1、POM依赖:
<?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.sh.xujf</groupId> <artifactId>flink01</artifactId> <version>1.0-SNAPSHOT</version> <properties> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> <encoding>UTF-8</encoding> <scala.version>2.11.2</scala.version> <scala.binary.version>2.11</scala.binary.version> <hadoop.version>2.7.6</hadoop.version> <flink.version>1.10.1</flink.version> </properties> <dependencies> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-java</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-java_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-scala_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-clients_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kafka-0.10_${scala.binary.version}</artifactId> <version>${flink.version}</version> </dependency> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.40</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>fastjson</artifactId> <version>1.2.70</version> </dependency> </dependencies> </project>
2、java代码:
package com.sh.xujf; import org.apache.flink.api.common.functions.FlatMapFunction; import org.apache.flink.api.java.utils.ParameterTool; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.datastream.DataStreamSource; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.windowing.time.Time; import org.apache.flink.util.Collector; /** * Author: xujf * Date: 2020-10-4 * Desc: 使用flink对指定窗口内的数据进行实时统计,最终把结果打印出来 * 先在node21机器上执行nc -l 9000 */ public class StreamingWindowWordCountJava { public static void main(String[] args) throws Exception { //定义socket的端口号 int port; try{ ParameterTool parameterTool = ParameterTool.fromArgs(args); port = parameterTool.getInt("port"); }catch (Exception e){ System.err.println("没有指定port参数,使用默认值9000"); port = 9000; } //获取运行环境 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); //连接socket获取输入的数据 DataStreamSource<String> text = env.socketTextStream("192.168.2.132", port, "\n"); //计算数据 DataStream<WordWithCount> windowCount = text.flatMap(new FlatMapFunction<String, WordWithCount>() { public void flatMap(String value, Collector<WordWithCount> out) throws Exception { String[] splits = value.split("\\s"); for (String word:splits) { System.out.println(word); out.collect(new WordWithCount(word,1L)); } } })//打平操作,把每行的单词转为<word,count>类型的数据 //针对相同的word数据进行分组 .keyBy("word") //指定计算数据的窗口大小和滑动窗口大小 .timeWindow(Time.seconds(2),Time.seconds(1)) .sum("count"); //把数据打印到控制台,使用一个并行度 windowCount.print().setParallelism(1); //注意:因为flink是懒加载的,所以必须调用execute方法,上面的代码才会执行 env.execute("streaming word count"); } /** * 主要为了存储单词以及单词出现的次数 */ public static class WordWithCount{ public String word; public long count; public WordWithCount(){} public WordWithCount(String word, long count) { this.word = word; this.count = count; } @Override public String toString() { return "WordWithCount{" + "word='" + word + '\'' + ", count=" + count + '}'; } } }
3、先在虚拟机上执行:nc -l 9000
4、idea启动程序,在虚拟机上输入字符串即可测试。