Spark数据处理与分析---留存率

Posted 2021-05-21 Shall潇

需求分析

• 日志数据清洗
• 用户留存分析
• 活跃用户分析

文章目录

• 一、数据清洗
• • 1、读入日志文件并转化为RDD[Row]类型
  • 2、对数据进行清洗
  • 3、将数据写入mysql表中
• 二、用户留存率分析
• 三、活跃用户统计

一、数据清洗

样例数据

1、读入日志文件并转化为RDD[Row]类型

• 按照Tab切割数据，过滤掉字段数量少于8个的
  

import java.util.Properties

import org.apache.commons.lang.StringUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Dataset, Row, SaveMode, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{LongType, StringType, StructField, StructType}

/*
* 数据清洗
*
* */

object ETLDemo {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("ETL")
    val sc = SparkContext.getOrCreate(conf)
    val spark = SparkSession.builder().config(conf).getOrCreate()
    import spark.implicits._

    //1.加载数据
    val rdd = sc.textFile("in/test.log")
//    println(rdd.count())

    //2.过滤为8个字段的数据，并封装为Row类型

    /*
    * event_time、url、method、status、sip、user_uip、action_prepend、action_client
    *
    * */
    val rowRDD = rdd.map(_.split("\\t")).filter(x=>x.length==8).map(x=>Row(x(0),x(1),x(2),x(3),x(4),x(5),x(6),x(7)))

    //创建Schema
    val logs_schema = StructType(
      Array(
        StructField("event_time",StringType),
        StructField("url",StringType),
        StructField("method",StringType),
        StructField("status",StringType),
        StructField("sip",StringType),
        StructField("user_uip",StringType),
        StructField("action_prepend",StringType),
        StructField("action_client",StringType)
      )
    )

    //创建DataFrame
    val logDF = spark.createDataFrame(rowRDD,logs_schema)
	}
}

2、对数据进行清洗

• 按照第一列和第二列对数据进行去重
• 过滤掉状态码非200
• 过滤掉event_time为空的数据
• 将url按照”&”以及”=”切割

//继续清洗
    val filterLogs = logDF.dropDuplicates("event_time", "url") //按照第一列和第二列对数据进行去重
      .filter(x => x(3) == "200") //过滤掉状态码非200
      .filter(x => StringUtils.isNotEmpty(x(0).toString)) //过滤掉event_time为空的数据
//将url按照”&”以及”=”切割
    val full_logs_RDD: RDD[Row] = filterLogs.map(line => {
      val str = line.getAs[String]("url")				//先获取到 url这一列数据
      val paramArray = str.split("\\\\?")					//按照 ? 进行切分
      var paramMap: Map[String, String] = null      	//先定义 存储 A=B 这样的键值对的类型
      if (paramArray.length == 2) {
        val strings = paramArray(1).split("&")      	//对 ?后面的字符串进行按 & 切分
        paramMap = strings.map(x => x.split("=")).filter(x => x.length == 2)
          .map(x => (x(0), x(1))).toMap					//按照 =切分后的数据放入 Map
      }
      ( //最好不要超过22个，如果非要那么多，建议使用元组中套元组
        line.getAs[String]("event_time"),              //再次获取数据
        paramMap.getOrElse[String]("userUID", ""),
        paramMap.getOrElse[String]("userSID", ""),
        paramMap.getOrElse[String]("actionBegin", ""),
        paramMap.getOrElse[String]("actionEnd", ""),
        paramMap.getOrElse[String]("actionType", ""),
        paramMap.getOrElse[String]("actionName", ""),
        paramMap.getOrElse[String]("actionValue", ""),
        paramMap.getOrElse[String]("actionTest", ""),
        paramMap.getOrElse[String]("ifEquipment", ""),
        line.getAs[String]("method"),
        line.getAs[String]("status"),
        line.getAs[String]("sip"),
        line.getAs[String]("user_uip"),
        line.getAs[String]("action_prepend"),
        line.getAs[String]("action_client")
      )
    }).toDF().rdd

    val full_logs_schema = StructType(					//重新定义结构
      Array(
        StructField("event_time",StringType),

        StructField("userUID",StringType),
        StructField("userSID",StringType),
        StructField("actionBegin",StringType),
        StructField("actionEnd",StringType),
        StructField("actionType",StringType),
        StructField("actionName",StringType),
        StructField("actionValue",StringType),
        StructField("actionTest",StringType),
        StructField("ifEquipment",StringType),

        StructField("method",StringType),
        StructField("status",StringType),
        StructField("sip",StringType),
        StructField("user_uip",StringType),
        StructField("action_prepend",StringType),
        StructField("action_client",StringType)
      )
    )

    val full_logDF = spark.createDataFrame(full_logs_RDD,full_logs_schema)  //得到最终的数据

3、将数据写入mysql表中

在执行写入数据程序之前，先创建好对应的数据库
进入到MySQL中

create database if not exists etl;

• 保存数据

//连接Mysql，将数据存入其中
val properties = new Properties()
properties.setProperty("url",JdbcUtil.url)   		//这里可以直接写自己的配置信息，但是最好封装一下
properties.setProperty("user",JdbcUtil.user)
properties.setProperty("password",JdbcUtil.password)
properties.setProperty("driver",JdbcUtil.driver)

