44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
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# Strict Business Action Condition Fields Design
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## Goal
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Prevent generated business-action SQL from using a column that does not exist on the configured target table.
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## Field Semantics
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`requestFields` are request payload keys. `ruleChecks.conditionFields` and
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`effects.conditionFields` are SQL condition columns and must exist on the
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corresponding `targetTable`.
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No alias mapping or legacy inference is supported. If a book table uses `id`,
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the business blueprint must use `id` as its condition field. A related record
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may still write that value into its own `book_id` column through
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`${param.id}`.
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## Validation
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Business blueprint normalization and validation use a table-to-column map.
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Every rule target field, effect target field, effect value key, and condition
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field is checked against its own target table instead of the union of all
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project columns.
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Condition-based effects require explicit condition fields. The generator must
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not fall back to all action request fields because request payloads can contain
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fields that are not columns of the effect target table.
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Invalid business blueprints fail before persistence and code generation with
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an error naming the target table and invalid condition field.
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## Generated Runtime
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The Qing service implementation builds SQL directly from validated
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`conditionFields`. It does not read `conditionColumns` and does not substitute
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request fields when effect conditions are absent.
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## Testing
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Regression tests cover a schema where `book_info` has primary key `id` while
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another table has `book_id`. A rule targeting `book_info` with
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`conditionFields: ["book_id"]` must be rejected. A valid rule using `id` must
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remain renderable as `where id = ?`.
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