Spark RDD Transformation 简单用例（三）

cache和persist

将RDD数据进行存储，persist(newLevel: StorageLevel)设置了存储级别，cache()和persist()是相同的，存储级别为MEMORY_ONLY。因为RDD的transformation是lazy的，只有action算子才会触发transformain真正的执行，如果一个rdd需要进行多次的action算子操作，最好能够使用cache或persist将rdd缓存至内存中，这样除第一次action会触发transformation操作，后面的action算子都不会再次触发transformation操作。

class StorageLevel private(

    private var _useDisk: Boolean,

    private var _useMemory: Boolean,

    private var _useOffHeap: Boolean,

    private var _deserialized: Boolean,

    private var _replication: Int = 1) /*复制份数，默认为1*/

extends Externalizable 

  val NONE = new StorageLevel(false, false, false, false)

  val DISK_ONLY = new StorageLevel(true, false, false, false)

  val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2)

  val MEMORY_ONLY = new StorageLevel(false, true, false, true)

  val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2)

  val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false)

  val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2)

  val MEMORY_AND_DISK = new StorageLevel(true, true, false, true)

  val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2)

  val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false)

  val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)

  val OFF_HEAP = new StorageLevel(true, true, true, false, 1)

/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */

def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)

/** Persist this RDD with the default storage level (`MEMORY_ONLY`). */

def cache(): this.type = persist()

/**
 * Set this RDD's storage level to persist its values across operations after the first time
 * it is computed. This can only be used to assign a new storage level if the RDD does not
 * have a storage level set yet. Local checkpointing is an exception.
 */
def persist(newLevel: StorageLevel): this.type

/**
 * Mark this RDD for persisting using the specified level.
 *
 * @param newLevel the target storage level
 * @param allowOverride whether to override any existing level with the new one
 */
private def persist(newLevel: StorageLevel, allowOverride: Boolean): this.type

mapValues(func)

元素是key-value对的RDD的每一个元素的value经过func映射(key不变)构建一个新的RDD
/**
 * Pass each value in the key-value pair RDD through a map function without changing the keys;
 * this also retains the original RDD's partitioning.
 */
def mapValues[U](f: V => U): RDD[(K, U)]

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

val rdd1 = rdd.mapValues(x=>1L)

rdd1.foreachPartition(it=>{

     while(it.hasNext){

       println(it.next)

     }

   println("================")

 }
)

scala> val rdd = sc.parallelize(List((1,1),(1,2),(1,3),(2,1),(2,2),(2,3)),3)

rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[1] at parallelize at <console>:24

scala> val rdd1 = rdd.mapValues(x=>1L)

rdd1: org.apache.spark.rdd.RDD[(Int, Long)] = MapPartitionsRDD[2] at mapValues at <console>:26

scala> rdd1.foreachPartition(it=>{

     | while(it.hasNext){

     | println(it.next)

     | }

     | println("================")

     | }

     | )

(1,1)

(1,1)

================

(1,1)

(2,1)

================

(2,1)

(2,1)

================

以上就是将(1,1),(1,2),(1,3),(2,1),(2,2),(2,3)中的value重新赋值为1，变为(1,1),(1,1),(1,1),(2,1),(2,1),(2,1)。下面使用reduceByKey计算每一个key出现的次数。

scala> val rdd1 = rdd.mapValues(x=>1L).reduceByKey(_ + _)

rdd1: org.apache.spark.rdd.RDD[(Int, Long)] = ShuffledRDD[4] at reduceByKey at <console>:26

scala> rdd1.collect.toMap

res4: scala.collection.immutable.Map[Int,Long] = Map(1 -> 3, 2 -> 3)

其实以上操作就是action算子countByKey()的实现。

collect(f: PartialFunction[T, U])

/**
 * Return an RDD that contains all matching values by applying `f`.
 */
def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U]

PartialFunction[T,U]是个什么东东呢？看一下PartialFunction的apply函数，即需要定义一个f(x)函数，入参类型为A，结果输出类型为B

