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xyz.florentforest.spark.ml.util

SchemaUtils

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object SchemaUtils

Utils for handling schemas.

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  4. def appendColumn(schema: StructType, col: StructField): StructType

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    Appends a new column to the input schema.

    Appends a new column to the input schema. This fails if the given output column already exists.

    schema

    input schema

    col

    New column schema

    returns

    new schema with the input column appended

  5. def appendColumn(schema: StructType, colName: String, dataType: DataType, nullable: Boolean = false): StructType

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    Appends a new column to the input schema.

    Appends a new column to the input schema. This fails if the given output column already exists.

    schema

    input schema

    colName

    new column name. If this column name is an empty string "", this method returns the input schema unchanged. This allows users to disable output columns.

    dataType

    new column data type

    returns

    new schema with the input column appended

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  7. def checkColumnType(schema: StructType, colName: String, dataType: DataType, msg: String = ""): Unit

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    Check whether the given schema contains a column of the required data type.

    Check whether the given schema contains a column of the required data type.

    colName

    column name

    dataType

    required column data type

  8. def checkColumnTypes(schema: StructType, colName: String, dataTypes: Seq[DataType], msg: String = ""): Unit

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    Check whether the given schema contains a column of one of the require data types.

    Check whether the given schema contains a column of one of the require data types.

    colName

    column name

    dataTypes

    required column data types

  9. def checkNumericType(schema: StructType, colName: String, msg: String = ""): Unit

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    Check whether the given schema contains a column of the numeric data type.

    Check whether the given schema contains a column of the numeric data type.

    colName

    column name

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