一：解釋

官網：https://spark.apache.org/docs/latest/sql-getting-started.html
這種場景是生活中的常態
When case classes cannot be defined ahead of time (for example, the structure of records is encoded in a string, or a text dataset will be parsed and fields will be projected differently for different users), a DataFrame can be created programmatically with three steps.

Create an RDD of Rows from the original RDD;
Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1.
Apply the schema to the RDD of Rows via createDataFrame method provided by SparkSession.

二：操作

package g5.learning

import org.apache.spark.sql.types.{LongType, StructField, StructType,StringType}
import org.apache.spark.sql.{Row, SparkSession}

object DFApp {
  def main(args: Array[String]): Unit = {
    val  sparksession= SparkSession.builder().appName("DFApp")
      .master("local[2]")
      .getOrCreate()

     //inferReflection(sparksession)

    programmatically(sparksession)
    sparksession.stop()
  }



  //第二種方法是程式設計的形式
  def programmatically(sparkSession: SparkSession)={
    val info = sparkSession.sparkContext.textFile("file:///E:\\data.txt")
    //Create an RDD of Rows from the original RDD;
    import sparkSession.implicits._
    val rdd =info.map(_.split("\t")).map(x=>Row(x(0),x(1),x(2).toLong))
    //這個rdd是個row型別
    //Create the schema
    val struct =StructType(Array(
      StructField("ip", StringType,true),
      StructField("time", StringType,false) ,
        StructField("responseSize",LongType,false)
    ))
    //Apply the schema to the RDD of Rows via createDataFrame method provided by SparkSession.
   val df = sparkSession.createDataFrame(rdd,struct)
    df.show()
        }
  }
 

三：注意

1.這種型別要比官網更好，適合大部分型別
2.要注意運算元的轉換（開始的時候，找運算元也是遇到了很多麻煩，運算元很多，要注意挑選自己的需求）

四：結果