大数据-Flink环境部署(Windows)及Flink编程

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发布于 2022-10-04 21:34:42

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发布于 2022-10-04 21:34:42

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部署与测试

下载地址

https://archive.apache.org/dist/flink/

本次以Flink 1.9.3版本为例

运行 Flink 需要安装 Java 7.x 或更高的版本

java -version

运行

访问 Flink UI

Flink有个UI界面，可以用于监控Flilnk的job运行状态 http://localhost:8081/

测试

运行自带的 WordCount 示例

以统计 Flink 自带的 README.txt 文件为例。

命令:

D:
cd D:\Tools\bigdata\flink-1.9.3\bin
.\flink.bat run ..\examples\batch\WordCount.jar -input ..\README.txt -output ..\README_CountWord_Result.txt

查看任务完成情况

http://localhost:8081/#/job/completed

编程开发

开发过程中并不需要在本地部署Flink环境。

创建项目

项目名WordCount

在项目名称WordCount上单击鼠标右键，在弹出的菜单中点击Add Framework Support

在java目录上单击鼠标右键，在弹出的菜单中选择Refactor，再在弹出的菜单中选择Rename，

然后，在出现的界面中把java目录名称修改为scala。

添加类WordCount

在IDEA开发界面中，打开pom.xml，清空里面的内容，输入如下内容：

<?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.psvmc</groupId>
  <artifactId>WordCount</artifactId>
  <version>1.0</version>

  <properties>
    <maven.compiler.source>8</maven.compiler.source>
    <maven.compiler.target>8</maven.compiler.target>
    <flink.version>1.9.3</flink.version>
    <scala.version>2.12.15</scala.version>
  </properties>

  <repositories>
    <repository>
      <id>alimaven</id>
      <name>aliyun maven</name>
      <url>https://maven.aliyun.com/repository/public</url>
    </repository>
  </repositories>
  <dependencies>
    <!-- flink核心API -->
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-java</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-scala_2.12</artifactId>
      <version>${flink.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-java_2.12</artifactId>
      <version>${flink.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.flink</groupId>
      <artifactId>flink-streaming-scala_2.12</artifactId>
      <version>${flink.version}</version>
    </dependency>
  </dependencies>
  <build>
    <plugins>
      <plugin>
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>3.4.6</version>
        <executions>
          <execution>
            <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>
    </plugins>
  </build>
</project>

测试

创建测试文件wordcount.txt

D:\bigdata_study\stu_list.txt

10010,张三,女,16,IS
10011,李四,男,18,IS
10012,王五,男,19,IS
10013,赵六,女,15,CS

然后，再打开WordCount.scala代码文件，清空里面的内容，输入如下内容：

import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
object WordCount {
  case class Student(id: String, name: String, sex: String, age: Int, department: String)
  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val environment = ExecutionEnvironment.getExecutionEnvironment
    val stu_list: DataSet[Student] = environment.readCsvFile[Student](
      filePath = "file:///D:/bigdata_study/stu_list.txt",
      lineDelimiter = "\n",
      fieldDelimiter = ",",
      quoteCharacter = null,
      ignoreFirstLine = false,
      ignoreComments = "#",
      lenient = false,
      includedFields = Array[Int](0, 1, 2, 3, 4),
      pojoFields = Array[String]("id", "name", "sex", "age", "department")
    )
    println("-------------原数据----------")
    stu_list.print
  }
}

运行就可以看到结果为

————-原数据———- Student(10012,王五,男,19,IS) Student(10010,张三,女,16,IS) Student(10013,赵六,女,15,CS) Student(10011,李四,男,18,IS)

打包运行

在IDEA开发界面的右侧，点击Maven图标，会弹出Maven调试界面

在Maven调试界面中点击package，就可以对应用程序进行打包，打包成JAR包。

这时，到IDEA开发界面左侧的项目目录树中，在“target”目录下，就可以看到生成了两个JAR文件，

分别是：WordCount-1.0.jar和WordCount-1.0-jar-with-dependencies.jar。

语法

加载数据

import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
object WordCount {
  case class Student(id: String, name: String, sex: String, age: Int, department: String)