//写入Mysql
println("将filterlogs写入Mysql")
filterLogs.write.mode(SaveMode.Overwrite).jdbc(JdbcUtil.url,"access_logs",properties)
println("写入完成")
println("将full_logDF写入Mysql")
full_logDF.write.mode(SaveMode.Overwrite).jdbc(JdbcUtil.url,"full_access_logs",properties)
println("写入完成")
spark.stop()

如果将写入Mysql和读取Mysql的封装成一个方法
就可以直接调用方法
例如下面

记得导包

package etl.util

import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
import java.util.Properties


object JdbcUtil {
  val url = "jdbc:mysql://192.168.XXX.100:3306/etl"
  val user = "root"
  val password = "root"
  val driver = "com.mysql.jdbc.Driver"

  val properties = new Properties()
  properties.setProperty("url",JdbcUtil.url)
  properties.setProperty("user",JdbcUtil.user)
  properties.setProperty("password",JdbcUtil.password)
  properties.setProperty("driver",JdbcUtil.driver)


  def getDataFrame(spark:SparkSession,tableName:String):DataFrame={
    val frame = spark.read.jdbc(url,tableName,properties)
    frame
  }

  def dataFrameToMysql(df:DataFrame, tableName:String)={
    df.write.mode(SaveMode.Overwrite).jdbc(url,tableName,properties)
    println("写入完成")
  }
}

二、用户留存率分析

import java.text.SimpleDateFormat
import java.util.Properties

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{SaveMode, SparkSession}
import etl.util.JdbcUtil
import org.apache.commons.lang.StringUtils
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.DoubleType

/*
* 留存率
*
* */

object Retention {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("ETL")
    val sc = SparkContext.getOrCreate(conf)
    val spark = SparkSession.builder().config(conf).getOrCreate()
    import spark.implicits._


    //连接Mysql，将数据存入其中：普通写法，后期可以封装

    val properties = new Properties()
    properties.setProperty("url",JdbcUtil.url)
    properties.setProperty("user",JdbcUtil.user)
    properties.setProperty("password",JdbcUtil.password)
    properties.setProperty("driver",JdbcUtil.driver)

    val logs = spark.read.jdbc(JdbcUtil.url,"full_access_logs",properties)
    logs.cache()

    //拉取actionName为注册的信息
    val registered = logs.filter($"actionName" === "Registered")
      .withColumnRenamed("event_time", "register_time")
      .select("userUID", "register_time")

    val signin = logs.filter($"actionName" === "Signin")
      .withColumnRenamed("event_time", "signin_time")
      .select("userUID", "signin_time")

    val joined = registered.join(signin,Seq("userUID"),"left")

    val sdf = new SimpleDateFormat("yyyy-MM-dd")
    //注册UDF，传入参数为时间：2018-09-04T20:34:45+08:00
    //输出为 Long类型的数字
    val datetrans = spark.udf.register("datetrans", (event_time: String) => {
      if (StringUtils.isEmpty(event_time))
        0
      else
        sdf.parse(event_time.substring(0, 10)).getTime
    })
    datetrans

    val joined2 = joined.withColumn("register_date",datetrans($"register_time"))
      .withColumn("signin_date",datetrans($"signin_time"))

//    joined2.show(false)

    //一天的毫秒数：8640,0000
    val daysc = 86400000

    val result = joined2.groupBy("register_date").agg(countDistinct("userUID").cast(DoubleType).as("num"))  //当天新增用户总量
    //    result.show()

	// 1 天留存率 ---- 7 天留存率 改一下daysc，>=
    val result1 = joined2.filter($"register_date" + daysc === $"signin_date")
      .groupBy($"register_date").agg(countDistinct("userUID").as("num1")) //1 日留存量

    // 355 381
    val result_one_day = result.join(result1, "register_date")
      .select($"register_date", round($"num1" / $"num", 2).as("一日留存率"))
    
    //使用封装好的方法进行写入
//    result_one_day.write.mode(SaveMode.Overwrite).jdbc(JdbcUtil.url,"",properties)
  }
}

三、活跃用户统计

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession
import etl.util.JdbcUtil
import org.apache.spark.sql.functions._

/*
* 活跃用户：买课或者看课的用户
* */

object Active {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[*]").setAppName("etl0")
    val spark = SparkSession.builder().config(conf).getOrCreate()
    val sc = SparkContext.getOrCreate(conf)

    import spark.implicits._

    val logs = JdbcUtil.getDataFrame(spark,"full_access_logs")
//    logs.printSchema()


    val ds = logs.filter($"actionName"==="BuyCourse" || $"actionName"==="StartLearn")
//    ds.show(false)
    val ds2 = ds.map(x=>(x.getAs[String]("userUID"),x.getAs[String]("event_time").substring(0,10)))
//    ds2.show(false)

    ds2.withColumnRenamed("_2","日期").groupBy($"日期").agg(countDistinct($"_1").as("活跃人数")).show()
  }
}