Spark RDD算子(七) groupByKey、cogroup_groupbykey cogroup-优快云博客

本文链接：https://blog.youkuaiyun.com/timicai/article/details/109611614

本文详细介绍了Spark中的两个关键算子——groupByKey和cogroup。groupByKey用于将相同key的元素聚合，返回结果为RDD[key, Iterable[value]]，类似于SQL的GROUP BY操作，但不建议在大规模数据中直接使用，因为可能会导致性能问题，推荐使用reduceByKey或combineByKey进行优化。cogroup则是对具有相同键的多个RDD进行分组，提供了一种同时处理多个RDD中相同键值的方法。" 114154618,10536023,Java利用高德API实现经纬度到地理位置转换,"['Java开发', '地图API', '地理编码']

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

一、groupByKey

groupByKey会将RDD[key,value] 按照相同的key进行分组，形成RDD[key,Iterable[value]]的形式，有点类似于sql中的groupby
groupByKey不能传算法，消耗性能，优化尽量使用reduceByKey或combineByKey

例：对学生的成绩进行分组

scala版本

val conf=new SparkConf().setMaster("local[2]").setAppName("groupby")
val sc=new SparkContext(conf)

val rdd=sc.makeRDD(List(("张三",97),("张三",87),
  ("李四",97),("李四",99),("小罗",100)))

val scoreGroupRdd = rdd.groupByKey()
//姓名,(分数,分数)的元组
scoreGroupRdd.collect.foreach(println)
//姓名,分数
scoreGroupRdd.collect.foreach(x=> {
  val name=x._1
  val scoreDetail=x._2
  scoreDetail.foreach(scoreDetail=>println(name,scoreDetail))
})

java版本

SparkConf conf=new SparkConf().setMaster("local[2]").setAppName("groupbyjava");
JavaSparkContext sc=new JavaSparkContext(conf);

//将JavaRDD<Tuple2<String,Float>> 类型转换为 JavaPairRDD<String, Float>
JavaPairRDD<String, Float> scoreMapRdd = JavaPairRDD.fromJavaRDD(scoreRdd);
JavaPairRDD<String, Iterable<Float>> scoreByKeyRdd = scoreMapRdd.groupByKey();
Map<String, Iterable<Float>> scoreMap = scoreByKeyRdd.collectAsMap();
Set<String> nameSet = scoreMap.keySet();
//增强for输出  name:score  和 name:scores的元组
for (String nameKey :nameSet) {
    Iterable<Float> floats = scoreMap.get(nameKey);
    Iterator<Float> iterator = floats.iterator();
    while(iterator.hasNext()){
        Float next = iterator.next();
        System.out.println(nameKey+":"+next);
    }
    System.out.println(nameKey+":"+scoreMap.get(nameKey));
}
System.out.println("------------------");
//通过Set迭代   name:score
Iterator<String> iterator = nameSet.iterator();
while(iterator.hasNext()){
    String nameKey=iterator.next();
    Iterable<Float> scores = scoreMap.get(nameKey);
    Iterator<Float> scoresiterator = scores.iterator();
    while(scoresiterator.hasNext()){
        Float value = scoresiterator.next();
        System.out.println(nameKey+":"+value);
    }
}

二、cogroup

groupByKey是对单个 RDD 的数据进行分组，还可以使用一个叫作 cogroup() 的函数对多个共享同一个键的 RDD 进行分组

scala版本

val rdd=sc.makeRDD(List(("张三",11),
  ("张三",31),("王五",11)))
val rdd2=sc.makeRDD(List(("张三",21),
  ("李四",21),("赵六",21)))
val rdd3=sc.makeRDD(List(("张三",31),
  ("李四",31),("小罗",31)))

println("--------2个RDD cogroup-------")
val rdd11 = rdd.cogroup(rdd2)
rdd11.collect.foreach(println)

println("--------3个RDD cogroup-------")
val rdd31 = rdd.cogroup(rdd2,rdd3).collect.foreach(println)

java版本

JavaRDD<Tuple2<String, Float>> scoreRdd1 = sc.parallelize(Arrays.asList(
        new Tuple2<>("张三", 11F),
        new Tuple2<>("张三", 12F),
        new Tuple2<>("李四", 13F)));

JavaRDD<Tuple2<String, Float>> scoreRdd2 = sc.parallelize(Arrays.asList(
        new Tuple2<>("张三", 21F),
        new Tuple2<>("张三", 22F),
        new Tuple2<>("李四", 23F)));

JavaRDD<Tuple2<String, Float>> scoreRdd3 = sc.parallelize(Arrays.asList(
        new Tuple2<>("张三", 31F),
        new Tuple2<>("张三", 32F),
        new Tuple2<>("李四", 33F)));

JavaPairRDD<String, Float> PairRDD1 = JavaPairRDD.fromJavaRDD(scoreRdd1);
JavaPairRDD<String, Float> PairRDD2 = JavaPairRDD.fromJavaRDD(scoreRdd2);
JavaPairRDD<String, Float> PairRDD3 = JavaPairRDD.fromJavaRDD(scoreRdd3);

//两个rdd进行cogroup
JavaPairRDD<String, Tuple2<Iterable<Float>, Iterable<Float>>> cogroup1
        = PairRDD1.cogroup(PairRDD2);

Map<String, Tuple2<Iterable<Float>, Iterable<Float>>> myMap = cogroup1.collectAsMap();
Set<String> keys = myMap.keySet();
for (String key:keys) {
    System.out.println(key+" "+myMap.get(key));
}

System.out.println("----------------");
//三个rdd进行cogroup
JavaPairRDD<String, Tuple3<Iterable<Float>, Iterable<Float>, Iterable<Float>>> cogroup2
        = PairRDD1.cogroup(PairRDD2, PairRDD3);
Map<String, Tuple3<Iterable<Float>, Iterable<Float>, Iterable<Float>>> myMap2 = cogroup2.collectAsMap();
Set<String> keys2 = myMap2.keySet();
for (String key:keys2) {
    System.out.println(key+" "+myMap2.get(key));
}