如下所示：importjava.text.DecimalFormatimportcom.alibaba.fastjson.JSONimportcom.donews.data.AppConfigimportcom.type
如下所示：
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'")
}
}
以上这篇DataFrame:通过SparkSql将scala类转为DataFrame的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
DataFrame SparkSql scala