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spark-shell 中rdd常用方法

作者:互联网

centos 7.2     spark 2.3.3      scala 2.11.11    java 1.8.0_202-ea

spark-shell中为scala语法格式

 

1.distinct 去重

val c = sc.parallerlize(List("Gnu","Cat","Rat","Dog","Gnu","Rat"),2)      初始化rdd,将数据均匀加载到2个partition中

c.distinct.collect

>>res1: Array[String]=Array(Dog,Gnu,Cat,Rat)

c.fisrt                                                                                                  first取RDD第一个Partition中的第一个记录

>>res2:String = Gnu 

 

2.filter  过滤

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)

 

3.filterByRange          返回指定范围内RDD记录,只能作用于排序RDD

val randRDD = sc.parallelize(List((2,"cat"),(6,"mouse"),(7,"cup),(3,"book"),(4,"tv"),(1,"screen"),(5,"heater")),3)

val sortedRDD = randRDD.sortByKey()

sortRDD.filterByRange(1,3).collect

>>res4:Array[(Int,String)] = Array((1,screen),(2,cat),(3,book))

 

4.foreach                    遍历RDD内每个记录

val c = sc.parallelize(List("cat","dog","tiger","lion","gnu"),3)

c.foreach(x => println(x + "is ym"))

>>lion is ym

gnu is ym

cat is ym

tiger is ym

dog is ym

 

5.foreachPartition        遍历RDD内每一个Partition(每个Partition对应一个值)

val b = sc.parallelize(List(1,2,3,4,5,6,7,8),3)

b.foreachPartition(x => println(x.reduce(_ + _ )))

>> 6

15

15

 

6.fullOuterJoin

rdd1.fullOuterJoin[rdd2]         对两个PairRDD进行外连接 ,相同的key值的全部value组合,没有相同key的也保留,值用None填充

val pairRDD1 = sc.parallelize(List(("cat",2),("cat",5),("book",40)))

val pairRDD2 = sc.parallelize(List(("cat",2),("cup",5),("book",40)))

pairRDD1.fullOuterJoin(pairRDD2).collect

>>res5: Array[(String,(Option[Int],Option[Int]))] = Array((book,(Some(40),Some(40))),  (cup,(None,Some(5))),  (cat,(Some(2),Some(2))),  (cat,(Some(5),Some(2)))

 

7.groupBy   根据给定的规则 来分组

val a = sc.parallelize(1 to 9,3)

a.groupBy(x => {if (x % 2 == 0) "even" else "odd" }).collect

>> res6:Array[(String,Seq[Int])] = Array((even,ArrayBuffer(2,4,6,8)),(odd,ArrayBuffer(1,3,5,7,9)))

 

groupBy中使用的方法函数写法还可写作:

def myfunc(a:Int):Int = 

{

a % 2

}

a.groupBy(myfunc).collect

def myfunc(a:Int):Int=

{

a % 2

}

a.groupBy(x => myfunc(x),3).collect

a.groupBy(myfunc(_),1).collect

 

例  将groupBy的条件设置为 partition ,同时自定义数据分区的规则

 

package sometest
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

object SparkApplication{
  def main(args:Array[String]){
    val conf = new SparkConf()
    val sc = new SparkContext(conf).setAppName("GroupPartition").setMaster("spark://master:7077")
    val a = sc.parallelize(1 to 9 , 3)
    val p = new MyPartitioner()
    val b = a.groupBy((x:Int) => {x},p) //这里按照自定义分区规则P重新分区,然后groupBy
   // b的形式为RDD[(Int,Iterable[Int])] 比如说 (1,CompactBuffer(1))

    def myfunc(index:Int,iter:Iterator[(Int,Iterable[Int])]): Iterator[(Int,(Iterable[Int],Int))] = {
      iter.map(a => (index,(a._2,a._1))) //a._2这种写法表示a中的第2个元素
    }
    val c = b.mapPartitionsWithIndex(myfunc)
    println("This is Result for My :")
    c.collect().foreach(println)
}



自定义分区规则


package sometest
import org.apache.spark.Partitioner

/**
*自定义数据分区规则
**/
class MyPartitioner extends Partitioner{
  def numPartitions:Int = 2 //设置分区数
  def getPartition(key:Any):Int =
  {
    val code = key match
      {
        case null => 0
        case key:Int => key % numPartitions //取余
        case _ => key.hashCode % numPartitions
      }
    if(code < 0 ){ // 对 hashCode为负数的结果进行处理
            code + numPartitions  
            }
    else{
        code
      }
  }
  override def equals(other:Any):Boolean = // java标准的判断相等的函数, Spark内部比较两个RDD的分区是否一样时 会用到这个这个函数
  {
    other match
    {
      case h:MyPartitioner => h.numPartitions == numPartitions
      case _ => false
    }
  }
}

 

打包成sparkAction.jar后 使用命令执行  spark-submit  --class sometest.SparkApplication  ~/sparkAction.jar

输出结果为:

