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IDEA 编写 Spark 程序
●创建 Maven 项目并补全目录、配置 pom.xml
<!-- 指定仓库位置，依次为aliyun、cloudera和jboss仓库 -->
<repositories>
<repository>
<id>aliyun</id>
<url>http://maven.aliyun.com/nexus/content/groups/public/</url>
</repository>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
<repository>
<id>jboss</id>
<url>http://repository.jboss.com/nexus/content/groups/public</url>
</repository>
</repositories>
<properties>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.11.8</scala.version>
<scala.compat.version>2.11</scala.compat.version>
<hadoop.version>2.7.4</hadoop.version>
<spark.version>2.2.0</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive-thriftserver_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- <dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
<version>${spark.version}</version>
</dependency>-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!--<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.0-mr1-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.2.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.2.0-cdh5.14.0</version>
</dependency>-->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.4</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.3.1</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>1.3.3</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.38</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<!-- 指定编译java的插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.5.1</version>
</plugin>
<!-- 指定编译scala的插件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.18.1</version>
<configuration>
<useFile>false</useFile>
<disableXmlReport>true</disableXmlReport>
<includes>
<include>**/*Test.*</include>
<include>**/*Suite.*</include>
</includes>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer
implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass></mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
编写代码：
package czxy
/**
* Created by 一个蔡狗 on 2020/4/2.
*/
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object WorldCount {
def main(args: Array[String]): Unit = {
//1.创建spark配置，设置应用程序名字
val config = new SparkConf().setAppName("ScalaWordCount1111")//.setMaster("local[*]")
//创建spark执行的入口
val sc = new SparkContext(config)
sc.setLogLevel("WARN")
//2.读取文件
//A Resilient Distributed Dataset (RDD)弹性分布式数据集
//可以简单理解为分布式的集合,但是spark对它做了很多的封装,
//让程序员使用起来就像操作本地集合一样简单,这样大家就很happy了
// 指定以后从哪里读取数据创建RDD(弹性分布式数据集)
val fileRDD: RDD[String] = sc.textFile(args(0)) //文件输入路径
//3.处理数据
//3.1对每一行按空切分并压平形成一个新的集合中装的一个个的单词
//flatMap是对集合中的每一个元素进行操作,再进行压平
//切分压平
val wordRDD: RDD[String] = fileRDD.flatMap(_.split(" "))
//3.2每个单词记为1 将单词和1组合
val wordAndOneRDD: RDD[(String, Int)] = wordRDD.map((_,1))
//3.3根据key进行聚合,统计每个单词的数量
//wordAndOneRDD.reduceByKey((a,b)=>a+b)
//第一个_:之前累加的结果
//第二个_:当前进来的数据
//按key进行聚合
val wordAndCount: RDD[(String, Int)] = wordAndOneRDD.reduceByKey(_+_)
//将结果保存到HDFS中
wordAndCount.saveAsTextFile(args(1))//文件输出路径
//4.收集结果
//val result: Array[(String, Int)] = wordAndCount.collect()
//result.foreach(println)
//释放资源
sc.stop()
}
}
打成 jar包 ：
导成jar包提交到集群：
启动集群 ：
（ 可以启动 spark 也可以不启动 因为 提交到的是 yarn ） 这里我就 启动一下
cd /export/servers/spark-2.2.0-bin-2.6.0-cdh5.14.0
./start-all.sh –master spark://node001:7077使用 spark-submit 命令将任务提交大 yarn 集群
/export/servers/spark-2.2.0-bin-2.6.0-cdh5.14.0/bin/spark-submit \ --class czxy.WorldCount \ //这里是你的 class 类 的 路径 Reference --master yarn \ // 提交到 yarn --deploy-mode cluster \ --driver-memory 1g \ --executor-memory 1g \ --executor-cores 2 \ /opt/package/Spark01-1.0-SNAPSHOT.jar \ // jar包 的名字 hdfs://node001:8020/tt.txt \ // hdfs 的 输入文件路径 hdfs://node001:8020/tttt // hdfs 的 输出文件路径关于 Reference （ 点击 class 类 右键）
执行结果 （数据在HDFS）
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