spark sortBy&&sortByKey

sortByKey和sortBy都是transforamation算子；

sortByKey   源码如下：

  def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)
      : RDD[(K, V)] = self.withScope
  {
    val part = new RangePartitioner(numPartitions, self, ascending)
    new ShuffledRDD[K, V, V](self, part)
      .setKeyOrdering(if (ascending) ordering else ordering.reverse)
  }

参数一：ascending 默认是True正序排列

参数二：分区数

即可以对标准RDD进行排序，也可以按照指定指定键值对pair Rdd进行排序

sortBy   源码如下

def sortBy[K](
      f: (T) => K,
      ascending: Boolean = true,
      numPartitions: Int = this.partitions.length)
      (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope {
    this.keyBy[K](f)
        .sortByKey(ascending, numPartitions)
        .values
  }

参数一： 输入一个匿名函数，匿名函数的返回值是排序的关键字

参数二：ascending 默认是True正序排列

参数三：分区数

案例对wordcount结果进行排序

数据

hadoop hadoop hadoop
spark spark spark spark spark
hadoop hadoop hadoop
spark spark spark spark spark
hive hive hive hive
hadoop hadoop hadoop
spark spark spark spark spark
hadoop hadoop hadoop
spark spark spark spark spark
apache apache apache apache
hadoop hadoop hadoop
spark spark spark spark spark
hive hive hive hive
hadoop hadoop hadoop
spark spark spark spark spark
apache apache apache apache

sortByKey

val data:RDD[String]=spark.sparkContext.textFile(datapath,2)
data.flatMap(x=>x.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(false).collect.foreach(println(_))

sortBykey

val data:RDD[String]=spark.sparkContext.textFile(datapath,2)

data.flatMap(x=>x.split(" ")).map((_,1)).reduceByKey(_+_).sortBy(x=>x._1,false).collect.foreach(println(_))

结果

(spark,30)
(hive,8)
(hadoop,18)
(apache,8)