JSON to Scala 3 Case Class Converter

JSON to Scala 3 Case Classes: Core Features and Principles

Manually writing Scala case classes based on complex JSON structures is not only tedious but also prone to errors. This tool automatically parses valid JSON input, recursively analyzing its objects, arrays, primitive types, and null values to accurately map them into Scala 3 case class definitions. At its core is a type inference engine that maps JSON objects to case classes, arrays to List[T], and optional fields to Option[T], ensuring the generated code complies with Scala 3 syntax while maintaining strict type safety.

Why Choose Our JSON to Scala 3 Converter?

• Smart type inference: Supports nested objects, multi-dimensional arrays, mixed types, and null values, automatically generating precise types like Option[T] and List[T].
• Real-time generation: Instantly outputs complete Scala 3 case class code upon entering JSON, with no manual configuration or waiting required.
• Syntax optimization: Strictly adheres to Scala 3 standards. The generated code can be directly integrated into your project, minimizing the need for subsequent modifications.

How to Use

1. Copy your JSON data and paste it into the input box.
2. The system will automatically parse the data and generate the Scala case class code.
3. Review the output and copy the code directly into your Scala project.

Frequently Asked Questions (FAQ)

What type is generated for an empty JSON array?
An empty array ([]) defaults to List[Nothing]. We recommend manually adjusting this to a specific type, such as List[String], based on your business logic.

How can I ensure date fields have the correct type?
Date strings in JSON (e.g., "2023-10-26T10:00:00Z") are mapped to String. If you need to use java.time types, you will need to modify the code manually.

Important Notes

Please ensure your input is valid JSON to prevent mixed-type arrays from being inferred as Any. This tool does not store user data, but we advise against processing sensitive information. Complex nested structures may require manual verification for type accuracy.

Technical Notes & Best Practices

For Scala developers, it is recommended to check if the Option type fields match your business logic after generation. For example, a common input like {"name":"Alice","age":30,"email":null} will output case class User(name: String, age: Int, email: Option[String]), where the email field requires Option operations to handle null values. Combining this with libraries like Circe or Play JSON can enable highly efficient serialization.