spark常用算子有哪些
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一些经常用到的RDD算子
map:将rdd的值输入，并返回一个自定义的类型，如下输入原始类型，输出一个tuple类型的数组
scala> val rdd1 = sc.parallelize(List("a","b","c","d"),2)
rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[1] at parallelize at :24
scala> rdd1.map((_,1)).collect
res1: Array[(String, Int)] = Array((a,1), (b,1), (c,1), (d,1))
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mapPartitionsWithIndex：输出数据对应的分区以及分区的值
scala> val rdd1 = sc.parallelize(List("a","b","c","d"),2)
rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[3] at parallelize at :24
scala> val func = (xpar:Int,y:Iterator[String])=>{
| y.toList.map(x=>"partition:"+xpar+" value:"+x).iterator
| }
func: (Int, Iterator[String]) => Iterator[String] =
scala> rdd1.mapPartitionsWithIndex(func).collect
res2: Array[String] = Array(partition:0 value:a, partition:0 value:b, partition:1 value:c, partition:1 value:d)
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aggregate(zeroValue)(seqOp, combOp)：对rdd的数据先按照分区汇总然后将分区的数据在汇总（迭代汇总，seqOp或者combOp的值会和下一个值进行比较）
scala> val rdd1 = sc.parallelize(List("a","b","c","d"),2)
rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[5] at parallelize at :24
scala> rdd1.aggregate("")(_+_,_+_)
res3: String = abcd
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aggregateByKey：适用于那种键值对类型的RDD,会根据key进行对value的操作，类似aggregate
scala> val rdd = sc.parallelize(List((1,1),(1,2),(2,2),(2,3)), 2)
rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[12] at parallelize at :24
scala> rdd.aggregateByKey(0)((x,y)=>x+y, (x,y)=>(x+y)).collect
res36: Array[(Int, Int)] = Array((2,5), (1,3))
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coalesce, repartition：repartition与coalesce相似，只不过repartition内部调用了coalesce，coalesce传入的参数比repartition传入的参数多一个，repartition有该参数的默认值，即：是否进行shuffule
scala> val rdd = sc.parallelize(List(1,2,3,4,5), 2)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[24] at parallelize at :24
scala> rdd.repartition(3)
res42: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[28] at repartition at :27
scala> res42.partitions.length
res43: Int = 3
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collectAsMap:将结果一map方式展示
scala> val rdd = sc.parallelize(List(("a",2),("b",10),("x",22)), 2)
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[29] at parallelize at :24
scala> rdd.collectAsMap
res44: scala.collection.Map[String,Int] = Map(b -> 10, a -> 2, x -> 22)
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combineByKey : 和reduceByKey是相同的效果。需要三个参数 第一个每个key对应的value 第二个，局部的value操作， 第三个：全局value操作
scala> val rdd = sc.parallelize(List(("a",2),("b",10),("x",22),("a",200),("x",89)), 2)
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[30] at parallelize at :24
scala> rdd.combineByKey(x=>x, (a:Int,b:Int)=>a+b, (a:Int,b:Int)=>a+b)
res45: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[31] at combineByKey at :27
scala> res45.collect
res46: Array[(String, Int)] = Array((x,111), (b,10), (a,202))
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countByKey：通过Key统计条数
scala> val rdd = sc.parallelize(List(("a",2),("b",10),("x",22),("a",200),("x",89)), 2)
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[33] at parallelize at :24
scala> rdd.countByKey
res49: scala.collection.Map[String,Long] = Map(x -> 2, b -> 1, a -> 2)
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filterByRange：返回符合过滤返回的数据
scala> val rdd = sc.parallelize(List(("a",2),("b",10),("x",22),("a",200),("x",89)), 2)
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[36] at parallelize at :24
scala> rdd.filterByRange("a","b")
res51: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[37] at filterByRange at :27
scala> res51.collect
res52: Array[(String, Int)] = Array((a,2), (b,10), (a,200))
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flatMapValues
scala> val rdd = sc.parallelize(List(("a"->"1 2 3 "),("b"->"1 2 3 "),("x"->"1 2 3 "),("a"->"1 2 3 "),("x"->"1 2 3 ")), 2)
rdd: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[39] at parallelize at :24
scala> rdd.flatMapValues(x=>x.split(" ")).collect
res53: Array[(String, String)] = Array((a,1), (a,2), (a,3), (b,1), (b,2), (b,3), (x,1), (x,2), (x,3), (a,1), (a,2), (a,3), (x,1), (x,2), (x,3))
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foldByKey：通过key聚集数据然后做操作
scala> val rdd = sc.parallelize(List(("a",2),("b",10),("x",22),("a",200),("x",89)), 2)
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[41] at parallelize at :24
scala> rdd.foldByKey(0)(_+_).collect
res55: Array[(String, Int)] = Array((x,111), (b,10), (a,202))
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keyBy : 以传入的参数做key
scala> val rdd1 = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[43] at parallelize at :24
scala> val rdd2 = rdd1.keyBy(_.length).collect
rdd2: Array[(Int, String)] = Array((3,dog), (6,salmon), (6,salmon), (3,rat), (8,elephant))
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keys values
scala> val rdd1 = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3)
rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[45] at parallelize at :24
scala> val rdd2 = rdd1.map(x=>(x.length,x))
rdd2: org.apache.spark.rdd.RDD[(Int, String)] = MapPartitionsRDD[47] at map at :26
scala> rdd2.keys
res63: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[48] at keys at :29
scala> rdd2.keys.collect
res64: Array[Int] = Array(3, 6, 6, 3, 8)
scala> rdd2.values.collect
res65: Array[String] = Array(dog, salmon, salmon, rat, elephant) 关于spark常用算子有哪些就分享到这里了，希望以上内容可以对大家有一定的帮助，可以学到更多知识。如果觉得文章不错，可以把它分享出去让更多的人看到。
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