spark核心编程；高级编程之基于排序机制的wordcount程序

/**
 * 排序的wordcount程序
 * @author Administrator
 *
 */
public class SortWordCount {
    public static void main(String[] args) {
        // 创建SparkConf和JavaSparkContext
        SparkConf conf = new SparkConf()
                .setAppName("SortWordCount")
                .setMaster("local"); 
        JavaSparkContext sc = new JavaSparkContext(conf);
        // 创建lines RDD
        JavaRDD<String> lines = sc.textFile("C://Users//Administrator//Desktop//spark.txt");
        // 执行我们之前做过的单词计数
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            private static final long serialVersionUID = 1L;
            @Override
            public Iterable<String> call(String t) throws Exception {
                return Arrays.asList(t.split(" "));  
            }
        });
        JavaPairRDD<String, Integer> pairs = words.mapToPair(
                new PairFunction<String, String, Integer>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Tuple2<String, Integer> call(String t) throws Exception {
                        return new Tuple2<String, Integer>(t, 1);
                    }
                });
        JavaPairRDD<String, Integer> wordCounts = pairs.reduceByKey(
                new Function2<Integer, Integer, Integer>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Integer call(Integer v1, Integer v2) throws Exception {
                        return v1 + v2;
                    }
                });
        // 到这里为止，就得到了每个单词出现的次数
        // 但是，问题是，我们的新需求，是要按照每个单词出现次数的顺序，降序排序
        // wordCounts RDD内的元素是什么？应该是这种格式的吧：(hello, 3) (you, 2)
        // 我们需要将RDD转换成(3, hello) (2, you)的这种格式，才能根据单词出现次数进行排序把！
        // 进行key-value的反转映射
        JavaPairRDD<Integer, String> countWords = wordCounts.mapToPair(
                new PairFunction<Tuple2<String,Integer>, Integer, String>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Tuple2<Integer, String> call(Tuple2<String, Integer> t)
                            throws Exception {
                        return new Tuple2<Integer, String>(t._2, t._1);
                    }
                });
        // 按照key进行排序
        JavaPairRDD<Integer, String> sortedCountWords = countWords.sortByKey(false);
        // 再次将value-key进行反转映射
        JavaPairRDD<String, Integer> sortedWordCounts = sortedCountWords.mapToPair(
                new PairFunction<Tuple2<Integer,String>, String, Integer>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Tuple2<String, Integer> call(Tuple2<Integer, String> t)
                            throws Exception {
                        return new Tuple2<String, Integer>(t._2, t._1);
                    }
                });
        // 到此为止，我们获得了按照单词出现次数排序后的单词计数
        // 打印出来
        sortedWordCounts.foreach(new VoidFunction<Tuple2<String,Integer>>() {
            private static final long serialVersionUID = 1L;
            @Override
            public void call(Tuple2<String, Integer> t) throws Exception {
                System.out.println(t._1 + " appears " + t._2 + " times.");      
            }
        });
        // 关闭JavaSparkContext
        sc.close();
    }

scala 版本

object SortWordCount {
  def main(args: Array[String]) {
    val conf = new SparkConf()
        .setAppName("SortWordCount")
        .setMaster("local") 
    val sc = new SparkContext(conf)
    val lines = sc.textFile("C://Users//Administrator//Desktop//spark.txt", 1)
    val words = lines.flatMap { line => line.split(" ") }  
    val pairs = words.map { word => (word, 1) }  
    val wordCounts = pairs.reduceByKey(_ + _)  
    val countWords = wordCounts.map(wordCount => (wordCount._2, wordCount._1))   
    val sortedCountWords = countWords.sortByKey(false)  
    val sortedWordCounts = sortedCountWords.map(sortedCountWord => (sortedCountWord._2, sortedCountWord._1))  
    sortedWordCounts.foreach(sortedWordCount => println(
        sortedWordCount._1 + " appear " + sortedWordCount._2 + " times."))
  }
}