Spark SQL读parquet文件及保存

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.hive._

/**
  * Created by soyo on 17-10-12.
  */
case class Persons(name:String,age:Int)
case class Record(key: Int, value: String)

object rdd_to_dataframe_parquet {
  val warehouseLocation = "file:${system:user.dir}/spark-warehouse"
          val spark=SparkSession.builder().config("spark.sql.warehouse.dir",warehouseLocation).enableHiveSupport().getOrCreate()
          import spark.implicits._
  def main(args: Array[String]): Unit = {
        val df =spark.sparkContext.textFile("file:///home/soyo/桌面/spark编程测试数据/people.txt")
           .map(_.split(",")).map(x=>Person(x(0),x(1).trim.toInt)).toDF()
             df.write.parquet("file:///home/soyo/桌面/spark编程测试数据/people.parquet")
     val parquetFile=spark.read.parquet("file:///home/soyo/桌面/spark编程测试数据/people.parquet")
        parquetFile.createOrReplaceTempView("people")
      val result=spark.sql("select * from people")
    result.show()
spark.stop()

 补充：需要多数据源整合查询时：

 val data=result1.union(result2)

data.createOrReplaceTempView("data")   之后执行后续查询