Spark学习:Distcp的spark实现

一、pom.xml

<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>Spark</groupId>
    <artifactId>Spark</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.2.0</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <id>compile-scala</id>
                        <phase>compile</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                    <execution>
                        <id>test-compile-scala</id>
                        <phase>test-compile</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <scalaVersion>2.12.15</scalaVersion>
                </configuration>
            </plugin>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.2</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <archive>
                        <manifest>
                            <mainClass></mainClass>
                        </manifest>
                    </archive>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <plugin>
                <groupId>org.codehaus.mojo</groupId>
                <artifactId>exec-maven-plugin</artifactId>
                <version>1.6.0</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>exec</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <executable>java</executable>
                    <includeProjectDependencies>true</includeProjectDependencies>
                    <includePluginDependencies>false</includePluginDependencies>
                    <classpathScope>compile</classpathScope>
                    <mainClass>cn.spark.study.App</mainClass>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

二、执行类,main方法

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import scala.concurrent.duration.{ Duration, DurationInt }
import scala.concurrent.ExecutionContext
import scala.concurrent.Future
import scala.collection.mutable.ListBuffer
import java.util.concurrent.Executors
import scala.concurrent.Await
import org.apache.spark.rdd.RDD
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path

object Distcp {
  def main(args: Array[String]): Unit = {
    type OptionMap = Map[Symbol, Any]
    if (args.length == 0) println("hhhhhh")
    val arglist = args.toList


    def nextOption(map : OptionMap, list: List[String]) : OptionMap = {
      def isSwitch(s : String) = (s(0) == '-')
      list match {
        case Nil => map
        case "-i" :: value =>  nextOption(map ++ Map('ignoreFailure -> 1), list.tail)
        case "-m" :: value :: tail =>
          nextOption(map ++ Map('maxconcurrency -> value.toInt), tail)
        case string :: Nil  => nextOption(map ++ Map('outfile -> string), list.tail)
        case string :: tail => nextOption(map ++ Map('infile -> string), tail)
        case option :: opt2 :: tail if isSwitch(opt2) =>
          println("Unknown option "+option)
          sys.exit(1)
      }
    }
    val options = nextOption(Map(),arglist)
    println(options)

    val sourceFolder  =  String.valueOf(options(Symbol("infile"))) //"hdfs://localhost:9000/test"
    val targetFolder = String.valueOf(options(Symbol("outfile")))//"hdfs://localhost:9000/target"
    val concurrency =  (options(Symbol("maxconcurrency"))).toString.toInt
    val ignoreFailure = options(Symbol("ignoreFailure")).toString.toInt

    val sparkConf = new SparkConf().setAppName("sparkbyexamples.com").setMaster("local[1]")
    val sc = new SparkContext(sparkConf)
    val sb = new StringBuffer();
    var fileNames = new ListBuffer[String]()

    val conf = new Configuration();
    conf.set("fs.defaultFS", "hdfs://localhost:9000")

    traverseDir(conf, sourceFolder, fileNames);
    fileNames.foreach(
      fileName =>
        try {
          sc.textFile(fileName, concurrency).saveAsTextFile(fileName.replace(sourceFolder, targetFolder));
        } catch {
          case t: Throwable => t.printStackTrace()
            if(ignoreFailure==0){
              throw new Exception("failed to copy "+fileName)
            }
        })
  }

  def traverseDir(hdconf: Configuration, path: String, filePaths: ListBuffer[String]) {
    val files = FileSystem.get(hdconf).listStatus(new Path(path))
    files.foreach { fStatus =>
    {
      if (!fStatus.isDirectory) {
        filePaths += fStatus.getPath.toString
      } else if (fStatus.isDirectory) {
        traverseDir(hdconf, fStatus.getPath.toString, filePaths)
      }
    }
    }
  }

}

三、生成jar包

四、运行
1、查看目录

hadoop fs -ls /home/student5/niezb

2、新建空目录bing

hadoop fs -mkdir -p /home/student5/niezb/bing

3、使用Distcp移动/home/student5/niezb下的所有文件和目录到/home/student5/niezb/bing下

spark-submit --class Distcp --master local[*] /home/student5/niezb/Spark-0.0.1-SNAPSHOT.jar -i -m 3 "/home/student5/niezb" "/home/student5/niezb/bing"

4、查看结果
嘻嘻嘻！