Building JSON Schema Validators into Formatters

Key capabilities of JSON Schema:

• Define required properties
• Specify data types (string, number, boolean, object, array, null)
• Set constraints on values (e.g., minimum/maximum length for strings, range for numbers)
• Describe array contents (e.g., all items must be strings)
• Handle complex structures and nested objects
• Use keywords like "required", "properties", "items", "type", "minLength", etc.

Benefits of combined tools:

• Single tool for both formatting and structural validation
• Catch data structure errors early in the development process
• Ensure data conforms to an expected contract
• Provide more specific error messages than just syntax errors
• Improve developer productivity by instantly highlighting schema violations

Syntax Errors:

• Occur during the initial parsing of the JSON string.
• JSON is not valid according to the fundamental JSON specification (RFC 8259).
• Examples: Missing commas, mismatched quotes, unescaped special characters, missing brackets/braces, trailing commas (in strict JSON).
• Prevent the JSON from being parsed into a data structure. Validation cannot happen if syntax is invalid.

Schema Validation Errors:

• Occur after the JSON has been successfully parsed and is syntactically valid.
• The data structure or values do not conform to the rules defined in the JSON Schema.
• Examples: Missing a required property, a property has the wrong data type (e.g., a number instead of a string), a string is too short/long, a number is outside a defined range, an array contains incorrect item types.
• Indicate that the *content* is invalid according to a specific contract, even if the format is correct.

Sample JSON Data:

{
  "product_id": 12345,
  "product_name": "Example Gadget",
  "price": "49.99",
  "tags": ["electronics", "gadget"],
  "in_stock": "true"
}

Sample JSON Schema:

{
  "type": "object",
  "properties": {
    "product_id": {
      "type": "integer"
    },
    "product_name": {
      "type": "string",
      "minLength": 5
    },
    "price": {
      "type": "number",
      "minimum": 0
    },
    "tags": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "in_stock": {
      "type": "boolean"
    },
    "rating": {
       "type": "number",
       "minimum": 0,
       "maximum": 5,
       "required": false
    }
  },
  "required": [
    "product_id",
    "product_name",
    "price",
    "tags",
    "in_stock"
  ]
}

Expected Validation Errors:

• Error at path "price": Expected type "number" but found type "string". (The value "49.99" is a string in the data, but the schema requires a number).
• Error at path "in_stock": Expected type "boolean" but found type "string". (The value "true" is a string in the data, but the schema requires a boolean).

Note that if the data had a syntax error (e.g., missing quote around "Example Gadget"), the formatter/validator would report that first, and schema validation might not even run until the syntax is corrected.

• Choosing a Validation Library: Select a robust, well-maintained library for your chosen programming language that fully implements the JSON Schema specification (e.g., `ajv` for JavaScript/Node.js, `jsonschema` for Python).
• User Interface: Design an intuitive UI that allows users to input both the JSON data and the schema. Clearly display formatting results and list validation errors, perhaps with links or highlighting to the relevant parts of the JSON data.
• Performance: For large JSON documents and complex schemas, validation can be computationally intensive. Optimize parsing and validation steps or provide feedback during processing.
• Error Reporting: Make validation error messages as helpful as possible. Include the error path, the violated schema rule, and a description.
• Schema Definition Help: Consider adding features to help users write correct schemas (e.g., schema syntax highlighting, basic schema validation on the schema input itself).