flink1.10.1--java 版--尚硅谷1-3简介/入门/安装/提交任务

青旅半醒 2023-01-19 04:35 155阅读 0赞

# 一.flink简介 #

# 1.传统数据处理架构 #

事务处理：java 后端->数据库

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分析处理：离线数仓

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有状态的流式处理：实时处理  
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流处理的演变：第二代流失处理架构(lambda)  
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# 2.Flink 的主要特点 #

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# 3.Flink vs Spark Streaming #

**延迟：flink 毫秒级延迟，sparkStreaming 秒级延迟**  
**架构：flink 真正的流，sparkStreaming 微批**

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flink没有Stage的概念，每个个节点的计算不需要等待。  
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# 4.WordCount #

## dataSet ##

dataSet是数据集不是数据流  
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package com.atguigu.wc;/** * Copyright (c) 2018-2028 尚硅谷 All Rights Reserved * <p> * Project: FlinkTutorial * Package: com.atguigu.wc * Version: 1.0 * <p> * Created by wushengran on 2020/11/6 11:22 */
    
    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.operators.DataSource;
    import org.apache.flink.api.java.tuple.Tuple2;
    import org.apache.flink.streaming.api.datastream.DataStream;
    import org.apache.flink.util.Collector;
    
    /** * @ClassName: WordCount * @Description: * @Author: wushengran on 2020/11/6 11:22 * @Version: 1.0 */
    
    // 批处理word count
    public class WordCount { 
        public static void main(String[] args) throws Exception{ 
            // 创建执行环境
            ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
            //data/sensor.txt
            // 从文件中读取数据
           // String inputPath = "D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\hello.txt";
            String inputPath = "data/hello.txt";
            DataSet<String> inputDataSet = env.readTextFile(inputPath);
    
            // 对数据集进行处理，按空格分词展开，转换成(word, 1)二元组进行统计
            DataSet<Tuple2<String, Integer>> resultSet = inputDataSet.flatMap(new MyFlatMapper())
                    .groupBy(0)    // 按照第一个位置的word分组
                    .sum(1);    // 将第二个位置上的数据求和
    
            resultSet.print();
        }
    
        // 自定义类，实现FlatMapFunction接口
        public static class MyFlatMapper implements FlatMapFunction<String, Tuple2<String, Integer>> { 
            @Override
            public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception { 
                // 按空格分词
                String[] words = value.split(" ");
                // 遍历所有word，包成二元组输出
                for (String word : words) { 
                    out.collect(new Tuple2<>(word, 1));
                }
            }
        }
    }

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## DataStream ##

流式处理API

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package com.atguigu.wc;/** * Copyright (c) 2018-2028 尚硅谷 All Rights Reserved * <p> * Project: FlinkTutorial * Package: com.atguigu.wc * Version: 1.0 * <p> * Created by wushengran on 2020/11/6 11:48 */
    
    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.DataStreamSource;
    import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    
    import java.net.URL;
    
    /** * @ClassName: StreamWordCount * @Description: * @Author: wushengran on 2020/11/6 11:48 * @Version: 1.0 */
    public class StreamWordCount { 
        public static void main(String[] args) throws Exception{ 
            // 创建流处理执行环境
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    // env.setParallelism(1);
    // env.disableOperatorChaining();
    
    // // 从文件中读取数据
    // String inputPath = "D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\hello.txt";
    // DataStream<String> inputDataStream = env.readTextFile(inputPath);
    
            // 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");
    
            // 从socket文本流读取数据
            DataStream<String> inputDataStream = env.socketTextStream(host, port);
    
    
            // 基于数据流进行转换计算
            DataStream<Tuple2<String, Integer>> resultStream = inputDataStream.flatMap(new WordCount.MyFlatMapper()).slotSharingGroup("green")
                    .keyBy(0)
                    .sum(1).setParallelism(2).slotSharingGroup("red");
    
            resultStream.print().setParallelism(1);
    
