Spark Core入门2【RDD的实质与RDD编程API】

﹏ヽ暗。殇╰゛Y 2022-10-02 00:38 190阅读 0赞

# 一、对RDD操作的本质 #

RDD的本质是一个函数,而RDD的变换不过是函数的嵌套.RDD有两类:

1.  输入的RDD: 典型如KafkaRDD、JDBCRDD
2.  转换的RDD: 如MapPartitionsRDD

**RDD的处理流程:**

以如下代码为例:

sc.textFile("abc.log").map().saveAsTextFile("")

1. textFile 会构建出一个NewHadoopRDD

2. map函数运行后会构建出一个MapPartitionsRDD

3. saveAsTextFile触发了实际流程代码的执行

**所以RDD不过是对一个函数的封装，当一个函数对数据处理完成后，我们就得到一个RDD的数据集(是一个虚拟的，后续会解释)。**

NewHadoopRDD是数据来源，每个parition(分布式并行执行)负责获取数据，获得过程是通过**iterator.next**获得一条一条记录的。假设某个时刻拿到了一条数据A,这个A会立刻被map里的函数处理得到B（完成了转换）,然后开始写入到HDFS(一条一条写入)上。其他数据重复如此。

1.  理论上某个MapPartitionsRDD里实际在内存里的数据等于其Partition的数目，是个非常小的数值。
2.  NewHadoopRDD则会略多些，因为属于数据源，读取文件，假设读取文件的buffer是1M，那么最多也就是partitionNum\*1M 数据在内存里
3.  saveAsTextFile也是一样的，往HDFS写文件，需要buffer，最多数据量为 buffer\* partitionNum(可以汇聚到Driver端写,也可以各个Executor直接写入到HDFS)

所以整个过程其实是流式(一般是一条一条或者一批一批)的过程，一条数据被各个RDD所包裹的函数处理。

(PS: 如果是mapPartition的话,那就是把整个partition的数据一起加载过来了，所以使用mapPartition函数比起map会容易造成内存溢出)

\------------------------------------------------------------

第一章节参考博客：[https://blog.csdn.net/zc19921215/article/details/82858585][https_blog.csdn.net_zc19921215_article_details_82858585]

# 二、RDD编程常用API #

**Transformation和Action的区别？**

Transformation不会立即执行，只是记录这些操作，操作后生成新的RDD  
Action会执行前边的Transformation所有操作，不再生成RDD，而是返回具体的结果

RDD中的所有转换都是延迟加载的，也就是说，它们并不会直接计算结果。相反的，它们只是记住这些应用到基础数据集（例如一个文件）上的转换动作。只有当发生一个要求返回结果给Driver的动作时，这些转换才会真正运行。这种设计让Spark更加有效率地运行。

**对于Transformation和Action的常用API**，可以参考官方文档：[http://spark.apache.org/docs/latest/rdd-programming-guide.html\#transformations][http_spark.apache.org_docs_latest_rdd-programming-guide.html_transformations]

常用的Transformation与Action API举例。

<table style="width:500px;"> 
 <tbody> 
  <tr> 
   <td style="width:286px;">aggregateByKey</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">reduceByKey</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">filter</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">map</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">flatMap</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">mapPartitions</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">mapPartitionsWithIndex</td> 
   <td style="width:562px;">Transformation</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">collect</td> 
   <td style="width:562px;">Action</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">aggregate</td> 
   <td style="width:562px;">Action</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">saveAsTextFile</td> 
   <td style="width:562px;">Action</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">foreach</td> 
   <td style="width:562px;">Action</td> 
  </tr> 
  <tr> 
   <td style="width:286px;">foreachPartition</td> 
   <td style="width:562px;">Action</td> 
  </tr> 
 </tbody> 
</table>

## 2.1  常用Transformation-API(即转换，延迟加载) ##

**\#通过并行化scala集合创建RDD**

scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8))
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

**\#查看该RDD的分区数量**

scala> rdd1.partitions.length

**\#排序操作1**

scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8)).map(_ * 2).sortBy(x => x, true)
    rdd1: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[7] at sortBy at <console>:24

**\#排序操作2**

scala> val rdd2 = sc.parallelize(List(5,6,4,7,3,8,2,9,1,10)).map(_*2).sortBy(x=>x+"",true)
    rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[14] at sortBy at <console>:24

