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Spark中利用Scala进行数据清洗（代码）

news 来源：原创 2024/5/19 18:57:02

2019-05-07 18:56:18
 1
　　package com.amoscloud.log.analyze
 2 
 3 import java.text.SimpleDateFormat
 4 import java.util.Date
 5 
 6 import org.apache.spark.rdd.RDD
 7 import org.apache.spark.{SparkConf, SparkContext}
 8 
 9 object LogAnalyze1 {
10   def main(args: Array[String]): Unit = {
11 
12 
13     val conf = new SparkConf().setMaster("local[2]").setAppName("LogAnalyze2")
14     val sc = new SparkContext(conf)
15 
16     val data = sc.textFile("C:\\Users\\Administrator\\Desktop\\HTTP.txt")
17     data.cache()
18     //    1.(手机号,归属地,设备品牌,设备型号,连接时长)
19     //    analyze1(data)
20     //    2.(时间段秒,访问流量)
21     analyze2(data)
22     //    3.(品牌,Array[(String,Int)]((型号1,个数1),(型号2,个数2)))
23     //    analyze(data)
24   }
25 
26   private def analyze(data: RDD[String]) = {
27     data.filter(_.split(",").length >= 72)
28       .map(x => {
29         val arr = x.split(",")
30         val brand = arr(70)
31         val model = arr(71)
32         ((brand, model), 1)
33       })
34       .reduceByKey(_ + _)
35       .map(t => {
36         val k = t._1
37         (k._1, (k._2, t._2))
38       })
39       .groupByKey()
40       .collect()
41       .foreach(println)
42   }
43 
44   private def analyze2(data: RDD[String]) = {
45     data.map(x => {
46       val arr = x.split(",")
47       val time = arr(16).take(arr(16).length - 4)
48       val flow = arr(7).toLong
49       (time, flow)
50     })
51       .reduceByKey(_ + _)
52       //      .map(x => (x._1, (x._2 / 1024.0).formatted("%.3f") + "KB"))
53       .map(x => (x._1, x._2))
54       .collect()
55       .foreach(println)
56   }
57 
58   private def analyze1(data: RDD[String]) = {
59     data
60       .filter(_.split(",").length >= 72)
61       .map(x => {
62         val arr = x.split(",")
63         val phoneNum = arr(3).takeRight(11)
64         val local = arr(61) + arr(62) + arr(63)
65         val brand = arr(70)
66         val model = arr(71)
67         val connectTime = timeDiff(arr(16), arr(17))
68         (phoneNum + "|" + local + "|" + brand + "|" + model, connectTime)
69         //        1.(手机号,归属地,设备品牌,设备型号,连接时长)
70       })
71       .reduceByKey(_ + _)
72       .map(t => (t._1, formatTime(t._2)))
73       .collect()
74       .foreach(println)
75   }
76 
77   def timeDiff(time1: String, time2: String): Long = {
78     val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
79     val timeStamp2 = sdf.parse(time2.take(time2.length - 4)).getTime + time2.takeRight(3).toLong
80     val timeStamp1 = sdf.parse(time1.take(time1.length - 4)).getTime + time1.takeRight(3).toLong
81     timeStamp2 - timeStamp1
82   }
83 
84 
85   def formatTime(time: Long): String = {
86     val timeS = time / 1000
87     val s = timeS % 60
88     val m = timeS / 60 % 60
89     val h = timeS / 60 / 60 % 24
90     h + ":" + m + ":" + s
91   }
92 
93 }

2：写spark程序统计iis网站请求日志中每天每个小时段成功访问ip的数量

package com.amoscloud.log.analyze

import org.apache.spark.rdd.RDD
import org.apache.spark.{HashPartitioner, SparkConf, SparkContext}

import scala.collection.mutable

object LogAnalyze {
  def main(args: Array[String]): Unit = {
    //    写spark程序统计iis网站请求日志中 每天每个小时段成功访问ip的数量

    //获取sc
    val conf = new SparkConf().setAppName("LogAnalyze").setMaster("local[2]")
    val sc = new SparkContext(conf)

    //读取数据
    val log: RDD[String] = sc.textFile("C:\\Users\\Administrator\\Desktop\\iis网站请求日志")


    //将日志中，日期，时间，IP和响应码 保留
    log
      .filter(_.split("\\s").length > 10)

      .map(line => {
        val strings = line.split("\\s+")
        //RDD[(String,String,String,String)]
        (strings(0), strings(1).split(":")(0), strings(8), strings(10))
      })
      //RDD[(String,String,String,String)]
      .filter(_._4 == "200")
      //RDD[(日期|时间,IP)]
      .map(t => (t._1 + "|" + t._2, t._3))
      //RDD[(日期|时间,Iterable[IP])]

      .groupByKey()
      .map(t => (t._1, t._2.toList.size, t._2.toList.distinct.size))

      .collect()
      .foreach(t => {
        val spl = t._1.split("\\|")
        printf("%s\t%s\t%d\t%d\n", spl(0), spl(1), t._2, t._3)
      })


    //数据按照 日期和时间进行分区  相同key的数据都在同一个分区中
    //      .partitionBy(new HashPartitioner(48))
    //      .mapPartitions((iter: Iterator[(String, String)]) => {
    //        val set = mutable.HashSet[String]()
    //        var count = 0
    //        var next = ("", "")
    //        while (iter.hasNext) {
    //          next = iter.next()
    //          count += 1
    //          set.add(next._2)
    //        }
    //        ((next._1, count, set.size) :: Nil).iterator
    //      })
    //      .filter(_._1.nonEmpty)
  }
}