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,可以使用combineByKey, 这个是各种聚集操作的鼻祖
def combineByKey[C](createCombiner : Function1[V, C],
mergeValue : Function2[C, V, C],
mergeCombiners : Function2[C, C, C]) : RDD[scala.Tuple2[K, C]]
combineByKey涉及三个方法:createCombiner、mergeValue、mergeCombiners
-
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。
案例:计算平均成绩
scala版本
object CombineByKeyScala {
//创建一个样例类
case class ScoreDetail(stuName:String,subject:String,score:Int)
def main(args: Array[String]): Unit = {
val conf=new SparkConf().setMaster("local[2]").setAppName("actionrdddemo")
val sc = new SparkContext(conf)
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)
)
val scoresWithKey =for {i<- scores} yield(i.stuName,i)
//创建Rdd,指定三个分区,并缓存
val scoreWithKeyRdd=sc.makeRDD(scoresWithKey).partitionBy(new HashPartitioner(3)).cache()
//输出元组,在元组的形式上可以进行操作
scoreWithKeyRdd.foreachPartition(partContext=>{
partContext.foreach(x=>println(x._1,x._2.subject)) //x是每个二元组
})
//聚合求平均值
val stuScoreInfoRdd = scoreWithKeyRdd.combineByKey(
//三个匿名函数
(x: ScoreDetail) => (x.score, 1),
(acc1: (Int, Int), x: ScoreDetail) => (acc1._1 + x.score, acc1._2 + 1),
(acc2: (Int, Int), acc3: (Int, Int)) => (acc2._1 + acc3._1, acc2._2 + acc3._2)
)
//方式一
val stuAvg=stuScoreInfoRdd.map({case (key,value)=>(key,value._1/value._2)})
//方式二
val stuAvg2=stuScoreInfoRdd.map(x=>(x,x._2._1/x._2._2))
stuAvg2.collect.foreach(println)
}
}
注释:
createCombiner: (x: ScoreDetail) => (x.score, 1)
这是第一次遇到xiaoming,创建一个函数,把map中的value转成另外一个类型 ,这里是把(xiaoming,(ScoreDetail类))转换成(xiaoming,(91,1))
mergeValue: (acc: (Int, Int), x: ScoreDetail) => (acc._1 + x.score, acc._2 + 1) 再次碰到xiaoming, 就把这两个合并, 这里是将(xiaoming,(91,1)) 这种类型 和 (xiaoming,(ScoreDetail类))这种类型合并,合并成了(xiaoming,(171,2))
mergeCombiners (acc1: (Int, Int), acc2: (Int, Int)) 这个是将多个分区中的xiaoming的数据进行合并, 如果xiaoming在同一个分区,这个函数就用不上
java版本
public class CombineByKeyJava {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("distinctJava");
JavaSparkContext sc = new JavaSparkContext(conf);
List<ScoreDetailsJava> scoreDetails=new ArrayList<>();
scoreDetails.add(new ScoreDetailsJava("xiaoming", "Math", 98));
scoreDetails.add(new ScoreDetailsJava("xiaoming", "English", 88));
scoreDetails.add(new ScoreDetailsJava("wangwu", "Math", 75));
scoreDetails.add(new ScoreDetailsJava("wangwu", "English", 78));
scoreDetails.add(new ScoreDetailsJava("lihua", "Math", 90));
scoreDetails.add(new ScoreDetailsJava("lihua", "English", 80));
scoreDetails.add(new ScoreDetailsJava("zhangsan", "Math", 91));
scoreDetails.add(new ScoreDetailsJava("zhangsan", "English", 80));
JavaRDD<ScoreDetailsJava> scoreDetailRdd=sc.parallelize(scoreDetails);
JavaPairRDD<String, ScoreDetailsJava> pairRdd = scoreDetailRdd.mapToPair(new PairFunction<ScoreDetailsJava, String, ScoreDetailsJava>() {
@Override
public Tuple2<String, ScoreDetailsJava> call(ScoreDetailsJava scoreDetailsJava) throws Exception {
return new Tuple2<>(scoreDetailsJava.sutName, scoreDetailsJava);
}
});
//createCombiner
Function<ScoreDetailsJava, Tuple2<Integer, Integer>> createCombiner = new Function<ScoreDetailsJava, Tuple2<Integer, Integer>>() {
@Override
public Tuple2<Integer, Integer> call(ScoreDetailsJava s) throws Exception {
return new Tuple2<>(s.score,1);
}
};
//mergeValue
Function2<Tuple2<Integer,Integer>, ScoreDetailsJava,Tuple2<Integer,Integer>> mergeValue
= new Function2<Tuple2<Integer, Integer>, ScoreDetailsJava,Tuple2<Integer, Integer>>() {
@Override
public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> t, ScoreDetailsJava s) throws Exception {
return new Tuple2<>(s.score+t._1,t._2+1);
}
};
//mergeCombiners
Function2<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>> mergeCombiners = new Function2<Tuple2<Integer, Integer>, Tuple2<Integer, Integer>, Tuple2<Integer, Integer>>() {
@Override
Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> t1, Tuple2<Integer, Integer> t2) throws Exception {
return new Tuple2<>(t1._1 + t2._1, t1._2 + t2._2);
}
};
JavaPairRDD<String, Tuple2<Integer, Integer>> stringTuple2JavaPairRdd = pairRdd.combineByKey(createCombiner, mergeValue, mergeCombiners);
List<Tuple2<String,Tuple2<Integer,Integer>>> collect=stringTuple2JavaPairRdd.collect();
for (Tuple2<String, Tuple2<Integer, Integer>> t2:collect){
System.out.println(t2._1+","+t2._2._1/t2._2._2);
}
}
}