发现返回的是RDD\[Int\]，因为sortBy中传递的仅仅是排序规则，排序仅仅改变数据的顺序，而不会改变数据的类型。collect的作用是将一系列的transformation操作提交到集群中执行，结果再返回到Driver所在的Array集合中。

scala> rdd2.collect
    res1: Array[Int] = Array(10, 12, 14, 16, 18, 2, 20, 4, 6, 8)

排序结果为字典顺序。

**\#区分是对RDD中的List操作还是对Scala中的List操作**

scala> val rdd5 = sc.parallelize(List(List("a b c", "a b b"),List("e f g", "a f g"), List("h i j", "a a b")))
    rdd5: org.apache.spark.rdd.RDD[List[String]] = ParallelCollectionRDD[16] at parallelize at <console>:24
    
    scala> val rdd6 = rdd5.flatMap(_.flatMap(_.split(" "))).collect
    rdd6: Array[String] = Array(a, b, c, a, b, b, e, f, g, a, f, g, h, i, j, a, a, b)

第一个flatMap调用的是rdd5的方法，取出的是一个个的List(如ListList("a b c", "a b b")和List("e f g", "a f g")等)，所以操作的是RDD中的List，第二个flatMap取出的是scala中的List元素"a b c"和"a b b"等元素。所以第一个flatMap会将任务分发到集群中不同的机器执行，而第二个flatMap会在集群中的某一台机器对某一个List进行计算。

**\#union求并集，注意类型要一致**

scala> val rdd6 = sc.parallelize(List(5,6,4,7))
    rdd6: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[18] at parallelize at <console>:24
    
    scala> val rdd7 = sc.parallelize(List(1,2,3,4))
    rdd7: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[19] at parallelize at <console>:24
    
    scala> val rdd8 = rdd6.union(rdd7)
    rdd8: org.apache.spark.rdd.RDD[Int] = UnionRDD[20] at union at <console>:27

使用DSL风格调用为：

scala> val rdd8 = rdd6 union rdd7
    rdd8: org.apache.spark.rdd.RDD[Int] = UnionRDD[21] at union at <console>:27
    
    scala> rdd8.collect
    res2: Array[Int] = Array(5, 6, 4, 7, 1, 2, 3, 4)

**\#intersection求交集**

scala> val rdd9 = rdd6.intersection(rdd7)
    rdd9: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[27] at intersection at <console>:27
    
    scala> rdd9.collect
    res3: Array[Int] = Array(4)

**\#join leftOuterJoin rightOuterJoin(连接)**

scala> val rdd1 = sc.parallelize(List(("tom", 1), ("jerry", 2), ("kitty", 3)))
    rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[28] at parallelize at <console>:24
    
    scala> val rdd2 = sc.parallelize(List(("jerry", 9), ("tom", 8), ("shuke", 7), ("tom", 2)))
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[29] at parallelize at <console>:24
    
    scala> val rdd3 = rdd1.join(rdd2)
    rdd3: org.apache.spark.rdd.RDD[(String, (Int, Int))] = MapPartitionsRDD[32] at join at <console>:27
    
    scala> val rdd3 = rdd1.join(rdd2).collect
    rdd3: Array[(String, (Int, Int))] = Array((tom,(1,2)), (tom,(1,8)), (jerry,(2,9)))
    
    scala> val rdd3 = rdd1.leftOuterJoin(rdd2).collect
    rdd3: Array[(String, (Int, Option[Int]))] = Array((tom,(1,Some(8))), (tom,(1,Some(2))), (kitty,(3,None)), (jerry,(2,Some(9))))
    
    scala> val rdd3 = rdd1.rightOuterJoin(rdd2).collect
    rdd3: Array[(String, (Option[Int], Int))] = Array((tom,(Some(1),8)), (tom,(Some(1),2)), (shuke,(None,7)), (jerry,(Some(2),9)))

**\#cogroup(全连接)**

scala> val rdd1 = sc.parallelize(List(("tom", 1), ("tom", 2), ("jerry", 3), ("kitty", 2)))
    rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[0] at parallelize at <console>:24
    
    scala> val rdd2 = sc.parallelize(List(("jerry", 2), ("tom", 1), ("shuke", 2)))
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[1] at parallelize at <console>:24
    
    scala> val rdd3 = rdd1.cogroup(rdd2)
    rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[Int], Iterable[Int]))] = MapPartitionsRDD[3] at cogroup at <console>:27
    
    scala> val rdd3 = rdd1.cogroup(rdd2).collect
    rdd3: Array[(String, (Iterable[Int], Iterable[Int]))] = Array((tom,(CompactBuffer(1, 2),CompactBuffer(1))), (jerry,(CompactBuffer(3),CompactBuffer(2))), (shuke,(CompactBuffer(),CompactBuffer(2))), (kitty,(CompactBuffer(2),CompactBuffer())))

