DataFrame：通过SparkSql将scala类转为DataFrame

import java.text.DecimalFormat
import com.alibaba.fastjson.JSON
import com.donews.data.AppConfig
import com.typesafe.config.ConfigFactory
import org.apache.spark.sql.types.{StructField, StructType}
import org.apache.spark.sql.{Row, SaveMode, DataFrame, SQLContext}
import org.apache.spark.{SparkConf, SparkContext}
import org.slf4j.LoggerFactory

/**
  * Created by silentwolf on 2016/6/3.
  */

case class UserTag(SUUID: String,
                   MAN: Float,
                   WOMAN: Float,
                   AGE10_19: Float,
                   AGE20_29: Float,
                   AGE30_39: Float,
                   AGE40_49: Float,
                   AGE50_59: Float,
                   GAME: Float,
                   MOVIE: Float,
                   MUSIC: Float,
                   ART: Float,
                   POLITICS_NEWS: Float,
                   FINANCIAL: Float,
                   EDUCATION_TRAINING: Float,
                   HEALTH_CARE: Float,
                   TRAVEL: Float,
                   AUTOMOBILE: Float,
                   HOUSE_PROPERTY: Float,
                   CLOTHING_ACCESSORIES: Float,
                   BEAUTY: Float,
                   IT: Float,
                   BABY_PRODUCT: Float,
                   FOOD_SERVICE: Float,
                   HOME_FURNISHING: Float,
                   SPORTS: Float,
                   OUTDOOR_ACTIVITIES: Float,
                   MEDICINE: Float
                  )

object UserTagTable {

  val LOG = LoggerFactory.getLogger(UserOverviewFirst.getClass)

  val REP_HOME = s"${AppConfig.HDFS_MASTER}/${AppConfig.HDFS_REP}"

  def main(args: Array[String]) {

    var startTime = System.currentTimeMillis()

    val conf: com.typesafe.config.Config = ConfigFactory.load()

    val sc = new SparkContext()

    val sqlContext = new SQLContext(sc)

    var df1: DataFrame = null

    if (args.length == 0) {
      println("请输入： appkey , StartTime ： 2016-04-10  ，StartEnd ：2016-04-11")
    }
    else {

      var appkey = args(0)

      var lastdate = args(1)

      df1 = loadDataFrame(sqlContext, appkey, "2016-04-10", lastdate)

      df1.registerTempTable("suuidTable")

      sqlContext.udf.register("taginfo", (a: String) => userTagInfo(a))
      sqlContext.udf.register("intToString", (b: Long) => intToString(b))
      import sqlContext.implicits._

      //***重点***：将临时表中的suuid和自定函数中Json数据，放入UserTag中。
    sqlContext.sql(" select distinct(suuid) AS suuid,taginfo(suuid) from  suuidTable group by suuid").map { case Row(suuid: String, taginfo: String) =>
        val taginfoObj = JSON.parseObject(taginfo)
        UserTag(suuid.toString,
          taginfoObj.getFloat("man"),
          taginfoObj.getFloat("woman"),
          taginfoObj.getFloat("age10_19"),
          taginfoObj.getFloat("age20_29"),
          taginfoObj.getFloat("age30_39"),
          taginfoObj.getFloat("age40_49"),
          taginfoObj.getFloat("age50_59"),
          taginfoObj.getFloat("game"),
          taginfoObj.getFloat("movie"),
          taginfoObj.getFloat("music"),
          taginfoObj.getFloat("art"),
          taginfoObj.getFloat("politics_news"),
          taginfoObj.getFloat("financial"),
          taginfoObj.getFloat("education_training"),
          taginfoObj.getFloat("health_care"),
          taginfoObj.getFloat("travel"),
          taginfoObj.getFloat("automobile"),
          taginfoObj.getFloat("house_property"),
          taginfoObj.getFloat("clothing_accessories"),
          taginfoObj.getFloat("beauty"),
          taginfoObj.getFloat("IT"),
          taginfoObj.getFloat("baby_Product"),
          taginfoObj.getFloat("food_service"),
          taginfoObj.getFloat("home_furnishing"),
          taginfoObj.getFloat("sports"),
          taginfoObj.getFloat("outdoor_activities"),
          taginfoObj.getFloat("medicine")
        )}.toDF().registerTempTable("resultTable")

