Spark中的常用算子
更多有用的例子和算子讲解参见:
http://homepage.cs.latrobe.edu.au/zhe/ZhenHeSparkRDDAPIExamples.html
map是对每个元素操作, mapPartitions是对其中的每个partition操作 ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- mapPartitionsWithIndex : 把每个partition中的分区号和对应的值拿出来, 看源码 val func = (index: Int, iter: Iterator[(Int)]) => { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9), 2) rdd1.mapPartitionsWithIndex(func).collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- aggregate def func1(index: Int, iter: Iterator[(Int)]) : Iterator[String] = { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9), 2) rdd1.mapPartitionsWithIndex(func1).collect ###是action操作, 第一个参数是初始值, 二:是2个函数[每个函数都是2个参数(第一个参数:先对个个分区进行合并, 第二个:对个个分区合并后的结果再进行合并), 输出一个参数] ###0 + (0+1+2+3+4 + 0+5+6+7+8+9) rdd1.aggregate(0)(_+_, _+_) rdd1.aggregate(0)(math.max(_, _), _ + _) ###5和1比, 得5再和234比得5 --> 5和6789比,得9 --> 5 + (5+9) rdd1.aggregate(5)(math.max(_, _), _ + _) val rdd2 = sc.parallelize(List("a","b","c","d","e","f"),2) def func2(index: Int, iter: Iterator[(String)]) : Iterator[String] = { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } rdd2.aggregate("")(_ + _, _ + _) rdd2.aggregate("=")(_ + _, _ + _) val rdd3 = sc.parallelize(List("12","23","345","4567"),2) rdd3.aggregate("")((x,y) => math.max(x.length, y.length).toString, (x,y) => x + y) val rdd4 = sc.parallelize(List("12","23","345",""),2) rdd4.aggregate("")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y) val rdd5 = sc.parallelize(List("12","23","","345"),2) rdd5.aggregate("")((x,y) => math.min(x.length, y.length).toString, (x,y) => x + y) ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- aggregateByKey val pairRDD = sc.parallelize(List( ("cat",2), ("cat", 5), ("mouse", 4),("cat", 12), ("dog", 12), ("mouse", 2)), 2) def func2(index: Int, iter: Iterator[(String, Int)]) : Iterator[String] = { iter.toList.map(x => "[partID:" + index + ", val: " + x + "]").iterator } pairRDD.mapPartitionsWithIndex(func2).collect pairRDD.aggregateByKey(0)(math.max(_, _), _ + _).collect pairRDD.aggregateByKey(100)(math.max(_, _), _ + _).collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- checkpoint sc.setCheckpointDir("hdfs://node-1.itcast.cn:9000/ck") val rdd = sc.textFile("hdfs://node-1.itcast.cn:9000/wc").flatMap(_.split(" ")).map((_, 1)).reduceByKey(_+_) rdd.checkpoint rdd.isCheckpointed rdd.count rdd.isCheckpointed rdd.getCheckpointFile ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- coalesce, repartition val rdd1 = sc.parallelize(1 to 10, 10) val rdd2 = rdd1.coalesce(2, false) rdd2.partitions.length ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- combineByKey : 和reduceByKey是相同的效果 ###第一个参数x:原封不动取出来, 第二个参数:是函数, 局部运算, 第三个:是函数, 对局部运算后的结果再做运算 ###每个分区中每个key中value中的第一个值, (hello,1)(hello,1)(good,1)-->(hello(1,1),good(1))-->x就相当于hello的第一个1, good中的1 val rdd1 = sc.textFile("hdfs://master:9000/wordcount/input/").flatMap(_.split(" ")).map((_, 1)) val rdd2 = rdd1.combineByKey(x => x, (a: Int, b: Int) => a + b, (m: Int, n: Int) => m + n) rdd1.collect rdd2.collect ###当input下有3个文件时(有3个block块, 不是有3个文件就有3个block, ), 每个会多加3个10 val rdd3 = rdd1.combineByKey(x => x + 10, (a: Int, b: Int) => a + b, (m: Int, n: Int) => m + n) rdd3.collect val rdd4 = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","wolf","bear","bee"), 3) val rdd5 = sc.parallelize(List(1,1,2,2,2,1,2,2,2), 3) val rdd6 = rdd5.zip(rdd4) val rdd7 = rdd6.combineByKey(List(_), (x: List[String], y: String) => x :+ y, (m: List[String], n: List[String]) => m ++ n) ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- countByKey val rdd1 = sc.parallelize(List(("a", 1), ("b", 2), ("b", 2), ("c", 2), ("c", 1))) rdd1.countByKey rdd1.countByValue ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- filterByRange val rdd1 = sc.parallelize(List(("e", 5), ("c", 3), ("d", 4), ("c", 2), ("a", 1))) val rdd2 = rdd1.filterByRange("b", "d") rdd2.collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- flatMapValues : Array((a,1), (a,2), (b,3), (b,4)) val rdd3 = sc.parallelize(List(("a", "1 2"), ("b", "3 4"))) val rdd4 = rdd3.flatMapValues(_.split(" ")) rdd4.collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- foldByKey val rdd1 = sc.parallelize(List("dog", "wolf", "cat", "bear"), 2) val rdd2 = rdd1.map(x => (x.length, x)) val rdd3 = rdd2.foldByKey("")(_+_) val rdd = sc.textFile("hdfs://node-1.itcast.cn:9000/wc").flatMap(_.split(" ")).map((_, 1)) rdd.foldByKey(0)(_+_) ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- foreachPartition val rdd1 = sc.parallelize(List(1, 2, 3, 4, 5, 6, 7, 8, 9), 3) rdd1.foreachPartition(x => println(x.reduce(_ + _))) ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- keyBy : 以传入的参数做key val rdd1 = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"), 3) val rdd2 = rdd1.keyBy(_.length) rdd2.collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- keys values val rdd1 = sc.parallelize(List("dog", "tiger", "lion", "cat", "panther", "eagle"), 2) val rdd2 = rdd1.map(x => (x.length, x)) rdd2.keys.collect rdd2.values.collect ------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------- mapPartitions
本文转自SummerChill博客园博客，原文链接：http://www.cnblogs.com/DreamDrive/p/6759638.html，如需转载请自行联系原作者
