flink-大数据之Flink（一）

1、简介

flink是一个分布式计算/处理引擎，用于对无界和有界数据流进行状态计算。

flink处理流程

应用场景：

• 电商销售：实时报表、广告投放、实时推荐
• 物联网：实时数据采集、实时报警
• 物流配送、服务：订单状态跟踪、信息推送
• 银行、金融：实时结算、风险检测

有状态的流式处理

用内存的本地状态替代传统的数据库

flink核心特点：高吞吐低延迟、结果准确性、精确一次的状态一致性保证、兼容性好、高可用和动态扩展。

分层API：

• SQL-最高层语言
• Table API-声明式领域专用语言
• DataStream/ DataSet API-核心API
• 有状态流处理-底层API

Flink与Spark对比

• 数据模型

  spark采用RDD模型，spark streaming的DStream实际上也是一组小批量数据集（RDD的集合）

  flink基本数据模型是数据流以及时间序列

• 运行时架构

  spark是批计算，将DAG划分为不同的stage，一个完成后才计算下一个

  flink是标准的流执行模式，一个事件在一个节点处理完后直接发往下一个节点进行处理

  2、测试wordcount

maven的pom文件

<?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>org.lizhe</groupId>
    <artifactId>flinkDemo</artifactId>
    <version>1.0-SNAPSHOT</version>
        <build>
        <plugins>
            <!-- 该插件用于将Scala代码编译成class文件 -->
<!--            <plugin>-->
<!--                <groupId>net.alchim31.maven</groupId>-->
<!--                <artifactId>scala-maven-plugin</artifactId>-->
<!--                <version>3.4.6</version>-->
<!--                <executions>-->
<!--                    <execution>-->
<!--                        &lt;!&ndash; 声明绑定到maven的compile阶段 &ndash;&gt;-->
<!--                        <goals>-->
<!--                            <goal>compile</goal>-->
<!--                        </goals>-->
<!--                    </execution>-->
<!--                </executions>-->
<!--            </plugin>-->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.0.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>8</source>
                    <target>8</target>
                </configuration>
            </plugin>
        </plugins>
    </build>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.13.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>1.13.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.13.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.8.2</version>
        </dependency>
    </dependencies>


</project>

java代码（批处理）

package FlinkDemo;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.DataSource;
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;


/**
 * @Title: FlinkWordCount
 * @Author lizhe
 * @Package FlinkDemo
 * @Date 2024/2/24 22:03
 * @description: FLinkDemo
 */
public class FlinkWordCount {
    
    public static void main(String[] args) throws Exception {
    
        //初始化环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        DataSource<String> dataSource = env.readTextFile("input/words.txt");
        //对每行进行分词，并转换成元组类型
        FlatMapOperator<String, Tuple2<String, Long>> wordAndOneTuple = dataSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {
    
            String[] words = line.split(" ");
            for (String word : words) {
    
                col.collect(Tuple2.of(word, 1L));
            }

        }) .returns(Types.TUPLE(Types.STRING,Types.LONG));//returns 方法指定的返回数据类型Tuple2，就是 Flink 自带的二元组数据类型。
        //按照第一个字段进行聚合
        UnsortedGrouping<Tuple2<String, Long>> group = wordAndOneTuple.groupBy(0);
        //按照第二个字段进行求和
        AggregateOperator<Tuple2<String, Long>> sum = group.sum(1);
        sum.print();

    }
}

java代码（流处理）

package FlinkDemo;

import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
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;

/**
 * @Title: BoundedStreamWordCount
 * @Author lizhe
 * @Package FlinkDemo
 * @Date 2024/2/25 18:57
 * @description: BoundedStreamWordCount
 */
public class BoundedStreamWordCount {
    
    public static void main(String[] args) throws Exception {
    
        StreamExecutionEnvironment env=StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> streamSource = env.readTextFile("input/words.txt");
        SingleOutputStreamOperator<Tuple2<String, Long>> singleOutputStreamOperator = streamSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {
    
