Spark修炼之道（进阶篇）——Spark入门到精通：第六节 Spark编程模型（三)

作者：周志湖

网名：摇摆少年梦

微信号：zhouzhihubeyond

本节主要内容

RDD transformation（续)

RDD actions

1. RDD transformation（续)

（1）repartitionAndSortWithinPartitions(partitioner)

repartitionAndSortWithinPartitions函数是repartition函数的变种，与repartition函数不同的是，repartitionAndSortWithinPartitions在给定的partitioner内部进行排序，性能比repartition要高。

函数定义：

/**

* Repartition the RDD according to the given partitioner and, within each resulting partition,

* sort records by their keys.

*

* This is more efficient than calling

repartition

and then sorting within each partition

* because it can push the sorting down into the shuffle machinery.

*/

def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)]

使用示例：

scala> val data = sc.parallelize(List((1,3),(1,2),(5,4),(1, 4),(2,3),(2,4)),3)
data: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[3] at parallelize at <console>:21

scala> data.repartitionAndSortWithinPartitions(new HashPartitioner(3)).collect
res3: Array[(Int, Int)] = Array((1,4), (1,3), (1,2), (2,3), (2,4), (5,4))

（2）aggregateByKey(zeroValue)(seqOp, combOp, [numTasks])

aggregateByKey函数对PairRDD中相同Key的值进行聚合操作，在聚合过程中同样使用了一个中立的初始值。其函数定义如下：

/**

* Aggregate the values of each key, using given combine functions and a neutral “zero value”.

* This function can return a different result type, U, than the type of the values in this RDD,

* V. Thus, we need one operation for merging a V into a U and one operation for merging two U’s,

* as in scala.TraversableOnce. The former operation is used for merging values within a

* partition, and the latter is used for merging values between partitions. To avoid memory

* allocation, both of these functions are allowed to modify and return their first argument

* instead of creating a new U.

*/

def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U,

combOp: (U, U) => U): RDD[(K, U)]

示例代码：

import org.apache.spark.SparkContext._
import org.apache.spark.{SparkConf, SparkContext}

object SparkWordCount{
  def main(args: Array[String]) {
    if (args.length == 0) {
      System.err.println("Usage: SparkWordCount <inputfile> <outputfile>")
      System.exit(1)
    }

    val conf = new SparkConf().setAppName("SparkWordCount").setMaster("local")
    val sc = new SparkContext(conf)

    val data = sc.parallelize(List((1,3),(1,2),(1, 4),(2,3),(2,4)))
    def seqOp(a:Int, b:Int) : Int ={
      println("seq: " + a + "\t " + b)
      math.max(a,b)
   }

   def combineOp(a:Int, b:Int) : Int ={
     println("comb: " + a + "\t " + b)
     a + b
   }

   val localIterator=data.aggregateByKey(1)(seqOp, combineOp).toLocalIterator
    for(i<-localIterator) println(i)
    sc.stop()
  }
}

执行结果：

seq: 1 3

seq: 3 2

seq: 3 4

seq: 1 3

seq: 3 4

(1,4)

(2,4)



从输出结果来看，seqOp函数起作用了，但comineOp函数并没有起作用，在Spark 1.5、1.4及1.3三个版本中测试，结果都是一样的。这篇文章http://www.iteblog.com/archives/1261给出了aggregateByKey的使用，其Spark版本是1.1，其返回结果符合预期。个人觉得是版本原因造成的，具体后面有时间再来分析。

RDD中还有其它非常有用的transformation操作，参见API文档：http://spark.apache.org/docs/latest/api/scala/index.html

2. RDD actions

本小节将介绍常用的action操作，前面使用的collect方法便是一种action，它返回RDD中所有的数据元素，方法定义如下：

/**

* Return an array that contains all of the elements in this RDD.

*/

def collect(): Array[T]

(1) reduce(func)

reduce采样累加或关联操作减少RDD中元素的数量，其方法定义如下：

/**

* Reduces the elements of this RDD using the specified commutative and

* associative binary operator.

*/

def reduce(f: (T, T) => T): T

使用示例：

scala> val data=sc.parallelize(1 to 9)
data: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:22

scala> data.reduce((x,y)=>x+y)
res12: Int = 45

scala> data.reduce(_+_)
res13: Int = 45

（2）count()

/**

* Return the number of elements in the RDD.

