}// 5 将转换结果打印输出valarray=wordToCount.collect() array.foreach(println)//关闭连接sc.stop() } } 实现三 Spark框架功能reduceByKey packagecom.hongpin.bigdata.spark_core.wordcountimportorg.apache.spark.{SparkConf,SparkContext}objectSpark03_wordcount{defmain(args:Array[String]):Unit= {//应用...
}// countByKeydef wordCount7(sc : SparkContext):Unit={valrdd: RDD[String] = sc.makeRDD(List("hello spark","hello scala"))// 扁平化操作:拆分所有句子,把所有单词放到一个list里valwords: RDD[String] = rdd.flatMap(_.split(" "))// 将每个单词变成 word => (word,1)valwordToOne: RDD[...
在Word Count示例中,如果数据集较大,可以考虑对RDD进行持久化,以避免重复切分和转换。 示例代码: words.persist() word_counts = words.map(lambdaword: (word,1)).reduceByKey(lambdaa, b: a + b) 3 调整分区数 默认情况下,Spark会根据集群的核数自动设置RDD的分区数。但在某些情况下,可以根据数据规模和...
//flatMapRDD: org.apache.spark.rdd.RDD[String] 去除空的字符串的操作 flatMapRDD.filter(word => word.nonEmpty) 3. 将每个单词进行计数 val mapRDD = flatMapRDD.map(word => (word,1)) 返回类型//mapRDD: org.apache.spark.rdd.RDD[(String, Int)] 4.将相同的单词放在一起进行value值得聚合 va...
//reduceRDD: org.apache.spark.rdd.RDD[(String, Int)]查看对比下（reduceByKey前后两个变量的collect）链式编程写法：val result = sc.textFile("file:///opt/modules/o2o23/spark/README.md").flatMap(line => line.split(" ")).filter(word => word.nonEmpty).map(word => (word,1)).reduceBy...
rdd.map(lambda r: r[0]) counts = lines.flatMap(lambda s: s.split(" "))\ .map(lambda word: (word, 1))\ .reduceByKey(add) output = counts.collect() with open(os.path.join(output_path, "result.txt"), "wt") as f: for (word, count) in output: f.write(str(word) +": ...
Map[String, Long]((word, 1)) } ) // Map 和 Map 聚合 val wordcount = mapRDD.reduce( (map1, map2) => { map2.foreach { case (word, count) => { val newCount = map1.getOrElse(word, 0L) + count map1.update(word, newCount) } } map1 } ) println(wordcount) } 方式10:...
return new Tuple2<String, Integer>(word, 1); } }); // 接着,需要以单词作为key,统计每个单词出现的次数 // 这里要使用reduceByKey这个算子,对每个key对应的value,都进行reduce操作 // 比如JavaPairRDD中有几个元素,分别为(hello, 1) (hello, 1) (hello, 1) (world, 1) ...
接下来,可以使用DataFrame中的数据实现一个简单的Word Count。首先,需要将DataFrame中的数据转换为一个RDD(弹性分布式数据集): valwords=dataFrame.flatMap(row=>row.getString(0).split(" ")) 1. 然后,使用RDD的map和reduceByKey方法来进行计数: valwordCounts=words.map(word=>(word,1)).reduceByKey(_+_...
方法一:map + reduceByKey package com.cw.bigdata.spark.wordcount import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} object WordCoun...