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When processing data through a schema transformer, validation errors are captured in the output’s errors property, providing detailed information about any data quality issues or schema violations.

Error Structure

The validation errors are organized in a tree structure where each path represents a field or nested object in your data. Each leaf node contains an array of ValidationErrorDetail objects:

Error Types

The system recognizes several categories of validation errors:
  • Basic Validation
    • pattern: Value doesn’t match the required pattern
    • required: Required field is missing
    • type: Value doesn’t match the expected type
    • enum: Value isn’t one of the allowed options
    • array: Array validation failures
    • duplicate: Duplicate value where uniqueness is required
  • Custom Validation
    • custom: Custom validation rule failures
    • unknown: Unrecognized validation issues
  • Unsupported Cases
    • Various unsupported_* types for special handling

Error Statistics

Each error includes detailed statistics about its occurrence:

Error Samples

Errors include sample data to help diagnose issues:

Example

Here’s an example of how validation errors might appear in the output:
In this example, we can see validation errors for email format and age type conversion, including statistics about how often these errors occur and sample problematic values.