转载地址：www.jianshu.com/p/027dd699b6d9
案例来源：http://homepage.cs.latrobe.edu.au/zhe/ZhenHeSparkRDDAPIExamples.html
Spark算子大致上可分三大类算子：
1、Value数据类型的Transformation算子，这种变换不触发提交作业，针对处理的数据项是Value型的数据。
2、Key-Value数据类型的Transformation算子，这种变换不触发提交作业，针对处理的数据项是Key-Value型的数据。
3、Action算子，这类算子会触发SparkContext提交作业。
一、Value型Transformation算子
1）map：map是对RDD中的每个元素都执行一个指定函数来产生一个新的RDD。任何原RDD中的元素在新RDD中都有且只有一个元素与之对应
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val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
val b = a.map(_.length)
val c = a.zip(b)c.collect
res0: Array[(String, Int)] = Array((dog,3), (salmon,6), (salmon,6), (rat,3), (elephant,8))
2）flatMap：与map类似，将原RDD中的每个元素通过函数f转换为新的元素，并将这些元素放入一个集合，构成新的RDD
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val a = sc.parallelize(1 to 10, 5) a.flatMap(1 to _).collect res47: Array[Int] = Array(1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) sc.parallelize(List(1, 2, 3), 2).flatMap(x => List(x, x, x)).collect res85: Array[Int] = Array(1, 1, 1, 2, 2, 2, 3, 3, 3)
3）mapPartiions：mapPartitions是map的一个变种。map的输入函数应用于RDD中的每个元素，而mapPartitions的输入函数应用于每个分区，也就是把每个分区中的内容作为整体来处理的。
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val x = sc.parallelize(1 to 10, 3) x.flatMap(List.fill(scala.util.Random.nextInt(10))(_)).collect res1: Array[Int] = Array(1, 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10)
4）glom（形成一个Array数组）：将每个分区内的元素组成一个数组，分区不变
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val a = sc.parallelize(1 to 100, 3) a.glom.collect res8: Array[Array[Int]] = Array(Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33), Array(34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66), Array(67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100))
5）union：合并同一数据类型元素，但不去重。合并后返回同类型的数据元素
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val a = sc.parallelize(1 to 3, 1) val b = sc.parallelize(5 to 7, 1) (a ++ b).collect res0: Array[Int] = Array(1, 2, 3, 5, 6, 7)
6）cartesian（笛卡尔操作）：对输入RDD内的所有元素计算笛卡尔积
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val x = sc.parallelize(List(1,2,3,4,5)) val y = sc.parallelize(List(6,7,8,9,10)) x.cartesian(y).collect res0: Array[(Int, Int)] = Array((1,6), (1,7), (1,8), (1,9), (1,10), (2,6), (2,7), (2,8), (2,9), (2,10), (3,6), (3,7), (3,8), (3,9), (3,10), (4,6), (5,6), (4,7), (5,7), (4,8), (5,8), (4,9), (4,10), (5,9), (5,10))
7）groupBy（生成相应的key，相同的放在一起）
val a = sc.parallelize(1 to 9, 3)
a.groupBy(x => { if (x % 2 == 0) "even" else "odd" }).collect
res42: Array[(String, Seq[Int])] = Array((even,ArrayBuffer(2, 4, 6, 8)), (odd,ArrayBuffer(1, 3, 5, 7, 9)))
8）filter
val a = sc.parallelize(1 to 10, 3) val b = a.filter(_ % 2 == 0) b.collect res3: Array[Int] = Array(2, 4, 6, 8, 10)
9）distinct（去重）
val c = sc.parallelize(List("Gnu", "Cat", "Rat", "Dog", "Gnu", "Rat"), 2)
c.distinct.collect
res6: Array[String] = Array(Dog, Gnu, Cat, Rat)
10）subtract（去掉含有重复的项）
val a = sc.parallelize(1 to 9, 3) val b = sc.parallelize(1 to 3, 3) val c = a.subtract(b) c.collect res3: Array[Int] = Array(6, 9, 4, 7, 5, 8)
11）sample
val a = sc.parallelize(1 to 10000, 3) a.sample(false, 0.1, 0).count res24: Long = 960
12）takesample
val x = sc.parallelize(1 to 1000, 3) x.takeSample(true, 100, 1) res3: Array[Int] = Array(339, 718, 810, 105, 71, 268, 333, 360, 341, 300, 68, 848, 431, 449, 773, 172, 802, 339, 431, 285, 937, 301, 167, 69, 330, 864, 40, 645, 65, 349, 613, 468, 982, 314, 160, 675, 232, 794, 577, 571, 805, 317, 136, 860, 522, 45, 628, 178, 321, 482, 657, 114, 332, 728, 901, 290, 175, 876, 227, 130, 863, 773, 559, 301, 694, 460, 839, 952, 664, 851, 260, 729, 823, 880, 792, 964, 614, 821, 683, 364, 80, 875, 813, 951, 663, 344, 546, 918, 436, 451, 397, 670, 756, 512, 391, 70, 213, 896, 123, 858)