/** Converts ordinary function to partial one

 *  @since   2.10

 */

def apply[A, B](f: A => B): PartialFunction[A, B] = { case x => f(x) }

val f : PartialFunction[Int,String] = {case 0 => "Sunday"

    case 1 => "Monday"

    case 2 => "Tuesday"

    case 3 => "Wednesday"

    case 4 => "Thursday"

    case 5 => "Friday"

    case 6 => "Saturday"

    case _ => "Unknown"

}

val rdd = sc.parallelize(0 to 9)

rdd.collect(f).collect

res3: Array[String] = Array(Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Unknown, Unknown, Unknown)

glom()

将每个partition中的元素合并成一个数组
/**
 * Return an RDD created by coalescing all elements within each partition into an array.
 */
def glom(): RDD[Array[T]]

scala> val rdd = sc.parallelize(1 to 9,3)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[9] at parallelize at <console>:24

scala> rdd.glom

res7: org.apache.spark.rdd.RDD[Array[Int]] = MapPartitionsRDD[10] at glom at <console>:27

scala> rdd.glom.collect

res8: Array[Array[Int]] = Array(Array(1, 2, 3), Array(4, 5, 6), Array(7, 8, 9))

subtract(other: RDD[T])

返回other中不存在的元素组成新的RDD，分区属性如果没有指定，则和原RDD保持一致。
/**
 * Return an RDD with the elements from `this` that are not in `other`.
 *
 * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
 * RDD will be &lt;= us.
 */
def subtract(other: RDD[T]): RDD[T]

val rdd1 = sc.parallelize(1 to 10,2)

val rdd2 = sc.parallelize(5 to 20,3)

val rdd = rdd1.subtract(rdd2)

rdd.collect

rdd.partitions.length

scala> val rdd1 = sc.parallelize(1 to 10,2)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(5 to 20,3)

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at <console>:24

scala> val rdd = rdd1.subtract(rdd2)

rdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[5] at subtract at <console>:28

scala> rdd.collect

res0: Array[Int] = Array(2, 4, 1, 3)                                            

scala> rdd.partitions.length

res2: Int = 2

指定分区数量

/**
 * Return an RDD with the elements from `this` that are not in `other`.
 */
def subtract(other: RDD[T], numPartitions: Int): RDD[T]

scala> val rdd = rdd1.subtract(rdd2,5)

rdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[9] at subtract at <console>:28

scala> rdd.partitions.length

res3: Int = 5

自定义分区属性partitioner

/**
 * Return an RDD with the elements from `this` that are not in `other`.
 */
def subtract(
    other: RDD[T],
    p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T]

class MyPartitioner(numParts:Int) extends org.apache.spark.Partitioner{

  override def numPartitions: Int = numParts

  override def getPartition(key: Any): Int = {

    key.toString.toInt%numPartitions

  }

}

val rdd = rdd1.subtract(rdd2,new MyPartitioner(3))

rdd.foreachPartition(

 x=>{

   while(x.hasNext){

     println(x.next)

   }

   println("============")

 }

 )

scala> class MyPartitioner(numParts:Int) extends org.apache.spark.Partitioner{

     |   override def numPartitions: Int = numParts

     |   override def getPartition(key: Any): Int = {

     |     key.toString.toInt%numPartitions

     |   }

     | }

defined class MyPartitioner

scala> val rdd = rdd1.subtract(rdd2,new MyPartitioner(3))

rdd: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[13] at subtract at <console>:29

scala> rdd.foreachPartition(

     |  x=>{

     |    while(x.hasNext){

     |      println(x.next)

     |    }

     |    println("============")

     |  }

     |  )

3

============

1

4

============

2

============

zip

两个RDD对应位置(按顺序）组成key-value对创建新的RDD，两个RDD的元素在每个分区中数量必须相同，partition数量必须相同。
/**
 * Zips this RDD with another one, returning key-value pairs with the first element in each RDD,
 * second element in each RDD, etc. Assumes that the two RDDs have the *same number of
 * partitions* and the *same number of elements in each partition* (e.g. one was made through
 * a map on the other).
 */
def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)]

val rdd1 = sc.parallelize(1 to 5,2)

val rdd2 = sc.parallelize(List("one","two","three","four","five"),2)

val rdd = rdd1.zip(rdd2)