同一个compactBuffer汇总的值为同一个RDD ，同一个RDD中如果有多个key相同，则把它们的value放入同一个compactBuffer中。

**\#cartesian笛卡尔积【以对偶元组的方式呈现笛卡尔积的结果】**

scala> val rdd1 = sc.parallelize(List("tom", "jerry"))
    rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[6] at parallelize at <console>:24
    
    scala> val rdd2 = sc.parallelize(List("tom", "kitty", "shuke"))
    rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[7] at parallelize at <console>:24
    
    scala> val rdd3 = rdd1.cartesian(rdd2)
    rdd3: org.apache.spark.rdd.RDD[(String, String)] = CartesianRDD[8] at cartesian at <console>:27
    
    scala> val rdd3 = rdd1.cartesian(rdd2).collect
    rdd3: Array[(String, String)] = Array((tom,tom), (tom,kitty), (tom,shuke), (jerry,tom), (jerry,kitty), (jerry,shuke))

**\#groupByKey(根据key分组)**

scala> val rdd3 = rdd1 union rdd2
    rdd3: org.apache.spark.rdd.RDD[(String, Int)] = UnionRDD[51] at union at <console>:27
    
    scala> (rdd1 union rdd2).collect
    res7: Array[(String, Int)] = Array((tom,1), (jerry,2), (kitty,3), (jerry,9), (tom,8), (shuke,7), (tom,2))
    
    scala> rdd3.groupByKey.collect
    res9: Array[(String, Iterable[Int])] = Array((tom,CompactBuffer(1, 2, 8)), (shuke,CompactBuffer(7)), (kitty,CompactBuffer(3)), (jerry,CompactBuffer(2, 9)))

**\#使用groupByKey进行WordCount**

scala> sc.textFile("hdfs://hdp-01:9000/wordcount").flatMap(_.split(" ")).map((_,1)).collect
    res13: Array[(String, Int)] = Array((hello,1), (hello,1), (hi,1), (java,1), (scala,1), (dianxin,1), (hi,1), (dianxin,1), (hello,1), (spark,1), (spark,1))
    
    scala> sc.textFile("hdfs://hdp-01:9000/wordcount").flatMap(_.split(" ")).map((_,1)).groupByKey.collect
    res14: Array[(String, Iterable[Int])] = Array((scala,CompactBuffer(1)), (hello,CompactBuffer(1, 1, 1)), (java,CompactBuffer(1)), (spark,CompactBuffer(1, 1)), (hi,CompactBuffer(1, 1)), (dianxin,CompactBuffer(1, 1)))
    
    scala> sc.textFile("hdfs://hdp-01:9000/wordcount").flatMap(_.split(" ")).map((_,1)).groupByKey.mapValues(_.sum).collect
    res15: Array[(String, Int)] = Array((scala,1), (hello,3), (java,1), (spark,2), (hi,2), (dianxin,2))

**当数据量较大的时候，groupBy和reduceByKey哪个效率高？**

groupBy是分组之后再聚合，分组这个过程会有大量的shuffle，key相同的value需要到同一台机器上计算。由于数据是分散在多态机器上的，需要shuffle到一起机器上，需要通过网络传输，而且发现都是大量的1进行累加，所以groupBy效率很低。

而reduceByKey会在局部先聚合， 聚合再shuffle，这样涉及的网络传输更少，效率更高。

## 2.2  常用Action-API ##

\#指定分区的Transformation，包含3个分区，意味着以后在触发Action时会生成三个Task，Task将List中的数据进行处理并写入到HDFS文件中，最后将会有3个结果文件。类似于MapReduce中一个Reduce生成一个结果文件。如果不指定分区数量，则根据集群中的总核数(实际上是集群中的总线程数)生成相等数量的结果文件。  
**一般来说  有多少个输入切片，就会产生多少个分区。**

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5), 3)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

