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通过编写Spark程序清洗酒店数据里的缺失数据、非法数据、重复数据

准备工作

1. 搭建 hadoop 伪分布或 hadoop 完全分布
2. 上传 hotal_data.csv 文件到 hadoop
3. idea 配置好 scala 环境

删除缺失值 >= 3 的数据

1. 读取 /hotel_data.csv
2. 删除缺失值 >= 3 的数据， 打印剔除的数量
3. 将清洗后的数据保存为/hotelsparktask1

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}object Demo01 {def main(args: Array[String]): Unit = {// System.setProperty("HADOOP_USER_NAME", "root")//解决保存文件权限不够的问题val config: SparkConf = new SparkConf().setMaster("local[1]").setAppName("1")val sc = new SparkContext(config)val hdfsUrl ="hdfs://192.168.226.129:9000"val filePath: String = hdfsUrl+"/file3_1/hotel_data.csv"val data: RDD[Array[String]] = sc.textFile(filePath).map(_.split(",")).cache()val total: Long = data.count()val dataDrop: RDD[Array[String]] = data.filter(_.count(_.equals("NULL")) <= 3)println("删除的数据条目有: " + (total - dataDrop.count()))dataDrop.map(_.mkString(",")).saveAsTextFile(hdfsUrl+ "/hotelsparktask1")sc.stop()}
}

删除星级、评论数、评分中任意字段为空的数据

1. 读取 /hotel_data.csv
2. 将字段{星级、评论数、评分}中任意字段为空的数据删除, 打印剔除的数量
3. 保存 /hotelsparktask2

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}object Demo02 {def main(args: Array[String]): Unit = {System.setProperty("HADOOP_USER_NAME", "root")val config: SparkConf = new SparkConf().setMaster("local[1]").setAppName("2")val sc = new SparkContext(config)val hdfsUrl ="hdfs://192.168.226.129:9000"val filePath: String = hdfsUrl+"/file3_1/hotel_data.csv"val data: RDD[Array[String]] = sc.textFile(filePath).map(_.split(",")).cache()val total: Long = data.count()val dataDrop: RDD[Array[String]] = data.filter {arr: Array[String] =>!(arr(6).equals("NULL") || arr(10).equals("NULL") || arr(11).equals("NULL"))}println("删除的数据条目有: " + (total - dataDrop.count()))dataDrop.map(_.mkString(",")).saveAsTextFile(hdfsUrl+ "/hotelsparktask2")sc.stop()}
}

删除非法数据

1. 读取第一题的 /hotelsparktask1
2. 剔除数据集中评分和星级字段的非法数据，合法数据是评分[0，5]的实数，星级是指星级字段内容中包含 NULL、二星、三星、四星、五星的数据
3. 剔除数据集中的重复数据
4. 分别打印 删除含有非法评分、星级以及重复的数据条目数
5. 保存 /hotelsparktask3

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}object Demo03 {def main(args: Array[String]): Unit = {System.setProperty("HADOOP_USER_NAME", "root")//解决权限问题val config: SparkConf = new SparkConf().setMaster(  "local[1]").setAppName("3")val sc = new SparkContext(config)val hdfsUrl ="hdfs://192.168.226.129:9000"val filePath: String = hdfsUrl+"/hotelsparktask1"val lines: RDD[String] = sc.textFile(filePath).cache()val data: RDD[Array[String]] = lines.map(_.split(","))val total: Long = data.count()val dataDrop: RDD[Array[String]] = data.filter {arr: Array[String] =>try {(arr(10).toDouble >= 0) && (arr(10).toDouble <= 5)} catch {case _: Exception => false}}val lab = Array("NULL", "一星", "二星", "三星", "四星", "五星")val dataDrop1: RDD[Array[String]] = data.filter { arr: Array[String] =>var flag = falsefor (elem <- lab) {if (arr(6).contains(elem)) {flag = true}}flag}val dataDrop2: RDD[String] = lines.distinctprintln("删除的非法评分数据条目有: " + (total - dataDrop.count()))println("删除的非法星级数据条目有: " + (total - dataDrop1.count()))println("删除重复数据条目有: " + (total - dataDrop2.count()))val wordsRdd: RDD[Array[String]] = lines.distinct.map(_.split(",")).filter {arr: Array[String] =>try {(arr(10).toDouble >= 0) && (arr(10).toDouble <= 5)} catch {case _: Exception => false}}.filter { arr: Array[String] =>var flag = falsefor (elem <- lab) {if (arr(6).contains(elem)) {flag = true}}flag}wordsRdd.map(_.mkString(",")).saveAsTextFile(hdfsUrl + "/hotelsparktask3")sc.stop()}
}

hotel_data.csv

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