Spark中的RDD就是一个不可变的分布式对象集合，是一种具有兼容性的基于内存的集群计算抽象方法，Spark则是这个方法的抽象。

Spark的RDD操作分为转化操作（transformation）和行动操作（action），两者的区别在于：

       a.转化操作返回一个新的RDD对象

       b.行动操作则会对RDD产生一个计算结果，并把结果返回到驱动器程序中或者把结果存储到外部存储系统（如HDFS）

常见的转化操作有：map，filter，flatMap，sample，union，distinct，

                                    groupByKey，reduceByKey，sortByKey，join，cogroup，cartesian  ......

常见的行动操作有：reduce，collect，count，first，take，taksSample，

                                    saveAsTextFile，saveAsSequenceFile，countByKey，foreach ......

下面我们以实例说明Saprk的RDD编程

1：创建RDD

      有两种方式：读取外部数据集，以及在驱动器程序中对一个集合进行并行化

       python：

>>> nums =sc.parallelize([1,2,3,4])
>>> nums
ParallelCollectionRDD[0] at parallelize at PythonRDD.scala:423

>>> words = sc.textFile("file:///usr/local/hadoop/spark/README.md")
>>> words
file:///usr/local/hadoop/spark/README.md MapPartitionsRDD[2] at textFile at NativeMethodAccessorImpl.java:-2
>>> 

val lines = sc.parallelize(List(1,2,3,4))
lines: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:27

val rdd = sc.makeRDD(1 to 10,2)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[10] at makeRDD at <console>:27

2：map()函数 和 take()函数

      RDD.map(func)，map接受一个函数作为参数，作用于RDD中的每个对象，并将返回结果作为结果RDD中对应的元素的值

      RDD.take(num)，用于取回num个value，在这里结合map使用，方便查看值

Python：

>>> nums = sc.parallelize([1,2,3,4])
>>> for num in nums.take(4):
...     print num
... 
1
2
3
4
>>> new_nums = nums.map(lambda x: x*2)
>>> for new_num in new_nums.take(4):
...     print new_num
... 
2
4
6
8

Scala：

scala> val nums = sc.parallelize(List(1,2,3,4))
nums: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[4] at parallelize at <console>:27

scala>nums.take(4).foreach(println)
1
2
3
4

3：flatMap()函数

      RDD.flatMap(func)，和map类似，只不过map返回的是一个个元素，而flatMap返回的则是一个返回值序列的迭代器

Python：

>>> string = sc.parallelize(["i love you"])
>>> new_str = string.flatMap(lambda str:str.split(" "))</span>
>>> for str in new_str.take(3):
...     print str
... 
i
love
you

Scala：

scala> val string = sc.parallelize(List("i love you"))
string: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[5] at parallelize at <console>:27

scala> val new_str = string.flatMap(line=>line.split(" "))
new_str: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[6] at flatMap at <console>:29

scala> new_str.take(3).foreach(println)
i
love
you

4：filter()函数和 first()函数

      RDD.filter(func)，接受一个函数作为参数，并将RDD中满足该函数的元素放入新的RDD中返回

      RDD.first()，返回的第一个

Python：

>>> string = sc.parallelize(["i love you"])
>>> new_str = string.filter(lambda line : "you" in line)
>>> new_str.first()
'i love you'

Scala：

scala> val string = sc.parallelize(List("i love you"))
string: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[7] at parallelize at <console>:27
scala> string.first()
res3: String = i love you

scala> 
<pre name="code" class="java">scala> val string = sc.parallelize(List("I love you"))
scala> val new_str = string.filter(line =>line.contains("love"))
new_str: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[6] at filter at <console>:23

scala> new_str.foreach(println)
I love you

5：union()函数

      RDD1.union(RDD2)，操作对象为两个RDD，返回一个新的RDD，转化操作可以操作任意数量的输入RDD

Python：

>>> num1 = sc.parallelize([1,2,3])
>>> num2 = sc.parallelize([4,5,6])
>>> num3 = num1.union(num2)
>>> for num in num3.take(6):
...     print num
... 
1
2
3
4
5
6

