Spark常用Transformations算子(一)

作者: 小猪Harry | 来源:发表于2018-10-17 09:53 被阅读0次

介绍以下Transformations算子：
map
flatMap
mapPartitions
mapPartitionsWithIndex
filter
sample
union
intersection
sortBy
sortByKey
groupByKey
reduceByKey
distinct
coalesce
repartition

(1) map、mapPartitions、mapPartitionsWithIndex

map以一条记录为单位进行操作

val arr = Array("Tom","Bob","Tony","Jerry")

//把4条数据分到两个分区中
val rdd = sc.parallelize(arr,2)

/*
 * 模拟把RDD中的元素写入数据库的过程
 */
rdd.map(x => {
  println("创建数据库连接...")
  println("写入数据库...")
  println("关闭数据库连接...")
  println()
}).count()

结果：

创建数据库连接...
写入数据库...
关闭数据库连接...

创建数据库连接...
写入数据库...
关闭数据库连接...

创建数据库连接...
写入数据库...
关闭数据库连接...

创建数据库连接...
写入数据库...
关闭数据库连接...

mapPartitions以分区为单位进行操作

val arr = Array("Tom","Bob","Tony","Jerry")
//把4条数据分到两个分区中
val rdd = sc.parallelize(arr,2)

/*
* 将RDD中的数据写入到数据库中，绝大部分使用mapPartitions算子来实现
*/
rdd.mapPartitions(x => {
  println("创建数据库连接...")
  val list = new ListBuffer[String]()
  while(x.hasNext) {
    // 模拟写入数据库
    list += x.next() + "写入数据库"
  }
  // 模拟执行SQL语句，批量插入
  list.iterator
}).foreach(println)

结果：

创建数据库
Tom:写入数据库
Bob:写入数据库 
创建数据库
Tony:写入数据库
Jerry:写入数据库

mapPartitionsWithIndex

val dataArr = Array("Tom01","Tom02","Tom03"
                  ,"Tom04","Tom05","Tom06"
                  ,"Tom07","Tom08","Tom09"
                  ,"Tom10","Tom11","Tom12")
val rdd = sc.parallelize(dataArr, 3);
val result = rdd.mapPartitionsWithIndex((index,x) => {
    val list = ListBuffer[String]()
    while (x.hasNext) {
      list += "partition:"+ index + " content:" + x.next
    }
    list.iterator
})
println("分区数量:" + result.partitions.size)
val resultArr = result.collect()
for(x <- resultArr){
  println(x)
}

结果：

分区数量:3
partition:0 content:Tom01
partition:0 content:Tom02
partition:0 content:Tom03
partition:0 content:Tom04
partition:1 content:Tom05
partition:1 content:Tom06
partition:1 content:Tom07
partition:1 content:Tom08
partition:2 content:Tom09
partition:2 content:Tom10
partition:2 content:Tom11
partition:2 content:Tom12

(2) flatMap

val conf = new SparkConf().setAppName("FlatMapTest").setMaster("local")
val sc = new SparkContext(conf)
val data = Array("hello hadoop","hello hive", "hello spark")
val rdd = sc.makeRDD(data)

rdd.flatMap(_.split(" ")).foreach(println)
/*
结果：
hello
hadoop
hello
hive
hello
spark
*/
rdd.map(_.split(" ")).foreach(println)
/*
[Ljava.lang.String;@3c986196
[Ljava.lang.String;@113116a6
[Ljava.lang.String;@542d75a6
*/

map 和 flatMap的区别
map：输入一条数据，返回一条数据
flatMap：输入一条数据，可能返回多条数据

image.png

以下scala程序可以说明map函数、flatMap函数和flatten函数的区别和联系：

scala> val arr = Array("hello hadoop","hello hive","hello spark")
arr: Array[String] = Array(hello hadoop, hello hive, hello spark)

scala> val map = arr.map(_.split(" "))
map: Array[Array[String]] = Array(Array(hello, hadoop), Array(hello, hive), Array(hello, spark))

scala> map.flatten
res1: Array[String] = Array(hello, hadoop, hello, hive, hello, spark)

scala> arr.flatMap(_.split(" "))
res2: Array[String] = Array(hello, hadoop, hello, hive, hello, spark)

(3) filter ：过滤

val rdd = sc.makeRDD(Array("hello","hello","hello","world"))
// filter(boolean) 返回的是判断条件为true的记录
rdd.filter(!_.contains("hello")).foreach(println)

结果：
world

(4) sample ：随机抽样

sample(withReplacement: Boolean, fraction: Double, seed: Long)  

withReplacement : 是否是放回式抽样  
    true代表如果抽中A元素，之后还可以抽取A元素
    false代表如果抽中了A元素，之后都不在抽取A元素  
fraction : 抽样的比例  
seed : 抽样算法的随机数种子，不同的数值代表不同的抽样规则，可以手动设置，默认为long的随机数

val rdd = sc.makeRDD(Array(
  "hello1","hello2","hello3","hello4","hello5","hello6",
  "world1","world2","world3","world4"
))
rdd.sample(false, 0.3).foreach(println)

