Flink的Java Api 实现WordCount的批处理和流处理

1 基础说明

Flink 是一款优秀的批处理和流处理的大数据计算引擎，本文将通过Flink的Java Api实现WordCount多版本案例。更多请查阅 Flink官网
说明：

1. Flink版本：1.13.5
2. Flink Web UI地址：http://192.168.18.88:7999
3. 服务器具备nc（netcat）环境，如果不具备，可在服务器执行安装命令

yum -y install netcat

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2 WordCount案例

2.1 项目目录结构

创建maven项目，目录结构如下：

2.2 maven依赖

依赖内容如下：

<dependencies>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_2.12</artifactId>
    <version>1.14.4</version>
  </dependency>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-java</artifactId>
    <version>1.14.4</version>
  </dependency>
  <dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-clients_2.12</artifactId>
    <version>1.14.4</version>
  </dependency>
</dependencies>

完整的pom.xml文件如下：

<?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>cn.mfox</groupId>
    <artifactId>wordcount-demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <name>wordcount-demo</name>
    <!-- FIXME change it to the project's website -->
    <url>http://www.example.com</url>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>1.14.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.14.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.14.4</version>
        </dependency>
    </dependencies>
    
    <build>
        <plugins>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

2.3 创建word.txt文件

创建src/main/java/cn/mfox/word.txt文件，word.txt文件的具体内容如下：

洪七公 王重阳 欧阳锋 黄药师
洪七公
周伯通
郭靖
黄药师
黄蓉
林朝英

word.txt文件位置及内容截图如下：

2.4 批处理WordCount

WordCountByBatch.java代码如下：

package cn.mfox;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * 基于批计算的wordcount案例
 *
 * @author hy
 * @version 1.0
 * @date 2022/3/24 16:52
 */
public class WordCountByBatch {
    public static void main(String[] args) throws Exception {
        // 1. 创建执行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        // 2. 从文件中读取文件，按行读取（存取的元素就是每行的文本）
        String inputPath = "src/main/java/cn/mfox/word.txt";
        DataSet<String> inputDataSet = env.readTextFile(inputPath);
        // 3. 转换数据格式
        // returns说明：当Lambda表达式使用Java泛型的时候，由于泛型擦除，需要显示的声明类型信息
        FlatMapOperator<String, Tuple2<String, Long>> wordAndOne = inputDataSet
                .flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
                    Arrays.stream(line.split(" "))
                            .map(word -> word.trim())
                            .filter(word -> !word.isEmpty())
                            .map(word -> Tuple2.of(word, 1L))
                            .forEach(out::collect);
                }).returns(Types.TUPLE(Types.STRING, Types.LONG));
        // 4. 安装word进行分组
        UnsortedGrouping<Tuple2<String, Long>> wordAndOneUG = wordAndOne.groupBy(0);
        // 5. 分组内聚合
        AggregateOperator<Tuple2<String, Long>> sumResult = wordAndOneUG.sum(1);
        // 6. 打印结果
        sumResult.print();
    }
}

程序运行结果如下：

2.5 流处理WordCount

WordCountByStream.java代码如下：

package cn.mfox;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * 基于socket流计算的wordcount案例
 *
 * @author hy
 * @version 1.0
 * @date 2022/3/25 14:52
 */
public class WordCountByStream {
    public static void main(String[] args) throws Exception {
        // 1. 创建流式执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 2. 从文件中读取文件
        String inputPath = "src/main/java/cn/mfox/word.txt";
        DataStream<String> inputDataStream = env.readTextFile(inputPath);

        // 3. 转换计算
        SingleOutputStreamOperator<Tuple2<String, Long>> wordAndOne = inputDataStream
                .flatMap((String line, Collector<String> words) -> {
                    // 分割及排空
                    Arrays.stream(line.split(" "))
                            .map(word -> word.trim())
                            .filter(word -> !word.isEmpty())
                            .forEach(words::collect);
                }).returns(Types.STRING)
                .map(word -> Tuple2.of(word, 1L))
                .returns(Types.TUPLE(Types.STRING, Types.LONG));
        // 4. 分组
        KeyedStream<Tuple2<String, Long>, String> wordAndOneKS = wordAndOne.keyBy(t -> t.f0);
        // 5. 求和
        SingleOutputStreamOperator<Tuple2<String, Long>> sumResult = wordAndOneKS.sum(1);
        // 6. 打印
        sumResult.print();
        // 7. 启动任务
        env.execute();
    }
}

程序运行结果如下：

2.6 Socket流处理WordCount

1. shell窗口中创建socket端口，命令如下

nc -lk 18888

2. socket端口中输入测试数据，截图如下：
   
3. 编写 WordCountBySocketStream.java 文件，代码如下：

package cn.mfox;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * 基于socket流计算的wordcount案例
 *
 * @author hy
 * @version 1.0
 * @date 2022/3/25 14:52
 */
public class WordCountBySocketStream {
    public static void main(String[] args) throws Exception {
        // 1. 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 2. 获取ParameterTool
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String host = parameterTool.get("host");
        int port = parameterTool.getInt("port");
        // 3. 从socket文本流读取数据
        DataStream<String> inputDataStream = env.socketTextStream(host, port);
        // 4. 转换计算
        SingleOutputStreamOperator<Tuple2<String, Long>> wordAndOne = inputDataStream
                .flatMap((String line, Collector<String> words) -> {
                    // 分割及排空
                    Arrays.stream(line.split(" "))
                            .map(word -> word.trim())
                            .filter(word -> !word.isEmpty())
                            .forEach(words::collect);
                }).returns(Types.STRING)
                .map(word -> Tuple2.of(word, 1L))
                .returns(Types.TUPLE(Types.STRING, Types.LONG));
        // 5. 分组
        KeyedStream<Tuple2<String, Long>, String> wordAndOneKS = wordAndOne.keyBy(t -> t.f0);
        // 6. 求和
        SingleOutputStreamOperator<Tuple2<String, Long>> sumResult = wordAndOneKS.sum(1);
        // 7. 打印
        sumResult.print();
        // 8. 启动任务
        env.execute();
    }
}

4. 在当前工程目录下执行maven打包命令

mvn clean package

流程截图如下：

5. 在Flink的Web界面（Submit New Job）菜单中上传Jar包，上传成功后的截图如下：
   
6. 界面点击刚上传的jar包，输入要运行的类、socket的地址、端口，输入的内容如下：

cn.mfox.WordCountBySocketStream
--host 192.168.18.88 --port 18888

截图如下：

7. 在Running Jobs菜单中可查看刚提交的Flink程序的执行情况，如各个算子的并行度，接受数据的条数大小等，截图如下：
   
8. 在Task Manages菜单下可以查看Flink程序的日志输出，截图如下：
   

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3 结束语

至此，文档篇幅结束…
因初次接触Flink及个人水平有限，如有错误，欢迎各位大佬点评 ！