Scala:

scala> val num1 = sc.parallelize(List(1,2,3))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:27

scala> val num2 = sc.parallelize(List(4,5,6))
num2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at <console>:27

scala> val num3 = num1.union(num2)
mum3: org.apache.spark.rdd.RDD[Int] = UnionRDD[2] at union at <console>:31

scala> num3.count()
res1: Long = 6                                                                  

scala> num3.foreach(println)
3
1
2
4
5
6

6：count()函数和collect()函数

      RDD.count()，是统计RDD中元素的个数，返回的是一个整数

      EDD.collect()，用来收集数据，保存在一个新的数据结构中，用来持久化，需要注意的是collect不能用在大规模数据集上

Python：

>>> nums = sc.parallelize([1,2,3,4])
>>> nums.count()
[Stage 0:>                                                          (0 +[Stage 0:>                                                          (0 +[Stage 0:==============>  
 (1 +
 4       
>>> 

>>> new_nums = nums.collect()
>>> new_nums
[1, 2, 3, 4]
>>> 

Scala：

scala> val num1 = sc.parallelize(List(1,2,3))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at parallelize at <console>:27

scala> num1.count()
res3: Long = 3

scala> val num2=num1.collect()
num2: Array[Int] = Array(1, 2, 3)

scala> num2
res4: Array[Int] = Array(1, 2, 3)

scala> 

7：伪集合操作

(1)：RDD.distinct，去重，但其操作的开销大，因为它需要所有数据通过网络进行混洗

>>> nums1 = sc.parallelize([1,2,3,3])
>>> nums1.count()
4
>>> nums2=nums1.distinct()
>>> nums2.count()
3
>>>

scala> val num1 = sc.parallelize(List(1,2,3,3))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[4] at parallelize at <console>:27

scala> val num2 = num1.distinct()
num2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[7] at distinct at <console>:29

scala> num2.foreach(println)
2
3
1

(2)：RDD1.intersection(RDD2),返回两个RDD中都有的元素，类似于集合中的交集

>>> nums_1=sc.parallelize([1,2,3,4,5])
>>> nums_2=sc.parallelize([3,4,5,6,7])
>>> nums_3=nums_1.intersection(nums_2)
>>> nums_3.count()
[Stage 7:>                                                          (0 +                                                                        3       
>>> for num in nums_3.take(3):
...     print num
...
3
4
5
>>>

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[9] at parallelize at <console>:27

scala> val num2 = sc.parallelize(List(3,4,5,6))
num2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[10] at parallelize at <console>:27

scala> val num3 = num1.intersection(num2)
num3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[16] at intersection at <console>:31

scala> num3.foreach(println)
4
3

(3)：RDD1.subtract(RDD2),接受一个RDD作为参数，返回一个由只存在第一个RDD1而不存在与第二个RDD2中的所有元素组成的RDD

>>> nums_4 = nums_1.subtract(nums_2)
>>> nums_4.count()
2
>>> for num in nums_4.take(2):
...     print num
...
1
2
>>>

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[17] at parallelize at <console>:27

scala> val num2 = sc.parallelize(List(3,4,5,6))
num2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[18] at parallelize at <console>:27

scala> val num3 = num1.subtract(num2)
num3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[22] at subtract at <console>:31

scala> num3.foreach(println)
2
1

(4)：RDD1.cartesian(RDD2),求笛卡尔积，求出所有可能的(a,b)对

>>> nums_5 = nums_1.cartesian(nums_2)
>>> nums_5
org.apache.spark.api.java.JavaPairRDD@5617ade8
>>> nums_5.first()
(1, 3)
>>>

Scala：

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[23] at parallelize at <console>:27

scala> val num2 = sc.parallelize(List(3,4,5,6))
num2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[24] at parallelize at <console>:27

scala> val num3 = num1.cartesian(num2)
num3: org.apache.spark.rdd.RDD[(Int, Int)] = CartesianRDD[25] at cartesian at <console>:31

scala> num3.foreach(println)
(1,3)
(1,5)
(1,6)
(1,4)
(2,3)
(2,4)
(3,3)
(2,5)
(2,6)
(3,4)
(3,6)
(4,3)
(3,5)
(4,5)
(4,4)
(4,6)

