实例描述
通过scala编写spark的单词计数程序。
测试数据
1.txt
hello world
hello hadoop
hadoop hivepom.xml文件
<?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.mn1024.spark</groupId>
<artifactId>spark_demo_wordcount</artifactId>
<version>1.0-SNAPSHOT</version>
<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>2.0.2</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-flume_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka_2.11</artifactId>
<version>1.6.2</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>
<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>cn.mn1024.spark.WordCount</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>程序代码
package cn.mn1024.spark
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* 功能：通过scala编写spark的单词计数程序
* @author GuangLing_Lin
*/
object WordCount {
def main(args: Array[String]): Unit = {
// 创建SparkConf对象,设置appName和master地址，local[2]表示本地使用2个线程来进行计算
val sparkConf: SparkConf = new SparkConf().setAppName("WordCount").setMaster("local[2]")
// 创建SparkContext对象，这个对象很重要，它会创建DAGScheduler和TaskScheduler，它是程序的入口，所有计算的资源
val sc: SparkContext = new SparkContext(sparkConf)
//设置日志输出级别
sc.setLogLevel("WARN")
// 读取数据文件
val file: RDD[String] = sc.textFile("F://wordcount//input//1.txt")
// 对文件中的每一行单词进行压平切分
val words: RDD[String] = file.flatMap(_.split(" "))
// 对每一个单词计数为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(_ + _)
// 按照单词出现的次数降序排序
val sortResult: RDD[(String, Int)] = result.sortBy(_._2, false)
// 收集数据，打印输出
val finalResult: Array[(String, Int)] = sortResult.collect()
// 打印结果
finalResult.foreach(x => println(x))
sc.stop()
}
}测试结果
(hello,2)
(hadoop,2)
(hive,1)
(world,1)Debug
- Error:scalac:bad option:'-make:transitive'
解决方法：scala版本问题，scala2.11不支持make参数，将pom.xml中的这个参数去掉即可解决
<configuration> <args> <!--arg>-make:transitive</arg--> <arg>-dependencyfile</arg> <arg>${project.build.directory}/.scala_dependencies</arg> </args> </configuration>解决方法:jar包冲突，将Maven仓库中的Log4j删掉，即可解决。
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/log4j/Level at org.apache.spark.internal.Logging$class.initializeLogging(Logging.scala:111) at org.apache.spark.internal.Logging$class.initializeLogIfNecessary(Logging.scala:102) at org.apache.spark.SparkContext.initializeLogIfNecessary(SparkContext.scala:74) at org.apache.spark.internal.Logging$class.log(Logging.scala:46) at org.apache.spark.SparkContext.log(SparkContext.scala:74) at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54) at org.apache.spark.SparkContext.logInfo(SparkContext.scala:74) at org.apache.spark.SparkContext.<init>(SparkContext.scala:185) at cn.mn1024.spark.WordCount$.main(WordCount.scala:14) at cn.mn1024.spark.WordCount.main(WordCount.scala) Caused by: java.lang.ClassNotFoundException: org.apache.log4j.Level at java.net.URLClassLoader.findClass(URLClassLoader.java:381) at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:335) at java.lang.ClassLoader.loadClass(ClassLoader.java:357) ... 10 more
每一个成功的背后都有无数个无人知晓的黑夜。
因为
夜晚，是超越对手的最佳时机。
===================== 码农1024 =====================#蔺光岭#
还不快抢沙发