spark应用----统计分析电商网站的用户行为数据_用spark进行用户行为分析

目录

项目说明

题目一：Top5热门品类

题目二：Top5热门品类中每个品类的Top5活跃Session统计

 scala实现

 新建maven项目结构如下

 配置pom.xml文件

scala代码

python实现

---

项目说明

本项目的数据是采集电商网站的用户行为数据，主要包含用户的4种行为：搜索、点击、下单和支付。需求说明：品类是指产品的分类，大型电商网站品类分多级，咱们的项目中品类只有一级，不同的公司可能对热门的定义不一样。我们按照每个品类的点击、下单、支付的量来统计热门品类。

数据格式如下：

下面是数据文件：
链接：https://pan.baidu.com/s/1w93qlqKGDp7bLbW569l2Aw?pwd=uvuw 

题目一：Top5热门品类

需求：品类是指产品的分类，大型电商网站品类分多级，咱们的项目中品类只有一级，不同的公司可能对热门的定义不一样。我们按照每个品类的点击、下单、支付的量来统计热门品类。本项目需求为：先按照点击数排名，靠前的就排名高；如果点击数相同，再比较下单数；下单数再相同，就比较支付数。输出结果如下：

 

1. 通过使用groupby实现，Top5热门品类，将聚合后的数据排序，取前5名
2. 通过使用reduceByKey实现，Top5热门品类，将聚合后的数据排序，取前5名

题目二：Top5热门品类中每个品类的Top5活跃Session统计

需求：对于排名前5的品类，分别获取每个品类点击次数排名前5的sessionId。（注意: 这里我们只关注点击次数，不关心下单和支付次数）这个就是说，对于top5的品类，每一个都要获取对它点击次数排名前5的sessionId。这个功能，可以让我们看到，对某个用户群体最感兴趣的品类，各个品类最感兴趣最典型的用户的session的行为。

1. 输出结果

---

 scala实现

 新建maven项目结构如下

 配置pom.xml文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
 
    <groupId>org.example</groupId>
    <artifactId>sparkrdd-141</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <properties>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.1.0</spark.version>
    </properties>
 
    <dependencies>
 
        <!-- Spark Core -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
 
        <!-- Scala 标准库 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
 
    </dependencies>
 
 
</project>

scala代码

import org.apache.log4j.{Level, Logger}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.storage.StorageLevel
 
object Main {
        def main(args: Array[String]): Unit = {
        Logger.getLogger("org").setLevel(Level.WARN)
 
        val conf = new SparkConf().setMaster("local[*]").setAppName("WordCountHelloWorld")
        val sc = new SparkContext(conf)
 
        // 加载数据
        val rdd01 = sc.textFile("src/main/resources/user_visit_action.txt")
        val rdd02 = rdd01.map(_.split("_"))
        val rdd03 = rdd02.map(x => x.slice(6, x.length)) // ['-1', '-1', 'null', 'null', 'null', 'null', '18'], ['9', '51', 'null', 'null', 'null', 'null', '6']
 
        // 过滤点击、订单和支付数据
        val click_RDD = rdd03.filter(_(0) != "-1")
        val order_RDD = rdd03.filter(_(2) != "null")
        val pay_RDD = rdd03.filter(_(4) != "null")
        // ==================================================================================================================
        // 题目1
        // 使用groupBy
        val click_RDD11 = click_RDD.map(x => (x(0), 1))
        val click_RDD12 = click_RDD11.groupBy(_._1)
        val click_RDD13 = click_RDD12.mapValues(_.map(_._2).sum)
        // println(click_RDD13.collect())
 
        val order_RDD11 = order_RDD.flatMap(x => x(2).split(','))
        val order_RDD12 = order_RDD11.map(x => (x, 1))
        val order_RDD13 = order_RDD12.groupBy(_._1)
        val order_RDD14 = order_RDD13.mapValues(_.map(_._2).sum)
        // println(order_RDD14.collect())
 
        val pay_RDD11 = pay_RDD.flatMap(x => x(4).split(','))
        val pay_RDD12 = pay_RDD11.map(x => (x, 1))
        val pay_RDD13 = pay_RDD12.groupBy(_._1)
        val pay_RDD14 = pay_RDD13.mapValues(_.map(_._2).sum)
        // println(pay_RDD14.collect())
 
        val end_RDD10 = click_RDD13.join(order_RDD14).join(pay_RDD14)  // ('16', ((5928, 1782), 1233)), ('19', ((6044, 1722), 1158)),
        val end_RDD11 = end_RDD10.map(x => (x._1.toInt, x._2._1._1, x._2._1._2, x._2._2))
 
        // 缓存一下
        // end_RDD11.cache()
        end_RDD11.persist(StorageLevel.DISK_ONLY)
 
        // 法1 使用top
        val end_RDD_top = end_RDD11.top(5)(Ordering[(Int, Int, Int)].on(x => (x._2, x._3, x._4)))
        println('\n' + end_RDD_top.mkString("\n"))
 
        // 法2 使用sortBy
        val end_RDD_sort = end_RDD11.sortBy(x => (-x._2, -x._3, -x._4))
        println('\n' + end_RDD_sort.take(5).mkString("\n"))
 
