spark RDD算子（五）之键值对聚合操作 combineByKey

combineByKey

聚合数据一般在集中式数据比较方便，如果涉及到分布式的数据集，该如何去实现呢。这里介绍一下combineByKey, 这个是各种聚集操作的鼻祖，应该要好好了解一下,参考scala API

简要介绍

def combineByKey[C](createCombiner: (V) => C,                      mergeValue: (C, V) => C,                       mergeCombiners: (C, C) => C): RD

• createCombiner: combineByKey() 会遍历分区中的所有元素，因此每个元素的键要么还没有遇到过，要么就
  和之前的某个元素的键相同。如果这是一个新的元素， combineByKey() 会使用一个叫作 createCombiner() 的函数来创建
  那个键对应的累加器的初始值
• mergeValue: 如果这是一个在处理当前分区之前已经遇到的键， 它会使用 mergeValue() 方法将该键的累加器对应的当前值与这个新的值进行合并
• mergeCombiners: 由于每个分区都是独立处理的， 因此对于同一个键可以有多个累加器。如果有两个或者更
  多的分区都有对应同一个键的累加器， 就需要使用用户提供的 mergeCombiners() 方法将各
  个分区的结果进行合并。

计算学生平均成绩例子

这里举一个计算学生平均成绩的例子,例子参考至https://www.edureka.co/blog/apache-spark-combinebykey-explained, github源码 我对此进行了解析
创建一个学生成绩说明的类

case class ScoreDetail(studentName: String, subject: String, score: Float)

下面是一些测试数据，加载测试数据集合 key = Students name and value = ScoreDetail instance

    val scores = List(      ScoreDetail("xiaoming", "Math", 98),      ScoreDetail("xiaoming", "English", 88),      ScoreDetail("wangwu", "Math", 75),      ScoreDetail("wangwu", "English", 78),      ScoreDetail("lihua", "Math", 90),      ScoreDetail("lihua", "English", 80),      ScoreDetail("zhangsan", "Math", 91),      ScoreDetail("zhangsan", "English", 80))

将集合转换成二元组， 也可以理解成转换成一个map, 利用了for 和 yield的组合

val scoresWithKey = for { i <- scores } yield (i.studentName, i)

创建RDD, 并且指定三个分区

    val scoresWithKeyRDD = sc.parallelize(scoresWithKey).partitionBy(new HashPartitioner(3)).cache

输出打印一下各个分区的长度和各个分区的一些数据

    println(">>>> Elements in each partition")    scoresWithKeyRDD.foreachPartition(partition => println(partition.length))    // explore each partition...    println(">>>> Exploring partitions' data...")    scoresWithKeyRDD.foreachPartition(      partition => partition.foreach(        item => println(item._2)))/*会输出 >>>> Elements in each partition620>>>> Exploring partitions' data...ScoreDetail(xiaoming,Math,98.0)ScoreDetail(xiaoming,English,88.0)ScoreDetail(lihua,Math,90.0)ScoreDetail(lihua,English,80.0)ScoreDetail(zhangsan,Math,91.0)ScoreDetail(zhangsan,English,80.0)ScoreDetail(wangwu,Math,75.0)ScoreDetail(wangwu,English,78.0)*/

聚合求平均值让后打印

      val avgScoresRDD = scoresWithKeyRDD.combineByKey(      (x: ScoreDetail) => (x.score, 1) /*createCombiner*/,      (acc: (Float, Int), x: ScoreDetail) => (acc._1 + x.score, acc._2 + 1) /*mergeValue*/,      (acc1: (Float, Int), acc2: (Float, Int)) => (acc1._1 + acc2._1, acc1._2 + acc2._2) /*mergeCombiners*/      // calculate the average    ).map( { case(key, value) => (key, value._1/value._2) })    avgScoresRDD.collect.foreach(println)/*输出:(zhangsan,85.5)(lihua,85.0)(xiaoming,93.0)(wangwu,76.5)*/

解释一下scoresWithKeyRDD.combineByKey
createCombiner: (x: ScoreDetail) => (x.score, 1)
这是第一次遇到zhangsan，创建一个函数，把map中的value转成另外一个类型 ，这里是把(zhangsan,(ScoreDetail类))转换成(zhangsan,(91,1))
mergeValue: (acc: (Float, Int), x: ScoreDetail) => (acc._1 + x.score, acc._2 + 1) 再次碰到张三， 就把这两个合并, 这里是将(zhangsan,(91,1)) 这种类型 和 (zhangsan,(ScoreDetail类))这种类型合并，合并成了(zhangsan,(171,2))
mergeCombiners (acc1: (Float, Int), acc2: (Float, Int)) 这个是将多个分区中的zhangsan的数据进行合并， 我们这里zhansan在同一个分区，这个地方就没有用上

java版本的介绍

ScoreDetail类

public class ScoreDetail implements Serializable{    //case class ScoreDetail(studentName: String, subject: String, score: Float)    public String studentName;    public String subject;    public float score;    public ScoreDetail(String studentName, String subject, float score) {        this.studentName = studentName;        this.subject = subject;        this.score = score;    }}

