【Spark】通过Spark实现点击流日志分析

文章目录

• 数据大致内容及格式
• 统计PV(PageViews)
• 统计UV(Unique Visitor)
• 求取TopN

---

数据大致内容及格式

194.237.142.21 - - [18/Sep/2013:06:49:18 +0000] "GET /wp-content/uploads/2013/07/rstudio-git3.png HTTP/1.1" 304 0 "-" "Mozilla/4.0 (compatible;)"
183.49.46.228 - - [18/Sep/2013:06:49:23 +0000] "-" 400 0 "-" "-"
163.177.71.12 - - [18/Sep/2013:06:49:33 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
163.177.71.12 - - [18/Sep/2013:06:49:36 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
101.226.68.137 - - [18/Sep/2013:06:49:42 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
101.226.68.137 - - [18/Sep/2013:06:49:45 +0000] "HEAD / HTTP/1.1" 200 20 "-" "DNSPod-Monitor/1.0"
60.208.6.156 - - [18/Sep/2013:06:49:48 +0000] "GET /wp-content/uploads/2013/07/rcassandra.png HTTP/1.0" 200 185524 "http://cos.name/category/software/packages/" "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36"
222.68.172.190 - - [18/Sep/2013:06:49:57 +0000] "GET /images/my.jpg HTTP/1.1" 200 19939 "http://www.angularjs.cn/A00n" "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36"
……
……

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统计PV(PageViews)

就是统计日志文件中有多少条数据

关于点击流日志的各种指标可以查看【Hadoop离线基础总结】网站流量日志数据分析系统

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

object PvCount {
  def main(args: Array[String]): Unit = {

    //获取SparkConf
    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("PV-Count").set("spark.driver.host", "localhost")
    //创建SparkContext
    val sparkContext = new SparkContext(sparkConf)
    //读取文件
    val fileRDD: RDD[String] = sparkContext.textFile("/Users/zhaozhuang/Desktop/4、Spark/Spark第二天/第二天教案/资料/运营商日志/access.log")
    //统计数量
    val count = fileRDD.count()

    println("一共有"+count+"行数据")

    sparkContext.stop()
  }
}

经统计后得出，数据有 14619条，也就是说PV量为14619

---

统计UV(Unique Visitor)

实际工作中，一般推荐用cookie而不是IP地址来对UV进行统计，但这里数据只有IP地址，所以目前就按IP算

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

object UvCount {
  def main(args: Array[String]): Unit = {
    //获取SparkConf
    val sparkConf = new SparkConf().setAppName("UV-Count").setMaster("local[2]").set("spark.driver.host","localhost")
    //创建SparkContext
    val sparkContext = new SparkContext(sparkConf)
    //筛选日志
    sparkContext.setLogLevel("WARN")
    //读取文件
    val fileRDD: RDD[String] = sparkContext.textFile("/Users/zhaozhuang/Desktop/4、Spark/Spark第二天/第二天教案/资料/运营商日志/access.log")
    //从所有数据中剔除掉不需要的数据，只拿到IP地址
    val getIpRDD: RDD[String] = fileRDD.map(_.split(" ")(0))
    //对IP地址进行去重，去重后数据减少，就可以将分区缩减为1个
    val distinctedRDD: RDD[String] = getIpRDD.distinct(1)
    //对去重后的数据进行计数统计
    val count: Long = distinctedRDD.count()

    println(count)

    sparkContext.stop()
  }
}

统计得出UV量为1050

---

求取TopN

有两种方法可以用，take()和top() 都可以

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

object GetTopN {
  def main(args: Array[String]): Unit = {
    //获取SparkConf
    val sparkConf = new SparkConf().setMaster("local[2]").set("spark.driver.host", "localhost").setAppName("getTopN")
    //获取SparkContext
    val sparkContext: SparkContext = new SparkContext(sparkConf)
    //读取文件
    val fileRDD: RDD[String] = sparkContext.textFile("/Users/zhaozhuang/Desktop/4、Spark/Spark第二天/第二天教案/资料/运营商日志/access.log")
    //筛选日志
    sparkContext.setLogLevel("WARN")

    //194.237.142.21 - - [18/Sep/2013:06:49:18 +0000] "GET /wp-content/uploads/2013/07/rstudio-git3.png HTTP/1.1" 304 0 "-" "Mozilla/4.0 (compatible;)"
    //以上是数据格式，首先对数据进行切割
    val valueRDD: RDD[Array[String]] = fileRDD.map(x => x.split(" "))
    /*
    数据切割后的形式
    194.237.142.21
    -
    -
    [18/Sep/2013:06:49:18
     +0000]
     "GET
     /wp-content/uploads/2013/07/rstudio-git3.png
     HTTP/1.1"
     304
     0
     "-"
     "Mozilla/4.0
     (compatible;)"
     */
    //日志数据中，下标为10的数据为我们要求取的数据（http_refer），所以切割后数组中少于10条的为无效数据
    //先将无效数据过滤掉
    val filterRDD: RDD[Array[String]] = valueRDD.filter(arr => arr.length > 10)
    //获取每一个http_refer的url，并计作一次
    val urlAndOne: RDD[(String, Int)] = filterRDD.map(x => (x(10), 1))
    //将url相同的次数相加
    val reduceRDD: RDD[(String, Int)] = urlAndOne.reduceByKey(_ + _)
    //将拿到的url+次数进行排序,false为降序，不填或true为升序
    val sortRDD: RDD[(String, Int)] = reduceRDD.sortBy(x => x._2, false)
    //求取TopN，两种方法take(N)或者top(N)
    val topRDD: Array[(String, Int)] = sortRDD.take(10)

    println(topRDD.toBuffer)
    sparkContext.stop()
  }
}

拿到控制台结果为：

ArrayBuffer(("-",5205), (“http://blog.fens.me/category/hadoop-action/”,547), (“http://blog.fens.me/”,377), (“http://blog.fens.me/wp-admin/post.php?post=2445&action=edit&message=10”,360), (“http://blog.fens.me/r-json-rjson/”,274), (“http://blog.fens.me/angularjs-webstorm-ide/”,271), (“http://blog.fens.me/wp-content/themes/silesia/style.css”,228), (“http://blog.fens.me/nodejs-express3/”,198), (“http://blog.fens.me/hadoop-mahout-roadmap/”,182), (“http://blog.fens.me/vps-ip-dns/”,176))