RDD转化为DataFrames的两种创建方式

1.通过反射的方式

         Scala的接口为SparkSQL提供了RDD通过转换成样例类，然后自动的转换成DataFrame，样例类定义了表的模式，使用反射读取case类参数的名称，并成为列的名称。case类也可以嵌套，或者包含复杂类型，如序列或数组。RDD可以隐式转换为一个DataFrame，然后被注册为一个表。表可用于后续SQL语句中。

// sc is an existing SparkContext
.val sqlContext = new org.apache.spark.sql.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,
// 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( "examples/src/main/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)

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{DataFrame, SQLContext}
case class Person(val name:String,val age:Integer)

object RDD2DataFrameByReflection {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("sparkSQL").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    val fileRDD: RDD[String] = sc.textFile("examples/src/main/resources/people.txt")

    val personRDD: RDD[Person] = fileRDD.map(_.split(",")).map(p => Person(p(0).trim,p(1).trim.toInt))

    import sqlContext.implicits._
    val df: DataFrame = personRDD.toDF()
    df.registerTempTable("student")

    val result: DataFrame = sqlContext.sql("select *from student")
    result.show()
    result.map(t =>"name:"+t(0)).foreach(println)
    result.map(_.getAs[String]("name")).foreach(println)
    result.map(_.getValuesMap[Any](List("name","age"))).foreach(println)

  }
}

2.通过将RDD转化成Row的方式

       当类不能被提前定义的时候（例如：结构化的记录被加密成是一个字符串，或者文本数据集将被解析给不同的用户使用）一个DataFrame的创建呀经历三个步骤：

     （1）从原始的RDD创建一个数据类型是Row的RDD

     （2）创建一个schema的结构和之前创建的Row RDD要匹配

      （3）将row RDD转换成DataFrame

// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// Create an RDD
val people = sc.textFile("examples/src/main/resources/people.txt")
// The schema is encoded in a string
val schemaString = "name age"
// Import Row.i
mport org.apache.spark.sql.Row;
// Import Spark SQL data types
import org.apache.spark.sql.types.{StructType,StructField,StringType};
// Generate the schema based on the string of schema
val schema =
  StructType(
    schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))
// Convert records of the RDD (people) to Rows.
val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim))
// Apply the schema to the RDD.
val peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema)
// Register the DataFrames as a table
.peopleDataFrame.registerTempTable("people")
// SQL statements can be run by using the sql methods provided by sqlContext.
val results = sqlContext.sql("SELECT name FROM people")
// 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 or by field name.
results.map(t => "Name: " + t(0)).collect().foreach(println)

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.{IntegerType, StructField, StructType,StringType}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{DataFrame, Row, SQLContext}

object RDD2DataFrameByProgrammatically {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("sparkSQL").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)

    val fileRDD: RDD[String] = sc.textFile("examples/src/main/resources/people.txt")
    val rowRDD: RDD[Row] = fileRDD.map(_.split(",")).map(p => Row(p(0).trim,p(1).trim.toInt))
     
    val schema = StructType(
       Array(
           StructField("name",StringType,true) ,
           StructField("age",IntegerType,true)
       )

    )
    val df: DataFrame = sqlContext.createDataFrame(rowRDD,schema)
    df.registerTempTable("student")
    sqlContext.sql("select *from student").show()



  }
}