Spark操作——转换操作(一)

2023-03-13 15:27 142阅读 0赞

1.  **基础转换操作**
2.  **键值转换操作**

### 基础转换操作 ###

*  map\[U\](f:(T)=>U):RDD\[U\]

对RDD中的每个元素都应用一个指定的函数，以此产生一个新的RDD

scala> var rdd = sc.textFile("/Users/lyf/Desktop/test/data1.txt")
    rdd: org.apache.spark.rdd.RDD[String] = /Users/lyf/Desktop/test/data1.txt MapPartitionsRDD[13] at textFile at <console>:24
    
    scala> rdd.map(line => line.split(" ")).collect
    res16: Array[Array[String]] = Array(Array(Hello, World), Array(Hello, Tom), Array(Hello, Jerry))

*  distince():RDD\[(T)\]

去除RDD中重复的元素，返回所有元素不重复的RDD

scala> var rdd = sc.parallelize(List(1,2,2,3,3,3,4,5))
    rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[15] at parallelize at <console>:24
    
    scala> rdd.distinct.collect
    res18: Array[Int] = Array(4, 1, 5, 2, 3)

*  distince(numPartions: Int):RDD\[T\]

scala> var rdd = sc.parallelize(List(1,2,2,3,3,3,4,5))
    rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[15] at parallelize at <console>:24
    
    scala> var rddDistinct = rdd.distinct
    rddDistinct: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[27] at distinct at <console>:25
    
    scala> rddDistinct.partitions.size
    res21: Int = 4
    
    scala> var rddDistinct = rdd.distinct(3)
    rddDistinct: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[30] at distinct at <console>:25
    
    scala> rddDistinct.partitions.size
    res22: Int = 3

*  flatMap\[U\](f:(T)=>TraversableOnce\[U\]):RDD\[U\]

scala> var rdd = sc.textFile("/Users/lyf/Desktop/test/data1.txt")
    rdd: org.apache.spark.rdd.RDD[String] = /Users/lyf/Desktop/test/data1.txt MapPartitionsRDD[32] at textFile at <console>:24
    
    scala> rdd.flatMap(line => line.split(" ")).collect
    res23: Array[String] = Array(Hello, World, Hello, Tom, Hello, Jerry)

*  coalesce(numPartitions:Int, shuffle:Boolean=false):RDD\[T\]
 *  repartition(numPartitions:Int):RDD\[T\]

两者都是对RDD进行重新分区。coalesce使用HashPartitioner进行分区，第一个参数为重分区数，第二个为是否进行shuffle，默认为false。repartition是coalesce操作shuffle为true的封装。

scala> var rdd = sc.textFile("/Users/lyf/Desktop/test/data1.txt")
    rdd: org.apache.spark.rdd.RDD[String] = /Users/lyf/Desktop/test/data1.txt MapPartitionsRDD[35] at textFile at <console>:24
    
    scala> rdd.partitions.size
    res24: Int = 2
    
    scala> var rdd_1 = rdd.coalesce(1)
    rdd_1: org.apache.spark.rdd.RDD[String] = CoalescedRDD[36] at coalesce at <console>:25
    
    // 如果分区数大于原来的分区数，则第二个参数必须要true，否则分区数不变
    scala> var rdd_2 = rdd.coalesce(3)
    rdd_2: org.apache.spark.rdd.RDD[String] = CoalescedRDD[37] at coalesce at <console>:25
    
    scala> rdd_2.partitions.size
    res26: Int = 2
    
    scala> var rdd_2 = rdd.coalesce(5, true)
    res37: Array[Array[Int]] = Array(Array(1, 2), Array(3, 4, 5), Array(6, 7), Array(8, 9, 10))
    rdd_2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[42] at coalesce at <console>:25
    
    scala> rdd_2.partitions.size
    res28: Int = 5
    
    scala> var rdd_3 = rdd.repartition(5)
    rdd_3: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[46] at repartition at <console>:25
    
    scala> rdd_3.partitions.size
    res29: Int = 5

*  randomSplit(weights: Array\[Double\], seed: Long=Utils.random.nextLong):Array\[RDD\[T\]\]

根据weights权重将一个RDD分割为多个RDD，组成RDD数组，权重越高，被划分到的概率就越大。

scala> var rdd = sc.parallelize(1 to 10, 10)
    rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[47] at parallelize at <console>:24
    
    // 将原RDD按照weights权重生成一个新的RDD数组
    scala> var rddSplit = rdd.randomSplit(Array(1.0, 2.0, 3.0, 4.0))
    rddSplit: Array[org.apache.spark.rdd.RDD[Int]] = Array(MapPartitionsRDD[48] at randomSplit at <console>:25, MapPartitionsRDD[49] at randomSplit at <console>:25, MapPartitionsRDD[50] at randomSplit at <console>:25, MapPartitionsRDD[51] at randomSplit at <console>:25)
    
    scala> rddSplit.size
    res30: Int = 4
    
    scala> rddSplit(0).collect
    res31: Array[Int] = Array()
    
    scala> rddSplit(1).collect
    res32: Array[Int] = Array(3, 8)
    
    scala> rddSplit(2).collect
    res33: Array[Int] = Array(1, 2, 9)
    
    scala> rddSplit(3).collect
    res34: Array[Int] = Array(4, 5, 6, 7, 10)

*  glom():RDD\[Array\[T\]\]

将RDD中每一个分区中所有类型为T的数据转变为元素类型为T的数组\[Array\[T\]\]

scala> var rdd = sc.parallelize(1 to 10, 4)
    rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[52] at parallelize at <console>:24
    
    scala> rdd.collect
    res36: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    
    scala> rdd.glom().collect
    res37: Array[Array[Int]] = Array(Array(1, 2), Array(3, 4, 5), Array(6, 7), Array(8, 9, 10))

*  union(other: RDD\[T\]): RDD\[T\]

返回两个RDD的并集，元素不进行去重

scala> var rdd1 = sc.makeRDD(1 to 3, 1)
    rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[54] at makeRDD at <console>:24
    
    scala> var rdd2 = sc.makeRDD(2 to 5, 1)
    rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[55] at makeRDD at <console>:24
    
    scala> rdd1.union(rdd2).collect
    res38: Array[Int] = Array(1, 2, 3, 2, 3, 4, 5)

*  intersection(other: RDD\[T\]): RDD\[T\]
 *  intersection(other: RDD\[T\], numPartitions:Int):RDD\[T\]
 *  intersection(other: RDD\[T\], partitioner: Partitioner): RDD\[T\]

返回两个RDD的交集，元素不进行不去重。参数numPartitions指定分区数，参数partitioner指定分区函数

scala> rdd1.intersection(rdd2).collect
    res39: Array[Int] = Array(3, 2)

*  subtract(other: RDD\[T\]): RDD\[T\]
 *  subtract(other: RDD\[T\], numPartitions:Int): RDD\[T\]
 *  subtract(other: RDD\[T\], p: Partitioner): RDD\[T\]

返回两个RDD的差集，元素不进行去重

scala> rdd1.subtract(rdd2).collect
    res40: Array[Int] = Array(1)

参考:

\[1\] 郭景瞻. 图解Spark:核心技术与案例实战\[M\]. 北京:电子工业出版社, 2017.