前言

在本文中将介绍spark中Task执行序列化的开发问题

开发环境准备

本实验Spark运行在Windows上，为了开发Spark应用程序，在本地机器上需要有Jdk1.8和Maven环境。
确保我们的环境配置正常，我们可以使用快捷键 Win+R 输入cmd：
环境如下：
图片说明
程序开发工具我们使用IDEA
#创建Maven项目
pom如下：

<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/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>SparkDemo</groupId>
  <artifactId>SparkDemo</artifactId>
  <version>1.0-SNAPSHOT</version>
  <inceptionYear>2008</inceptionYear>
  <properties>
    <scala.version>2.11.1</scala.version>
  </properties>

  <repositories>
    <repository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </repository>
  </repositories>

  <pluginRepositories>
    <pluginRepository>
      <id>scala-tools.org</id>
      <name>Scala-Tools Maven2 Repository</name>
      <url>http://scala-tools.org/repo-releases</url>
    </pluginRepository>
  </pluginRepositories>

  <dependencies>
    <dependency>
      <groupId>org.scala-lang</groupId>
      <artifactId>scala-library</artifactId>
      <version>${scala.version}</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.2.0</version>
    </dependency>


    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.4</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs</groupId>
      <artifactId>specs</artifactId>
      <version>1.2.5</version>
      <scope>test</scope>
    </dependency>
  </dependencies>

  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
          <args>
            <arg>-target:jvm-1.5</arg>
          </args>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-eclipse-plugin</artifactId>
        <configuration>
          <downloadSources>true</downloadSources>
          <buildcommands>
            <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
          </buildcommands>
          <additionalProjectnatures>
            <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
          </additionalProjectnatures>
          <classpathContainers>
            <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
            <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
          </classpathContainers>
        </configuration>
      </plugin>
    </plugins>
  </build>
  <reporting>
    <plugins>
      <plugin>
        <groupId>org.scala-tools</groupId>
        <artifactId>maven-scala-plugin</artifactId>
        <configuration>
          <scalaVersion>${scala.version}</scalaVersion>
        </configuration>
      </plugin>
    </plugins>
  </reporting>
</project>

编写Spark程序

目录结构如下:
图片说明
创建Serializable.scala:
首先我们需要了解
RDD中的函数传递：
在实际开发中我们往往需要自己定义一些对于RDD的操作，那么此时需要主要的是，初始化工作是在Driver端进行的，而实际运行程序是在Executor端进行的，这就涉及到了跨进程通信，是需要序列化的。
如果我们对我们自定义的类不进行序列化：

package SparkDemo

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * @Author: luomo
  * @CreateTime: 2019/10/29
  * @Description: Serializable from Driver to Executor
  */
object Serializable {

  def main(args: Array[String]): Unit = {

    //创建Spark上下文对象
    val config:SparkConf =new SparkConf().setMaster("local[*]").setAppName("Serializable")

    //创建Spark上下文对象
    val sc = new SparkContext(config)

    val rdd:RDD[String] = sc.parallelize(Array("hadoop","spark","hive","Flink"))

    val search = new Search("h")

    val match1:RDD[String] =search.getMatch1(rdd)
    match1.collect().foreach(println)
    sc.stop()
  }
  class Search(query:String){
    //过滤出包含字符串的数据
    def  isMatch(s:String):Boolean ={
      s.contains(query)
    }
    //过滤出包含字符串的RDD
    def getMatch1(rdd:RDD[String]) :RDD[String] = {
      rdd.filter(isMatch)
    }
    //过滤出包含字符串的RDD
    def getMatche2(rdd: RDD[String]): RDD[String] ={
      rdd.filter(x=> x.contains(query))
    }
  }
}

如图：
图片说明
可见，对于自己定义的普通类，Spark是无法直接将其序列化的。
需要我们自定义的类继承java.io.Serializable

package SparkDemo

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * @Author: luomo
  * @CreateTime: 2019/10/29
  * @Description: Serializable from Driver to Executor
  */
object Serializable {

  def main(args: Array[String]): Unit = {

    //创建Spark上下文对象
    val config:SparkConf =new SparkConf().setMaster("local[*]").setAppName("Serializable")

    //创建Spark上下文对象
    val sc = new SparkContext(config)

    val rdd:RDD[String] = sc.parallelize(Array("hadoop","spark","hive","Flink"))

    val search = new Search("h")

    val match1:RDD[String] =search.getMatch1(rdd)
    match1.collect().foreach(println)
    sc.stop()
  }
  //自定义类
  class Search(query:String) extends  java.io.Serializable {
    //过滤出包含字符串的数据
    def  isMatch(s:String):Boolean ={
      s.contains(query)
    }
    //过滤出包含字符串的RDD
    def getMatch1(rdd:RDD[String]) :RDD[String] = {
      rdd.filter(isMatch)
    }
    //过滤出包含字符串的RDD
    def getMatche2(rdd: RDD[String]): RDD[String] ={
      rdd.filter(x=> x.contains(query))
    }
  }
}

运行程序

如图我们过滤出包含字符h的字符串：
图片说明

SparkDemo(Serializable)

前言

开发环境准备

编写Spark程序

运行程序