spark03

spark03

map遍历每一个元素

mapPartitions每次遍历一个分区

foreach action算子

foreachPartitions action算子

collect

nginx  flume  hdfs  hbase  spark  mysql

如果是插入数据，那么foreachPartition比较好，因为每个分区建立一个连接

提交的一个任务中，存在几个job? action算子有几个就存在几个job

 

reduce得出一个结果

scala> var arr=Array(1,2,3,4,5,6)

arr: Array[Int] = Array(1, 2, 3, 4, 5, 6)

 

scala> sc.makeRDD(arr,2)

res0: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at <console>:27

 

scala> res0.reduce(_+_)

res1: Int = 21     

 

count算子

scala> res0.count

res2: Long = 6     

 

first

scala> res0.first

res4: Int = 1

 

take算子

scala> sc.makeRDD(arr,3)

res9: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at makeRDD at <console>:27

 

scala> res9.take(2)

res10: Array[Int] = Array(1, 2)

 

scala> res9.take(4)

res11: Array[Int] = Array(1, 2, 3, 4)

take算子可以产生多个job

 

 

take算子每次提交任务的时候都是sc.runJob,扫描的元素个数和总的元素个数比对，扫描的分区数量和总的分区数量比对

 

 

top算子

var arr=Array(1,2,3,4,5,6,7,8,9)

sc.makeRDD(arr,2)

 

scala> res9.top(3)

res14: Array[Int] = Array(9, 8, 7)     

先将数据进行排序，然后倒序截取前N个

 

takeOrdered正序排序，然后截取前N个

scala> res9.takeOrdered(3)

res15: Array[Int] = Array(1, 2, 3)

 

countByKey按照key得出value的数量

scala> var arr = Array(("a",1),("b",1),("a",1))

arr: Array[(String, Int)] = Array((a,1), (b,1), (a,1))

 

scala> sc.makeRDD(arr,3)

res16: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[4] at makeRDD at <console>:27

 

scala> res16.countByKey()

res17: scala.collection.Map[String,Long] = Map(a -> 2, b -> 1)

 

collect将数据收集到driver端，一般都是为了测试显示 Array

collectAsMap 将数据从executors端收集到driver端。Map

var arr=Array(1,2,3,4,5,6,7,8,9)

scala> sc.makeRDD(arr,3)

 

scala> res9.collect

res18: Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9)

 

scala> res9.collectAsMap

<console>:29: error: value collectAsMap is not a member of org.apache.spark.rdd.RDD[Int]

       res9.collectAsMap

            ^

 

 

scala> var arr=Array(("a",1),("b",1),("a",1))

arr: Array[(String, Int)] = Array((a,1), (b,1), (a,1))

 

scala> sc.makeRDD(arr,3)

res14: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[9] at makeRDD at <console>:27

 

res14.countByKey()

scala> res14.collectAsMap

res15: scala.collection.Map[String,Int] = Map(b -> 1, a -> 1)

 

算子在executor中执行的原理和过程

 

rdd是一个弹性的分布式的数据集，默认带有分区的，每个分区会被一个线程处理

rdd中存在数据？

 

 

 

 

 

 

 

sortBy排序

scala> res27.collect

res30: Array[Int] = Array(1, 2, 3, 4, 6, 7, 9)                                  

 

scala> res26.sortBy(t=>t,false,4)

res31: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[27] at sortBy at <console>:29

 

scala> res31.partitions.size

res32: Int = 4

sortBy可以改变分区数量，同时排序可以正序和倒序，并且带有shuffle

 

sortByKey按照key进行排序

scala> var arr = Array((1,2),(2,1),(3,3),(6,9),(5,0))

arr: Array[(Int, Int)] = Array((1,2), (2,1), (3,3), (6,9), (5,0))

 

scala> sc.makeRDD(arr,3)

res33: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[28] at makeRDD at <console>:27

