Scala2.11.8 spark2.3.1 mongodb connector 2.3.0

import java.sql.DriverManager
import com.mongodb.spark._
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
object mongospark20180830consume_amount {

  // 关于记录多个相同字段的处理方法 https://blog.csdn.net/qq_14950717/article/details/62425563
  // https://blog.csdn.net/qq_27234661/article/details/78344435?locationNum=3&fps=1
  def main(args: Array[String]): Unit = {

   // val mgohost = "dds-m5e6e56a3b0cf7b42784-pub.mongodb.rds.aliyuncs.com"
   // spark-submit --driver-class-path /usr/local/jdk/lib/mysql-connector-java-5.1.46.jar   --packages org.mongodb.spark:mongo-spark-connector_2.11:2.3.0 --class  "mongospark20180830consume_amount"  /testdata/u3.jar


    //  "org.mongodb.spark" %% "mongo-spark-connector" % "2.3.0",
  //  val conn = DriverManager.getConnection(url)
    val conf = new SparkConf().setAppName("appName").setMaster("local")
    val sparkConf = new SparkConf().setAppName("adver").setMaster("local[*]")
    val spark = SparkSession.builder().config(sparkConf).enableHiveSupport().getOrCreate()

    val inputUri="mongodb://saas:saas2018yundinglixin@dds-m5e6e56a3b0cf7b42784-pub.mongodb.rds.aliyuncs.com:3717/saas.elemeterPowerHistory"

    val df=spark.read.format("com.mongodb.spark.sql").options(
      Map("spark.mongodb.input.uri" -> inputUri,
        "spark.mongodb.input.partitioner" -> "MongoPaginateBySizePartitioner",
        "spark.mongodb.input.partitionerOptions.partitionKey"  -> "_id",
        "spark.mongodb.input.partitionerOptions.partitionSizeMB"-> "32"))
      .load()

    val currentTimestamp = System.currentTimeMillis()
    val df2 = df.select("time".toString,"uuid".toString,"consume_amount".toString,"room_id".toString)
      .toDF("time","uuid","consume_amount","room_id")


    spark.sql("use saas")
    df2.write.mode("overwrite").saveAsTable("consume_amount20180831")
//       df2.foreach(println)
//

//    val rddf=spark.sql( "select uuid,from_unixtime(cast(`time`/1000 AS bigint),'yyyyMMddHH'),consume_amount from consume where time>=1533115788000").toDF("uuid", "time","consume_amount")
//
  //  spark.sql("use saas")
//
//    rddf.write.saveAsTable("consume_amount20180830")
//   // val p=df.printSchema()
//
//   // val select=spark.sql("select s.sn,s.uuid,e.time,e.consume_amount from staonly2  s join elem e on s.uuid=e.uuid").take(10)
//    val select=spark.sql("select consume_amount from elem limit 5").take(5)
//     select.foreach(println)




  }
}