> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lume.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Validation Errors

> Understanding and parsing validation errors in mapping results

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:

```typescript theme={null}
interface ValidationErrorDetail {
  error_type: string;
  statistics: ErrorStats;
  schema_path: string;
  error_sample: SampleItem[];
  check: any;
}
```

### 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:

```typescript theme={null}
interface ErrorStats {
  error_count: number;   // Number of times this error occurred
  null_count: number;    // Number of null values
  total_count: number;   // Total number of records processed
  missing_count: number; // Number of missing values
}
```

### Error Samples

Errors include sample data to help diagnose issues:

```typescript theme={null}
interface SampleItem {
  index: number;  // Index of the record in the source data
  value: any;     // The problematic value
}
```

## Example

Here's an example of how validation errors might appear in the output:

```json theme={null}
{
  "errors": {
    "user": {
      "email": [{
        "error_type": "pattern",
        "statistics": {
          "error_count": 3,
          "null_count": 0,
          "total_count": 100,
          "missing_count": 0
        },
        "schema_path": "user.email",
        "error_sample": [
          { "index": 5, "value": "invalid-email" },
          { "index": 12, "value": "also-invalid" }
        ],
        "check": {
          "pattern": "^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Za-z]{2,}$"
        }
      }],
      "age": [{
        "error_type": "type",
        "statistics": {
          "error_count": 1,
          "null_count": 0,
          "total_count": 100,
          "missing_count": 0
        },
        "schema_path": "user.age",
        "error_sample": [
          { "index": 23, "value": "thirty" }
        ],
        "check": {
          "type": "number"
        }
      }]
    }
  }
}
```

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.