 

scala> res33.sortByKey()

res34: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[31] at sortByKey at <console>:29

 

scala> res34.collect

res35: Array[(Int, Int)] = Array((1,2), (2,1), (3,3), (5,0), (6,9))

 

scala> res33.sortByKey

   def sortByKey(ascending: Boolean,numPartitions: Int): org.apache.spark.rdd.RDD[(Int, Int)]

 

scala> res33.sortByKey(false)

res36: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[34] at sortByKey at <console>:29

 

scala> res36.collect

res37: Array[(Int, Int)] = Array((6,9), (5,0), (3,3), (2,1), (1,2))

 

scala> res33.sortByKey(false,10)

res38: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[37] at sortByKey at <console>:29

 

scala> res38.partitions,size

<console>:1: error: ';' expected but ',' found.

res38.partitions,size

                ^

 

scala> res38.partitions.size

res39: Int = 6

 

scala> res33.partitions.size

res40: Int = 3

 

scala> res38.partitions.size

res41: Int = 6

sortBykey产生shuffle，他的分区器就是rangePartitioner groupByKey reduceBykey他们的分区器都是hashPartitioner

比如：sortByKey它的分区器是rangePartitioner，这个分区器是按照范围和数据量进行自适配的，如果元素个数大于等于分区的个数，这样的分区不会产生差别，如果数据量比分区数量还要小，那么指定的分区个数和真正的分区个数就会产生差别

 

union intersection subtract

scala> var arr = Array(1,2,3,4,5)

arr: Array[Int] = Array(1, 2, 3, 4, 5)

 

scala> var arr1 = Array(3,4,5,6,7)

arr1: Array[Int] = Array(3, 4, 5, 6, 7)

 

scala> sc.makeRDD(arr)

res42: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[38] at makeRDD at <console>:27

 

scala> sc.makeRDD(arr1)

res43: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[39] at makeRDD at <console>:27

 

scala> res42 union res41

<console>:35: error: type mismatch;

 found   : Int

 required: org.apache.spark.rdd.RDD[Int]

       res42 union res41

                   ^

 

scala> res42 union res43

res45: org.apache.spark.rdd.RDD[Int] = UnionRDD[40] at union at <console>:33

 

scala> res45.collect

res46: Array[Int] = Array(1, 2, 3, 4, 5, 3, 4, 5, 6, 7)              

union只是结果集的联合，没有任何业务逻辑，所以分区数量是两个rdd的分区数量总和

 

scala> res42 intersection res43

res47: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[46] at intersection at <console>:33

 

scala> res47.collect

res48: Array[Int] = Array(3, 4, 5)                                              

 

scala> res42 substract res43

<console>:33: error: value substract is not a member of org.apache.spark.rdd.RDD[Int]

       res42 substract res43

             ^

 

scala> res42 subtract res43

res50: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[50] at subtract at <console>:33

 

scala> res50.collect

res51: Array[Int] = Array(1, 2)  

交集和差集都是原来分区中的数据，所以分区数量不会改变

 

distinct

 

scala> var arr = Array(1,1,1,1,1,12,2,3,3,3,3,3,4,4,4,4,4)

arr: Array[Int] = Array(1, 1, 1, 1, 1, 12, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4)

 

scala> sc.makeRDD(arr,3)

res52: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[51] at makeRDD at <console>:27

 

scala> res52.distinct

res53: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[54] at distinct at <console>:29

 

scala> res53.collect

res54: Array[Int] = Array(3, 12, 4, 1, 2)

使用groupbyKey

scala> res52.map((_,null))

res55: org.apache.spark.rdd.RDD[(Int, Null)] = MapPartitionsRDD[55] at map at <console>:29

 

scala> res55.groupByKey()

res56: org.apache.spark.rdd.RDD[(Int, Iterable[Null])] = ShuffledRDD[56] at groupByKey at <console>:31

 

scala> res55.map(_._1)

res57: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[57] at map at <console>:31