            // 执行任务
            env.execute();
        }
    }

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nc -lk 7777

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# 第三章 Flink 部署 #

flink下载  
https://flink.apache.org/zh/  
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![在这里插入图片描述][20210509152032151.png]  
flink 1.10之前的版本都包含了对hadoop的依赖  
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flink1.10之后的版本flink的安装包和hadoop的依赖分离开来了  
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![在这里插入图片描述][20210509153253786.png]  
flink/conf/flink-conf.yaml的主要内容  
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## 3.1 Standalone 模式 ##

### 3.1.1 安装 ###

解压缩 flink-1.10.1-bin-scala\_2.12.tgz，进入 conf 目录中。  
1）修改 flink/conf/flink-conf.yaml 文件：  
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3）分发给另外两台机子：  
![在这里插入图片描述][20210509155155849.png]  
4）启动 ：  
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flink/conf/flink-conf.yaml  
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访问 http://localhost:8081 可以对 flink 集群和任务进行监控管理。  
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### 3.1.2 提交任务 ###

web页面提交任务

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task任务需要的taskSloat 一般等于执行任务需要最大一个并行度  
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命令提交任务

1.  准备数据文件（如果需要）  
    ![在这里插入图片描述][20210509155816954.png]
2.  把含数据文件的文件夹，分发到 taskmanage 机器  
    ![在这里插入图片描述][20210509160412206.png]  
    如 果 从 文 件 中 读 取 数 据 ， 由 于 是 从 本 地 磁 盘 读 取 ， 实 际 任 务 会 被 分 发 到  
    taskmanage 的机器中，所以要把目标文件分发
3.  执行程序  
    ![在这里插入图片描述][20210509162521578.png]  
    ![在这里插入图片描述][20210525152011927.png]

./flink run -p 2  -c com.atguigu.wc.StreamWordCount  /root/ysw/flink/jar/FlinkTutorial-1.0-SNAPSHOT.jar --host kafka1 --port 6666

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4) 查看计算结果  
注意：如果输出到控制台，应该在 taskmanager 下查看；如果计算结果输出到文  
件，同样会保存到 taskmanage 的机器下，不会在 jobmanage 下。

1.  在 webui 控制台查看计算过程  
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## 3.2 Yarn 模式 ##

**现在流行的资源管理平台主要是yarn和k8s**  
![在这里插入图片描述][20210509163407162.png]

### 3.2.1 Flink on Yarn ###

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### 3.2.2 Session Cluster ###

1.  启动 hadoop 集群（略)
2.  启动 yarn-session

/yarn-session.sh -n 2 -s 2 -jm 1024 -tm 1024 -nm test -d

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3) 执行任务

./flink run -c com.atguigu.wc.StreamWordCount FlinkTutorial-1.0-SNAPSHOT-jar-with-dependencies.jar --host lcoalhost –port 7777

1.  去 yarn 控制台查看任务状态  
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2.  取消 yarn-session

yarn application --kill application_1577588252906_0001

### 3.2.2 Per Job Cluster ###

1.  启动 hadoop 集群（略)
2.  不启动 yarn-session，直接执行 job

./flink run –m yarn-cluster -c com.atguigu.wc.StreamWordCount FlinkTutorial-1.0-SNAPSHOT-jar-with-dependencies.jar --host lcoalhost –port 7777

## 3.3 Kubernetes 部署 ##

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// 启动 jobmanager-service 服务
    kubectl create -f jobmanager-service.yaml
    // 启动 jobmanager-deployment 服务
    kubectl create -f jobmanager-deployment.yaml
    // 启动 taskmanager-deployment 服务
    kubectl create -f taskmanager-deployment.yaml

4）访问 Flink UI 页面  
集群启动后，就可以通过 JobManagerServicers 中配置的 WebUI 端口，用浏览器  
输入以下 url 来访问 Flink UI 页面了：  
http://\{JobManagerHost:Port\}/api/v1/namespaces/default/services/flink-jobmanage  
r:ui/proxy

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