Flink算子和入门案例（wordcount）

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文章目录

- - - 1.Flink入门案例wordcount
    - 2.基于本地构建DataStream,基于文件构建DataStream，基于socket构建DataStream，自定义source
    - 3.使用自定义source去读取MySQL数据库数据
    - 4.map
    - 5.flatMap
    - 6.filter
    - 7.KeyBy
    - 8.Reduce
    - 9.Agg
    - 10.Window

1.Flink入门案例wordcount

先导入pom依赖

 <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <flink.version>1.11.2</flink.version>
        <scala.binary.version>2.11</scala.binary.version>
        <scala.version>2.11.12</scala.version>
        <log4j.version>2.12.1</log4j.version>
    </properties>

    <dependencies>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-walkthrough-common_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>


        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <version>${log4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <version>${log4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>${log4j.version}</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.36</version>
        </dependency>


    </dependencies>


    <build>

        <plugins>


            <!-- Java Compiler -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.1</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <!-- Scala Compiler -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <args>
                        <arg>-nobootcp</arg>
                    </args>
                </configuration>
            </plugin>


        </plugins>

    </build>

package com.liu.core

import org.apache.flink.streaming.api.scala._

/**
 * @ Author : ld
 * @ Description : 实时统计word个数
 * @ Date : 2021/11/23 18:57
 * @ Version : 1.0
 */
object FlinkWordCount {
  def main(args: Array[String]): Unit = {
    //创建flink的环境
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    //设置并行度
    env.setParallelism(2)
    //读取socket数据
    //启动master开启nc,没有的执行yum -install nc安装
    //nc -lk 8888
    env.socketTextStream("master",8888)
    //把单词拆分
      .flatMap(_.split(","))
    //转换成kv格式
      .map((_,1))
    //按单词分组
      .keyBy(_._1)
    //统计单词数量
      .sum(1)
    //打印结果
      .print()

    //启动flink
    env.execute()
  }
}

在这里插入图片描述

2.基于本地构建DataStream,基于文件构建DataStream，基于socket构建DataStream，自定义source

package com.liu.source

import org.apache.flink.streaming.api.functions.source.SourceFunction
import org.apache.flink.streaming.api.scala._


/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 19:26
 * @ Version : 1.0
 */
object Demo1Source {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    /**
     * 基于本地构建DataStream -- 有界流
     */
    val lisrDS: DataStream[Int] = env.fromCollection(List(1, 2, 3, 4, 5, 6, 7, 8, 9))
    lisrDS.print()

    /**
     * 基于文件构建DataStream --有界流
     */
    val studentDS: DataStream[String] = env.readTextFile("Flink/data/student.txt")

    studentDS
      .map(stu=>(stu.split(",")(4),1))
      .keyBy(_._1)
      .sum(1)
      .print()

    /**
     * 基于socket构建DataStream-- 无界流
     */
//    env.socketTextStream("master11",8888)
//      .print()


    /**
     * 自定义socket，实现SourceFunction接口
     */
    env.addSource(new MySource).print()

    env.execute()
  }
}

/**
 * 自定义source，实现SourceFunction接口
 * 实现run方法
 */
class MySource extends SourceFunction[Int]{
  /**
   * run方法只执行一次
   * @ param ctx:用于发送数据到下游task
   */
  override def run(ctx: SourceFunction.SourceContext[Int]): Unit = {
    var i=0
    while(true){//死循环，看完发送到下游结果就关闭吧
      //把数据发送到下游
      ctx.collect(i)
      //休眠50毫秒
      Thread.sleep(50)
      i+=1
    }

  }

  /**
   * cancel()方法再任务取消时执行用于回收资源
   */

  override def cancel(): Unit = {}
}

3.使用自定义source去读取MySQL数据库数据

package com.liu.source

import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.source.{RichSourceFunction, SourceFunction}
import org.apache.flink.streaming.api.scala._

import java.sql.{Connection, DriverManager, ResultSet}

/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 20:05
 * @ Version : 1.0
 */
object Demo2MysqlSource {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(2)
    //使用自定义source
    val mysqlDS: DataStream[(Int, String, Int, String, String)] = env.addSource(new MysqlSource)
    mysqlDS.print()

    env.execute()
  }
}

/**
 * 自定义读取mysql---有界流
 * SourceFunction -- 单一source,run方法只会执行一次
 * ParallelSourceFunction-- 并行的source，并行度决定source个数
 * RichSourceFunction -- 比sourceFunction多了open和close方法
 * RichParallelSourceFunction --结合上面两个方法
 */

class MysqlSource extends RichSourceFunction[(Int, String, Int, String, String)] {
  /**
   * open方法会在run方法之前执行
   * @ param ctx
   */
  var conn: Connection = _

  override def open(parameters: Configuration): Unit = {
    //加载驱动
    Class.forName("com.mysql.jdbc.Driver")
    //建立连接
    conn = DriverManager.getConnection("jdbc:mysql://master:3306/test", "root", "123456")
  }
  /**
   * 在run方法后执行
   */
  override def close(): Unit = {
    //关闭连接
    conn.close()
  }


  override def run(ctx: SourceFunction.SourceContext[(Int, String, Int, String, String)]): Unit = {

    //查看数据
    val stat = conn.prepareStatement("select * from student")
    val res: ResultSet = stat.executeQuery()
    //解析数据
    while (res.next()) {
      val id: Int = res.getInt("id")
      val name: String = res.getString("name")
      val age: Int = res.getInt("age")
      val gender: String = res.getString("gender")
      val clazz: String = res.getString("clazz")
      //数据发送到下游
      ctx.collect((id, name, age, gender, clazz))