13）cache、persist
val c = sc.parallelize(List("Gnu", "Cat", "Rat", "Dog", "Gnu", "Rat"), 2)
c.getStorageLevel
res0: org.apache.spark.storage.StorageLevel = StorageLevel(false, false, false, false, 1)
c.cache
c.getStorageLevel
res2: org.apache.spark.storage.StorageLevel = StorageLevel(false, true, false, true, 1)
二、Key-Value型Transformation算子
处理数据类型为Key-Value的Transmation算子，大致可以分为三类：
1．输入输出分区1对1
2．聚集操作
3．连接
输入输出分区1对1
1）mapValues:顾名思义就是输入函数应用于RDD中KV（Key-Value）类型元素中的Value，原RDD中的Key保持不变，与新的Value一起组成新的RDD中的元素。因此，该函数只适用于元素为Key-Value对的RDD
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val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2)
val b = a.map(x => (x.length, x))
b.mapValues("x" + _ + "x").collect
res5: Array[(Int, String)] = Array((3,xdogx), (5,xtigerx), (4,xlionx), (3,xcatx), (7,xpantherx), (5,xeaglex))
聚集操作-对一个RDD聚集
2）combineByKey:与reduceByKey类似，相当于将元素（int, int）KV对变换为（int, Seq[int]）新的KV对
val a = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)
val b = sc.parallelize(List(1,1,2,2,2,1,2,2,2), 3)
val c = b.zip(a)
val d = c.combineByKey(List(_), (x:List[String], y:String) => y :: x, (x:List[String], y:List[String]) => x ::: y)
d.collect
res16: Array[(Int, List[String])] = Array((1,List(cat, dog, turkey)), (2,List(gnu, rabbit, salmon, bee, bear, wolf)))
3）reduceByKey:对元素为KV对的RDD中Key相同的元素的Value进行reduce操作，即两个值合并为一个值。因此，Key相同的多个元素的值被合并为一个值，然后与原RDD中的Key组成一个新的KV对
val a = sc.parallelize(List("dog", "cat", "owl", "gnu", "ant"), 2)
val b = a.map(x => (x.length, x))
b.reduceByKey(_ + _).collect
res86: Array[(Int, String)] = Array((3,dogcatowlgnuant))
val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2)
val b = a.map(x => (x.length, x))
b.reduceByKey(_ + _).collect
res87: Array[(Int, String)] = Array((4,lion), (3,dogcat), (7,panther), (5,tigereagle))
聚集操作-对一个RDD聚集
4）partitionBy:根据KV对的Key对RDD进行分区
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（对RDD进行分区操作）
聚集操作-对两个RDD聚集
5）cogroup:一组强大的函数，可以对多达3个RDD根据key进行分组，将每个Key相同的元素分别聚集为一个集合
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val a = sc.parallelize(List(1, 2, 1, 3), 1) val b = a.map((_, "b")) val c = a.map((_, "c")) b.cogroup(c).collect res7: Array[(Int, (Iterable[String], Iterable[String]))] = Array( (2,(ArrayBuffer(b),ArrayBuffer(c))), (3,(ArrayBuffer(b),ArrayBuffer(c))), (1,(ArrayBuffer(b, b),ArrayBuffer(c, c))) )
连接
6）join:本质是对两个含有KV对元素的RDD进行coGroup算子协同划分，再通过flatMapValues将合并的数据分散。
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)
val d = c.keyBy(_.length)
b.join(d).collect
res0: Array[(Int, (String, String))] = Array((6,(salmon,salmon)), (6,(salmon,rabbit)), (6,(salmon,turkey)), (6,(salmon,salmon)), (6,(salmon,rabbit)), (6,(salmon,turkey)), (3,(dog,dog)), (3,(dog,cat)), (3,(dog,gnu)), (3,(dog,bee)), (3,(rat,dog)), (3,(rat,cat)), (3,(rat,gnu)), (3,(rat,bee)))
7）leftOutJoin:相当于在join基础上判断一侧的RDD是否为空，如果为空，则填充空，如果有数据，则将数据进行连接计算，然后返回结果。
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)
val d = c.keyBy(_.length)
b.leftOuterJoin(d).collect
res1: Array[(Int, (String, Option[String]))] = Array((6,(salmon,Some(salmon))), (6,(salmon,Some(rabbit))), (6,(salmon,Some(turkey))), (6,(salmon,Some(salmon))), (6,(salmon,Some(rabbit))), (6,(salmon,Some(turkey))), (3,(dog,Some(dog))), (3,(dog,Some(cat))), (3,(dog,Some(gnu))), (3,(dog,Some(bee))), (3,(rat,Some(dog))), (3,(rat,Some(cat))), (3,(rat,Some(gnu))), (3,(rat,Some(bee))), (8,(elephant,None)))
8）rightOutJoin:相当于在join基础上判断一侧的RDD是否为空，如果为空，则填充空，如果有数据，则将数据进行连接计算，然后返回结果。
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3)
val d = c.keyBy(_.length)
b.rightOuterJoin(d).collect