  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val env = ExecutionEnvironment.getExecutionEnvironment
    val stu: DataSet[(Int, String, Double)] = env.fromElements(
      (19, "Wilson", 178.8),
      (17, "Edith", 168.8),
      (18, "Joyce", 174.8),
      (18, "May", 195.8),
      (18, "Gloria", 182.7),
      (21, "Jessie", 184.8)
    )
    stu.print
  }
}

从文件中加载

import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
object WordCount {
  case class Student(id: String, name: String, sex: String, age: Int, department: String)
  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val environment = ExecutionEnvironment.getExecutionEnvironment
    val stu_list: DataSet[Student] = environment.readCsvFile[Student](
      filePath = "file:///D:/bigdata_study/stu_list.txt",
      lineDelimiter = "\n",
      fieldDelimiter = ",",
      quoteCharacter = null,
      ignoreFirstLine = false,
      ignoreComments = "#",
      lenient = false,
      includedFields = Array[Int](0, 1, 2, 3, 4),
      pojoFields = Array[String]("id", "name", "sex", "age", "department")
    )
    println("-------------原数据----------")
    stu_list.print
  }
}

数据导出

导出到HDFS

//写入到HDFS
val output2 = "hdfs://bdedev/flink/Student002.csv"
ds2.writeAsCsv(output2, rowDelimiter = "\n", fieldDelimiter = "|||", WriteMode.OVERWRITE)
env.execute()

导出到文件

//写入到文件
val output2 = "file:///D:/bigdata_study/result001.txt"
ds3.writeAsCsv(output2, rowDelimiter = "\n", fieldDelimiter = ",", WriteMode.OVERWRITE)
env.execute()

值转换

Flink的Transformation转换主要包括四种：单数据流基本转换、基于Key的分组转换、多数据流转换和数据重分布转换。

单数据流基本转换

基于Key的分组转换

对数据分组主要是为了进行后续的聚合操作，即对同组数据进行聚合分析。

groupBy会将一个DataSet转化为一个GroupedDataSet，聚合操作会将GroupedDataSet转化为DataSet。如果聚合前每个元素数据类型是T，聚合后的数据类型仍为T。

aggregation

常见的聚合操作有sum、max、min等，这些聚合操作统称为aggregation。aggregation需要一个参数来指定按照哪个字段进行聚合。跟groupBy相似，我们可以使用数字位置来指定对哪个字段进行聚合，也可以使用字段名。

与批处理不同，这些聚合函数是对流数据进行数据，流数据是依次进入Flink的，聚合操作是对之前流入的数据进行统计聚合。sum算子的功能对该字段进行加和，并将结果保存在该字段上。min操作无法确定其他字段的数值。

val tupleStream = env.fromElements(
  (0, 0, 0), (0, 1, 1), (0, 2, 2),
  (1, 0, 6), (1, 1, 7), (1, 2, 8)
)
tupleStream.groupBy(0).sum(1).print()

第0个分组，第1个求和

结果

(1,3,8) (0,3,2)

reduce

import org.apache.flink.api.common.operators.Order
import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}

object WordCount {
  def main(args: Array[String]): Unit = {
    //设置用户名，避免权限错误
    System.setProperty("HADOOP_USER_NAME", "hadoop");
    val env = ExecutionEnvironment.getExecutionEnvironment
    val ds1: DataSet[String] = env.fromElements(
      "good good study", "day day up"
    )
    val group_ds  = ds1.flatMap(line => line.split(" ")).map(word => (word, 1)).groupBy(0)
    val ds3 = group_ds.reduce((a, b) => (a._1, a._2 + b._2))
    ds3.sortPartition(0, Order.ASCENDING).print
  }
}

结果