        // ==================================================================================================================
        // 题目2
        // reduceByKey实现
        // 点击
        val click_RDD01 = click_RDD.map(x => (x(0), 1))
        val click_RDD02 = click_RDD01.reduceByKey(_ + _)
        // println('\n' + click_RDD02.collect())
 
        // 订单
        val order_RDD01 = order_RDD.flatMap(x => x(2).split(','))
        val order_RDD02 = order_RDD01.map(x => (x, 1))
        val order_RDD03 = order_RDD02.reduceByKey(_ + _)
        // println('\n' + order_RDD03.collect())
 
        // 支付
        val pay_RDD01 = pay_RDD.flatMap(x => x(4).split(','))
        val pay_RDD02 = pay_RDD01.map(x => (x, 1))
        val pay_RDD03 = pay_RDD02.reduceByKey(_ + _)
        // println('\n' + pay_RDD03.collect())
 
        val end_RDD = click_RDD02.join(order_RDD03).join(pay_RDD03)
        val end_RDD01 = end_RDD.map(x => (x._1.toInt, x._2._1._1, x._2._1._2, x._2._2))
 
        // 缓存一下
        // end_RDD01.cache()
        end_RDD01.persist(StorageLevel.DISK_ONLY)
 
        // 法1 使用top
        val end_RDD02 = end_RDD01.top(5)(Ordering[(Int, Int, Int)].on(x => (x._2, x._3, x._4)))
        println('\n' + end_RDD02.mkString("\n"))
 
        // 法2 使用sortBy
        val end_RDD03 = end_RDD01.sortBy(x => (-x._2, -x._3, -x._4))
        println('\n' + end_RDD03.take(5).mkString("\n"))
 
 
        // ==================================================================================================================
        // 题目3
        //     过滤出top5点击品类id 中的会话id
        val list_clickID_top = end_RDD_top.map(_._1).toList
        println(list_clickID_top)
        val session_RDD = rdd02.map(x => (x(6), x(2)))
        val session_RDD01 = session_RDD.filter(x => list_clickID_top.contains(x._1.toInt))
 
        //    构造 (clickid_sessionid,1)
        val session_RDD02 = session_RDD01.map(x => (x._1 + "_" + x._2, 1))
        //    聚合
        val session_RDD03 = session_RDD02.reduceByKey(_ + _)
 
        //    构造 (clickid,(sessionid,sum))
        val session_RDD04 = session_RDD03.map(x => (x._1.split("_"), x._2)).map(x => (x._1(0), (x._1(1), x._2)))
        //    根据clickid分组
        val session_RDD05 = session_RDD04.groupBy(_._1)
 
        // 排序取Top5热门品类中每个品类的Top5活跃Session统计
        val sorted_rdd = session_RDD05.mapValues(x => x.toList.sortBy(_._2._2).reverse.take(5))
        val sorted_rdd2 = sorted_rdd.mapValues(x => x.map(_._2))
 
        sorted_rdd2.foreach(x => println("(" + x._1 + ", " + x._2.toList + ")"))
        // println(sorted_rdd.collect())
        // val session_RDD06 = session_RDD05.map(x => (x._1, x._2.toList)).mapValues(x => (x.sorted(Ordering.by((_: (String, Int))._2).reverse).take(5)))  // [('12', ('6502cdc9-cf95-4b08-8854-f03a25baa917', 1)),
        // println(sorted_rdd.collect())
        }
}

python实现

from pyspark import SparkConf, SparkContext, StorageLevel
 
if __name__ == '__main__':
    conf = SparkConf().setSparkHome('spark_hw').setMaster("local[*]")
    sc = SparkContext(conf=conf)
 
    rdd01 = sc.textFile(r"D:\TASK\PythonProject\pythonSpark\01_RDD\user_visit_action.txt")
    rdd02 = rdd01.map(lambda x: x.split('_'))
    rdd03 = rdd02.map(lambda x: x[
                                6:])  # ['-1', '-1', 'null', 'null', 'null', 'null', '18'], ['9', '51', 'null', 'null', 'null', 'null', '6']]
 
    click_RDD = rdd03.filter(lambda x: x[0] != '-1')
    order_RDD = rdd03.filter(lambda x: x[2] != 'null')
    pay_RDD = rdd03.filter(lambda x: x[4] != 'null')
 
    # ==================================================================================================================
    # reduceByKey实现
 
    # 点击
    click_RDD01 = click_RDD.map(lambda x: (x[0], 1))
    click_RDD02 = click_RDD01.reduceByKey(lambda sum1, x: sum1 + x)
    # print('\n', click_RDD02.collect())
 
    # 订单
    order_RDD01 = order_RDD.flatMap(lambda x: x[2].split(','))
    order_RDD02 = order_RDD01.map(lambda x: (x, 1))
    order_RDD03 = order_RDD02.reduceByKey(lambda sum1, x: sum1 + x)
    # print('\n', order_RDD03.collect())
 
