required
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Engine and version whose constraint semantics matter. Default: PostgreSQL 16
DDL or precise table definitions. Default: projects(id uuid primary key, tenant_id uuid not null); tasks(id uuid primary key, tenant_id uuid not null, project_id uuid references projects(id));
Rules, cardinalities and frequent reads/writes. Default: Every task must belong to a project in the same tenant. Task lists filter by tenant and project. Project deletion should be blocked while tasks exist.
Review this relational schema for PostgreSQL 16:
projects(id uuid primary key, tenant_id uuid not null);
tasks(id uuid primary key, tenant_id uuid not null, project_id uuid references projects(id));
Business invariants and main access patterns:
Every task must belong to a project in the same tenant. Task lists filter by tenant and project. Project deletion should be blocked while tasks exist.
Create a table mapping each stated invariant to the constraint or application behavior that enforces it. Flag missing enforcement, ambiguous cardinality, nullable-key surprises, and delete/update behavior that could violate a rule. Cite exact tables and columns.
Prioritize findings by concrete failure scenario. For each, provide the smallest corrective design and its tradeoff. Distinguish data-integrity constraints from query-driven index suggestions; do not recommend an index without an access pattern.
End with a short list of existing-data checks needed before applying changes and questions that require a domain decision. Do not claim a schema is production-ready or generate destructive migration commands.No public examples yet for version 1
Curated prompt only. No example output or model test is attached; model compatibility has not been verified. Default inputs are illustrative practice material; replace them with your own facts and context.
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