 

scala> res57.collect

res58: Array[Int] = Array(1, 1, 1, 1, 1, 12, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4)

 

scala> res56.map(_._1)

res59: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[58] at map at <console>:33

 

scala> res59.collect

res60: Array[Int] = Array(3, 12, 4, 1, 2)

使用reduceByKey

scala> res52.map((_,null))

res61: org.apache.spark.rdd.RDD[(Int, Null)] = MapPartitionsRDD[59] at map at <console>:29

 

scala> res61.reduceByKey((a,b)=>a)

res62: org.apache.spark.rdd.RDD[(Int, Null)] = ShuffledRDD[60] at reduceByKey at <console>:31

 

scala> res62.map(_._1)

res63: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[61] at map at <console>:33

 

scala> res63.collect

res64: Array[Int] = Array(3, 12, 4, 1, 2)

scala> res52.distinct(10)

res66: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[64] at distinct at <console>:29

 

scala> res66.partitions.size

res67: Int = 10

 

 

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全局topN，整个学校里面的老师访问的排名的前几个（不区分专业）

学科topN，每个专业的老师的排名前几个？

第一种分组方式 第二种过滤器的方式

 

join  leftOuterJoin  rightOuterJoin  cogroup

以上是join的关联操作，rdd必须是对偶元组的

scala> var arr = Array(("zhangsan",200),("lisi",250),("zhaosi",300),("wangwu",400))

arr: Array[(String, Int)] = Array((zhangsan,200), (lisi,250), (zhaosi,300), (wangwu,400))

 

scala> var arr1 = Array(("zhangsan",30),("lisi",25),("zhaosi",12),("liuneng",5))

arr1: Array[(String, Int)] = Array((zhangsan,30), (lisi,25), (zhaosi,12), (liuneng,5))

 

scala> sc.makeRDD(arr,3)

res68: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[65] at makeRDD at <console>:27

 

scala> sc.makeRDD(arr1,3)

res69: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[66] at makeRDD at <console>:27

 

scala> res68 join res69

res70: org.apache.spark.rdd.RDD[(String, (Int, Int))] = MapPartitionsRDD[69] at join at <console>:33

 

scala> res70.collect

res71: Array[(String, (Int, Int))] = Array((zhangsan,(200,30)), (lisi,(250,25)), (zhaosi,(300,12)))

 

scala> res68 leftOuterJoin res69

res72: org.apache.spark.rdd.RDD[(String, (Int, Option[Int]))] = MapPartitionsRDD[72] at leftOuterJoin at <console>:33

 

scala> res72.collect

res73: Array[(String, (Int, Option[Int]))] = Array((zhangsan,(200,Some(30))), (wangwu,(400,None)), (lisi,(250,Some(25))), (zhaosi,(300,Some(12))))

 

scala> res68 rightOuterJoin res69

res74: org.apache.spark.rdd.RDD[(String, (Option[Int], Int))] = MapPartitionsRDD[75] at rightOuterJoin at <console>:33

 

scala> res74.collect

res75: Array[(String, (Option[Int], Int))] = Array((zhangsan,(Some(200),30)), (lisi,(Some(250),25)), (zhaosi,(Some(300),12)), (liuneng,(None,5)))

 

scala> res68 cogroup res69

res76: org.apache.spark.rdd.RDD[(String, (Iterable[Int], Iterable[Int]))] = MapPartitionsRDD[77] at cogroup at <console>:33

 

scala> res76.collect

res77: Array[(String, (Iterable[Int], Iterable[Int]))] = Array((zhangsan,(CompactBuffer(200),CompactBuffer(30))), (wangwu,(CompactBuffer(400),CompactBuffer())), (lisi,(CompactBuffer(250),CompactBuffer(25))), (zhaosi,(CompactBuffer(300),CompactBuffer(12))), (liuneng,(CompactBuffer(),CompactBuffer(5))))