8：reduce()函数

  RDD.reduce(func),接受一个函数作为参数，操作两个RDD的元素类型的数据并返回一个同样类型的新元素

Python:

>>> nums=sc.parallelize([1,2,3,4,5,6])
>>> nums.reduce(lambda x,y:x+y)
21
>>> 

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[27] at parallelize at <console>:27

scala> val num2 = num1.reduce((x,y)=>x+y)
num2: Int = 10

9：aggregate()函数

aggregate()函数需要我们提供期待返回的类型的初始值，然后通过一个函数把RDD中的元素合并起来放入累加器，考虑到每个节点是在本地累加的，最终，还需要通过第二个函数把累加器兩兩合并

Python：

>>> nums = sc.parallelize([1,2,3,4])
>>> sumCount = nums.aggregate( (0,0),
... (lambda acc,value:(acc[0]+value,acc[1]+1)),
... (lambda acc1,acc2:(acc1[0]+acc2[0],acc1[1]+acc2[1])))
>>> sumCount[0]/float(sumCount[1])
2.5
>>>

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[30] at parallelize at <console>:27

scala> val result = num1.aggregate((0,0))(
     | (acc,value) => (acc._1 + value,acc._2+1),
     | (acc1,acc2) =>(acc1._1+acc2._1,acc1._2+acc2._2)
     | )
result: (Int, Int) = (10,4)

scala> val avg = result._1/result._2.toDouble
avg: Double = 2.5

scala> 

10：top()函数和 foreach()函数

        RDD.top(num)，从RDD中返回前边的num个元素

Python：

>>> nums = sc.parallelize([1,2,3,4])
>>> new_nums = nums.top(3)
>>> new_nums
[4, 3, 2]

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[31] at parallelize at <console>:27

scala> num1.top(2)
res10: Array[Int] = Array(4, 3)

scala> 

Python：

>>> nums = sc.parallelize([1,2,3])
>>> def add(x):
...     print "\n","x+2:",x+2
... 
>>> nums.foreach(add)

x+2: 5

x+2: 3

x+2: 4

Scala：

scala> def add(x:Int)={
     |  println (x+2)
     | }
add: (x: Int)Unit

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at parallelize at <console>:27

scala> num1.foreach(add)
6
5
3
4

11：sample()函数 和 takeSample()函数

            sample(withReplacement,traction,[send])：对RDD采样以及是否转换

Python：

>>> nums = sc.parallelize([1,2,3,4,5,6,7])
>>> new_nums = nums.sample(False,0.5)
>>> new_nums
PythonRDD[106] at RDD at PythonRDD.scala:43
>>> new_nums.count()
5
>>> for n in new_nums.take(5):
...     print n
... 
1
3
5
6
7

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[37] at parallelize at <console>:27

scala> val num2 = num1.sample(false,0.5)
num2: org.apache.spark.rdd.RDD[Int] = PartitionwiseSampledRDD[38] at sample at <console>:29

scala> num2.foreach(println)
2
3

Python：

>>> nums = sc.parallelize([1,2,3,4,5,6,7])
>>> new_nums= nums.takeSample(False,5)
>>> new_nums
[5, 3, 4, 6, 7]

Scala：

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[39] at parallelize at <console>:27

scala> val num2 = num1.takeSample(false,2)
num2: Array[Int] = Array(3, 4)

12：persist  和 unpersist

        RDD.persist()，不带参数默认把数据以序列化的形式缓存在JVM的堆空间中

        RDD.unpersist()，手动把持久化的RDD从内存中删除

>>> nums = sc.parallelize([1,2,3,4,5,6,7])
>>> new_nums = nums.persist()
>>> new_nums
ParallelCollectionRDD[124] at parallelize at PythonRDD.scala:423
>>> new_nums.unpersist()
ParallelCollectionRDD[124] at parallelize at PythonRDD.scala:423
>>> 

scala> val num1 = sc.parallelize(List(1,2,3,4))
num1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[41] at parallelize at <console>:27

scala> val num2 = num1.persist()
num2: num1.type = ParallelCollectionRDD[41] at parallelize at <console>:27

scala> num2.foreach(println)
3
1
2
4

scala> num2.unpersist()
res17: num2.type = ParallelCollectionRDD[41] at parallelize at <console>:27