Java函数式编程如何与Hadoop或Spark框架集成实现并行计算？

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;

public class MapReduceWithLambda {
    public static void main(String[] args) {
        // 创建 Spark 配置和上下文
        SparkConf conf = new SparkConf().setAppName("MapReduce with Lambda");
        SparkContext sc = new SparkContext(conf);

        // 创建 RDD
        JavaRDD<String> inputRDD = sc.parallelize(Arrays.asList("hello", "world", "this", "is", "an", "example"));

        // 使用 Lambda 实现 Map 任务
        JavaPairRDD<String, Integer> mapRDD = inputRDD
                .mapToPair((PairFunction<String, String, Integer>) s -> new Tuple2<>(s, 1));

        // 使用 Lambda 实现 Reduce 任务
        JavaPairRDD<String, Integer> reduceRDD = mapRDD
                .reduceByKey((Integer a, Integer b) -> a + b);

        // 收集结果并打印
        for (Tuple2<String, Integer> result : reduceRDD.collect()) {
            System.out.println("Word: " + result._1() + ", Count: " + result._2());
        }
    }
}

import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

public class StreamingWithLambda {
    public static void main(String[] args) {
        // 创建 SparkSession 和流数据集
        SparkSession spark = SparkSession.builder()
                .appName("Streaming with Lambda")
                .master("local[*]")
                .getOrCreate();
        Dataset<Row> streamingDataset = spark.readStream()
                .format("socket")
                .option("host", "localhost")
                .option("port", 9999)
                .load();

        // 使用 Lambda 过滤数据
        Dataset<Row> filteredDataset = streamingDataset
                .filter((Row row) -> row.getLong(0) % 2 == 0);

        // 使用 Lambda 进行聚合
        Dataset<Row> aggregatedDataset = filteredDataset
                .groupBy("field1")
                .agg(functions.sum("field2"));

        // 输出结果
        aggregatedDataset.writeStream()
                .outputMode("update")
                .format("console")
                .start()
                .awaitTermination();
    }
}