array

45. Jump Game II Given an array of non-negative integers, you are initially positioned at the first index of the array. Each element in the array represents your maximum jump length at that position. Your goal is to reach the last index in the minimum number of jumps. For example: Given array A = [2,3,1,1,4] The minimum number of jumps to reach the last index is 2 . (Jump 1 step from index 0 to 1, then 3 steps to the last index.) Note: You can assume that you can always reach the last index. 给定一个非负整数数组，给定的初始化位置在数组的起始位置。数组中的每个元素代表着你能都在此位置跳跃的最大的距离。你的目标是用最少的跳跃数达到数组的末尾。 算法：贪心 第一种方法：（贪心） public int

map: 对RDD每个元素转换 flatMap: 对RDD每个元素转换, 然后再 扁平化 （即将所有对象合并为一个对象） Example: data 有两行数据： a,b,c 1,2,3 scala>data.map(line1 => line1.split(",")).collect() res11: Array[Array[String]] = Array(Array(a, b, c),Array(1, 2, 3)) scala>data.flatMap(line1 => line1.split(",")).collect() res13: Array[String] = Array(a, b, c, 1, 2, 3) 来源： CSDN 作者： 杨鑫newlfe 链接： https://blog.csdn.net/u012965373/article/details/60879642

map(function) map是对RDD中的每个元素都执行一个指定的函数来产生一个新的RDD。任何原RDD中的元素在新RDD中都有且只有一个元素与之对应。 举例： val a = sc.parallelize( 1 to 9 , 3 ) val b = a .map(x => x* 2 )//x => x*2是一个函数，x是传入参数即RDD的每个元素，x*2是返回值 a .collect //结果Array[Int] = Array(1, 2, 3, 4, 5, 6, 7, 8, 9) b.collect //结果Array[Int] = Array(2, 4, 6, 8, 10, 12, 14, 16, 18) 当然map也可以把Key变成Key-Value对 val a = sc .parallelize (List( "dog" , "tiger" , "lion" , "cat" , "panther" , " eagle" ), 2 ) val b = a .map ( x => ( x , 1 )) b .collect .foreach (println(_)) /* (dog,1) (tiger,1) (lion,1) (cat,1) (panther,1) ( eagle,1) */ mapPartitions(function) map(

以前总是分不清楚spark中flatmap和map的区别，现在弄明白了，总结分享给大家，先看看flatmap和map的定义。 map()是将函数用于RDD中的每个元素，将返回值构成新的RDD。 flatmap()是将函数应用于RDD中的每个元素，将返回的迭代器的所有内容构成新的RDD 有些拗口，看看例子就明白了。 val rdd = sc.parallelize( List ( "coffee panda" , "happy panda" , "happiest panda party" )) 输入 rdd. map (x=>x).collect 结果 res9: Array [ String ] = Array (coffee panda, happy panda, happiest panda party) 输入 rdd.flatMap(x=>x.split( " " )).collect 结果 res8: Array [ String ] = Array (coffee, panda, happy, panda, happiest, panda, party) flatMap说明白就是先map然后再flat，再来看个例子 val rdd1 = sc.parallelize( List ( 1 , 2 , 3 , 3 )) scala> rdd1.map(x=>x+ 1 )

文章目录 数组: map flatten flatMap foreach worldcount 数组: val array = new Array[Int](4) //数组的声明 array(0) = 1 array(0) = 100 //数组的长度不可变,内容可变 map 将数组中的每个元素进行某种映射操作 val array = Array[Int](2,3,5,6,7) //使用 val y = array.map((x:Int) => x*2) array.map(_*2) flatten 扁平化操作 val arr = Array("hello h1 h2", "nihao zs lisi") // Array[String] = Array(hello h1 h2, nihao zs lisi) arr.map(_.split(" ")) //Array[Array[String]] = Array(Array(hello, h1, h2), Array(nihao, zs, lisi)) arr.map(_.split(" ")).flatten // Array[String] = Array(hello, h1, h2, nihao, zs, lisi) flatMap 等同于 map+flatten arr.flatMap(_.split(" ")) //

