Spark RDD练习

工程添加的依赖

<?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.edu.hust</groupId>
    <artifactId>rdd</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>1.7</maven.compiler.source>
        <maven.compiler.target>1.7</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <scala.version>2.10.6</scala.version>
        <spark.version>1.6.1</spark.version>
        <hadoop.version>2.6.4</hadoop.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_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</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>-make:transitive</arg>
                                <arg>-dependencyfile</arg>
                                <arg>${project.build.directory}/.scala_dependencies</arg>
                            </args>
                        </configuration>
                    </execution>
                </executions>
            </plugin>

            <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>


</project>

聚合函数练习

具体的代码如下:

import org.apache.spark.{SparkConf, SparkContext}

object aggregateDemo {
  def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setAppName("aggregateDemo").setMaster("local[2]")
    val sc=new SparkContext(conf)
    val rdd1=sc.parallelize(Array(1,2,3,4,5,6,7,7,8),2)
    //聚合函数第一个值，表示初始值，这里需要传递两个函数，第一个用于表示局部计算的，第二个表示全局计算的
    val rdd2=rdd1.aggregate(0)(_+_,_+_)
    println(rdd2)
    //这里初始值为10，因为有两个分区，所以需要加3次
    val rdd3=rdd1.aggregate(10)(_+_,_+_)
    println(rdd3)
    //如果需要求两个partition的最大值相加，则使用下列函数
    val rdd4=rdd1.aggregate(0)(math.max(_,_),_+_)
    println(rdd4)

    val rdd5=sc.parallelize(List("a","b","c","d","e","f","g"))
    //这是一将所有的字符串连接操作，返回的结果不确定，因为是并行计算，不知道哪一个先返回
    val rdd6=rdd5.aggregate("")(_+_,_+_)
    println(rdd6)
    //这里需要考虑""字符串的长度，然后转化为"1"
    val rdd7=sc.parallelize(List("a1","b342","c234","d5235","e22","f1",""))
    val rdd8=rdd7.aggregate("")((x:String,y:String)=>math.max(x.length,y.length).toString,(x,y)=>x+y)
    println(rdd8)

    //val rddf9=rdd7.combineByKey()
  }
}

计算基站以及定位

        基站数据和定位的数据，具体数据如下https://pan.baidu.com/s/1CeCXsSxbVQk8aezKMK5hOw

代码如下：

import org.apache.spark.{SparkConf, SparkContext}

object LocationDemo2 {
  def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setAppName("loc").setMaster("local[2]");
    val sc=new SparkContext(conf)
    //计算分组
    val rdd1=sc.textFile("/Users/youyujie/Documents/location").map(line=>{
      val text=line.split(",")
      val time=if(text(3)=="1") -text(1).toLong else text(1).toLong
      ((text(0),text(2)),time)
    })
    val locRDD=sc.textFile("/Users/youyujie/Documents/loc").map(line=>{
      val text=line.split(",")
      (text(0),text(1),text(2))
    })
    val rdd2=rdd1.reduceByKey(_+_).map(x=>{
      (x._1._2,x._1._1,x._2)
    })
    //rdd2.join()
    println(locRDD.collect().toBuffer)
  }
}

本地模式运行实例

import org.apache.spark.{SparkConf, SparkContext}

object Test {
  def main(args: Array[String]): Unit = {
    //设置spark配置文件，开启本地模式,使用两个线程
    val conf=new SparkConf().setAppName("RDD").setMaster("local[2]")
    //获取spark上下文
    val sc=new SparkContext(conf)
    //使用parallelize获取rdd,分区数量是2个
    val rdd1=sc.parallelize(List(12,3,4,5,6,7,8,9),2)

    val func1=(index:Int,it:Iterator[(Int)])=>{
      //写一个函数，这里使用标记出每一个元素的分区号然后将list转化为toIterator返回
        it.toList.map(x => "[partID:" +  index + ", val: " + x + "]").toIterator
    }
    //可查看mapPartitionsWithIndex的定义，可以看出需要传递一个函数。
    val rdd2=rdd1.mapPartitionsWithIndex(func1)
    println( rdd2.collect().toBuffer)
  }
}