rdd.collect

scala> val rdd1 = sc.parallelize(1 to 5,2)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[14] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(List("one","two","three","four","five"),2)

rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[15] at parallelize at <console>:24

scala> val rdd = rdd1.zip(rdd2)

rdd: org.apache.spark.rdd.RDD[(Int, String)] = ZippedPartitionsRDD2[16] at zip at <console>:28

scala> rdd.collect

res5: Array[(Int, String)] = Array((1,one), (2,two), (3,three), (4,four), (5,five))

combineByKey

/**
 * Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
 * existing partitioner/parallelism level. This method is here for backward compatibility. It
 * does not provide combiner classtag information to the shuffle.
 *
 * @see [[combineByKeyWithClassTag]]
 */
def combineByKey[C](
    createCombiner: V => C,
    mergeValue: (C, V) => C,
    mergeCombiners: (C, C) => C): RDD[(K, C)]

var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd.combineByKey(

  (v : Int) => v + "$",

  (c : String, v : Int) => c + "@" + v,

  (c1 : String, c2 : String) => c1 + "||" + c2

).collect

scala> var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[29] at parallelize at <console>:24

scala> rdd.combineByKey(

     |   (v : Int) => v + "$",

     |   (c : String, v : Int) => c + "@" + v,

     |   (c1 : String, c2 : String) => c1 + "||" + c2

     | ).collect

res20: Array[(String, String)] = Array((B,1$@2), (A,1$@2@3), (C,1$))

没看明白啊！没看明白啊！没看明白啊！没看明白啊！没看明白啊！

flatMapValues

与flatMap类似，只是value经过函数f映射后得到1个或多个元素与key组成新的key-value，然后创建新的RDD。
/**
 * Pass each value in the key-value pair RDD through a flatMap function without changing the
 * keys; this also retains the original RDD's partitioning.
 */
def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)]

var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd.flatMapValues(x => { x to 3}).collect

scala> var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[32] at parallelize at <console>:24

scala> rdd.flatMapValues(x => { x to 3})

res24: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[33] at flatMapValues at <console>:27

scala> rdd.flatMapValues(x => { x to 3}).collect

res25: Array[(String, Int)] = Array((A,1), (A,2), (A,3), (A,2), (A,3), (A,3), (B,1), (B,2), (B,3), (B,2), (B,3), (C,1), (C,2), (C,3))

foldByKey

对每一个key的value进行聚合运算，其中zeroValue会与每一个key组成一个key-value对参与运算。
/**
 * Merge the values for each key using an associative function and a neutral "zero value" which
 * may be added to the result an arbitrary number of times, and must not change the result
 * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
 */
def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)]

var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd.foldByKey(0)(_+_)

rdd.foldByKey(0)(_+_).collect

rdd.foldByKey(1)(_+_)

rdd.foldByKey(1)(_+_).collect

scala> var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[35] at parallelize at <console>:24

scala> rdd.foldByKey(0)(_+_)

res26: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[36] at foldByKey at <console>:27

scala> rdd.foldByKey(0)(_+_).collect

res27: Array[(String, Int)] = Array((B,3), (A,6), (C,1))

scala> rdd.foldByKey(1)(_+_)

res28: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[38] at foldByKey at <console>:27

scala> rdd.foldByKey(1)(_+_).collect

res29: Array[(String, Int)] = Array((B,4), (A,7), (C,2))

keys

返回key-value的所有key.
/**
 * Return an RDD with the keys of each tuple.
 */

def keys: RDD[K] = self.map(_._1)

scala> var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[42] at parallelize at <console>:24

scala> rdd.keys.collect

res32: Array[String] = Array(A, A, A, B, B, C)

values

返回key-value的所有value.

/**
 * Return an RDD with the values of each tuple.
 */
def values: RDD[V] = self.map(_._2)

scala> var rdd = sc.parallelize(Array(("A",1),("A",2),("A",3),("B",1),("B",2),("C",1)))

rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[44] at parallelize at <console>:24

scala> rdd.values

res33: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[45] at values at <console>:27

scala> rdd.values.collect

res34: Array[Int] = Array(1, 2, 3, 1, 2, 1)