Spark中WordCount示例

原创
2017/04/06 16:23
阅读数 94

创建工程

在eclipse中创建一个Scala工程,名为WordCountSpark。

  • File -> New -> Other -> Scala Wizards -> Scala Project 点击创建 截图1

  • 点击Finish创建

  • 在WordCountSpark上点击右键 -> Configure -> Convert to Maven Project 输入图片说明

  • 在Scala library container上右键点击,修改Scala Library 输入图片说明 输入图片说明

  • 在JRE System Library上右键点击
    输入图片说明 输入图片说明

  • 修改pom.xml文件,添加repository和dependency,pom.xml文件如下

    <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/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>WordCountSpark</groupId>
    <artifactId>WordCountSpark</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    
    <repositories>
      	<repository>
      		<id>cloudera</id>
      		<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
      	</repository>
      </repositories>
      <dependencies>
      	<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.10 -->
      	<dependency>
      		<groupId>org.apache.spark</groupId>
      		<artifactId>spark-core_2.10</artifactId>
      		<version>1.3.0-cdh5.4.3</version>
      	</dependency>
    
      </dependencies>
    
    <build>
      <sourceDirectory>src</sourceDirectory>
      <plugins>
        <plugin>
          <artifactId>maven-compiler-plugin</artifactId>
          <version>3.1</version>
          <configuration>
            <source/>
            <target/>
          </configuration>
        </plugin>
      </plugins>
    </build>
    </project>    
    
  • 创建包examples
    输入图片说明

  • 创建Object WordCount
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WordCount示例代码

package examples

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
object WordCount {
  def main(args: Array[String]) {
    if (args.length < 1) {
      System.err.println("Usage: WordCount <file>")
      System.exit(1)
    }
    val sc = new SparkContext()
    val counts = sc.textFile(args(0)).
      flatMap(line => line.split("\\s+")).
      map(word => (word, 1)).reduceByKey(_ + _)
    counts.take(5).foreach(println)
    sc.stop()
  }
}
  • 打成jar包,工程WordCount右键 -> Export -> JAR file
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  • 执行命令WordCountSpark.jar

    [training@ localhost /tmp]$ spark-submit --master local --class examples.WordCount WordCountSpark.jar file:///tmp/sparktest/2.txt (AARDVARK,1) (MAT,1) (ON,2) (SAT,2) (SOFA,1)

  • 执行命令spark-submit --help

    [training@ localhost /tmp]$ spark-submit --help Usage: spark-submit [options] <app jar | python file> [app arguments] Usage: spark-submit --kill [submission ID] --master [spark://...] Usage: spark-submit --status [submission ID] --master [spark://...]

    Options: --master MASTER_URL spark://host:port, mesos://host:port, yarn, or local. --deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or on one of the worker machines inside the cluster ("cluster") (Default: client). --class CLASS_NAME Your application's main class (for Java / Scala apps). --name NAME A name of your application. --jars JARS Comma-separated list of local jars to include on the driver and executor classpaths. --packages Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. Will search the local maven repo, then maven central and any additional remote repositories given by --repositories. The format for the coordinates should be groupId:artifactId:version. --repositories Comma-separated list of additional remote repositories to search for the maven coordinates given with --packages. --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place on the PYTHONPATH for Python apps. --files FILES Comma-separated list of files to be placed in the working directory of each executor.

    --conf PROP=VALUE           Arbitrary Spark configuration property.
    --properties-file FILE      Path to a file from which to load extra properties. If not
                                specified, this will look for conf/spark-defaults.conf.
    
    --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 512M).
    --driver-java-options       Extra Java options to pass to the driver.
    --driver-library-path       Extra library path entries to pass to the driver.
    --driver-class-path         Extra class path entries to pass to the driver. Note that
                                jars added with --jars are automatically included in the
                                classpath.
    
    --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).
    
    --proxy-user NAME           User to impersonate when submitting the application.
    
    --help, -h                  Show this help message and exit
    --verbose, -v               Print additional debug output
    --version,                  Print the version of current Spark
    

    Spark standalone with cluster deploy mode only: --driver-cores NUM Cores for driver (Default: 1). --supervise If given, restarts the driver on failure. --kill SUBMISSION_ID If given, kills the driver specified. --status SUBMISSION_ID If given, requests the status of the driver specified.

    Spark standalone and Mesos only: --total-executor-cores NUM Total cores for all executors.

    YARN-only: --driver-cores NUM Number of cores used by the driver, only in cluster mode (Default: 1). --executor-cores NUM Number of cores per executor (Default: 1). --queue QUEUE_NAME The YARN queue to submit to (Default: "default"). --num-executors NUM Number of executors to launch (Default: 2). --archives ARCHIVES Comma separated list of archives to be extracted into the working directory of each executor.

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