小记--------spark-Wordcount经典案例之对结果根据词频进行倒序排序

　　

还是以经典案例Wordcount为例：

 

逻辑思路：

1.先把文本按空格切分成每个单词    flatMap()

2.将每个单词都转换成Tuple2类型(hello ,1)    map()

3.将key相同的次数相加(hello , 5)    reduceByKey()

4.将(hello , 5) 反转成(5 , hello)    map()

5.将反转好的RDD 通过key排序    sortByKey()

6.将排序好的RDD 再反转成(hello , 5)     map()

7.打印    foreach()

 

scala版本

/**
  * sortwordcount 案例
  * 通过单词出现的次数 倒序排序
  */
object SortWordCountScala {
 
 
  def main(args: Array[String]): Unit = {
    //创建SparkContext sparkconf
    val conf = new SparkConf()
      .setAppName("sortWordCount")
      .setMaster("local")
 
 
    val sc = new SparkContext(conf)
 
 
    //读取文件
    val lines = sc.textFile("D:\daima\work\1011\spark-test-zhonghuashishan\file\data_syn.txt")
    //原始Wordcount案例
    val split = lines.flatMap(line => line.split(" "))
    val map = split.map(m => (m ,1))
    val reduce = map.reduceByKey(_ + _ )
    //将(you ,2 ) (hello ,3) 反转成(2 , you) (3 , hello)
    val fanzhuan = reduce.map(r => (r._2 , r._1))
    //通过key排序 sortByKey 参数为false ：倒序(3 , hello) (2 , you)
    val sort = fanzhuan.sortByKey(false)
    //在将反转之后排序好的rdd在反转成：(hello ,3) (you , 2)
    val put = sort.map(m => (m._2 , m._1))
    //打印
    put.foreach(p => println(p._1 +"单词"+p._2+"个数"))
  }
}

 

 

Java版本

/**
* 排序的Wordcount程序
* 通过单词出现的次数 倒序排序
*/
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linesrdd
        JavaRDD<String> linesRDD = sc.textFile("D:\daima\work\1011\spark-test-zhonghuashishan\file\data_syn.txt");
 
 
        //执行之前我们做过的单词计数
        JavaRDD<String> stringJavaRDD = linesRDD.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterator<String> call(String s) throws Exception {
                return Arrays.asList(s.split(" ")).iterator();
            }
        });
        //给每个单词都拼成 (q,1)
        JavaPairRDD<String, Integer> stringIntegerJavaPairRDD = stringJavaRDD.mapToPair(new PairFunction<String, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(String s) throws Exception {
                return new Tuple2<String, Integer>(s, 1);
            }
        });
 
 
        //结果为 (yu , 3) (hello ,2)
        JavaPairRDD<String, Integer> stringIntegerJavaPairRDD1 = stringIntegerJavaPairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer integer, Integer integer2) throws Exception {
                return integer + integer2;
            }
        });
 
 
        //通过new Tuple2  将(yu , 3) (hello ,2)  反转成 (3 , yu) (2 , hello)
        JavaPairRDD<Integer, String> integerStringJavaPairRDD = stringIntegerJavaPairRDD1.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
            @Override
            public Tuple2<Integer, String> call(Tuple2<String, Integer> stringIntegerTuple2) throws Exception {
                return new Tuple2<>(stringIntegerTuple2._2, stringIntegerTuple2._1);
            }
        });
 
 
        //按照key进行排序 倒序 (3 , yu) (2 , hello)
        JavaPairRDD<Integer, String> integerStringJavaPairRDD1 = integerStringJavaPairRDD.sortByKey(false);
 
 
        //将排序好的(3 , yu) (2 , hello) 反转成 (yu , 3) (hello ,2)
        JavaPairRDD<String, Integer> stringIntegerJavaPairRDD2 = integerStringJavaPairRDD1.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(Tuple2<Integer, String> integerStringTuple2) throws Exception {
 
 
                return new Tuple2<String, Integer>(integerStringTuple2._2, integerStringTuple2._1);
            }
        });
 
 
        //打印
        stringIntegerJavaPairRDD2.foreach(new VoidFunction<Tuple2<String, Integer>>() {
            @Override
            public void call(Tuple2<String, Integer> stringIntegerTuple2) throws Exception {
                System.out.println(stringIntegerTuple2._1 + "单词"+ stringIntegerTuple2._2 +"个数");
            }
        });
        //关闭JavaSparkContext
        sc.close();
    }
}