**\#collect**

scala> rdd1.collect
    res0: Array[Int] = Array(1, 2, 3, 4, 5)

**\#reduce**

scala> rdd1.reduce(_ + _)
    res1: Int = 15

**\#count**

scala> rdd1.count
    res2: Long = 5

**\#top(num: Int)  将数据进行降序排列，求topN。 将返回的多个元素放入Array中返回到Driver端**

scala> rdd1.top(3)
    res3: Array[Int] = Array(5, 4, 3)

**\#take(num: Int)  从头开始取n个元素**

scala> rdd1.take(2)
    res4: Array[Int] = Array(1, 2)

**\#first 取第一个元素返回Int类型**

scala> rdd1.first
    res5: Int = 1

**\#takeOrdered   将数据进行升序排列，取n个数据**

scala> rdd1.takeOrdered(3)
    res6: Array[Int] = Array(1, 2, 3)

## 2.3  高级的RDD-API ##

**\#mapPartitionsWithIndex**【取分区中的数据，并且可以将分区的编号取出，这样就可以知道数据属于哪个分区对应的Task】

"一次取出一个分区"(分区中并没有存储数据，而是记录要读取哪些数据，真正在Worker的Executor中生成的Task会读取多条数据，并且可以将分区的编号取出，我们可以认为就是分区对应的数据)

scala> val rdd1 = sc.parallelize(List.apply(1,2,3,4,5,6,7,8,9),2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at parallelize at <console>:24
    
    scala> val mapWithIndexFunction = (index: Int, iterator: Iterator[Int]) => { 
         | iterator.map(e => s"part: $index, ele: $e")
         | }
    mapWithIndexFunction: (Int, Iterator[Int]) => Iterator[String] = <function2>
    
    scala> val rdd2 = rdd1.mapPartitionsWithIndex(mapWithIndexFunction)
    rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1] at mapPartitionsWithIndex at <console>:27
    
    scala> val rdd2 = rdd1.mapPartitionsWithIndex(mapWithIndexFunction).collect
    rdd2: Array[String] = Array(part: 0, ele: 1, part: 0, ele: 2, part: 0, ele: 3, part: 0, ele: 4, part: 1, ele: 5, part: 1, ele: 6, part: 1, ele: 7, part: 1, ele: 8, part: 1, ele: 9)

数据0~4都属于0号分区，数据5~9都属于1号分区。总共9个数据，一个分区4个数据一个分区5个数据，目的是为了使两个分区生成的Task在计算的时候尽量均衡。

**\#aggregate 【先局部聚合再全局聚合】 是Action，只能对一个个元素操作，而不能对(K,V)操作**

scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8,9),2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[5] at parallelize at <console>:24
    
    scala> val sum = rdd1.aggregate(0)(_ + _, _ + _) #先在分区内局部聚合,再全局聚合
    sum: Int = 45

总共有两个分区：分区1为1,2,3,4  分区2为5,6,7,8,9  第一个\_ + \_ 执行的是两个分区内的求和1+2+3+4=10和5+6+7+8+9=25，再全局聚合10+25=35，返回结果。

将每个分区内的最大值进行求和，初始值为0

scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8,9),2)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[6] at parallelize at <console>:24
    
    scala> val maxSum = rdd1.aggregate(0)(math.max(_, _), _ + _)
    maxSum: Int = 13

总共有两个分区：分区0为1,2,3,4  分区1为5,6,7,8,9   第一个分区最大值为4，第二个分区最大值为9，全局聚合后的结果为13

将每个分区内的最大值进行求和，初始值为5

scala> val maxSum = rdd1.aggregate(5)(math.max(_, _), _ + _)
    maxSum: Int = 19

总共有两个分区：分区0为1,2,3,4  分区1为5,6,7,8,9   第一个分区最大值为5(初始值)，第二个分区最大值为9，全局聚合后的结果还需与初始值相加，结果为14+5=19

