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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! **/
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