sparkrdd转dataframe的两种方式

package l847164916

import java.sql.{DriverManager, ResultSet}
import java.util.Properties

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Row, SQLContext, SaveMode}
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types._

/**
  * Created by Administrator on 2016/9/2.
  */
object Test {
  def main(args: Array[String]): Unit = {
    val url = "jdbc:mysql://xxxxxxx:3306/hyn_profile"
    val prop = new Properties()
    prop.setProperty("user", "root")
    prop.setProperty("password", "xxxxx")
    prop.setProperty("driver","com.mysql.jdbc.Driver")
    val conf = new SparkConf().setAppName("test").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    val schema = StructType(
      StructField("name",StringType) ::
        StructField("rate1",DoubleType) ::
        StructField("rate2",DoubleType)
        :: Nil
    )
    val rdd1 = sc.textFile("D:/LCH/hellomoto/data").map{r => r.split(" ")}.map{r => (r(0),r(1))}
    val rdd2 = sc.textFile("D:/LCH/hellomoto/data1").map{r => r.split(" ")}.map{r => (r(0),r(1))}
    //1.外连接才会产生option类型
    //2.getOrElse()设置join后空值的默认值,注意类型要与schema匹配
    val rdd = rdd1.fullOuterJoin(rdd2).map{case ((name,(rate1,rate2)))=>(name,rate1,rate2)}.map(r=> Row.apply(r._1,r._2.getOrElse(0.0).toString.toDouble,r._3.getOrElse(0.0).toString.toDouble))
    val df = sqlContext.createDataFrame(rdd,schema)
    df.write.mode(SaveMode.Overwrite).jdbc(url,"test",prop)
    /*
    （二）隐式转换DF：
    import sqlContext.implicits._
    val df = rdd1.fullOuterJoin(rdd2).map{case ((name,(rate1,rate2)))=>(name,rate1,rate2)}.toDF("name","rate1","rate2")
    df.write.mode(SaveMode.Overwrite).jdbc(url,"test",prop)

     */
    }
    }