**aggregate全局聚合是无序的**

scala> val rdd2 = sc.parallelize(List("a","b","c","d","e","f"),2)
    rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[7] at parallelize at <console>:24
    
    scala> val rdd3 = rdd2.aggregate("")(_ + _, _ + _)
    rdd3: String = defabc
    
    scala> val rdd3 = rdd2.aggregate("")(_ + _, _ + _)
    rdd3: String = abcdef

scala> val rdd3 = sc.parallelize(List("12","23","345","4567"),2)
    rdd3: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[8] at parallelize at <console>:24
    
    scala> rdd3.aggregate("")((x,y) => math.max(x.length, y.length).toString, (x,y) => x + y)
    res6: String = 24
    
    scala> rdd3.aggregate("")((x,y) => math.max(x.length, y.length).toString, (x,y) => x + y)
    res7: String = 42

scala> val rdd4 = sc.parallelize(List("12","23","345",""),2)
    rdd4: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at parallelize at <console>:24
    
    scala> rdd4.aggregate("")((x, y) => math.min(x.length, y.length).toString, (x, y) => x + y)
    res0: String = 01                                                               
    
    scala> rdd4.aggregate("")((x, y) => math.min(x.length, y.length).toString, (x, y) => x + y)
    res1: String = 10

"".length的值为0，与"12".length=2 相比，0更小，执行0.toString，返回"0"。注意，此时"0".length的值为1，1再与"23".length即2比较，返回1。同理分区2字符串长度最小值为0，聚合后的结果则为10或01。

**\#collectAsMap**

scala> val rdd = sc.parallelize(List(("a", 1), ("b", 2)))
    rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[4] at parallelize at <console>:24
    
    scala> val rdd1 = rdd.mapValues(_ * 100).collect
    rdd1: Array[(String, Int)] = Array((a,100), (b,200))
    
    
    scala> val rdd1 = rdd.mapValues(_ * 100).collectAsMap
    rdd1: scala.collection.Map[String,Int] = Map(b -> 200, a -> 100)

注意：实际生产环境中，不要直接将数据收集到Driver端，再存入数据库或文件系统，这样会存在性能瓶颈。

**\#countByKey 求某个Key出现的次数，与数值没有关系**

scala> val rdd1 = sc.parallelize(List(("a", 1), ("b", 2), ("b", 2), ("c", 2), ("c", 1)))
    rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[7] at parallelize at <console>:24
    
    scala> rdd1.countByKey
    res4: scala.collection.Map[String,Long] = Map(a -> 1, b -> 2, c -> 2)

**\#countByValue**

scala> rdd1.countByValue
    res5: scala.collection.Map[(String, Int),Long] = Map((b,2) -> 2, (c,2) -> 1, (a,1) -> 1, (c,1) -> 1)

**\#filterByRange   \[是一个闭区间，包含了例子所示的"b"与"d"\]**

scala> val rdd1 = sc.parallelize(List(("e", 5), ("c", 3), ("d", 4), ("c", 2), ("a", 1),("b", 1)))
    rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[13] at parallelize at <console>:24
    
    scala> val rdd2 = rdd1.filterByRange("b", "d")
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[14] at filterByRange at <console>:25
    
    scala> rdd2.collect
    res7: Array[(String, Int)] = Array((c,3), (d,4), (c,2), (b,1))

**\#flatMapValues **

scala> val a = sc.parallelize(List(("a", "1 2"), ("b", "3 4")))
    a: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[16] at parallelize at <console>:24
    
    scala> a.flatMapValues(_.split(" "))
    res9: org.apache.spark.rdd.RDD[(String, String)] = MapPartitionsRDD[17] at flatMapValues at <console>:26
    
    scala> res9.collect
    res10: Array[(String, String)] = Array((a,1), (a,2), (b,3), (b,4))

**\#foldByKey 【属于PairRDDFunctions中的方法，foldByKey和reduceByKey，aggregateByKey，combineByKey一样，它们底层调用的都是combineByKeyWithClassTag方法，都是Transformation】**

scala> val rdd1 = sc.parallelize(List("dog", "wolf", "cat", "bear"), 2)
    rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[18] at parallelize at <console>:24
    
    scala> val rdd2 = rdd1.map(x => (x.length, x))
    rdd2: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD[19] at map at <console>:25
    
    scala> rdd2.collect
    res13: Array[(Int, String)] = Array((3,dog), (4,wolf), (3,cat), (4,bear))

