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package com.gmei
import java.io.{File, PrintWriter, Serializable}
import com.gmei.lib.AbstractParams
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.{DataFrame, SaveMode, TiContext}
import scopt.OptionParser
object Data2FFM {
Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)
case class Params(env: String = "dev"
) extends AbstractParams[Params] with Serializable
val defaultParams = Params()
val parser = new OptionParser[Params]("Feed_EDA") {
head("EsmmData")
opt[String]("env")
.text(s"the databases environment you used")
.action((x, c) => c.copy(env = x))
note(
"""
|For example, the following command runs this app on a tidb dataset:
|
| spark-submit --class com.gmei.EsmmData ./target/scala-2.11/feededa-assembly-0.1.jar \
""".stripMargin +
s"| --env ${defaultParams.env}"
)
}
def writecsv(path:String,str:String): Unit ={
val writer = new PrintWriter(new File(path))
writer.write(str)
writer.close()
println("写入成功")
}
def main(args: Array[String]): Unit = {
parser.parse(args, defaultParams).map { param =>
GmeiConfig.setup(param.env)
val spark_env = GmeiConfig.getSparkSession()
val sc = spark_env._2
val ti = new TiContext(sc)
ti.tidbMapTable(dbName = "jerry_test", tableName = "esmm_train_data")
ti.tidbMapTable(dbName = "jerry_test", tableName = "esmm_pre_data")
val train_sep_date = GmeiConfig.getMinusNDate(10)
val esmm_data = sc.sql(
s"""
|select device_id,y,z,stat_date,ucity_id,cid_id,clevel1_id,ccity_name from esmm_train_data
|where stat_date > '${train_sep_date}'
""".stripMargin
).repartition(200).na.drop()
val column_list = esmm_data.columns.filter(x => x != "y" && x != "z")
val max_stat_date = sc.sql(
s"""
|select max(stat_date) from esmm_train_data
""".stripMargin
)
println("------------------------")
val max_stat_date_str = max_stat_date.collect().map(s => s(0).toString).head
println(max_stat_date_str)
println(column_list.slice(0,2).toList)
esmm_data.persist()
val column_number = scala.collection.mutable.Map[String,Array[String]]()
for (i <- column_list){
column_number(i) = esmm_data.select(i).collect().map(x => x(0).toString).distinct
}
esmm_data.unpersist()
println("dict")
val rdd = esmm_data.rdd
.map(x => (x(0).toString,x(1).toString,x(2).toString,x(3).toString,
x(4).toString,x(5).toString,x(6).toString, x(7).toString))
rdd.persist()
import sc.implicits._
val train = rdd.filter(x => x._4 != max_stat_date_str)
.map(x => (x._1,x._2,x._3,column_number("device_id").indexOf(x._1),
column_number("stat_date").indexOf(x._4), column_number("ucity_id").indexOf(x._5),
column_number("cid_id").indexOf(x._6), column_number("clevel1_id").indexOf(x._7),
column_number("ccity_name").indexOf(x._8),x._5,x._6))
.map(x => ((new util.Random).nextInt(2147483647),x._2,x._3,"1:%d:1.0 2:%d:1.0 3:%d:1.0 4:%d:1.0 5:%d:1.0 6:%d:1.0".
format(x._4,x._5,x._6,x._7,x._8,x._9),x._1,x._10,x._11)).zipWithIndex()
.map(x => (x._1._1,x._2,x._1._2,x._1._3,x._1._4,x._1._5,x._1._6,x._1._7))
.map(x => (x._1,x._2+","+x._3+","+x._4+","+x._5,x._6,x._7,x._8)).toDF("number","data","device_id","city_id","cid")
println("train")
train.show(6)
val jdbcuri = "jdbc:mysql://10.66.157.22:4000/jerry_test?user=root&password=3SYz54LS9#^9sBvC&rewriteBatchedStatements=true"
GmeiConfig.writeToJDBCTable(jdbcuri, train, "esmm_data2ffm_train", SaveMode.Overwrite)
val test = rdd.filter(x => x._4 == max_stat_date_str)
.map(x => (x._1,x._2,x._3,column_number("device_id").indexOf(x._1),
column_number("stat_date").indexOf(x._4), column_number("ucity_id").indexOf(x._5),
column_number("cid_id").indexOf(x._6), column_number("clevel1_id").indexOf(x._7),
column_number("ccity_name").indexOf(x._8),x._5,x._6))
.map(x => ((new util.Random).nextInt(2147483647),x._2,x._3,"1:%d:1.0 2:%d:1.0 3:%d:1.0 4:%d:1.0 5:%d:1.0 6:%d:1.0".