            String[] words = line.split(" ");
            for (String word : words) {
    
                col.collect(Tuple2.of(word, 1L));
            }
        }).returns(Types.TUPLE(Types.STRING, Types.LONG));
        KeyedStream<Tuple2<String, Long>, Object> keyedStream = singleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {
    
            @Override
            public Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {
    
                return stringLongTuple2.f0;
            }
        });
        SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyedStream.sum(1);
        sum.print();
        env.execute();

    }

}

输出结果：线程号>(单词，个数)

7> (flink,1)
8> (hadoop,1)
3> (hello,1)
5> (world,1)
3> (hello,2)
3> (hello,3)

监听socket流

1. 启动netcat

   nc -lk 7777
   

2. 编写代码

   package FlinkDemo;
   
   import org.apache.flink.api.common.typeinfo.Types;
   import org.apache.flink.api.java.ExecutionEnvironment;
   import org.apache.flink.api.java.functions.KeySelector;
   import org.apache.flink.api.java.tuple.Tuple2;
   import org.apache.flink.streaming.api.datastream.DataStreamSource;
   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;
   
   /**
    * @Title: StreamWordCount
    * @Author lizhe
    * @Package FlinkDemo
    * @Date 2024/2/25 20:34
    * @description: StreamWordCount
    */
   public class StreamWordCount {
         
       public static void main(String[] args) throws Exception {
         
           StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
           DataStreamSource<String> dataStreamSource = env.socketTextStream("192.168.132.101", 7777);
           SingleOutputStreamOperator<Tuple2<String, Long>> singleOutputStreamOperator = dataStreamSource.flatMap((String line, Collector<Tuple2<String, Long>> col) -> {
         
               String[] words = line.split(" ");
               for (String word : words) {
         
                   col.collect(Tuple2.of(word, 1L));
               }
           }).returns(Types.TUPLE(Types.STRING, Types.LONG));
           KeyedStream<Tuple2<String, Long>, Object> keyedStream = singleOutputStreamOperator.keyBy(new KeySelector<Tuple2<String, Long>, Object>() {
         
               @Override
               public Object getKey(Tuple2<String, Long> stringLongTuple2) throws Exception {
         
                   return stringLongTuple2.f0;
               }
           });
           SingleOutputStreamOperator<Tuple2<String, Long>> sum = keyedStream.sum(1);
           sum.print();
           env.execute();
   
       }
   }
   
   

3. netcat发送

   

4. 查看结果

   3> (hello,1)
   7> (flink,1)
   5> (world,1)
   3> (hello,2)
   3> (hello,3)
   8> (hadoop,1)
   

3、flink快速部署

fink提交作业和执行任务需要：客户端（Client）、作业管理器（JobManager）、任务管理器（TaskManager）

JobManager对作业进行调度管理，把任务分发给TaskManager，TaskManager对数据进行处理。

1. 本地启动

下载flink，地址：https://flink.apache.org/downloads/#apache-flink-downloads

解压后直接启动

[root@hadoop101 soft] cd /export/soft/flink-1.13.0
[root@hadoop101 flink-1.13.0] bin/start-cluster.sh
Starting cluster.
Starting standalonesession daemon on host hadoop101.
Starting taskexecutor daemon on host hadoop101.

访问webUI：http://hadoop101:8081/

关闭命令

bin/stop-cluster.sh

2. 集群启动

   修改flink下的conf/flink-conf.yaml，修改jobmanager.rpc.address:hadoop100，并修改works文件，其中添加hadoop101，hadoop102
   
   

3. 在master节点输入启动命令，得到

   [root@hadoop100 flink-1.13.0]# bin/start-cluster.sh
   Starting cluster.
   Starting standalonesession daemon on host hadoop100.
   Starting taskexecutor daemon on host hadoop101.
   Starting taskexecutor daemon on host hadoop102.
   
   

   集群停止同本地启动。

4. 向集群提交作业

   程序使用maven打包，并提交到web端
   
   启动netcat，并在netcat中发送数据