【零基础学flink】flink实战(idea版本)

回顾

上一小节中我们介绍了flink的安装，以及flink word count的实现，本文主要介绍如何使用idea开发flink项目

一、新建Maven项目

这一步比较简单，网上也有很多教程，主要是使用maven来管理依赖包

二、pom的配置

        <dependency>
            <groupId>org.apache.flinkgroupId>
            <artifactId>flink-javaartifactId>
            <version>1.8.0version>
        dependency>
        <dependency>
            <groupId>org.apache.flinkgroupId>
            <artifactId>flink-streaming-java_2.11artifactId>
            <version>1.8.0version>
        dependency>
        <dependency>
            <groupId>org.apache.flinkgroupId>
            <artifactId>flink-clients_2.11artifactId>
            <version>1.8.0version>
        dependency>

三、wordcount实战（在线版本）

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: Mr.Deng
 * Date: 2018/10/15
 * 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6100");
            port = 6100;
        }
        //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("211.1xx.1xx.6x", port, "\n");
// 本文填写的是远程linux ip，在远程linux上需要执行：nc -l 6100命令
        //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) {
                    out.collect(new WordWithCount(word,1L));
                }
            }
        })//打平操作，把每行的单词转为类型的数据
                //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(5),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 +
                    '}';
        }
    }

}

四、离线版本wordCount

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
/**
 * Author: Mr.Deng
 * Date: 2018/10/19
 * Desc:
 */
public class WordCountJava {
    public static void main(String[] args) throws Exception {
        //构建环境
        final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //通过字符串构建数据集
        DataSet<String> text = env.fromElements(
                " Who is there ? ",
                " I think I hear them. Stand, ho ! Who is there ? ");
        //分割字符串、按照key进行分组、统计相同的key个数
        DataSet<Tuple2<String, Integer>> wordCounts = text
                .flatMap(new LineSplitter())
                .groupBy(0)
                .sum(1);
        //打印
        wordCounts.print();
    }
    //分割字符串的方法
    public static class LineSplitter implements FlatMapFunction<String, Tuple2<String, Integer>> {
        public void flatMap(String line, Collector<Tuple2<String, Integer>> out) {
            for (String word : line.split(" ")) {
                out.collect(new Tuple2<String, Integer>(word, 1));
            }
        }
    }
}

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