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import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;

import java.util.ArrayList;
import java.util.List;

public class SparkDatasetConverter {
    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder()
                .appName("SparkDatasetConverter")
                .getOrCreate();

        // Create a sample dataset of ThirdClass instances
        Dataset<ThirdClass> thirdClassDataset = spark.createDataset(
                List.of(
                    new ThirdClass("stringAttribute1", 1, "var11", "var21", "var31"),
                    new ThirdClass("stringAttribute1", 1, "var12", "var22", "var32")
                ),
                Encoders.bean(ThirdClass.class)
        );

        // Convert the ThirdClass dataset to a RandomClass dataset
        Dataset<RandomClass> randomClassDataset = convertToRandomClass(thirdClassDataset);

        // Print the resulting RandomClass dataset
        randomClassDataset.show();
    }

    private static Dataset<RandomClass> convertToRandomClass(Dataset<ThirdClass> thirdClassDataset) {
        return thirdClassDataset
                .groupByKey(
                        functions.struct("stringAttribute", "intAttribute"),
                        Encoders.product(Encoders.STRING(), Encoders.INT())
                )
                .flatMapGroups(
                    (key, iterator) -> {
                        List<RandomClass> randomClassList = new ArrayList<>();
                        List<AnotherClass> anotherClassList = new ArrayList<>();
                        String stringAttribute = key.getString(0);
                        Integer intAttribute = key.getInt(1);

                        iterator.forEachRemaining(thirdClass -> {
                            anotherClassList.add(new AnotherClass(thirdClass.getVar1(), thirdClass.getVar2(), thirdClass.getVar3()));
                        });

                        randomClassList.add(new RandomClass(stringAttribute, intAttribute, anotherClassList));
                        return randomClassList.iterator();
                    },
                    Encoders.bean(RandomClass.class)
                );
    }

    public static class RandomClass {
        private String stringAttribute;
        private Integer intAttribute;
        private List<AnotherClass> listAttribute;

        public RandomClass(String stringAttribute, Integer intAttribute, List<AnotherClass> listAttribute) {
            this.stringAttribute = stringAttribute;
            this.intAttribute = intAttribute;
            this.listAttribute = listAttribute;
        }

        // Getters and setters omitted for brevity
    }

    public static class AnotherClass {
        private String var1;
        private String var2;
        private String var3;

        public AnotherClass(String var1, String var2, String var3) {
            this.var1 = var1;
            this.var2 = var2;
            this.var3 = var3;
        }

        // Getters and setters omitted for brevity
    }

    public static class ThirdClass {
        private String stringAttribute;
        private Integer intAttribute;
        private String var1;
        private String var2;
        private String var3;

        public ThirdClass(String stringAttribute, Integer intAttribute, String var1, String var2, String var3) {
            this.stringAttribute = stringAttribute;
            this.intAttribute = intAttribute;
            this.var1 = var1;
            this.var2 = var2;
            this.var3 = var3;
        }

        // Getters and setters omitted for brevity
    }
}

/**
The main steps are:

1. Create a sample Spark Dataset of `ThirdClass` instances.
2. Call the `convertToRandomClass` method, which performs the following:
   - Group the `ThirdClass` Dataset by `stringAttribute` and `intAttribute` using `groupByKey`.
   - For each group, flatten the `ThirdClass` instances into a single `RandomClass` instance by creating a list of `AnotherClass` objects.
   - Return the resulting `RandomClass` Dataset.
3. Print the resulting `RandomClass` Dataset.

The key points are:

- Use `groupByKey` to group the `ThirdClass` Dataset by the `stringAttribute` and `intAttribute` columns.
- In the `flatMapGroups` function, iterate through the grouped `ThirdClass` instances, create `AnotherClass` objects, and add them to the `listAttribute` of the `RandomClass` instance.
- Return the `RandomClass` Dataset using the appropriate `Encoder`.

Let me know if you have any questions! **/