value toDF is not a member of org.apache.spark.rdd

问题:

Exception :

val people = sc.textFile("resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)).toDF()
value toDF is not a member of org.apache.spark.rdd.RDD[Person]

Here is TestApp.scala file:

package main.scala

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf


case class Record1(k: Int, v: String)


object RDDToDataFramesWithCaseClasses {

    def main(args: Array[String]) {
        val conf = new SparkConf().setAppName("Simple Spark SQL Application With RDD To DF")

        // sc is an existing SparkContext.
        val sc = new SparkContext(conf)

        val sqlContext = new SQLContext(sc)

        // this is used to implicitly convert an RDD to a DataFrame.
        import sqlContext.implicits._

        // Define the schema using a case class.
        // Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,package main.scala

And TestApp.scala

import org.apache.spark.SparkContext    
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf


case class Record1(k: Int, v: String)


object RDDToDataFramesWithCaseClasses {
    def main(args: Array[String]) {
        val conf = new SparkConf().setAppName("RDD To DF")

        // sc is an existing SparkContext.
        // you can use custom classes that implement the Product interface.
        case class Person(name: String, age: Int)

        // Create an RDD of Person objects and register it as a table.
        val people = sc.textFile("resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt)).toDF() 
        people.registerTempTable("people")

        // SQL statements can be run by using the sql methods provided by sqlContext.
        val teenagers = sqlContext.sql("SELECT name, age FROM people WHERE age >= 13 AND age <= 19")

        // The results of SQL queries are DataFrames and support all the normal RDD operations.
        // The columns of a row in the result can be accessed by field index:
        teenagers.map(t => "Name: " + t(0)).collect().foreach(println)

        // or by field name:
        teenagers.map(t => "Name: " + t.getAs[String]("name")).collect().foreach(println)

        // row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]

        teenagers.map(_.getValuesMap[Any](List("name", "age"))).collect().foreach(println)

        // Map("name" -> "Justin", "age" -> 19)

    }
}

And SBT File

name := "SparkScalaRDBMS"
version := "1.0"
scalaVersion := "2.11.7"
libraryDependencies += "org.apache.spark" %% "spark-core" % "1.5.1"
libraryDependencies += "org.apache.spark" %% "spark-sql" % "1.5.1"

回答1:

now i found the reason, you should define case class in the object and outof the main function. look at here

Ok, I finally fixed the issue. 2 things needed to be done:

1. Import implicits: Note that this should be done only after an instance of org.apache.spark.sql.SQLContext is created. It should be written as:

   val sqlContext= new org.apache.spark.sql.SQLContext(sc)

   import sqlContext.implicits._

2. Move case class outside of the method: case class, by use of which you define the schema of the DataFrame, should be defined outside of the method needing it. You can read more about it here: https://issues.scala-lang.org/browse/SI-6649

回答2:

In Spark 2, you need to import the implicits from the SparkSession:

val spark = SparkSession.builder().appName(appName).getOrCreate()
import spark.implicits._

See the Spark documentation for more options when creating the SparkSession.

回答3:

i.  scala> case class Employee(id: Int, name: String, age: Int)
defined class Employee
scala> val sqlContext= new org.apache.spark.sql.SQLContext(sc)
warning: there was one deprecation warning; re-run with -deprecation for details
sqlContext: org.apache.spark.sql.SQLContext = org.apache.spark.sql.SQLContext@1f94e3a

scala> import sqlContext.implicits._
import sqlContext.implicits._
scala> var empl1=   empl.map(_.split(",")).map(e=>Employee(e(0).trim.toInt,e(1),e(2).trim.toInt)).toDF
empl1: org.apache.spark.sql.DataFrame = [id: int, name: string ... 1 more field]
scala> val allrecords = sqlContext.sql("SELECT * FROM employee")
allrecords: org.apache.spark.sql.DataFrame = [id: int, name: string ... 1 more field]

scala> allrecords.show();
+----+--------+---+
|  id|    name|age|
+----+--------+---+
|1201|  satish| 25|
|1202| krishna| 28|
|1203|   amith| 39|
|1204|   javed| 23|
|1205|  prudvi| 23|
+----+--------+---+

回答4:

There are two problems with your code

1. You need to import import sqlContext.implicits._ for Spark V 1.0 or import spark.implicits._ if you are using Spark V 2.0 or above

2. Secondly case class Record1(k: Int, v: String) needs to be inside main function but outside def main(args: Array[String]) { val conf = new SparkConf().setAppName("RDD To DF") …

}