需求和分词测试练习

搜狗搜索日志分析

数据网址：http://www.sogou.com/labs/resource/q.php

搜狗实验室提供【用户查询日志(SogouQ)】数据分为三个数据集，大小不一样

迷你版(样例数据, 376KB)：http://download.labs.sogou.com/dl/sogoulabdown/SogouQ/SogouQ.mini.zip

精简版(1天数据，63MB)：http://download.labs.sogou.com/dl/sogoulabdown/SogouQ/SogouQ.reduced.zip

完整版(1.9GB)：http://www.sogou.com/labs/resource/ftp.php?dir=/Data/SogouQ/SogouQ.zip

需求

分词工具测试

 添加依赖：

<dependency>
<groupId>com.hankcs</groupId>
<artifactId>hanlp</artifactId>
<version>portable-1.7.7</version>
</dependency>

测试：

package cn.itcast.core

import java.util

import com.hankcs.hanlp.HanLP
import com.hankcs.hanlp.seg.common.Term


/**
 * Author itcast
 * Desc HanLP入门案例
 */
object HanLPTest {
  def main(args: Array[String]): Unit = {
    val words = "[HanLP入门案例]"
    val terms: util.List[Term] = HanLP.segment(words)
    println(terms) //直接打印java的list:[[/w, HanLP/nx, 入门/vn, 案例/n, ]/w]
    import scala.collection.JavaConverters._
    println(terms.asScala.map(_.word)) //转为scala的list:ArrayBuffer([, HanLP, 入门, 案例, ])

    val cleanWords1: String = words.replaceAll("\[|\]", "") //将"["或"]"替换为空"" //"HanLP入门案例"
    println(cleanWords1) //HanLP入门案例
    println(HanLP.segment(cleanWords1).asScala.map(_.word)) //ArrayBuffer(HanLP, 入门, 案例)

    val log = """00:00:00 2982199073774412    [360安全卫士]   8 3 download.it.com.cn/softweb/software/firewall/antivirus/20067/17938.html"""
    val cleanWords2 = log.split("\s+")(2) //[360安全卫士]
      .replaceAll("\[|\]", "") //360安全卫士
    println(HanLP.segment(cleanWords2).asScala.map(_.word)) //ArrayBuffer(360, 安全卫士)
  }
}

 案例：

package cn.itcast.core

import com.hankcs.hanlp.HanLP
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import shapeless.record
import spire.std.tuples

import scala.collection.immutable.StringOps
import scala.collection.mutable

/**
 * Author itcast
 * Desc 需求:对SougouSearchLog进行分词并统计如下指标:
 * 1.热门搜索词
 * 2.用户热门搜索词(带上用户id)
 * 3.各个时间段搜索热度
 */
object SougouSearchLogAnalysis {
  def main(args: Array[String]): Unit = {
    //TODO 0.准备环境
    val conf: SparkConf = new SparkConf().setAppName("spark").setMaster("local[*]")
    val sc: SparkContext = new SparkContext(conf)
    sc.setLogLevel("WARN")
    //TODO 1.加载数据
    val lines: RDD[String] = sc.textFile("data/input/SogouQ.sample")

    //TODO 2.处理数据
    //封装数据
    val SogouRecordRDD: RDD[SogouRecord] = lines.map(line => {//map是一个进去一个出去
      val arr: Array[String] = line.split("\s+")
      SogouRecord(
        arr(0),
        arr(1),
        arr(2),
        arr(3).toInt,
        arr(4).toInt,
        arr(5)
      )
    })

    //切割数据
   /* val wordsRDD0: RDD[mutable.Buffer[String]] = SogouRecordRDD.map(record => {
      val wordsStr: String = record.queryWords.replaceAll("\[|\]", "") //360安全卫士
      import scala.collection.JavaConverters._ //将Java集合转为scala集合
      HanLP.segment(wordsStr).asScala.map(_.word) //ArrayBuffer(360, 安全卫士)
    })*/

    val wordsRDD: RDD[String] = SogouRecordRDD.flatMap(record => { //flatMap是一个进去,多个出去(出去之后会被压扁) //360安全卫士==>[360, 安全卫士]
      val wordsStr: String = record.queryWords.replaceAll("\[|\]", "") //360安全卫士
      import scala.collection.JavaConverters._ //将Java集合转为scala集合
      HanLP.segment(wordsStr).asScala.map(_.word) //ArrayBuffer(360, 安全卫士)
    })

    //TODO 3.统计指标
    //--1.热门搜索词
    val result1: Array[(String, Int)] = wordsRDD
      .filter(word => !word.equals(".") && !word.equals("+"))    //过滤
      .map((_, 1))
      .reduceByKey(_ + _)
      .sortBy(_._2, false)
      .take(10)

    //--2.用户热门搜索词(带上用户id)
    val userIdAndWordRDD: RDD[(String, String)] = SogouRecordRDD.flatMap(record => { //flatMap是一个进去,多个出去(出去之后会被压扁) //360安全卫士==>[360, 安全卫士]
      val wordsStr: String = record.queryWords.replaceAll("\[|\]", "") //360安全卫士
      import scala.collection.JavaConverters._ //将Java集合转为scala集合
      val words: mutable.Buffer[String] = HanLP.segment(wordsStr).asScala.map(_.word) //ArrayBuffer(360, 安全卫士)
      val userId: String = record.userId
      words.map(word => (userId, word))
    })
    val result2: Array[((String, String), Int)] = userIdAndWordRDD
      .filter(t => !t._2.equals(".") && !t._2.equals("+"))
      .map((_, 1))
      .reduceByKey(_ + _)
      .sortBy(_._2, false)
      .take(10)

    //--3.各个时间段搜索热度
    val result3: Array[(String, Int)] = SogouRecordRDD.map(record => {
      val timeStr: String = record.queryTime
      val hourAndMitunesStr: String = timeStr.substring(0, 5)
      (hourAndMitunesStr, 1)
    }).reduceByKey(_ + _)
      .sortBy(_._2, false)
      .take(10)

    //TODO 4.输出结果
    result1.foreach(println)
    result2.foreach(println)
    result3.foreach(println)

    //TODO 5.释放资源
    sc.stop()
  }
  //准备一个样例类用来封装数据
  /**
   * 用户搜索点击网页记录Record
   * @param queryTime  访问时间，格式为：HH:mm:ss
   * @param userId     用户ID
   * @param queryWords 查询词
   * @param resultRank 该URL在返回结果中的排名
   * @param clickRank  用户点击的顺序号
   * @param clickUrl   用户点击的URL
   */
  case class SogouRecord(
                          queryTime: String,
                          userId: String,
                          queryWords: String,
                          resultRank: Int,
                          clickRank: Int,
                          clickUrl: String
                        )
}