Spark RDD轉換爲DataFrame

 
#構造case class，利用反射機制隱式轉換
scala> import spark.implicits._
scala> val rdd= sc.textFile("input/textdata.txt")
scala> case class Person(id:Int,name:String)
scala> val df = rdd.map(_.split(",")).map(x=>Person(x(0).toInt,x(1))).toDF
scala> df.show
+---+--------+
| id| name|
+---+--------+
| 1|zhangsan|
| 2| lisi|
| 3| wangwu|
| 4| zhaoliu|
+---+--------+

#經過schema，Row構造dataframe
scala> import org.apache.spark.sql.types._
scala> import org.apache.spark.sql.Row
scala> val structFields = Array(StructField("id",IntegerType,true),StructField("name",StringType,true))
scala> val structType = StructType(structFields) #建立schema結構
scala> val lines= sc.textFile("input/textdata.txt")
scala> val rdd = lines.map(_.split(",")).map(x=>Row(x(0).toInt,x(1))) #建立RDD[Row]
scala> val df = spark.createDataFrame(rdd,structType) #經過RDD[Row],schema構建DataFrame
scala> df.show
+---+--------+
| id| name|
+---+--------+
| 1|zhangsan|
| 2| lisi|
| 3| wangwu|
| 4| zhaoliu|
+---+--------+

　　

cat textdata.txt
1,zhangsan
2,lisi
3,wangwu
4,zhaoliu