    # 支付
    pay_RDD01 = pay_RDD.flatMap(lambda x: x[4].split(','))
    pay_RDD02 = pay_RDD01.map(lambda x: (x, 1))
    pay_RDD03 = pay_RDD02.reduceByKey(lambda sum1, x: sum1 + x)
    # print('\n', pay_RDD03.collect())
 
    end_RDD = click_RDD02.join(order_RDD03).join(
        pay_RDD03)  # ('16', ((5928, 1782), 1233)), ('19', ((6044, 1722), 1158)),
    end_RDD01 = end_RDD.map(lambda x: (int(x[0]), x[1][0][0], x[1][0][1], x[1][1]))
 
    # 缓存一下
    # end_RDD01.cache()
    end_RDD01.persist(StorageLevel.DISK_ONLY)
 
    # 法1 使用top
    end_RDD02 = end_RDD01.top(5, key=lambda x: (x[1], x[2], x[3]))
    print('\n', end_RDD02)
    # 法2 使用sortBy
    end_RDD02 = end_RDD01.sortBy(lambda x: (-x[1], -x[2], -x[3]))
    print('\n', end_RDD02.take(5))
 
    # ==================================================================================================================
    # 使用sortBy
    click_RDD11 = click_RDD.map(lambda x: (x[0], 1))
    click_RDD12 = click_RDD11.groupBy(lambda x: x[0])
    click_RDD13 = click_RDD12.mapValues(lambda x: sum(i for j, i in x))
    # print(click_RDD13.collect())
 
    order_RDD11 = order_RDD.flatMap(lambda x: x[2].split(','))
    order_RDD12 = order_RDD11.map(lambda x: (x, 1))
    order_RDD13 = order_RDD12.groupBy(lambda x: x[0])
    order_RDD14 = order_RDD13.mapValues(lambda x: sum(i for j, i in x))
    # print(order_RDD14.collect())
 
    pay_RDD11 = pay_RDD.flatMap(lambda x: x[4].split(','))
    pay_RDD12 = pay_RDD11.map(lambda x: (x, 1))
    pay_RDD13 = pay_RDD12.groupBy(lambda x: x[0])
    pay_RDD14 = pay_RDD13.mapValues(lambda x: sum(i for j, i in x))
    # print(pay_RDD14.collect())
 
    end_RDD10 = click_RDD13.join(order_RDD14).join(
        pay_RDD14)  # ('16', ((5928, 1782), 1233)), ('19', ((6044, 1722), 1158)),
    end_RDD11 = end_RDD10.map(lambda x: (int(x[0]), x[1][0][0], x[1][0][1], x[1][1]))
 
    # 缓存一下
    # end_RDD11.cache()
    end_RDD11.persist(StorageLevel.DISK_ONLY)
 
    # 法1 使用top
    end_RDD_top = end_RDD11.top(5, key=lambda x: (x[1], x[2], x[3]))
    print('\n', end_RDD_top)
 
    # 法2 使用sortBy
    end_RDD_sort = end_RDD11.sortBy(lambda x: (-x[1], -x[2], -x[3]))
    print('\n', end_RDD_sort.take(5))
 
    # ==================================================================================================================
    # 过滤出top5点击品类id 中的会话id
    list_clickID_top = [x[0] for x in end_RDD_top]
    print(list_clickID_top)
    session_RDD = rdd02.map(lambda x: (x[6], x[2]))
    session_RDD01 = session_RDD.filter(lambda x: int(x[0]) in list_clickID_top)
 
    # 构造 (clickid_sessionid,1)
    session_RDD02 = session_RDD01.map(lambda x: (x[0] + '_' + x[1], 1))
 
    # 聚合
    session_RDD03 = session_RDD02.reduceByKey(lambda sum1, x: sum1 + x)
 
    # 构造 (clickid,(sessionid,sum))
    session_RDD04 = session_RDD03.map(lambda x: (x[0].split('_'), x[1])).map(lambda x: (x[0][0], (x[0][1], x[1])))
    # 根据 clcikid分组
    session_RDD05 = session_RDD04.groupBy(lambda x: x[0])
    session_RDD05.cache()
    session_RDD05.foreach(print)
    # for i in session_RDD05.collect():
    #     for j in i:
    #         print(list(j))
    # session_RDD05.saveAsTextFile("../my.txt")
 
    # x为迭代器对象 y为迭代器对象中的每一个元素
    sorted_rdd = session_RDD05.mapValues(lambda x: sorted(x, key=lambda y: y[1][1], reverse=True)[:5])
    sorted_rdd = sorted_rdd.mapValues(lambda x: (j for i, j in x))
 
    # modified_rdd = sorted_rdd.mapValues(lambda values: [y for _, y in values])
 
    sorted_rdd.foreach(lambda x: print("(", x[0], list(x[1]), ")"))
    # print(sorted_rdd.collect())
    # session_RDD06 = session_RDD05.map(lambda x: (x[0], list(x[1]))).mapValues(lambda x: (sorted(-i[1][1] for i in x)[:5]))  # [('12', ('6502cdc9-cf95-4b08-8854-f03a25baa917', 1)),
    # print(sorted_rdd.collect())