Spark-sql结果保存指定位置

//1.读取数据,将每一行的数据使用列分隔符分割
val lineRDD = sc.textFile("hdfs://node1.itcast.cn:9000/person.txt", 1).map(_.split(" "))


//2.定义case class(相当于表的schema)
case class Person(id:Int, name:String, age:Int)


//3.导入隐式转换,在当前版本中可以不用导入
import sqlContext.implicits._


//4.将lineRDD转换成personRDD
val personRDD = lineRDD.map(x => Person(x(0).toInt, x(1), x(2).toInt))


//5.将personRDD转换成DataFrame
val personDF = personRDD.toDF


6.对personDF进行处理




#(SQL风格语法)
personDF.registerTempTable("t_person")
sqlContext.sql("select * from t_person order by age desc limit 2").show
sqlContext.sql("desc t_person").show
val result = sqlContext.sql("select * from t_person order by age desc")


7.保存结果
result.save("hdfs://hadoop.itcast.cn:9000/sql/res1")
result.save("hdfs://hadoop.itcast.cn:9000/sql/res2", "json")


#以JSON文件格式覆写HDFS上的JSON文件
import org.apache.spark.sql.SaveMode._
result.save("hdfs://hadoop.itcast.cn:9000/sql/res2", "json" , Overwrite)


8.重新加载以前的处理结果(可选)
sqlContext.load("hdfs://hadoop.itcast.cn:9000/sql/res1")
sqlContext.load("hdfs://hadoop.itcast.cn:9000/sql/res2", "json")



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