Data Schemas Generation From Existing DataFrame

Special utility functions that generate code of useful Kotlin definitions (returned as a String) based on the current DataFrame schema.

generateInterfaces

inline fun <reified T> DataFrame<T>.generateInterfaces(): CodeString

fun <T> DataFrame<T>.generateInterfaces(markerName: String): CodeString

Generates @DataSchema interfaces for this DataFrame (including all nested DataFrame columns and column groups) as Kotlin interfaces.

This is useful when working with the compiler plugin in cases where the schema cannot be inferred automatically from the source.

Arguments

markerName: String? — The base name to use for generated interfaces.
If null, uses the T type argument of DataFrame simple name.
Default: null.
extensionProperties: Boolean – Whether to generate extension properties in addition to interface declarations.
Useful if you don't use the compiler plugin, otherwise they are not needed; the compiler plugin, notebooks, and older Gradle/KSP plugin generate them automatically. Default: false.
visibility: MarkerVisibility – Visibility modifier for the generated declarations.
Default: MarkerVisibility.IMPLICIT_PUBLIC.
useFqNames: Boolean – If true, fully qualified type names will be used in generated code.
Default: false.
nameNormalizer: NameNormalizer – Strategy for converting column names (with spaces, underscores, etc.) to Kotlin-style identifiers. Generated properties will still refer to columns by their actual name using the @ColumnName annotation. Default: NameNormalizer.default.
nestedMarkerNameProvider: MarkerNameProvider – Strategy for generating names for nested data schema declarations (markers).
- MarkerNameProvider.fromColumnName (default) generates descriptive names from the column names.
- MarkerNameProvider.PredefinedName uses the name of the root marker for all nested declarations and appends a numerical suffix to resolve name conflicts.
  Default: MarkerNameProvider.fromColumnName.

Returns

CodeString – A value class wrapper for String, containing
the generated Kotlin code of @DataSchema interfaces. It also contains generated extension properties for extensionProperties=true.

Examples

df.generateInterfaces(markerName = "Customer")

By adding these interfaces to your project with the compiler plugin enabled,
you'll gain full support for the extension properties API and type-safe operations.

Use cast to apply the generated schema to a DataFrame. Right after that, you can use extension properties in the following operations:

df.cast<Customer>().filter { orders.all { orderId >= 102 } }

generateDataClasses

inline fun <reified T> DataFrame<T>.generateDataClasses(
    markerName: String? = null,
    extensionProperties: Boolean = false,
    visibility: MarkerVisibility = MarkerVisibility.IMPLICIT_PUBLIC,
    useFqNames: Boolean = false,
    nameNormalizer: NameNormalizer = NameNormalizer.default,
    nestedMarkerNameProvider: MarkerNameProvider = MarkerNameProvider.fromColumnName,
): CodeString

Generates Kotlin data classes corresponding to the DataFrame schema (including all nested DataFrame columns and column groups).

Useful when you want to:

Work with the data as regular Kotlin data classes.
Convert a dataframe to instantiated data classes with df.toListOf<DataClassType>().
Work with data classes serialization.
Extract structured types for further use in your application.

Arguments

markerName: String? — The base name to use for generated data classes.
If null, uses the T type argument of DataFrame simple name.
Default: null.
extensionProperties: Boolean – Whether to generate extension properties in addition to interface declarations.
Useful if you don't use the compiler plugin, otherwise they are not needed; the compiler plugin, notebooks, and older Gradle/KSP plugin generate them automatically. Default: false.
visibility: MarkerVisibility – Visibility modifier for the generated declarations.
Default: MarkerVisibility.IMPLICIT_PUBLIC.
useFqNames: Boolean – If true, fully qualified type names will be used in generated code.
Default: false.
nameNormalizer: NameNormalizer – Strategy for converting column names (with spaces, underscores, etc.) to Kotlin-style identifiers. Generated properties will still refer to columns by their actual name using the @ColumnName annotation. Default: NameNormalizer.default.
nestedMarkerNameProvider: MarkerNameProvider – Strategy for generating names for nested data schema declarations (markers).
- MarkerNameProvider.fromColumnName (default) generates descriptive names from the column names.
- MarkerNameProvider.PredefinedName uses the name of the root marker for all nested declarations and appends a numerical suffix to resolve name conflicts.
  Default: MarkerNameProvider.fromColumnName.

Returns

CodeString – A value class wrapper for String, containing
the generated Kotlin code of @DataSchema data classes. It also contains generated extension properties for extensionProperties=true.

Examples

df.generateDataClasses("Customer")

By adding these interfaces to your project with the compiler plugin enabled,
you'll gain full support for the extension properties API and type-safe operations.

Use cast to apply the generated schema to a DataFrame. Right after that, you can use extension properties in the following operations:

df.cast<Customer>().filter { orders.all { orderId >= 102 } }

You can use these classes for the DataFrame conversion to a list of typed objects:

val customers: List<Customer> = df.cast<Customer>().toList()

27 May 2026