This is Result for My :
(0,(CompactBuffer(4),4))
( 0,( CompactBuffer(6),6))
( 0,( CompactBuffer(8),8))
( 0,( CompactBuffer(2),2))
( 0,( CompactBuffer(1),1))
( 0,( CompactBuffer(3),3))
( 0,( CompactBuffer(7),7))
( 0,( CompactBuffer(9),9))
( 0,( CompactBuffer(5),5))

 
8.groupByKey [Pair]
类似于groupBy ,不过函数作用于key,而groupBy的函数是作用于每个数据的
val a = sc.parallelize(List("dog","tiger","lion","cat","spider","eagle"),2)
val b = a.keyBy(_.length)
b.groupByKey.collect

输出res11:Array[(Int,Iterable[String])] = Array((4,CompactBuffer(lion)),(6,CompactBuffer(spider)),(3,CompactBuffer(dog,cat)),(5,CompactBuffer(tiger,eagle)))





9.histogram[Double] 计算数据直方图 (数值数据分布的精确图形表示)

计算给定数据中的最大值和最小值 ,然后将这个范围段平均分成n组,统计给定数据中每组的频数
一般来说,范围段为横轴 ,各组的统计个数为纵坐标

val a = sc.parallelize(List(1.1,1.2,1.3,2.0,2.1,7.4,7.5,7.6,8.8,9.0),3)
a.histogram(5) //将样本数据分成 5 组
res11: (Array[Double],Array[Long]) = (Array(1.1,2.68,4.26,5.84,7.42,9.0),Array(5,0,0,1,4))



10.intersection 返回两个RDD的交集(内连接)
val x=sc.parallelize(1 to 20)
val y =sc.parallelize(10 to 30)
val z = x.intersection(y)
z.collect
res74: Array[Int] = Array(16,17,18,10,19,11,20,12,13,14,15)

内连接
val a = sc.parallelize(List("dog","salmon","salmon","rat","elephant"),3)
val b = a.keyBy(_.length) //Array[(Int,String)]=Array((3,dog),(3,rat),(6,salmon),(6(salmon),(8,elephant))
val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf",bear","bee"),3)

val d = c.keyBy(_.length)
b.join(d).collect
输出 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)), (,(rat,bee)))


11.keys[Pair] 返回 key,value列表中的所有key

val a = sc.parallelize(List((3,"dog"),(5,"tiger"),(4,"lion"),(3,"cat"),(7,"panther"),(5,"eagle")),2)
a.keys.collect
res2: Array[Int] = Array(3,5,4,3,7,5)


12. lookup 查找指定记录
val a = sc.parallelize(List((3,"dog"),(5,"tiger"),(4,"lion"),(3,"cat"),,(7,"panther"),(5,"eagle")),2)
a.lookup(5)
res8: Seq[String] = WrappedArray(tiger,eagle)

13.max 返回最大值
借用上述的a
a.max
res9: (Int,String) = (7,panther)

val y =sc.parallelize(10 to 30)
y.max
res10: Int = 30
14. mean 平均值
y.mean
res13: Double = 20.0


15. persist,cache 设置RDD的存储级别
val c = sc.parallelize(List("Gnu","Cat","Rat","Dog","Gnu","Rat"),2)
c.getStorageLevel
res14: org.apache.spark.storage.StorageLevel = StorageLevel(1 replicas)
c.cache
res15: c.type = ParallelCollectionRDD[41] at parallelize at <console>:24
c.getStorageLevel
res16:org.apache.spark.storage.StorageLevel = StorageLevel(memory, deserialized, 1 replicas)


16. sample 根据给定比例对数据进行采样
sample(withReplacement, fraction, seed)
withReplacement : 是否使用随机数替换
fraction : 对数据进行采样的比例
seed : 随机数生成器种子
val a = sc.parallelize(1 to 10000,3)
a.sample(false,0.1,0).count
res17:Long = 1032

a.sample(true,0.3,0).count
res18: Long = 3110

a.sample(true,0.3,13).count
res20 : Long = 2952


17.saveAsTextFile保存到文本数据 (默认 文件系统是hdfs)
textFile读取文本数据

val a = sc.parallelize(11 to 19,3)
a.saveAsTextFile("test/tf") //实际上是保存到文件夹 test/tf ,由于并行化因子为3,一个Partition对应一个par-000x
val b = sc.textFile("test/tf")
b.collect
res4: Array[String] = Array(11,12,13,14,15,16,17,18,19)


18.take 返回数据集中的前N个数据
val b = sc.parallelize(List("dog","cat","ape","salmon","gnu"),2)
b.take(2)
res5: Array[String] = Array(dog,cat)


19.union,++ 对两个RDD数据进行并集 ,合并两个RDD
val a = sc.parallelize( 1 to 5,1)
val b = sc.parallelize(5 to 7,1)
(a++b).collect
Array[Int] = Array(1,2,3,4,5,5,6,7)







 

标签:shell,parallelize,val,Int,cat,rdd,sc,spark,Array
来源: https://www.cnblogs.com/Ting-light/p/11115455.html