    }
  }


  override def cancel(): Unit = {

  }
}

4.map

package com.liu.transformation

import org.apache.flink.api.common.functions.MapFunction
import org.apache.flink.streaming.api.scala._

/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 20:54
 * @ Version : 1.0
 */
object Demo1Map {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val lineDS: DataStream[String] = env.socketTextStream("master11", 8888)

    /**
     * map函数
     * 传入一个函数
     * 传入一个接口的实现类 --MapFunction
     */
    lineDS.map(new MapFunction[String,String]{
      override def map(t: String): String = {
      t +"ok"
      }
    }).print()

    env.execute()

  }
}

在这里插入图片描述

5.flatMap

package com.liu.transformation

import org.apache.flink.api.common.functions.{FlatMapFunction, RichFlatMapFunction}
import org.apache.flink.streaming.api.scala._
import org.apache.flink.util.Collector
/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 21:03
 * @ Version : 1.0
 */
object Demo2FlatMap {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(4)//设置并行度为4
    val linesDS: DataStream[String] = env.socketTextStream("master", 8888)

    /**
     * FlatMapFunction
     * RichSourceFunction--多了open和close方法，可以做初始化操作
     */

    val flatMapDS: DataStream[String] = linesDS.flatMap(new RichFlatMapFunction[String, String] {
      override def flatMap(line: String, out: Collector[String]): Unit = {
        /**
         * flatMap函数，每一条数据执行一次
         *
         * @ param line ： 一行数据
         * @ param out  ; 用于将数据发送到下游
         */
        line
          .split(",")
          .foreach(out.collect) //下面释内容简写
        //          .foreach(word=>{
        //            //发送数据
        //            out.collect(word)
        //          })
      }
    })

    flatMapDS.print()

    env.execute()
  }
}

在这里插入图片描述

6.filter

package com.liu.transformation

import org.apache.flink.api.common.functions.FilterFunction
import org.apache.flink.streaming.api.scala._
/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 21:17
 * @ Version : 1.0
 */
object Demo3Filter {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    /**
     * filter的算子不是懒执行
     */
    val studentDS: DataStream[String] = env.readTextFile("Flink/data/student.txt")

    studentDS.filter(new FilterFunction[String]{
      override def filter(stu: String): Boolean = {
        //过滤出性别为男的所有学生
        stu.split(",")(3)=="男"
      }
    }).print()

    env.execute()
  }
}

在这里插入图片描述

7.KeyBy

package com.liu.transformation

import org.apache.flink.api.java.functions.KeySelector
import org.apache.flink.streaming.api.scala._
/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 21:25
 * @ Version : 1.0
 */
object Demo4KeyBy {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(3)

    val linesDS: DataStream[String] = env.socketTextStream("master", 8888)

    /**
     * keyBy把相同的key发送到同一个task中
     */
    linesDS.keyBy(new KeySelector[String,String] {
      override def getKey(line: String): String ={
        line
      }
    }).print()

    env.execute()


  }
}

在这里插入图片描述

8.Reduce

package com.liu.transformation

import org.apache.flink.api.common.functions.ReduceFunction
import org.apache.flink.streaming.api.scala._

/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 22:01
 * @ Version : 1.0
 */
object Demo5Reduce {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val lineDS: DataStream[String] = env.socketTextStream("master11", 8888)

    val keyByDS: KeyedStream[(String, Int), String] = lineDS
      .flatMap(_.split(","))
      .map((_, 1))
      .keyBy(_._1)

    /**
     * reduce:在keyBy之后进行聚合
     */
    keyByDS.reduce(new ReduceFunction[(String,Int)]{
      override def reduce(t: (String,Int), t1: (String,Int)): (String,Int) = {
        (t._1,t1._2+t1._2)
      }
    }).print()

    env.execute()
  }
}

9.Agg

package com.liu.transformation

import org.apache.flink.streaming.api.scala._

/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 22:09
 * @ Version : 1.0
 */
object Demo6Agg {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val studentDS: DataStream[String] = env.readTextFile("Flink/data/student.txt")

    var stuDS: DataStream[Student] = studentDS.map(line => {
      val split = line.split(",")
      Student(split(0), split(1), split(2).toInt, split(3), split(4))
    })

    stuDS.keyBy(_.clazz)
      .sum("age")
      .print()
    /**
     * max 和 maxBy 之间的区别在于 max 返回流中的最大值，但 maxBy 返回具有最大值的键，
     */
    stuDS.keyBy(_.clazz)
      .maxBy("age")
      .print()

    env.execute()
  }
  case class Student(id:String,name:String,age:Int,gender:String,clazz:String)
}

10.Window

package com.liu.transformation

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
/**
 * @ Author : ld
 * @ Description : 
 * @ Date : 2021/11/23 21:52
 * @ Version : 1.0
 */
object Demo7Window  {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val linesDS: DataStream[String] = env.socketTextStream("master11", 8888)

    /**
     * 每5秒统计一次单词数量
     */
    linesDS
      .flatMap(_.split(","))
      .map((_,1))
      .keyBy(_._1)
      .timeWindow(Time.seconds(5))
      .sum(1)
      .print()

    env.execute()



  }
}

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Flink算子和入门案例（wordcount）

文章目录

1.Flink入门案例wordcount

2.基于本地构建DataStream,基于文件构建DataStream，基于socket构建DataStream，自定义source

3.使用自定义source去读取MySQL数据库数据

4.map

5.flatMap

6.filter

7.KeyBy

8.Reduce

9.Agg

10.Window

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