scala> rdd5.flatMap(t=>{t.split("\t")}).collect res8: Array[String] = Array(75, 2018-09-17 19:15:34.0, BK-HTML5-JY-1819, BK181713017, 张帅杰, 2018-09-17 19:50:37.0, 34, 0, 55, 55, 75, 2018-09-17 19:16:07.0, BK-HTML5-JY-1819, BK181913016, 王润芝, 2018-09-17 19:39:30.0, 23, 0, 62, 62, 75, 2018-09-17 19:16:08.0, BK-HTML5-JY-1819, BK181913062, 唐颖, 2018-09-17 19:39:31.0, 23, 0, 60, 60, 75, 2018-09-17 19:16:47.0, BK-HTML5-JY-1819, BK181913007, 周营营, 2018-09-17 19:43:32.0, 0, 0, 61, 61) scala> rdd5.map(t=>{t.split("\t")}).collect res9: Array[Array[String]] = Array(Array(75, 2018-09-17 19:15:34.0, BK-HTML5

以前总是分不清楚spark中flatmap和map的区别，现在弄明白了，总结分享给大家，先看看flatmap和map的定义。 map()是将函数用于RDD中的每个元素，将返回值构成新的RDD。 flatmap()是将函数应用于RDD中的每个元素，将返回的迭代器的所有内容构成新的RDD 有些拗口，看看例子就明白了。 val rdd = sc.parallelize(List( "coffee panda" , "happy panda" , "happiest panda party" )) 输入 rdd .map ( x => x ) .collect 结果 res9: Array[String] = Array(coffee panda, happy panda, happiest panda party) 输入 rdd .flatMap ( x => x .split ( " " )) .collect 结果: res8: Array[String] = Array(coffee, panda, happy, panda, happiest, panda, party) flatMap说明白就是先map然后再flat，再来看个例子 val rdd1 = sc .parallelize (List( 1 , 2 , 3 , 3 )) scala> rdd1 .map ( x =>

map()是将函数用于RDD中的每个元素，将返回值构成新的RDD。 flatmap()是将函数应用于RDD中的每个元素，将返回的迭代器的所有内容构成新的RDD,这样就得到了一个由各列表中的元素组成的RDD,而不是一个列表组成的RDD。 有些拗口，看看例子就明白了。 val rdd = sc. parallelize (List( "coffee panda" , "happy panda" , "happiest panda party" ) ) 输入 rdd. map (x=>x) .collect 结果 res9: Array [ String ] = Array (coffee panda, happy panda, happiest panda party) 输入 rdd. flatMap (x=>x.split( " " ) ).collect 结果 res8: Array [ String ] = Array (coffee, panda, happy, panda, happiest, panda, party) flatMap说明白就是先map然后再flat，再来看个例子 val rdd1 = sc. parallelize (List( 1 , 2 , 3 , 3 ) ) scala> rdd1. map (x=>x+ 1 ) .collect res10:

map()是将函数用于RDD中的每个元素，将返回值构成新的RDD。 flatmap()是将函数应用于RDD中的每个元素，将返回的迭代器的所有内容构成新的RDD,这样就得到了一个由各列表中的元素组成的RDD,而不是一个列表组成的RDD。 有些拗口，看看例子就明白了。 val rdd = sc. parallelize (List( "coffee panda" , "happy panda" , "happiest panda party" ) ) 输入 rdd. map (x=>x) .collect 结果 res9: Array [ String ] = Array (coffee panda, happy panda, happiest panda party) 输入 rdd. flatMap (x=>x.split( " " ) ).collect 结果 res8: Array [ String ] = Array (coffee, panda, happy, panda, happiest, panda, party) flatMap说明白就是先map然后再flat，再来看个例子 val rdd1 = sc. parallelize (List( 1 , 2 , 3 , 3 ) ) scala> rdd1. map (x=>x+ 1 ) .collect res10:

题意： Today at the lesson Vitya learned a very interesting function — mex. Mex of a sequence of numbers is the minimum non-negative number that is not present in the sequence as element. For example, mex ([4, 33, 0, 1, 1, 5]) = 2 and mex ([1, 2, 3]) = 0. Vitya quickly understood all tasks of the teacher, but can you do the same? You are given an array consisting of n non-negative integers, and m queries. Each query is characterized by one number x and consists of the following consecutive steps: Perform the bitwise addition operation modulo 2 ( xor) of each array element with the number x . Find