Lets convert the dataframe of string into the dataframe of Row using the rowencoder. We create the structfield and add it to the list of struct field and pass the list into the createStructType function. Pass structType into the map function.

import java.util.ArrayList;
import java.util.List;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalyst.encoders.RowEncoder;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;

public class DataFrameDocumentation {

public static void main(String[] args) {

SparkSession spark = SparkSession.builder().appName("documentation").master("local").getOrCreate();

spark.sparkContext().setLogLevel("ERROR");

Dataset<String> dataframe = spark.read().text("C:\\codebase\\scala-project\\inputdata\\rating_data").as(Encoders.STRING());

List<org.apache.spark.sql.types.StructField> listOfStructField = new ArrayList<org.apache.spark.sql.types.StructField>();

listOfStructField.add(DataTypes.createStructField("test", DataTypes.StringType, true));
listOfStructField.add(DataTypes.createStructField("test2", DataTypes.StringType, true));

StructType structType = DataTypes.createStructType(listOfStructField);

Dataset<Row> dataframeRowEncoder = dataframe.map(new MapFunction<String, Row>() {

private static final long serialVersionUID = 445454;

@Override
public Row call(String arg0) throws Exception {
// TODO Auto-generated method stub

return RowFactory.create(arg0.split(",")[0], "Test_Data");
}
}, RowEncoder.apply(structType));

dataframeRowEncoder.show();

}
}