*/

def count(): Long

使用示例：

scala> val data=sc.parallelize(1 to 9)
data: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:22
scala> data.count
res14: Long = 9

（3）first()

/**

* Return the first element in this RDD.

*/

def first()

scala> val data=sc.parallelize(1 to 9)
data: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:22
scala> data.first
res15: Int = 1

（4）take(n)

/**

* Take the first num elements of the RDD. It works by first scanning one partition, and use the

* results from that partition to estimate the number of additional partitions needed to satisfy

* the limit.

*

* @note due to complications in the internal implementation, this method will raise

* an exception if called on an RDD of

Nothing

or

Null

.

*/

def take(num: Int): Array[T]

scala> val data=sc.parallelize(1 to 9)
data: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:22
scala> data.take(2)
res16: Array[Int] = Array(1, 2)

(5) takeSample(withReplacement, num, [seed])

对RDD中的数据进行采样

/**

* Return a fixed-size sampled subset of this RDD in an array

*

* @param withReplacement whether sampling is done with replacement

* @param num size of the returned sample

* @param seed seed for the random number generator

* @return sample of specified size in an array

*/

// TODO: rewrite this without return statements so we can wrap it in a scope

def takeSample(

withReplacement: Boolean,

num: Int,

seed: Long = Utils.random.nextLong): Array[T]

scala> val data=sc.parallelize(1 to 9)
data: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:22

scala> data.takeSample(false,5)
res17: Array[Int] = Array(6, 7, 4, 1, 2)

scala> data.takeSample(true,5)
res18: Array[Int] = Array(3, 3, 8, 3, 8)

(6) takeOrdered(n, [ordering])

/**

* Returns the first k (smallest) elements from this RDD as defined by the specified

* implicit Ordering[T] and maintains the ordering. This does the opposite of [[top]].

* For example:

* {{{

* sc.parallelize(Seq(10, 4, 2, 12, 3)).takeOrdered(1)

* // returns Array(2)

*

* sc.parallelize(Seq(2, 3, 4, 5, 6)).takeOrdered(2)

* // returns Array(2, 3)

* }}}

*

* @param num k, the number of elements to return

* @param ord the implicit ordering for T

* @return an array of top elements

*/

def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T]

(6) saveAsTextFile(path)

将RDD保存到文件，本地模式时保存在本地文件，集群模式指如果在Hadoop基础上则保存在HDFS上

/**

* Save this RDD as a text file, using string representations of elements.

*/

def saveAsTextFile(path: String): Unit

scala> data.saveAsTextFile("/data.txt")

(7) countByKey()

将RDD中的数据按Key计数

/**

* Count the number of elements for each key, collecting the results to a local Map.

*

* Note that this method should only be used if the resulting map is expected to be small, as

* the whole thing is loaded into the driver’s memory.

* To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which

* returns an RDD[T, Long] instead of a map.

*/

def countByKey(): Map[K, Long]

使用示例：

scala> val data = sc.parallelize(List((1,3),(1,2),(5,4),(1, 4),(2,3),(2,4)),3)
data: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[10] at parallelize at <console>:22

scala> data.countByKey()
res22: scala.collection.Map[Int,Long] = Map(1 -> 3, 5 -> 1, 2 -> 2)

（8）foreach(func)

foreach方法遍历RDD中所有的元素

// Actions (launch a job to return a value to the user program)

/**

* Applies a function f to all elements of this RDD.

*/

def foreach(f: T => Unit): Unit

import org.apache.spark.SparkContext._
import org.apache.spark.{SparkConf, SparkContext}

object ForEachDemo{
  def main(args: Array[String]) {
    if (args.length == 0) {
      System.err.println("Usage: SparkWordCount <inputfile> <outputfile>")
      System.exit(1)
    }

    val conf = new SparkConf().setAppName("SparkWordCount").setMaster("local")
    val sc = new SparkContext(conf)

    val data = sc.parallelize(List((1,3),(1,2),(1, 4),(2,3),(2,4)))

    data.foreach(x=>println("key="+x._1+",value="+x._2))
    sc.stop()
  }
}

Sparkh中还存在其它非常有用的action操作，如foldByKey、sampleByKey等，参见API文档：http://spark.apache.org/docs/latest/api/scala/index.html