IDEA中编写Spark的WordCount程序 06

1. 创建一个项目

2. 选择Maven项目,然后点击next

3. 填写Maven的坐标,然后点击next

4. 填写项目名称,然后点击finish

5. 创建好Maven项目后,点击Enable Auto-Import

6. 配置Maven的pom.xml

<properties>
        <scala.version>2.11.8scala.version>
        <spark.version>2.1.3spark.version>
    properties>
    <dependencies>
        <dependency>
            <groupId>org.scala-langgroupId>
            <artifactId>scala-libraryartifactId>
            <version>${scala.version}version>
        dependency>
        <dependency>
            <groupId>org.apache.sparkgroupId>
            <artifactId>spark-core_2.11artifactId>
            <version>${spark.version}version>
        dependency>
    dependencies>
    <build>
        <sourceDirectory>src/main/scalasourceDirectory>
        <testSourceDirectory>src/test/scalatestSourceDirectory>
        <plugins>
            <plugin>
                <groupId>net.alchim31.mavengroupId>
                <artifactId>scala-maven-pluginartifactId>
                <version>3.2.2version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compilegoal>
                            <goal>testCompilegoal>
                        goals>
                        <configuration>
                            <args>
                                <arg>-dependencyfilearg>
                                <arg>${project.build.directory}/.scala_dependenciesarg>
                            args>
                        configuration>
                    execution>
                executions>
            plugin>
            <plugin>
                <groupId>org.apache.maven.pluginsgroupId>
                <artifactId>maven-shade-pluginartifactId>
                <version>2.4.3version>
                <executions>
                    <execution>
                        <phase>packagephase>
                        <goals>
                            <goal>shadegoal>
                        goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SFexclude>
                                        <exclude>META-INF/*.DSAexclude>
                                        <exclude>META-INF/*.RSAexclude>
                                    excludes>
                                filter>
                            filters>
                            <transformers>
                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>mainClass>
                                transformer>
                            transformers>
                        configuration>
                    execution>
                executions>
            plugin>
        plugins>
build>

7. 添加src/main/scala和src/test/scala,与pom.xml中的配置保持一致

8. 新建一个scala class,类型为Object

9. 编写Spark程序

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

object WordCount{
	def main(args:Array[String]):Unit = {
		//设置spark的配置文件信息
		val sparkConf:SparkConf = new SparkConf().setAppName("WordCount")
		//构建SparkContext上下文对象,它是程序的入口,所有计算的源头
		val sc:SparkContext = new SparkContext(sparkConf)
		//读取文件
		val file:RDD[String] = sc.textFile(args(0))

		//对文件中每一行单词进行压平切分
		val words:RDD[String] = file.flatMap(_.split(" "))
		//对每一个单词计数1, 转化为(单词,1)
		val wordAndOne:RDD[(String,Int)] = words.map(x=>(x,1))
		//相同的单词进行汇总,前一个下划线表示累加数据,后一个下划线表示新数据
		val result:RDD[(String,Int)] = wordAndOne.reduceByKey(_+_)
		//保存数据到hdfs
		result.saveAsTextFile(args(1))
		sc.stop()
	}
}

10. 使用Maven打包

点击idea右侧的Maven Project选项

点击Lifecycle,选择package，然后点击Run Maven Build

11. 选择编译成功的jar包,并将该jar上传到Spark集群的某个节点上

12. 首先启动hdfs和Spark集群

• 启动hdfs
  /export/servers/hadoop/sbin/start-dfs.sh
• 启动spark
  /export/servers/spark/sbin/start-all.sh

13. 使用spark-submit命令提交Spark应用(注意参数的顺序)

spark-submit \
--class cn.test.spark.WordCount \
--master spark://node1:7077 \
--executor-memory 1g \
--total-executor-cores 2 \
/root/spark-1.0-SNAPSHOT.jar \
/words.txt \
/spark_out

这里通过spark-submit提交任务到集群上。用的是spark的Standalone模式
Standalone模式是Spark内部默认实现的一种集群管理模式，这种模式是通过集群中的Master来统一管理资源。

1. 查看Spark的web管理界面
   地址： 192.168.200.160:8080
   
2. 查看hdfs上的结果文件