将key相同的元组的值都组装在一起

scala> val rdd3 = rdd2.aggregateByKey("")(_ + _, _ + _)
    rdd3: org.apache.spark.rdd.RDD[(Int, String)] = ShuffledRDD[20] at aggregateByKey at <console>:25
    
    scala> rdd3.collect
    res14: Array[(Int, String)] = Array((4,wolfbear), (3,catdog))
    
    scala> val rdd4 = rdd2.reduceByKey(_ + _)
    rdd4: org.apache.spark.rdd.RDD[(Int, String)] = ShuffledRDD[21] at reduceByKey at <console>:25
    
    scala> rdd4.collect
    res15: Array[(Int, String)] = Array((4,wolfbear), (3,catdog))
    
    scala> val rdd5 = rdd3.foldByKey("")(_ + _)
    rdd5: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD[22] at foldByKey at <console>:25
    
    scala> rdd5.collect
    res16: Array[(Int, String)] = Array((4,wolfbear), (3,catdog))

**\#foreach**

scala> val rdd = sc.parallelize(List(1,2,3,4,5,6))
    rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[23] at parallelize at <console>:24
    
    scala> rdd.foreach(x => print(x * 100))
    
    scala>

并没有返回结果，**foreach是一个Action操作，实际打印在Executor中打印**，控制台即(Driver端)并没有从Worker中的Executor中拉取数据，所以看不到结果，结果可以在spark后台管理界面看到。

**\#foreachPartition**

scala> rdd.foreachPartition(it => it.foreach(x => println(x * 100)))
    
    scala>

foreach为条为单位打印，而foreachPartition以分区为单位打印。应用场景：比如要将计算好的数据写入到HDFS/Hive/MySQL等中，需要使用foreachPartition效率更高。foreach每写入一条都需要与MySQL建立一个JDBC连接，假设写入1000万条数据，就需要创建1000万个JDBC连接，资源消耗巨大。而foreachPartition每建立一个JDBC连接就可以将整个分区数据写入MySQL中，资源消耗小更高效。

**\#combineByKey【因为是比较底层的方法，使用时候需要指定类型】**

scala> val rdd = sc.parallelize(List.apply(("hello", 2), ("hi", 3), ("hello", 4), ("hi", 6), ("hello", 8), ("hi", 1)),2)
    rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[2] at parallelize at <console>:24
    
    scala> val rdd2 = rdd.combineByKey(x => x, (m: Int, n: Int) => m + n, (a: Int, b: Int) => a + b)
    rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[3] at combineByKey at <console>:25
    
    scala> rdd2.collect
    res1: Array[(String, Int)] = Array((hello,14), (hi,10))

将rdd6中key相同的进行分组并存入ListBuffer中

scala> val rdd4 = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)
    rdd4: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[4] at parallelize at <console>:25
    
    scala> val rdd5 = sc.parallelize(List(1,1,2,2,2,1,2,2,2), 3)
    rdd5: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[5] at parallelize at <console>:25
    
    scala> val rdd6 = rdd5.zip(rdd4)
    rdd6: org.apache.spark.rdd.RDD[(Int, String)] = ZippedPartitionsRDD2[6] at zip at <console>:28
    
    scala> rdd6.collect
    res6: Array[(Int, String)] = Array((1,dog), (1,cat), (2,gnu), (2,salmon), (2,rabbit), (1,turkey), (2,wolf), (2,bear), (2,bee))
    
    scala> import scala.collection.mutable.ListBuffer
    import scala.collection.mutable.ListBuffer
    
    scala> rdd6.combineByKey(x => ListBuffer(x), (a: ListBuffer[String], b: String) => a += b, (m: ListBuffer[String], n: ListBuffer[String]) => m ++= n)
    res4: org.apache.spark.rdd.RDD[(Int, scala.collection.mutable.ListBuffer[String])] = ShuffledRDD[7] at combineByKey at <console>:29
    
    scala> res4.collect
    res5: Array[(Int, scala.collection.mutable.ListBuffer[String])] = Array((1,ListBuffer(dog, cat, turkey)), (2,ListBuffer(salmon, rabbit, gnu, wolf, bear, bee)))

**实质：**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0Y2F0c19jbg_size_16_color_FFFFFF_t_70][]

[https_blog.csdn.net_zc19921215_article_details_82858585]: https://blog.csdn.net/zc19921215/article/details/82858585
[http_spark.apache.org_docs_latest_rdd-programming-guide.html_transformations]: http://spark.apache.org/docs/latest/rdd-programming-guide.html#transformations
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2l0Y2F0c19jbg_size_16_color_FFFFFF_t_70]: /images/20220113/a61af5955daf4ed8b73407be4b75768e.png