> For the complete documentation index, see [llms.txt](https://docs.pyck.cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pyck.cloud/graphql-api/mutations/updatedatatype.md).

# Update Data Type

Update DataType Mutation

Updates an existing data type definition in the system.

## Arguments

| Name    | Type                   | Description                           |
| ------- | ---------------------- | ------------------------------------- |
| `id`    | `ID!`                  | The ID of the data type to update     |
| `input` | `UpdateDataTypeInput!` | Input data for updating the data type |

## Return Type

`DataType` - The updated data type object.

## Input Fields

`UpdateDataTypeInput`:

| Field                 | Type      | Description                           |
| --------------------- | --------- | ------------------------------------- |
| `name`                | `String`  | Name of the data type                 |
| `description`         | `String`  | Optional description of the data type |
| `clearDescription`    | `Boolean` | Clear the description field           |
| `jsonSchema`          | `String`  | JSON schema definition                |
| `frontendSchema`      | `String`  | Optional frontend schema definition   |
| `clearFrontendSchema` | `Boolean` | Clear the frontend schema field       |

## Usage Example

```graphql
mutation {
  updateDataType(
    id: "dt_123456789"
    input: {
      name: "Enhanced Customer Profile"
      description: "Updated customer data type with additional fields for loyalty and preferences"
      jsonSchema: "{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\",\"minLength\":1},\"email\":{\"type\":\"string\",\"format\":\"email\"},\"phone\":{\"type\":\"string\"},\"address\":{\"type\":\"object\",\"properties\":{\"street\":{\"type\":\"string\"},\"city\":{\"type\":\"string\"},\"state\":{\"type\":\"string\"},\"zip\":{\"type\":\"string\"},\"country\":{\"type\":\"string\"}}},\"loyaltyTier\":{\"type\":\"string\",\"enum\":[\"bronze\",\"silver\",\"gold\",\"platinum\"]},\"preferences\":{\"type\":\"object\",\"properties\":{\"language\":{\"type\":\"string\"},\"currency\":{\"type\":\"string\"},\"timezone\":{\"type\":\"string\"}}}},\"required\":[\"name\",\"email\"]}"
      frontendSchema: "{\"fields\":[{\"name\":\"name\",\"type\":\"text\",\"label\":\"Full Name\",\"required\":true},{\"name\":\"email\",\"type\":\"email\",\"label\":\"Email Address\",\"required\":true},{\"name\":\"phone\",\"type\":\"tel\",\"label\":\"Phone Number\"},{\"name\":\"loyaltyTier\",\"type\":\"select\",\"label\":\"Loyalty Tier\",\"options\":[\"bronze\",\"silver\",\"gold\",\"platinum\"]}]}"
    }
  ) {
    id
    name
    description
    jsonSchema
    frontendSchema
    updatedAt
  }
}
```

```graphql
mutation {
  # Update schema to add new validation rules
  updateDataType(
    id: "dt_inventory_item"
    input: {
      jsonSchema: "{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\",\"minLength\":1,\"maxLength\":200},\"sku\":{\"type\":\"string\",\"pattern\":\"^[A-Z0-9-]+$\"},\"description\":{\"type\":\"string\",\"maxLength\":1000},\"weight\":{\"type\":\"number\",\"minimum\":0},\"dimensions\":{\"type\":\"object\",\"properties\":{\"length\":{\"type\":\"number\",\"minimum\":0},\"width\":{\"type\":\"number\",\"minimum\":0},\"height\":{\"type\":\"number\",\"minimum\":0}},\"required\":[\"length\",\"width\",\"height\"]},\"category\":{\"type\":\"string\"},\"tags\":{\"type\":\"array\",\"items\":{\"type\":\"string\"}},\"hazardous\":{\"type\":\"boolean\",\"default\":false},\"perishable\":{\"type\":\"boolean\",\"default\":false},\"serialTracked\":{\"type\":\"boolean\",\"default\":false}},\"required\":[\"name\",\"sku\"]}"
    }
  ) {
    id
    jsonSchema
    updatedAt
  }
}
```

```graphql
mutation {
  # Clear frontend schema and update description
  updateDataType(
    id: "dt_supplier_basic"
    input: {
      description: "Basic supplier information without advanced features"
      clearFrontendSchema: true
    }
  ) {
    id
    description
    frontendSchema
    updatedAt
  }
}
```

## Update Strategies

### Schema Evolution

When updating JSON schemas, consider:

* **Backward Compatibility**: Ensure existing data remains valid
* **Field Addition**: New optional fields are safe to add
* **Field Removal**: Removing fields may break existing data
* **Validation Changes**: Stricter validation may invalidate existing records

### Frontend Schema Updates

The `frontendSchema` field provides UI hints for form generation:

* Update field types and labels
* Add new form fields for new schema properties
* Remove obsolete UI elements
* Update validation rules and help text

### Field Clearing

Use clear flags to remove optional fields:

* `clearDescription: true` - Removes the description
* `clearFrontendSchema: true` - Removes the frontend schema

## Common Update Scenarios

### Schema Enhancement

* Add new optional fields to accommodate evolving business needs
* Update validation rules to enforce data quality
* Modify field types to support new data formats
* Add enum values for new category options

### UI Improvements

* Update frontend schema to improve form usability
* Add better field labels and help text
* Reorganize field order for better user experience
* Add conditional field visibility rules

### Maintenance Updates

* Fix validation issues in existing schemas
* Update descriptions for better documentation
* Align schemas with current business processes
* Remove deprecated fields and constraints

## Validation Considerations

### JSON Schema Validation

* Ensure the JSON schema is syntactically valid
* Test schema against existing data before updating
* Consider migration path for existing records
* Document breaking changes for dependent systems

### Impact Assessment

Before updating a data type:

* Identify entities using this data type
* Check for data compatibility issues
* Plan data migration if needed
* Notify affected users and systems

## Best Practices

1. **Version Control**: Keep track of schema changes over time
2. **Testing**: Validate new schemas against sample data
3. **Documentation**: Update descriptions to reflect changes
4. **Communication**: Notify stakeholders of significant changes
5. **Rollback Plan**: Have a plan to revert problematic changes
6. **Gradual Updates**: Make incremental changes rather than major overhauls

## Error Handling

Updates may fail due to:

* **Invalid JSON**: Malformed JSON schema syntax
* **Schema Conflicts**: New schema incompatible with existing data
* **Permission Issues**: Insufficient rights to modify data types
* **Dependency Violations**: Other systems depend on current schema structure

## Notes

* Schema changes affect all entities using this data type
* Consider creating new data types instead of modifying critical existing ones
* Test schema changes in development environments first
* Monitor system performance after schema updates
* Keep backup copies of working schemas before making changes


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