【菜鸟系列】spark常用算子总结（scala、java）--groupByKey，reduceByKey

groupByKey和reduceByKey是常用的聚合函数，作用的数据集为PairRDD

scala
reduceByKey函数原型

def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope {
combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)
}

我们可以发现，输入参数是两个，第一个参数是分区函数，第二个是作用在value上的一个函数，如果第一个参数为空，则使用默认的hashPartitioner;

reduceByKey样例

  val conf = new SparkConf().setAppName("jiangtao_demo").setMaster("local")
  val sc = new SparkContext(conf)
  val data = sc.makeRDD(List("pandas","numpy","pip","pip","pip"))
  //mapToPair
  val dataPair = data.map((_,1))

  //reduceByKey
  val result1 = dataPair.reduceByKey(_+_)
  //或者
  // val result2 = dataPair.reduceByKey((x,y)=>(x+y))

groupByKey函数原型

def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
// groupByKey shouldn’t use map side combine because map side combine does not
// reduce the amount of data shuffled and requires all map side data be inserted
// into a hash table, leading to more objects in the old gen.
val createCombiner = (v: V) => CompactBuffer(v)
val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)
bufs.asInstanceOf[RDD[(K, Iterable[V])]]
}

我们发现groupByKey输入参数为分区方式，参数为空，代表采用默认的hashPartitioner，返回值为RDD[(K, Iterable[V])

其实reduceByKey与groupByKey底层调用的都是combineByKeyWithClassTag，我们都知道，reduceByKey是分区内先进行一个合并，在进行shuffle混洗；而groupByKey是直接进行shuffle混洗，效率上来看，一定是reduceByKey更优，下面我们看下combineByKeyWithClassTag，会发现，有个参数叫mapSideCombine，这个参数是控制在shuffle前是否进行分区内聚合操作，reduceByKey默认是true

def combineByKeyWithClassTag[C](
createCombiner: V => C,
mergeValue: (C, V) => C,
mergeCombiners: (C, C) => C,
partitioner: Partitioner,
mapSideCombine: Boolean = true,
serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
require(mergeCombiners != null, “mergeCombiners must be defined”) // required as of Spark 0.9.0
if (keyClass.isArray) {
if (mapSideCombine) {
throw new SparkException(“Cannot use map-side combining with array keys.”)
}
if (partitioner.isInstanceOf[HashPartitioner]) {
throw new SparkException(“Default partitioner cannot partition array keys.”)
}
}
val aggregator = new Aggregator[K, V, C](
self.context.clean(createCombiner),
self.context.clean(mergeValue),
self.context.clean(mergeCombiners))
if (self.partitioner == Some(partitioner)) {
self.mapPartitions(iter => {
val context = TaskContext.get()
new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
}, preservesPartitioning = true)
} else {
new ShuffledRDD[K, V, C](self, partitioner)
.setSerializer(serializer)
.setAggregator(aggregator)
.setMapSideCombine(mapSideCombine)
}
}

groupByKey样例

  val conf = new SparkConf().setAppName("jiangtao_demo").setMaster("local")
  val sc = new SparkContext(conf)
  val data = sc.makeRDD(List("pandas","numpy","pip","pip","pip"))
  //mapToPair
  val dataPair = data.map((_,1))
  //groupByKey
  val result3 = dataPair.groupByKey()
  //此时返回的是RDD:String,Iterable,下面的代码对  x._2进行一个累加
  val result4 = result3.map(x=>(x._1,x._2.sum))

================================================================
java
reduceByKey样例

SparkConf conf = new SparkConf().setAppName("jiangtao_demo").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        //并行集合生成JavaRDD
        JavaRDD lines = jsc.parallelize(Arrays.asList("pandas","numpy","pip","pip","pip"));
JavaPairRDD<String,Integer> mapToPairResult = lines.mapToPair(new PairFunction<String,String,Integer>() {
            @Override
            public Tuple2<String,Integer> call(String o) throws Exception {
                Tuple2 tuple2 = new Tuple2(o,1);
                //System.out.println(tuple2._1()+":"+tuple2._2());
                return tuple2;
            }
        });
        //reduceByKey 统计词频
        JavaPairRDD reduceByKeyResult = mapToPairResult.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer i1, Integer i2) throws Exception {
                return i1+i2;
            }
        });

groupByKey样例

SparkConf conf = new SparkConf().setAppName("jiangtao_demo").setMaster("local");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        //并行集合生成JavaRDD
        JavaRDD lines = jsc.parallelize(Arrays.asList("pandas","numpy","pip","pip","pip"));
JavaPairRDD<String,Integer> mapToPairResult = lines.mapToPair(new PairFunction<String,String,Integer>() {
            @Override
            public Tuple2<String,Integer> call(String o) throws Exception {
                Tuple2 tuple2 = new Tuple2(o,1);
                //System.out.println(tuple2._1()+":"+tuple2._2());
                return tuple2;
            }
        });
        //groupByKey 统计词频
        JavaPairRDD groupByKeyResult = mapToPairResult.groupByKey();
        //此时返回的结果是JavaPairRDD
        //[(pip,[1, 1, 1]), (pandas,[1]), (numpy,[1])]
        System.out.println(groupByKeyResult.collect());
        JavaPairRDD<String,Integer> gr = groupByKeyResult.mapToPair(new PairFunction<Tuple2<String,Iterable>,String,Integer>(){

            public Tuple2<String,Integer> call(Tuple2<String,Iterable> tuple2){
                int sum = 0;
                Iterator<Integer> it = tuple2._2.iterator();
                while(it.hasNext()){
                    sum += it.next();
                }
                return new Tuple2<String,Integer>(tuple2._1,sum);
            }
        });
        System.out.println(gr.collect());
        //返回结果[(pip,3), (pandas,1), (numpy,1)]

后续会补全更多的常用算子，敬请关注，此文原创，转载请注明出处