format(x._4,x._5,x._6,x._7,x._8,x._9),x._1,x._10,x._11)).zipWithIndex()
.map(x => (x._1._1,x._2,x._1._2,x._1._3,x._1._4,x._1._5,x._1._6,x._1._7))
.map(x => (x._1,x._2+","+x._3+","+x._4+","+x._5,x._6,x._7,x._8)).toDF("number","data","device_id","city_id","cid")
println("test")
test.show(6)
rdd.unpersist()
GmeiConfig.writeToJDBCTable(jdbcuri, test, "esmm_data2ffm_cv", SaveMode.Overwrite)
val esmm_pre_data = sc.sql(
s"""
|select device_id,y,z,stat_date,ucity_id,cid_id,clevel1_id,ccity_name,label
|from esmm_pre_data
""".stripMargin
).repartition(200).na.drop()
esmm_pre_data.persist()
val esmm_pre_cids = esmm_pre_data.select("cid_id").distinct().collect().map(
s => s(0).toString
)
val esmm_pre_city = esmm_pre_data.select("ucity_id").distinct().collect().map(
s => s(0).toString)
val esmm_pre_device = esmm_pre_data.select("device_id").distinct().collect().map(
s => s(0).toString)
val esmm_join_cids = esmm_pre_cids.intersect(column_number("cid_id"))
val esmm_join_city = esmm_pre_city.intersect(column_number("ucity_id"))
val esmm_join_device = esmm_pre_device.intersect(column_number("device_id"))
val rdd_pre = esmm_pre_data.rdd.repartition(200)
.map(x => (x(0).toString,x(1).toString,x(2).toString,x(3).toString,
x(4).toString,x(5).toString,x(6).toString,
x(7).toString,x(8).toString)).filter(x => esmm_join_cids.indexOf(x._6) != -1)
.filter(x => esmm_join_city.indexOf(x._5) != -1).filter(x => esmm_join_device.indexOf(x._1) != -1)
val native_pre = rdd_pre.filter(x => x._9 == "0").map(x => (x._1,x._2,x._3,column_number("device_id").indexOf(x._1),
column_number("stat_date").indexOf(x._4), column_number("ucity_id").indexOf(x._5),
column_number("cid_id").indexOf(x._6), column_number("clevel1_id").indexOf(x._7),
column_number("ccity_name").indexOf(x._8),x._5,x._6))
.map(x => ((new util.Random).nextInt(2147483647),x._2,x._3,"1:%d:1.0 2:%d:1.0 3:%d:1.0 4:%d:1.0 5:%d:1.0 6:%d:1.0".
format(x._4,x._5,x._6,x._7,x._8,x._9),x._1,x._10,x._11)).zipWithIndex()
.map(x => (x._1._1,x._2,x._1._2,x._1._3,x._1._4,x._1._5,x._1._6,x._1._7))
.map(x => (x._1,x._2+","+x._3+","+x._4+","+x._5,x._6,x._7,x._8)).toDF("number","data","device_id","city_id","cid")
println("pre")
native_pre.show(6)
GmeiConfig.writeToJDBCTable(jdbcuri, native_pre, "esmm_data2ffm_infer_native", SaveMode.Overwrite)
val nearby_pre = rdd_pre.filter(x => x._9 == "1").map(x => (x._1,x._2,x._3,column_number("device_id").indexOf(x._1),
column_number("stat_date").indexOf(x._4), column_number("ucity_id").indexOf(x._5),
column_number("cid_id").indexOf(x._6), column_number("clevel1_id").indexOf(x._7),
column_number("ccity_name").indexOf(x._8),x._5,x._6))
.map(x => ((new util.Random).nextInt(2147483647),x._2,x._3,"1:%d:1.0 2:%d:1.0 3:%d:1.0 4:%d:1.0 5:%d:1.0 6:%d:1.0".
format(x._4,x._5,x._6,x._7,x._8,x._9),x._1,x._10,x._11)).zipWithIndex()
.map(x => (x._1._1,x._2,x._1._2,x._1._3,x._1._4,x._1._5,x._1._6,x._1._7))
.map(x => (x._1,x._2+","+x._3+","+x._4+","+x._5,x._6,x._7,x._8)).toDF("number","data","device_id","city_id","cid")
println("pre")
nearby_pre.show(6)
GmeiConfig.writeToJDBCTable(jdbcuri, nearby_pre, "esmm_data2ffm_infer_nearby", SaveMode.Overwrite)
sc.stop()
}
}
}