结果：理论上会随机抽取30%的数据，但是在数据量不大的时候，不一定很准确

hello1
hello3
world3

(5) union：把两个RDD进行逻辑上的合并

val rdd1 =sc.makeRDD(1 to 3)
val rdd2 = sc.parallelize(4 until 6)
rdd1.union(rdd2).foreach {println}

结果：

1
2
3
4
5

(6) intersection：求两个RDD的交集

val rdd1 =sc.makeRDD(1 to 3)
val rdd2 = sc.parallelize(2 to 5)

rdd1.intersection(rdd2).foreach(println)

结果：
2
3

(7) sortBy和sortByKey

sortBy：手动指定排序的字段

val arr = Array(
        Tuple3(190,100,"Jed"),
        Tuple3(100,202,"Tom"),
        Tuple3(90,111,"Tony")
    )
val rdd = sc.parallelize(arr)
rdd.sortBy(_._1).foreach(println)
/* 按第一个元素排序
   (90,111,Tony)
   (100,202,Tom)
   (190,100,Jed)
 */

rdd.sortBy(_._2, false).foreach(println)
/* 按照第二个元素排序，降序
   (100,202,Tom)
   (90,111,Tony)
   (190,100,Jed)
 */

rdd.sortBy(_._3).foreach(println)
/* 按照第三个元素排序
   (190,100,Jed)
   (100,202,Tom)
   (90,111,Tony)
 */

}

sortByKey：按key进行排序

val rdd = sc.makeRDD(Array(
      (5,"Tom"),(10,"Jed"),(3,"Tony"),(2,"Jack")
    ))
rdd.sortByKey().foreach(println)

结果：

(2,Jack)
(3,Tony)
(5,Tom)
(10,Jed)

(8) groupByKey和reduceByKey

val rdd = sc.makeRDD(Array(
      ("Tom",1),("Tom",1),("Tony",1),("Tony",1)
    ))

rdd.groupByKey().foreach(println)
/*
(Tom,CompactBuffer(1, 1))
(Tony,CompactBuffer(1, 1))
*/

rdd.reduceByKey(_+_).foreach(println)
/*
(Tom,2)
(Tony,2)
*/

image.png

(9) distinct：去掉重复数据

val rdd = sc.makeRDD(Array(
      "hello",
      "hello",
      "hello",
      "world"
    ))

rdd.distinct().foreach {println}
/*
hello
world
*/

// dinstinct = map + reduceByKey + map
val distinctRDD = rdd
  .map {(_,1)}
  .reduceByKey(_+_)
  .map(_._1)
distinctRDD.foreach {println}
/*
hello
world
*/

image.png

(10) coalesce、repartition：改变RDD分区数

coalesce

/*
 * false:不产生shuffle
 * true:产生shuffle
 * 如果重分区的数量大于原来的分区数量,必须设置为true,否则分区数不变
 * 增加分区会把原来的分区中的数据随机分配给设置的分区中
 * 默认为false
 */
object CoalesceTest {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("MapTest").setMaster("local")
    val sc = new SparkContext(conf)
    val arr = Array(
      "partition:0 content:Tom01",
      "partition:0 content:Tom02",
      "partition:0 content:Tom03",
      "partition:0 content:Tom04",
      "partition:1 content:Tom05",
      "partition:1 content:Tom06",
      "partition:1 content:Tom07",
      "partition:1 content:Tom08",
      "partition:2 content:Tom09",
      "partition:2 content:Tom10",
      "partition:2 content:Tom11",
      "partition:2 content:Tom12")

    val rdd = sc.parallelize(arr, 3);

    val coalesceRdd = rdd.coalesce(6,true)

    val results = coalesceRdd.mapPartitionsWithIndex((index,x) => {
      val list = ListBuffer[String]()
      while (x.hasNext) {
        list += "partition:"+ index + " content:[" + x.next + "]"
      }
      list.iterator
    })

    println("分区数量:" + results.partitions.size)
    results.foreach(println)
    /*
    分区数量:6
    partition:0 content:[partition:1 content:Tom07]
    partition:0 content:[partition:2 content:Tom10]
    partition:1 content:[partition:0 content:Tom01]
    partition:1 content:[partition:1 content:Tom08]
    partition:1 content:[partition:2 content:Tom11]
    partition:2 content:[partition:0 content:Tom02]
    partition:2 content:[partition:2 content:Tom12]
    partition:3 content:[partition:0 content:Tom03]
    partition:4 content:[partition:0 content:Tom04]
    partition:4 content:[partition:1 content:Tom05]
    partition:5 content:[partition:1 content:Tom06]
    partition:5 content:[partition:2 content:Tom09]
    */

