bc-code-intelligence-mcp

--- title: "API Page Validation Rule Execution" domain: "quinn-tester" difficulty: "intermediate" bc_versions: "14+" tags: ["validation", "business-rules", "data-integrity"] samples: "samples/rule-execution.md" --- # API Page Validation Rule Execution ## Overview API Page validation rule execution ensures data integrity and business rule compliance for data operations performed through OData endpoints. Understanding validation execution patterns is critical for implementing reliable API solutions that maintain data quality and business logic consistency. Validation rules execute automatically during API operations, applying the same business logic that governs interactive user operations. This ensures consistent data validation regardless of the interface used to modify business data. Proper validation implementation provides meaningful error feedback to API consumers while preventing invalid data from entering the system and maintaining overall data integrity standards. ## Validation Execution Timing Validation rules execute at multiple points during API operations including field-level validation during data entry, record-level validation before commit operations, and cross-table validation for related data integrity. Field validation occurs immediately when field values are modified through API operations, providing early detection of invalid data before complex business logic execution. Record validation executes before database commit operations, ensuring all business rules are satisfied before persisting changes to the database system. ## Standard Table Validation API Pages inherit all standard table validation logic including field constraints, table relations, and business logic implemented in table triggers and validation procedures. Standard validations include data type validation, range checking, required field validation, and referential integrity constraints that ensure data consistency with existing business rules. Custom table validation logic executes during API operations with the same behavior as interactive data entry, ensuring consistent business rule enforcement across all data entry mechanisms. ## Field-Level Validation Patterns Individual field validation rules execute when field values are modified through PATCH or PUT operations, providing immediate feedback for invalid field values before processing related business logic. Field validation includes format validation, range checking, lookup validation, and custom business logic specific to individual fields or field combinations. Consider the user experience implications of field-level validation errors in API scenarios, providing clear error messages that help API consumers understand and correct validation issues. ## Cross-Field and Record Validation Record-level validation rules execute after all field modifications are processed, enabling validation logic that depends on multiple field values or complex business rule combinations. Cross-field validation ensures data consistency across related fields and implements business rules that require analysis of complete record state rather than individual field values. Advanced validation scenarios may require access to related table data or external system information to complete validation rule execution. ## Related Table Validation API Page operations may trigger validation rules in related tables when modifications affect data relationships or referential integrity constraints. Related table validation ensures system-wide data integrity is maintained even when modifications originate from API operations rather than direct table access. Consider the performance implications of related table validation, especially in scenarios involving complex table relationships or large data volumes. ## Custom Business Logic Integration Custom business logic implemented in table triggers, page triggers, or specialized validation procedures executes during API operations with the same behavior as interactive operations. Integration with custom business logic ensures API operations respect all business rules and constraints without requiring separate validation implementations for API scenarios. Design custom validation logic with API operations in mind, ensuring error messages and validation feedback are appropriate for both interactive and programmatic consumers. ## Error Handling and Feedback Validation errors result in appropriate HTTP status codes and error messages that provide meaningful feedback to API consumers about the nature of validation failures. Error messages should provide sufficient information for API consumers to understand and correct validation issues without exposing sensitive system information or internal implementation details. Consider implementing structured error responses that include field-specific validation errors and suggested corrective actions for common validation scenarios. ## Validation Performance Considerations Complex validation rules can impact API operation performance, especially in high-volume scenarios where validation overhead becomes significant compared to basic data operations. Monitor validation performance to identify optimization opportunities including caching validation results, optimizing validation logic, or restructuring validation approaches for better performance. Balance validation thoroughness with performance requirements, ensuring critical business rules are enforced while maintaining acceptable API response times. ## Batch Operation Validation Batch API operations require special consideration for validation rule execution, as validation errors in individual records may affect the entire batch operation depending on transaction and error handling strategies. Implement appropriate error handling for batch scenarios including partial success scenarios, rollback strategies, and error reporting that identifies specific validation failures within batch operations. Consider the business impact of validation failures in batch operations and design appropriate handling strategies that balance data integrity requirements with operational efficiency. ## Integration Testing Strategies Implement comprehensive testing for validation rule execution in API scenarios, including positive validation cases, negative validation cases, and edge cases that may not occur in interactive usage. Test validation behavior across different API operation types including creation, modification, and deletion operations to ensure consistent validation enforcement. Validate error handling and feedback mechanisms to ensure API consumers receive appropriate information for resolving validation issues. ## Security and Validation Validation rules may include security-related constraints that prevent unauthorized data modifications or enforce data access restrictions based on user context or business rules. Ensure validation logic properly integrates with BC security systems to prevent security bypass through API operations while maintaining appropriate functionality for legitimate use cases. Consider the security implications of validation error messages, ensuring they provide helpful feedback without exposing sensitive information that could be exploited by unauthorized users. ## Documentation and Communication Document validation behavior for API consumers including common validation scenarios, error message formats, and recommended approaches for handling validation failures in client applications. Provide clear guidance on validation requirements during API integration development to help API consumers implement appropriate error handling and data validation strategies. Maintain documentation consistency between interactive validation behavior and API validation behavior to ensure developers have accurate expectations for validation rule execution. ## Best Practices Design validation rules with both interactive and API usage in mind, ensuring validation logic provides appropriate feedback and behavior for different consumption scenarios. Implement comprehensive error handling for validation scenarios including graceful degradation, meaningful error messages, and appropriate HTTP status codes for different validation failure types. Monitor validation rule performance and effectiveness in API scenarios, identifying optimization opportunities and ensuring validation rules continue to meet business requirements over time. Test validation behavior thoroughly across different API usage patterns and data scenarios to ensure robust validation enforcement that maintains data integrity without compromising API usability.