CombineByKey的测试类

public class CombineTest {    public static void main(String[] args) {        SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount").setMaster("local");        JavaSparkContext sc = new JavaSparkContext(sparkConf);        ArrayList<ScoreDetail> scoreDetails = new ArrayList<>();        scoreDetails.add(new ScoreDetail("xiaoming", "Math", 98));        scoreDetails.add(new ScoreDetail("xiaoming", "English", 88));        scoreDetails.add(new ScoreDetail("wangwu", "Math", 75));        scoreDetails.add(new ScoreDetail("wangwu", "Englist", 78));        scoreDetails.add(new ScoreDetail("lihua", "Math", 90));        scoreDetails.add(new ScoreDetail("lihua", "English", 80));        scoreDetails.add(new ScoreDetail("zhangsan", "Math", 91));        scoreDetails.add(new ScoreDetail("zhangsan", "English", 80));        JavaRDD<ScoreDetail> scoreDetailsRDD = sc.parallelize(scoreDetails);        JavaPairRDD<String, ScoreDetail> pairRDD = scoreDetailsRDD.mapToPair(new PairFunction<ScoreDetail, String, ScoreDetail>() {            @Override            public Tuple2<String, ScoreDetail> call(ScoreDetail scoreDetail) throws Exception {                return new Tuple2<>(scoreDetail.studentName, scoreDetail);            }        });//        new Function<ScoreDetail, Float,Integer>();        Function<ScoreDetail, Tuple2<Float, Integer>> createCombine = new Function<ScoreDetail, Tuple2<Float, Integer>>() {            @Override            public Tuple2<Float, Integer> call(ScoreDetail scoreDetail) throws Exception {                return new Tuple2<>(scoreDetail.score, 1);            }        };        // Function2传入两个值，返回一个值        Function2<Tuple2<Float, Integer>, ScoreDetail, Tuple2<Float, Integer>> mergeValue = new Function2<Tuple2<Float, Integer>, ScoreDetail, Tuple2<Float, Integer>>() {            @Override            public Tuple2<Float, Integer> call(Tuple2<Float, Integer> tp, ScoreDetail scoreDetail) throws Exception {                return new Tuple2<>(tp._1 + scoreDetail.score, tp._2 + 1);            }        };        Function2<Tuple2<Float, Integer>, Tuple2<Float, Integer>, Tuple2<Float, Integer>> mergeCombiners = new Function2<Tuple2<Float, Integer>, Tuple2<Float, Integer>, Tuple2<Float, Integer>>() {            @Override            public Tuple2<Float, Integer> call(Tuple2<Float, Integer> tp1, Tuple2<Float, Integer> tp2) throws Exception {                return new Tuple2<>(tp1._1 + tp2._1, tp1._2 + tp2._2);            }        };        JavaPairRDD<String, Tuple2<Float,Integer>> combineByRDD  = pairRDD.combineByKey(createCombine,mergeValue,mergeCombiners);        //打印平均数        Map<String, Tuple2<Float, Integer>> stringTuple2Map = combineByRDD.collectAsMap();        for ( String et:stringTuple2Map.keySet()) {            System.out.println(et+" "+stringTuple2Map.get(et)._1/stringTuple2Map.get(et)._2);        }    }}

注意有个坑的地方 createCombine方法必须是这样的

        Function<ScoreDetail, Tuple2<Float, Integer>> createCombine = new Function<ScoreDetail, Tuple2<Float, Integer>>() {            @Override            public Tuple2<Float, Integer> call(ScoreDetail scoreDetail) throws Exception {                return new Tuple2<>(scoreDetail.score, 1);            }        };

而不能是这样的, 即使最后的RDD都类似

        PairFunction<ScoreDetail, Float, Integer> createCombine = new PairFunction<ScoreDetail, Float, Integer>() {            @Override            public Tuple2<Float, Integer> call(ScoreDetail scoreDetail) throws Exception {                return new Tuple2<>(scoreDetail.score, 1);            }        };

再推荐几篇比较好的文章 LXW的大数据田地: combineByKey
推荐图书快速大数据分析中combineByKey的讲解