修改esmm测试项目的预测集sql

eda/feededa/src/main/scala/com/gmei/esmm_app_list.scala

+package com.gmei
+
+
+import java.io.Serializable
+import java.time.LocalDate
+
+import com.gmei.lib.AbstractParams
+import org.apache.log4j.{Level, Logger}
+import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession, TiContext}
+import scopt.OptionParser
+
+import scala.util.parsing.json.JSON
+
+
+object esmm_app_list {
+
+  Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
+  Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)
+
+  case class Params(env: String = "dev",
+                    date: String = "2018-08-01"
+                   ) extends AbstractParams[Params] with Serializable
+
+  val defaultParams = Params()
+
+  val parser = new OptionParser[Params]("Feed_EDA") {
+    head("WeafareStat")
+    opt[String]("env")
+      .text(s"the databases environment you used")
+      .action((x, c) => c.copy(env = x))
+    opt[String] ("date")
+      .text(s"the date you used")
+      .action((x,c) => c.copy(date = x))
+    note(
+      """
+        |For example, the following command runs this app on a tidb dataset:
+        |
+        | spark-submit --class com.gmei.WeafareStat ./target/scala-2.11/feededa-assembly-0.1.jar \
+      """.stripMargin +
+        s"|   --env ${defaultParams.env}"
+    )
+  }
+
+  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 yesterday = LocalDate.now().minusDays(118).toString.replace("-","")
+    println(yesterday)
+
+    get_applist(sc,yesterday)
+    sc.stop()
+  }}
+
+  def get_applist(spark:SparkSession,yesterday:String): Unit ={
+    val df = spark.sql(
+      s"""
+         |select device["device_id"] as device_id,cl_type,params["installed_app_info"]
+         |from online.tl_hdfs_maidian_view where partition_date = $yesterday
+         |and action = 'user_installed_all_app_info'
+       """.stripMargin).dropDuplicates("device_id")
+    df.persist()
+    val ti = new TiContext(spark)
+    ti.tidbMapTable(dbName = "jerry_test",tableName = "device_app_list")
+    val old = spark.sql("select device_id from device_app_list").collect().map(x => x(0).toString)
+
+    import spark.implicits._
+    val android = df.rdd.map(x => (x(0).toString,x(1).toString,x(2).toString))
+      .filter(x => x._2 == "android").map(x => (x._1,x._2,parse_json(x._3),yesterday))
+
+    val ios = df.rdd.map(x => (x(0).toString,x(1).toString,x(2).toString))
+      .filter(x => x._2 == "ios").map(x => (x._1,x._2,x._3,yesterday))
+
+    val rdd = android.union(ios)
+
+    val new_user = rdd.filter(x => old.indexOf(x._1)== -1)
+      .toDF("device_id","os","app_list","update_date")
+    if (new_user.take(1).nonEmpty){
+      val jdbc = "jdbc:mysql://10.66.157.22:4000/jerry_test?user=root&password=3SYz54LS9#^9sBvC&rewriteBatchedStatements=true"
+      GmeiConfig.writeToJDBCTable(jdbc, new_user,"device_app_list", SaveMode.Append)
+      val tecent_jdbc = "jdbc:mysql://152.136.44.138:4000/jerry_test?user=root&password=3SYz54LS9#^9sBvC&rewriteBatchedStatements=true"
+      GmeiConfig.writeToJDBCTable(tecent_jdbc, new_user,"device_app_list", SaveMode.Append)
+
+    }else{
+      println("没有新用户需要写入")
+    }
+    df.unpersist()
+
+
+
+
+  }
+  def parse_json(str:String): String ={
+    var t = List[Map[String, Any]]()
+    val result = JSON.parseFull(str)
+    result match {
+      case Some(b: List[Map[String, Any]]) => t = t ++ b
+      case None => println("Parsing failed")
+      case other => println("Unknown data structure: " + other)
+    }
+    var x = List[String]()
+    if (t.nonEmpty){
+      for (i <- t){
+        x = x:+i("appName").toString
+      }
+    }
+    x.mkString(",")
+  }
+}
+

tensnsorflow/es/feature.py

@@ -143,7 +143,7 @@ def get_predict(date,value_map,app_list_map,level2_map,level3_map):
           "left join cid_type_top c on e.device_id = c.device_id " \
           "left join cid_time_cut cut on e.cid_id = cut.cid " \
           "left join device_app_list dl on e.device_id = dl.device_id " \
-          "left join diary_feat feat on e.cid_id = feat.diary_id"
+          "left join diary_feat feat on e.cid_id = feat.diary_id limit 600"
     df = con_sql(db, sql)
     df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
                             6: "device_type", 7: "manufacturer", 8: "channel", 9: "top",10: "device_id",