dd_06_validation

error handling and validation example

this example demonstrates comprehensive error handling patterns, validation strategies, and production-ready error management for the df library.

key concepts demonstrated

error types and categories

• binding errors: field type mismatches, missing required fields
• validation errors: custom validation failures, business rule violations
• conversion errors: type conversion and coercion failures
• reference errors: missing or invalid pointer references
• file i/o errors: json/yaml parsing and file access errors

validation strategies

• struct tag validation: built-in validation through df tags
• custom validators: implement validation in Unmarshaler interfaces
• business rule validation: domain-specific validation logic
• cross-field validation: validation that requires multiple fields
• conditional validation: validation based on other field values

error handling patterns

• graceful degradation: continue processing despite non-critical errors
• error accumulation: collect multiple validation errors before failing
• error context: provide detailed information about failure location
• error recovery: attempt to fix or work around common errors
• error reporting: structured error information for debugging

production considerations

• logging integration: proper error logging for monitoring
• user-friendly messages: convert technical errors to user messages
• error codes: categorize errors for automated handling
• performance impact: minimize validation overhead
• security: avoid exposing sensitive information in errors

workflow demonstrated

1. basic error scenarios: demonstrate common binding and validation errors
2. custom validation: implement domain-specific validation logic
3. error accumulation: collect and report multiple validation errors
4. error recovery: attempt to fix common data issues automatically
5. production patterns: logging, monitoring, and user-friendly error handling

example scenarios

the example demonstrates:

• user registration: comprehensive validation of user input
• configuration loading: robust handling of config file errors
• data import: bulk data processing with error reporting
• api validation: request validation with detailed error responses

usage

go run main.go

benefits

• robustness: handle errors gracefully without crashing
• debuggability: detailed error information for troubleshooting
• user experience: clear, actionable error messages
• maintainability: structured error handling throughout the application
• monitoring: comprehensive error reporting for operations