（1）示例数据：`people.txt

Michael,29
Andy,30
Justin,19

```
（2）示例代码

```scala
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}

object RDDtoDataFrame {

  case class People(name:String, age:Int)

  def main(args: Array[String]): Unit = {
    val spark:SparkSession = SparkSession.builder()
      .master("local[4]")
      .appName(this.getClass.getName)
      .getOrCreate()
    val sc:SparkContext = spark.sparkContext
    import spark.implicits._

    /***** 方式1：将RDD切割，然后关联case class，最后转换成DataFrame *****/
    val peopleRDD:RDD[String] = sc.textFile("file:///E:\\hadoop\\input\\people.txt")
    // 对RDD切割并关联到case class
    val peopleDF:DataFrame = peopleRDD
      .map(_.split(","))
      .map(x=>People(x(0), x(1).toInt))
      .toDF()

    peopleDF.show()
    // +-------+---+
    // |   name|age|
    // +-------+---+
    // |Michael| 29|
    // |   Andy| 30|
    // | Justin| 19|
    // +-------+---+

    // 创建临时表
    peopleDF.createOrReplaceTempView("people")
    spark.sql("select * from people where name='Andy'").show()
    // +----+---+
    // |name|age|
    // +----+---+
    // |Andy| 30|
    // +----+---+

    /***** 方式2：将RDD通过和Schema信息关联, 得到DataFrame *****/
    // 1. 通过StructType构建Schema
    // StructFile(字段名, 字段类型, 字段的值是否可以为null)，默认为true可以为null
    val schema = StructType(Array(
      StructField("name", StringType, true),
      StructField("age", IntegerType, true)
    ))

    // 2. 将每行字符串切割,切割成Array, 然后将其转化为RDD[Row]类型
    val peopleRowRDD:RDD[Row] = peopleRDD
      .map(_.split(","))
      .map(x=>Row(x(0), x(1).toInt))

    // 3. 将Row类型的RDD和Schema信息关联, 创建一个DataFrame
    val df:DataFrame = spark.createDataFrame(peopleRowRDD, schema)

    df.createOrReplaceTempView("people2")
    spark.sql("select * from people2").show()
    // +-------+---+
    // |   name|age|
    // +-------+---+
    // |Michael| 29|
    // |   Andy| 30|
    // | Justin| 19|
    // +-------+---+
  }
}

```