第四章 Flink实战之WordCount基础入门

1、Flink开发环境搭建 1.1、创建Maven项目

1、选择“File” -->“New”–>“Project”
2、选择 Maven，设置JDK版本，选择maven项目的模板

org.apache.maven.archetypes:maven-archetype-quickstart
#代表普通的maven项目面板

3、设置Groupid和Artifactid

Groupid:公司名称
Artifactid:项目模块

4、检查Maven环境，选择”Next“

主要选择maven路径和settings路径

5、检查项目名和工作空间，选择finish
6、等待项目创建，下载资源，创建完成后目录结构如下

1.2、pom文件配置



    4.0.0

    com.hainiu
    hainiuflink
    1.0
    
    
        1.8
        2.11
        1.9.3
        1.10.0
        2.7.3
        1.2.72
        2.9.0
        5.1.35
        1.2.17
        1.7.7
        1.8
        1.8
        1.8
        UTF-8
        compile

        com.hainiu.Driver
    

    
        
            org.slf4j
            slf4j-log4j12
            ${slf4j.version}
            ${project.build.scope}
        

        
            log4j
            log4j
            ${log4j.version}
            ${project.build.scope}
        
        
        
            org.apache.hadoop
            hadoop-client
            ${hadoop.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-hadoop-compatibility_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-java
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-streaming-java_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-scala_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-streaming-scala_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-runtime-web_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-statebackend-rocksdb_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-hbase_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-connector-elasticsearch5_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-connector-kafka-0.10_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            org.apache.flink
            flink-connector-filesystem_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            mysql
            mysql-connector-java
            ${mysql.version}
            ${project.build.scope}
        
        
        
            redis.clients
            jedis
            ${redis.version}
            ${project.build.scope}
        
        
        
            org.apache.parquet
            parquet-avro
            ${parquet.version}
            ${project.build.scope}
        
        
            org.apache.parquet
            parquet-hadoop
            ${parquet.version}
            ${project.build.scope}
        
        
            org.apache.flink
            flink-parquet_${scala.version}
            ${flink.version}
            ${project.build.scope}
        
        
        
            com.alibaba
            fastjson
            ${fastjson.version}
            ${project.build.scope}
        
    

    
        
            
                src/main/resources
            
        
        
            
                org.apache.maven.plugins
                maven-assembly-plugin
                
                    
                        src/assembly/assembly.xml
                    
                    
                        
                            ${mainClass}
                        
                    
                
                
                    
                        make-assembly
                        package
                        
                            single
                        
                    
                
            
            
                org.apache.maven.plugins
                maven-surefire-plugin
                2.12
                
                    true
                    once
                    
                        **/**
                    
                
            
            
                org.apache.maven.plugins
                maven-compiler-plugin
                3.1
                
                    ${java.version}
                    ${java.version}
                    ${project.build.sourceEncoding}

2、Flink开发流程 2.1、获取执行环境 2.1.1、批处理环境获取

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

2.1.2、流处理环境获取

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

2.1.3、自动模式

Flink.12开始，流批一体，自动获取模式 - AUTOMATIC()

env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

2.2、创建/加载数据源 2.1.2、从本地文件加载数据源 - 批处理

String path = "H:\flink_demo\flink_test\src\main\resources\wordcount.txt";
DataSet inputDataSet = env.readTextFile(path);

2.1.2、从scoket中读取数据 - 流处理

DataStreamSource elementsSource= env.socketTextStream("10.50.40.131", 9999);

scoket：

2.1.3、从文件中动态读取数据 - 流处理

DataStream lines = env.readTextFile("file:///path");

2.3、数据转换处理

对数据加工转换：扁平化 + 分组 + 聚合

DataSet> resultDataSet = inputDataSet.flatMap(new MyFlatMapFunction())
    .groupBy(0) // (word, 1) -> 0 表示 word
    .sum(1);

2.4、数据输出/存储

输出位置：输出到文件，输出到控制台，输出到MQ，输出到DB,输出到scoket…

//print()它会对流中的每个元素都调用 toString() 方法。
resultDataSet.print();