      val resultDF = sqlContext.sql(s"select '$appkey' AS APPKEY, '$lastdate' AS DATE,SUUID ,MAN,WOMAN,AGE10_19,AGE20_29,AGE30_39 ," +
        "AGE40_49 ,AGE50_59,GAME,MOVIE,MUSIC,ART,POLITICS_NEWS,FINANCIAL,EDUCATION_TRAINING,HEALTH_CARE,TRAVEL,AUTOMOBILE," +
        "HOUSE_PROPERTY,CLOTHING_ACCESSORIES,BEAUTY,IT,BABY_PRODUCT ,FOOD_SERVICE ,HOME_FURNISHING ,SPORTS ,OUTDOOR_ACTIVITIES ," +
        "MEDICINE from resultTable WHERE SUUID IS NOT NULL")
      resultDF.write.mode(SaveMode.Overwrite).options(
        Map("table" -> "USER_TAGS", "zkUrl" -> conf.getString("Hbase.url"))
      ).format("org.apache.phoenix.spark").save()

    }
  }

  def intToString(suuid: Long): String = {
    suuid.toString()
  }

  def userTagInfo(num1: String): String = {

    var de = new DecimalFormat("0.00")
    var mannum = de.format(math.random).toFloat
    var man = mannum
    var woman = de.format(1 - mannum).toFloat

    var age10_19num = de.format(math.random * 0.2).toFloat
    var age20_29num = de.format(math.random * 0.2).toFloat
    var age30_39num = de.format(math.random * 0.2).toFloat
    var age40_49num = de.format(math.random * 0.2).toFloat

    var age10_19 = age10_19num
    var age20_29 = age20_29num
    var age30_39 = age30_39num
    var age40_49 = age40_49num
    var age50_59 = de.format(1 - age10_19num - age20_29num - age30_39num - age40_49num).toFloat

    var game = de.format(math.random * 1).toFloat
    var movie = de.format(math.random * 1).toFloat
    var music = de.format(math.random * 1).toFloat
    var art = de.format(math.random * 1).toFloat
    var politics_news = de.format(math.random * 1).toFloat

    var financial = de.format(math.random * 1).toFloat
    var education_training = de.format(math.random * 1).toFloat
    var health_care = de.format(math.random * 1).toFloat
    var travel = de.format(math.random * 1).toFloat
    var automobile = de.format(math.random * 1).toFloat

    var house_property = de.format(math.random * 1).toFloat
    var clothing_accessories = de.format(math.random * 1).toFloat
    var beauty = de.format(math.random * 1).toFloat
    var IT = de.format(math.random * 1).toFloat
    var baby_Product = de.format(math.random * 1).toFloat

    var food_service = de.format(math.random * 1).toFloat
    var home_furnishing = de.format(math.random * 1).toFloat
    var sports = de.format(math.random * 1).toFloat
    var outdoor_activities = de.format(math.random * 1).toFloat
    var medicine = de.format(math.random * 1).toFloat

    "{" + "\"man\"" + ":" + man + "," + "\"woman\"" + ":" + woman + "," + "\"age10_19\"" + ":" + age10_19 + "," + "\"age20_29\"" + ":" + age20_29 + "," +
      "\"age30_39\"" + ":" + age30_39 + "," + "\"age40_49\"" + ":" + age40_49 + "," + "\"age50_59\"" + ":" + age50_59 + "," + "\"game\"" + ":" + game + "," +
      "\"movie\"" + ":" + movie + "," + "\"music\"" + ":" + music + "," + "\"art\"" + ":" + art + "," + "\"politics_news\"" + ":" + politics_news + "," +
      "\"financial\"" + ":" + financial + "," + "\"education_training\"" + ":" + education_training + "," + "\"health_care\"" + ":" + health_care + "," +
      "\"travel\"" + ":" + travel + "," + "\"automobile\"" + ":" + automobile + "," + "\"house_property\"" + ":" + house_property + "," + "\"clothing_accessories\"" + ":" + clothing_accessories + "," +
      "\"beauty\"" + ":" + beauty + "," + "\"IT\"" + ":" + IT + "," + "\"baby_Product\"" + ":" + baby_Product + "," + "\"food_service\"" + ":" + food_service + "," +
      "\"home_furnishing\"" + ":" + home_furnishing + "," + "\"sports\"" + ":" + sports + "," + "\"outdoor_activities\"" + ":" + outdoor_activities + "," + "\"medicine\"" + ":" + medicine +
      "}";

  }

  def loadDataFrame(ctx: SQLContext, appkey: String, startDay: String, endDay: String): DataFrame = {
    val path = s"$REP_HOME/appstatistic"
    ctx.read.parquet(path)
      .filter(s"timestamp is not null and appkey='$appkey'  and day>='$startDay' and day<='$endDay'")
  }


}

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