众所周知，wordcount在大数据中的地位相当于helloworld在各种编程语言中的地位。本文并不分析wordcount的计算方法，而是直接给出代码，目的是为了比较Spark中Java，Python，Scala的区别。
　　显然，Java写法较为复杂，Python简单易懂，Scala是Spark的原生代码，故即为简洁。
Java完整代码：

import java.util.Arrays;
import java.util.Iterator;
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 org.apache.spark.api.java.function.VoidFunction;
import scala.Tuple2;
public class wordcount {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setMaster("local").setAppName("wc");
        JavaSparkContext sc = new JavaSparkContext(conf);
        //read a txtfile
        JavaRDD<String> text = sc.textFile("/home/vagrant/speech.txt");
        //split(" ")
        JavaRDD<String> words = text.flatMap(new FlatMapFunction<String, String>() {
            private static final long serialVersionUID = 1L;
            @Override
            public Iterator<String> call(String line) throws Exception {
                return Arrays.asList(line.split(" ")).iterator();
            }
        });
        //word => (word,1)
        JavaPairRDD<String,Integer> counts=words.mapToPair(
                new PairFunction<String, String, Integer>() {
                    public Tuple2<String, Integer> call(String s) throws Exception {
                        return new Tuple2(s, 1);
                    }
                }
        );
        //reduceByKey
        JavaPairRDD <String,Integer> results=counts.reduceByKey(
                new Function2<Integer, Integer, Integer>() {
                    public Integer call(Integer v1, Integer v2) throws Exception {
                        return v1 + v2;
                    }
                }
        ) ;
        //print
        results.foreach(new VoidFunction<Tuple2<String, Integer>>(){
              @Override
              public void call(Tuple2<String, Integer> t) throws Exception { 
                              System.out.println("("+t._1()+":"+t._2()+")");
              }
              });           
    }
}

Pyspark完整代码：

# Imports the PySpark libraries
from pyspark import SparkConf, SparkContext  
# Configure the Spark context to give a name to the application
sparkConf = SparkConf().setAppName("MyWordCounts")
sc = SparkContext(conf = sparkConf)
# The text file containing the words to count (this is the Spark README file)
textFile = sc.textFile('/home/vagrant/speech.txt')
# The code for counting the words (note that the execution mode is lazy)
# Uses the same paradigm Map and Reduce of Hadoop, but fully in memory
wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
# Executes the DAG (Directed Acyclic Graph) for counting and collecting the result
for wc in wordCounts.collect():
    print(wc)

Scala完整代码：

import org.apache.spark.{SparkContext,SparkConf}

object test{
  def main(args:Array[String]){
    val sparkConf = new SparkConf().setMaster("local").setAppName("MyWordCounts")
    val sc = new SparkContext(sparkConf)
    sc.textFile("/home/vagrant/speech.txt").flatMap(_.split(' ')).map((_,1)).reduceByKey(_+_).foreach(println)
  }
}