2.5、计算模型

获取执行环境（execution environment）
加载/创建初始数据集
对数据集进行各种转换操作（生成新的数据集）
指定将计算的结果放到何处去
触发APP执行

3、实战案例 3.1、批处理开发样例

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;

public class WordCount {
    public static void main(String[] args) throws Exception {
        // 1、创建执行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // 2、读取数据
        String path = "H:\flink_demo\flink_test\src\main\resources\wordcount.txt";
        // DataSet -> Operator -> DataSource
        DataSet inputDataSet = env.readTextFile(path);

        // 3、扁平化 + 分组 + sum
        DataSet> resultDataSet = inputDataSet.flatMap(new MyFlatMapFunction())
                .groupBy(0) // (word, 1) -> 0 表示 word
                .sum(1);

        resultDataSet.print();
    }

    public static class MyFlatMapFunction implements FlatMapFunction> {

        @Override
        public void flatMap(String input, Collector> collector) throws Exception {
            String[] words = input.split(" ");
            for (String word : words) {
                collector.collect(new Tuple2<>(word, 1));
            }
        }
    }
}

运行结果

3.2、流处理开发样例

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class StreamCount {
    public static void main(String[] args) throws Exception {
        // 1、创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2、读取 文件数据 数据
        DataStreamSource inputDataStream = env.readTextFile("H:\flink_demo\flink_test\src\main\resources\wordcount.txt");

        // 3、计算
        SingleOutputStreamOperator> resultDataStream = inputDataStream.flatMap(new FlatMapFunction>() {
            @Override
            public void flatMap(String input, Collector> collector) throws Exception {
                String[] words = input.split(" ");
                for (String word : words) {
                    collector.collect(new Tuple2<>(word, 1));
                }
            }
        }).keyBy(0)
                .sum(1);

        // 4、输出
        resultDataStream.print();

        // 5、启动 env
        env.execute();
    }
}

运行结果

统计文本:Flink
输出:Flink,1

统计文本:增加Spark
输出:Flink,2 Spark,1

统计文本:新增python
输出:Flink,3 Spark,2 python,1

3.3、流/批不同

流式处理的结果:是不断刷新的，在这个例子中，数据时一行一行进入处理任务的，进来一批处理一批，没有数据就处于等待状态。

4、Flink在Yarn部署 4.1、代码打包

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

public class StreamCount {
    public static void main(String[] args) throws Exception {
        // 1、创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2、读取 文件数据 数据
        //DataStreamSource inputDataStream = env.readTextFile("H:\flink_demo\flink_test\src\main\resources\wordcount.txt");
        //2.2、用parameter tool工具从程序启动参数中提取配置项
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String host = parameterTool.get("host");
        int port = parameterTool.getInt("port");
        DataStreamSource inputDataStream = env.socketTextStream(host,port);
        
        // 3、计算
        SingleOutputStreamOperator> resultDataStream = inputDataStream.flatMap(new FlatMapFunction>() {
            @Override
            public void flatMap(String input, Collector> collector) throws Exception {
                String[] words = input.split(" ");
                for (String word : words) {
                    collector.collect(new Tuple2<>(word, 1));
                }
            }
        }).keyBy(0)
                .sum(1);

        // 4、输出
        resultDataStream.print().setParallelism(1);

        //打印执行计划图
        System.out.println(env.getExecutionPlan());
        // 5、启动 env
        env.execute();
    }
}

先编译后打包

4.2、通过UI界面部署到集群

（1）将jar包上传到flink集群

Flink-ui界面 -》submit new job -》Add New

（2）配置参数

entry class:入口类
program Arguments:入口参数
parallelism:并行度,作业提交job时,如果代码没有配,以全局并行度为准,没有以此并性都为准,没有,以全局并行度为准
savapoint path:保存点,从之前存盘的地方启动

（3）运行结果

未成功，待解决

5、疑难杂症 5.1、maven的Plugin文件爆红

解决方式 - 设置maven为本地maven库

New -> Settings -> maven

6.2、Flink Web UI截面提交时显示Server reponse message

解决方式

vim {flink_home}/log

第四章 Flink实战之WordCount基础入门

Java相关栏目本月热门文章