//3.通过监控端口创建DStream，读进来的数据为一行行 val lineStreams = ssc.socketTextStream(“hadoop102”, 9999)//将每一行数据做切分，形成一个个单词 val wordStreams = lineStreams.flatMap(_.split(” “))//将单词映射成元组（word,1）val wordAndOneStreams = wordStreams.map((_, 1))//将相同...
public static int countWords(Stream<Character> stream) { WordCounterTuple wordCounter = stream.reduce( new WordCounterTuple( true,0), new WordCountAccumulator(), new WordCountCombiner()); System.out.println(wordCounter.getCounter()); return wordCounter.getCounter(); } 1. 2. 3. 4. 5. 6....
*/publicstaticintcountWordsParallel(Stream<Character>stream){WordCounterTuple wordCounter=stream.parallel().reduce(newWordCounterTuple(true,0),newWordCountAccumulator(),newWordCountCombiner());System.out.println(wordCounter.getCounter());returnwordCounter.getCounter();} 运行下看看结果,没有报错,但是运行...
publicstaticvoidmain(String[]args){List<String>list=Arrays.asList("beijing shanghai guangzhou","beijing guangzhou","beijing","beijing");Map<String,Long>collect=list.stream().flatMap(o->Stream.of(o.split(" "))).collect(Collectors.groupingBy(o->o,Collectors.counting()));System.out.println(c...
java8 stream流操作实现List Count/Word Count 话不多说,直接上代码 List Count 代码语言:javascript 复制 publicstaticvoidmain(String[]args){List<String>list=Arrays.asList("beijing","shanghai","guangzhou","shenzhen","beijing");Map<String,Long>collect=list.stream().collect(Collectors.groupingBy(o->...
Word Count import java.util.Arrays; import java.util.List; import java.util.Map; import java.util.stream.Collectors; public class TestStream { static void wordCount() { List<String> lines = Arrays.asList("hello java", "hello python"); Map<String, Long> words = lines.stream() .flatMap...
* 第三步:创建Spark Streaming 输入数据来源:input Stream * 1.数据输入来源可以基于File、HDFS、Flume、Kafka、Socket * * 2.在这里我们指定数据来源于网络Socket端口, * Spark Streaming连接上该端口并在运行的时候一直监听该端口的数据(当然该端口服务首先必须存在,并且在后续会根据业务需要不断的有数据产生)。
java单机版word-count java.nio.file.Path.of方法需要jdk11的支持 publicstaticvoidmain(String[] args){// 1.利用try-with-resource语句从文件读取每行文字,形成以行为单位的字符串流try(Stream<String> lineStream = Files.lines(Path.of("/Users/yang/test.txt"), Charset.defaultCharset())) {...
Stream<String> stream = list.stream(); 1. 2. 1.2 从数组创建 String[] array = {"A", "B", "C"}; Stream<String> stream = Arrays.stream(array); 1. 2. 1.3 生成 Stream Stream<Integer> stream = Stream.generate(() -> new Random().nextInt()); ...
Flink的数据处理方法基于流式处理架构,是一种真正的流处理、流计算框架,其中的很多概念及思想模式对于大数据处理方法具有启发意义。Flink 官网对于Stream,State,Time等组件做了详细的解释和说明。在下文中完成大数据版Hello,World的编写与运行,同时继续理解官方文档。