res2: Array[(Int, (Option[String], String))] = Array((6,(Some(salmon),salmon)), (6,(Some(salmon),rabbit)), (6,(Some(salmon),turkey)), (6,(Some(salmon),salmon)), (6,(Some(salmon),rabbit)), (6,(Some(salmon),turkey)), (3,(Some(dog),dog)), (3,(Some(dog),cat)), (3,(Some(dog),gnu)), (3,(Some(dog),bee)), (3,(Some(rat),dog)), (3,(Some(rat),cat)), (3,(Some(rat),gnu)), (3,(Some(rat),bee)), (4,(None,wolf)), (4,(None,bear)))
三、Actions算子
Action算子—无输出
Action算子—HDFS
Action算子—Scala集合及数据类型
1）foreach:
是对RDD中的每个元素执行无参数的f函数，返回Unit。定义如下：
def foreach(f: T => Unit)
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val c = sc.parallelize(List("cat", "dog", "tiger", "lion", "gnu", "crocodile", "ant", "whale", "dolphin", "spider"), 3)
c.foreach(x => println(x + "s are yummy"))
lions are yummy
gnus are yummy
crocodiles are yummy
ants are yummy
whales are yummy
dolphins are yummy
spiders are yummy
2）saveAsTextFile:函数将RDD保存为文本至HDFS指定目录，每次输出一行。
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val a = sc.parallelize(1 to 10000, 3)
a.saveAsTextFile("mydata_a")
14/04/03 21:11:36 INFO FileOutputCommitter: Saved output of task 'attempt_201404032111_0000_m_000002_71' to file:/home/cloudera/Documents/spark-0.9.0-incubating-bin-cdh4/bin/mydata_a
3）saveAsObjectFile:将RDD分区中每10个元素保存为一个数组并将其序列化，映射为（null, BytesWritable（Y））的元素，以SequenceFile的格式写入HDFS
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val x = sc.parallelize(1 to 100, 3)
x.saveAsObjectFile("objFile")
val y = sc.objectFile[Int]("objFile")
y.collect
res52: Array[Int] = Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100)
4）collect:将RDD分散存储的元素转换为单机上的Scala数组并返回，类似于toArray功能
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val c = sc.parallelize(List("Gnu", "Cat", "Rat", "Dog", "Gnu", "Rat"), 2)
c.collect
res29: Array[String] = Array(Gnu, Cat, Rat, Dog, Gnu, Rat)
5）collectAsMap:与collect类似，将元素类型为key-value对的RDD，转换为Scala Map并返回，保存元素的KV结构。
val a = sc.parallelize(List(1, 2, 1, 3), 1) val b = a.zip(a) b.collectAsMap res1: scala.collection.Map[Int,Int] = Map(2 -> 2, 1 -> 1, 3 -> 3)
6）reduceByKeyLocally:先reduce，然后collectAsMap。
val a = sc.parallelize(List("dog", "cat", "owl", "gnu", "ant"), 2)
val b = a.map(x => (x.length, x))
b.reduceByKey(_ + _).collect
res86: Array[(Int, String)] = Array((3,dogcatowlgnuant))
7）lookup:扫描RDD的所有元素，选择与参数匹配的Key，并将其Value以Scala sequence的形式返回
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val a = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2)
val b = a.map(x => (x.length, x))
b.lookup(5)
res0: Seq[String] = WrappedArray(tiger, eagle)
8）count:返回RDD中的元素个数。
val c = sc.parallelize(List("Gnu", "Cat", "Rat", "Dog"), 2)
c.count
res2: Long = 4
9）top:返回RDD中最大/最小的K个元素。
val c = sc.parallelize(Array(6, 9, 4, 7, 5, 8), 2) c.top(2) res28: Array[Int] = Array(9, 8)
10）reduce:对RDD中的所有元素进行reduceLeft操作。
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val a = sc.parallelize(1 to 100, 3) a.reduce(_ + _) res41: Int = 5050
11）fold:与reduce类似，不同的是每次对分区内的value聚集时，分区内初始化的值为zero value。
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val a = sc.parallelize(List(1,2,3), 3) a.fold(0)(_ + _) res59: Int = 6
12）aggregate:允许用户对RDD使用两个不同的reduce函数，第一个reduce函数对各个分区内的数据聚集，每个分区得到一个结果。第二个reduce函数对每个分区的结果进行聚集，最终得到一个总的结果。aggregate相当于对RDD内的元素数据归并聚集，且这种聚集是可以并行的。而fold与reduced的聚集是串行的。
val z = sc.parallelize(List(1,2,3,4,5,6), 2)
// lets first print out the contents of the RDD with partition labels
def myfunc(index: Int, iter: Iterator[(Int)]) : Iterator[String] = {
iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator
}
z.mapPartitionsWithIndex(myfunc).collect
res28: Array[String] = Array([partID:0, val: 1], [partID:0, val: 2], [partID:0, val: 3], [partID:1, val: 4], [partID:1, val: 5], [partID:1, val: 6])
z.aggregate(0)(math.max(_, _), _ + _)
res40: Int = 9
参考：http://www.ithao123.cn/content-2407104.html
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