本文章主要介绍了Spark编写WordCount(scala编写),具有不错的的参考价值,希望对您有所帮助,如解说有误或未考虑完全的地方,请您留言指出,谢谢!
一、创建maven项目
二、导入依赖
<!-- 定义了一些常量 -->
<properties>
<maven.compiler.source>1.8</maven.compiler.source>
<maven.compiler.target>1.8</maven.compiler.target>
<scala.version>2.12.10</scala.version>
<spark.version>3.0.0</spark.version>
<encoding>UTF-8</encoding>
</properties>
<dependencies>
<!-- 导入scala的依赖 -->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
<!-- 打包时不会将依赖打入jar包 -->
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>${spark.version}</version>
<!-- 打包时不会将依赖打入jar包 -->
<scope>provided</scope>
</dependency>
</dependencies>
<build>
<pluginManagement>
<plugins>
<!-- 编译scala的插件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
</plugin>
<!-- 编译java的插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.5.1</version>
</plugin>
</plugins>
</pluginManagement>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<executions>
<execution>
<id>scala-compile-first</id>
<phase>process-resources</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>scala-test-compile</id>
<phase>process-test-resources</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<executions>
<execution>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
</executions>
</plugin>
<!-- 打jar插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.3</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>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
三、编写程序
package cn._51doit.day01
import org.apache.spark.rdd.RDD
import org.apache.spark.{
SparkConf, SparkContext}
object WordCount {
def main(args: Array[String]): Unit = {
//创建SparkContext
val conf = new SparkConf().setAppName("WordCount")
//SparkContext是用来创建最原始的RDD的
val sc = new SparkContext(conf)
//创建RDD
val lines: RDD[String] = sc.textFile(args(0))
//切分压平
val words: RDD[String] = lines.flatMap(_.split(" "))
//将单词和1组合
val wordAndOne = words.map((_, 1))
//分组聚合
val reduced = wordAndOne.reduceByKey(_ + _)
//排序
val sorted = reduced.sortBy(_._2, false)
//Action算子,会触发任务执行
//保存数据到hdfs中
sorted.saveAsTextFile(args(1))
//释放资源
sc.stop()
}
}
四、打包
五、上传到集群
六、启动
/opt/apps/spark-3.0.0-bin-hadoop2.7/bin/
spark-submit --master
spark://linux01:7077 --executor-memory
1g --total-executor-cores
5 --class
cn._51doit.day01.WordCount /root/spark-in-action-1.0-SNAPSHOT.jar hdfs://linux01:9000/sp-data hdfs://linux01:9000/out-spark/out2
原创文章,作者:Maggie-Hunter,如若转载,请注明出处:https://blog.ytso.com/tech/bigdata/228146.html