    // 增加分区肯定会发生shuffle，如果设置为false，结果是不变的
    val coalesceRdd2 = rdd.coalesce(6,false)
    val results2 = coalesceRdd2.mapPartitionsWithIndex((index,x) => {
      val list = ListBuffer[String]()
      while (x.hasNext) {
        list += "partition:"+ index + " content:[" + x.next + "]"
      }
      list.iterator
    })

    println("分区数量:" + results2.partitions.size)
    results2.foreach(println)
    /*
    分区数量:3
    partition:0 content:[partition:0 content:Tom01]
    partition:0 content:[partition:0 content:Tom02]
    partition:0 content:[partition:0 content:Tom03]
    partition:0 content:[partition:0 content:Tom04]
    partition:1 content:[partition:1 content:Tom05]
    partition:1 content:[partition:1 content:Tom06]
    partition:1 content:[partition:1 content:Tom07]
    partition:1 content:[partition:1 content:Tom08]
    partition:2 content:[partition:2 content:Tom09]
    partition:2 content:[partition:2 content:Tom10]
    partition:2 content:[partition:2 content:Tom11]
    partition:2 content:[partition:2 content:Tom12]
    */

    val coalesceRdd3 = rdd.coalesce(2,true)
    val results3 = coalesceRdd3.mapPartitionsWithIndex((index,x) => {
      val list = ListBuffer[String]()
      while (x.hasNext) {
        list += "partition:"+ index + " content:[" + x.next + "]"
      }
      list.iterator
    })

    println("分区数量:" + results3.partitions.size)
    results3.foreach(println)
    /*
    分区数量:2
    partition:0 content:[partition:0 content:Tom01]
    partition:0 content:[partition:0 content:Tom03]
    partition:0 content:[partition:1 content:Tom05]
    partition:0 content:[partition:1 content:Tom07]
    partition:0 content:[partition:2 content:Tom09]
    partition:0 content:[partition:2 content:Tom11]
    partition:1 content:[partition:0 content:Tom02]
    partition:1 content:[partition:0 content:Tom04]
    partition:1 content:[partition:1 content:Tom06]
    partition:1 content:[partition:1 content:Tom08]
    partition:1 content:[partition:2 content:Tom10]
    partition:1 content:[partition:2 content:Tom12]
    */

    val coalesceRdd4 = rdd.coalesce(2,false)
    val results4 = coalesceRdd4.mapPartitionsWithIndex((index,x) => {
      val list = ListBuffer[String]()
      while (x.hasNext) {
        list += "partition:"+ index + " content:[" + x.next + "]"
      }
      list.iterator
    })

    println("分区数量:" + results4.partitions.size)
    results4.foreach(println)
    /*
    分区数量:2
    partition:0 content:[partition:0 content:Tom01]
    partition:0 content:[partition:0 content:Tom02]
    partition:0 content:[partition:0 content:Tom03]
    partition:0 content:[partition:0 content:Tom04]
    partition:1 content:[partition:1 content:Tom05]
    partition:1 content:[partition:1 content:Tom06]
    partition:1 content:[partition:1 content:Tom07]
    partition:1 content:[partition:1 content:Tom08]
    partition:1 content:[partition:2 content:Tom09]
    partition:1 content:[partition:2 content:Tom10]
    partition:1 content:[partition:2 content:Tom11]
    partition:1 content:[partition:2 content:Tom12]
    */
  }

}

以下图片说明这些情况：

image.png

repartition

repartition(int n) = coalesce(int n, true)

partitionBy：自定义分区器，重新分区

package com.aura.transformations

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

/**
  * Author: Jed
  * Description: 自定义分区规则
  * Date: Create in 2018/1/12
  */
class MyPartition extends Partitioner {

  // 分区数量为2
  override def numPartitions: Int = 2

  // 自定义分区规则
  override def getPartition(key: Any): Int = {
    if(key.hashCode() % 2 == 0) {
      0
    }else {
      1
    }
  }
}

object PartitionByTest {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("PartitionByTest").setMaster("local")
    val sc = new SparkContext(conf)

    val arr = Array((1,1),(2,2),(3,3),(4,4),(5,5),(6,6),(7,7),(8,8),(9,9))
    val rdd = sc.makeRDD(arr,3)
    println("分区数量：" + rdd.partitions.length)
    rdd.foreachPartition(x => {
      println("*******")
      while(x.hasNext) {
        println(x.next())
      }
    })
    /*
    分区数量：3
    *******
    (1,1)
    (2,2)
    (3,3)
    *******
    (4,4)
    (5,5)
    (6,6)
    *******
    (7,7)
    (8,8)
    (9,9)
     */

    val partitionRDD = rdd.partitionBy(new MyPartition)
    println("分区数量：" + partitionRDD.partitions.length)
    partitionRDD.foreachPartition(x => {
      println("*******")
      while(x.hasNext) {
        println(x.next())
      }
    })
    /*
    分区数量：2
    *******
    (2,2)
    (4,4)
    (6,6)
    (8,8)
    *******
    (1,1)
    (3,3)
    (5,5)
    (7,7)
    (9,9)
     */
  }

}

Spark常用Transformations算子(一)

(1) map、mapPartitions、mapPartitionsWithIndex

(2) flatMap

(3) filter ：过滤

(4) sample ：随机抽样

(5) union：把两个RDD进行逻辑上的合并

(6) intersection：求两个RDD的交集

(7) sortBy和sortByKey

(8) groupByKey和reduceByKey

(9) distinct：去掉重复数据

(10) coalesce、repartition：改变RDD分区数

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