系列文章目录

spark第一章：环境安装
spark第二章：sparkcore实例
spark第三章：工程化代码

---

---

前言

我们上一次博客，完成了一些案例的练习，现在我要要进行一些结构上的完善，上一次的案例中，代码的耦合性非常高，想要修改就十分复杂，而且有很多代码都在重复使用，我们想要把一些重复的代码抽取出来，进而完成解耦合的操作，提高代码的复用。

---

一、三层架构

大数据的三层架构其中包括
controller(控制层)：负责调度各模块
service(服务层)：存放逻辑代码
dao(持久层)：进行文件交互
现在我们分别给各层创建一个包

解释一下其中几个
application:项目的启动文件
bean:存放实体类
common:存放这个项目的通用代码
util:存放通用代码（所有项目均可）

二、拆分WordCount

万物皆可WordCount我们就以上次的WordCount为例操作。放一下源代码

object WordCount {def main(args: Array[String]): Unit = {//  创建 Spark 运行配置对象val sparkConf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WordCount")// 创建 Spark 上下文环境对象（连接对象）val sc : SparkContext = new SparkContext(sparkConf)// 读取文件 获取一行一行的数据val lines: RDD[String] = sc.textFile("datas/word.txt")// 将一行数据进行拆分val words: RDD[String] = lines.flatMap(_.split(" "))// 将数据根据单次进行分组，便于统计val wordToOne: RDD[(String, Int)] = words.map(word => (word, 1))// 对分组后的数据进行转换val wordToSum: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)// 打印输出val array: Array[(String, Int)] = wordToSum.collect()array.foreach(println)sc.stop()}}

1.三层拆分

在进行数据抽取之前，我们先进行简单的三层架构拆分
记得把包名路径换成自己的

WordCountDao.scala
负责文件交互，也就是第一步的读取文件

package com.atguigu.bigdata.spark.core.rdd.framework1.daoimport com.atguigu.bigdata.spark.core.rdd.framework1.application.WordCountApplication.scclass WordCountDao {def readFile(path:String) ={sc.textFile(path)}
}

WordCountService.scala
负责逻辑运算

package com.atguigu.bigdata.spark.core.rdd.framework1.serviceimport com.atguigu.bigdata.spark.core.rdd.framework1.dao.WordCountDaoimport org.apache.spark.rdd.RDDclass WordCountService {private val wordCountDao =new WordCountDao()def dataAnalysis(): Array[(String, Int)] ={val lines: RDD[String] =wordCountDao.readFile("datas/word.txt")val words: RDD[String] = lines.flatMap(_.split(" "))val wordToOne: RDD[(String, Int)] = words.map(word => (word, 1))val wordToSum: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)val array: Array[(String, Int)] = wordToSum.collect()array}
}

WordCountController.scala
负责调度项目

package com.atguigu.bigdata.spark.core.rdd.framework1.controllerimport com.atguigu.bigdata.spark.core.rdd.framework1.service.WordCountServiceclass WordCountController {private val wordCountService =new WordCountService()def dispath(): Unit ={val array=wordCountService.dataAnalysis()array.foreach(println)}
}

WordCountApplication.scala
main方法启动项目

package com.atguigu.bigdata.spark.core.rdd.framework1.applicationimport com.atguigu.bigdata.spark.core.rdd.framework1.controller.WordCountController
import org.apache.spark.{SparkConf, SparkContext}object WordCountApplication extends App {val sparkConf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("WordCount")val sc : SparkContext = new SparkContext(sparkConf)val controller = new WordCountController()controller.dispath()sc.stop()
}

2.代码抽取

接下来我们把一些常用或者会重复实用的代码抽取出来。
创建四个Train，用来抽取四个文件

TApplication.scala
其中通用代码为环境创建

package com.atguigu.bigdata.spark.core.rdd.framework.commonimport com.atguigu.bigdata.spark.core.rdd.framework.util.EnvUtil
import org.apache.spark.{SparkConf, SparkContext}trait TApplication {def start(master: String="local[*]", app: String="Application")(op: =>Unit): Unit ={val sparkConf: SparkConf = new SparkConf().setMaster(master).setAppName(app)val sc : SparkContext = new SparkContext(sparkConf)EnvUtil.put(sc)try {op}catch {case ex=>println(ex.getMessage)}sc.stop()EnvUtil.clear()}
}

TController.scala
定义调度Train之后由Controller进行重写

package com.atguigu.bigdata.spark.core.rdd.framework.commontrait TController {def dispatch():Unit
}

TDao.scala
WordCount通用读取，路径为参数

package com.atguigu.bigdata.spark.core.rdd.framework.commonimport com.atguigu.bigdata.spark.core.rdd.framework.util.EnvUtil
import org.apache.spark.rdd.RDDtrait TDao {def readFile(path:String): RDD[String] ={EnvUtil.take().textFile(path)}
}

TService.scala
和Controller类似，由Service重写

package com.atguigu.bigdata.spark.core.rdd.framework.commontrait TService {def dataAnalysis():Any
}

定义环境，确保所有类都能访问sc线程
EnvUtil.scala

package com.atguigu.bigdata.spark.core.rdd.framework.utilimport org.apache.spark.SparkContextobject EnvUtil {private val scLocal =new ThreadLocal[SparkContext]()def put(sc:SparkContext): Unit ={scLocal.set(sc)}def take(): SparkContext = {scLocal.get()}def clear(): Unit ={scLocal.remove()}
}

修改三层架构
WordCountApplication.scala

package com.atguigu.bigdata.spark.core.rdd.framework.applicationimport com.atguigu.bigdata.spark.core.rdd.framework.common.TApplication
import com.atguigu.bigdata.spark.core.rdd.framework.controller.WordCountControllerobject WordCountApplication extends App with TApplication{start(){val controller = new WordCountController()controller.dispatch()}}

WordCountController.scala

package com.atguigu.bigdata.spark.core.rdd.framework.controllerimport com.atguigu.bigdata.spark.core.rdd.framework.common.TController
import com.atguigu.bigdata.spark.core.rdd.framework.service.WordCountServiceclass WordCountController extends TController{private val WordCountService = new WordCountService()def dispatch(): Unit ={val array: Array[(String, Int)] = WordCountService.dataAnalysis()array.foreach(println)}
}

WordCountDao.scala

package com.atguigu.bigdata.spark.core.rdd.framework.daoimport com.atguigu.bigdata.spark.core.rdd.framework.common.TDaoclass WordCountDao extends TDao{}

WordCountService.scala

package com.atguigu.bigdata.spark.core.rdd.framework.serviceimport com.atguigu.bigdata.spark.core.rdd.framework.common.TService
import com.atguigu.bigdata.spark.core.rdd.framework.dao.WordCountDao
import org.apache.spark.rdd.RDDclass WordCountService extends TService{private val wordCountDao=new WordCountDao()def dataAnalysis(): Array[(String, Int)] = {val lines: RDD[String] = wordCountDao.readFile("datas/word.txt")val words: RDD[String] = lines.flatMap(_.split(" "))val wordToOne: RDD[(String, Int)] = words.map(word => (word, 1))val wordToSum: RDD[(String, Int)] = wordToOne.reduceByKey(_ + _)val array: Array[(String, Int)] = wordToSum.collect()array}}

再次运行

---

总结

对spark项目代码的规范就到这里，确实有点复杂，我也不知道说清楚没有。