Spark学习之wordcount程序

实例代码：

import org.apache.spark.SparkConf;  
import org.apache.spark.api.java.JavaPairRDD;  
import org.apache.spark.api.java.JavaRDD;  
import org.apache.spark.api.java.JavaSparkContext;  
import org.apache.spark.api.java.function.FlatMapFunction;  
import org.apache.spark.api.java.function.Function2;  
import org.apache.spark.api.java.function.PairFunction;

import scala.Tuple2;

import java.util.ArrayList;
import java.util.Arrays;  
import java.util.Iterator;
import java.util.List;

//Driver
public class SparkWordCount1{
    public static void main(String[] args) {
        
        SparkConf sparkConf = new SparkConf()  // 第一步：创建SparkConf对象,设置相关配置信息  
                                .setMaster("local")
                                .setAppName("wordcount");  
        JavaSparkContext ctx = new JavaSparkContext(sparkConf); // 第二步：创建JavaSparkContext对象，是Spark的所有功能的入口
        ctx.setLogLevel("WARN");
//      final JavaRDD<String> linesRdd = ctx.textFile(args[0]); // 根据文件类型的输入源创建RDD的方法textFile()
        ArrayList<String> lines = new ArrayList<String>();
        lines.add("Hello Java Hi Ok");
        lines.add("Ok No House Hello");
        JavaRDD<String> linesRdd = ctx.parallelize(lines);  // 第三步：创建一个初始的RDD  并行化 parallelize
        JavaRDD<String> words = linesRdd.flatMap((s) -> Arrays.asList(s.split(" ")).iterator());
        JavaPairRDD<String, Integer> ones = words.mapToPair(s->new Tuple2<String, Integer>(s, 1));
        JavaPairRDD<String, Integer> counts =  ones.reduceByKey((x,y)->x+y); 
        List<Tuple2<String, Integer>> results = counts.collect();
//      System.out.println(counts.collect());
//      System.out.println(results.toString());
//      counts.foreach(System.out::println);
        counts.foreach(x->System.out.println(x));
        ctx.close();
    }  
}

实例结果：