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package com.amex.matching;

import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.rdd.RDD;
import org.apache.spark.sql.*;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.LongType;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import scala.Tuple2;
import scala.collection.mutable.WrappedArray;

import java.io.Serializable;

public final class GenRecId implements Serializable {

    public static void main(String[] args) throws AnalysisException {
        String in_table = args[0];
        String out_table = args[1];
        String start_rid = args[2];

        SparkSession spark = SparkSession.builder()
                .appName("Generate recid")

        Dataset<Row> rddhive = spark.sql("select * from " + in_table);
        StructType hiveschema = rddhive.schema();

        // Convert RDD to CustomJavaPairRDD
        CustomJavaPairRDD<Row, Object> inputPairRDD = new CustomJavaPairRDD<>(rddhive.rdd().toJavaRDD().rdd().toJavaRDD());

        // Transformation function
        Function<Tuple2<Row, Object>, Tuple2<Row, Object>> mapFunction = new Function<Tuple2<Row, Object>, Tuple2<Row, Object>>() {
            public Tuple2<Row, Object> call(Tuple2<Row, Object> tuple) throws Exception {
                Row r = tuple._1();
                long recid = (Long) tuple._2();
                long id1 = Long.parseLong(start_rid) + recid;

                // Convert id1 to WrappedArray to include it in the row
                WrappedArray<Long> wrappedArray = WrappedArray.make(new Long[]{id1});

                // Create a new Row with id1 appended to the existing fields
                Object[] rowData = new Object[r.size() + 1];
                for (int i = 0; i < r.size(); i++) {
                    rowData[i] = r.get(i);
                rowData[r.size()] = wrappedArray;

                // Create a new Tuple2 with the updated Row and the original Object
                Tuple2<Row, Object> updatedTuple = new Tuple2<>(RowFactory.create(rowData), tuple._2());

                // Return the updated Tuple2
                return updatedTuple;

        // Apply the transformation using map transformation on CustomJavaPairRDD
        CustomJavaPairRDD<Row, Object> transformedPairRDD = inputPairRDD.mapToPair(new PairFunction<Tuple2<Row, Object>, Row, Object>() {
            public Tuple2<Row, Object> call(Tuple2<Row, Object> tuple) throws Exception {
                return mapFunction.call(tuple);

        // Extract the Rows from the updated Tuple2s
        JavaRDD<Row> transformedRowsJavaRDD = transformedPairRDD.values().toJavaRDD();

        StructField structField = new StructField("id", (DataType) LongType$.MODULE$, false,
        Dataset<Row> wcID = spark.createDataFrame(transformedRowsJavaRDD, new StructType(hiveschema.fields()).add(structField));

        spark.sql("drop table if exists " + out_table);
        spark.sql("create table " + out_table +
                " as select id as recid, pdate, listcd, vendorname, mid, seqnum, matchtype, business, businessid, " +
                "person, personid, address, phone, email, siccode, stdstat, hashraw from sprk_to_hive");

        System.out.println("End of Program!!!");

    public static class CustomJavaPairRDD<K, V> extends JavaPairRDD<K, V> {
        public CustomJavaPairRDD(RDD<Tuple2<K, V>> rdd) {
            super(rdd, scala.reflect.ClassTag$.MODULE$.apply(Tuple2.class));