spark将RDD解析字段转为dataFream

在spark中，将datafream数据源解析为RDD后，通过添加对应的字段信息，再转换为dataFream，直接上代码，记录下

package com.ku.test

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Dataframe, Row, SparkSession}

import scala.collection.mutable

object TestAddField1 {


  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder()
      .appName("test")
      .master("local")
      .getOrCreate()

    spark.sparkContext.setLogLevel("WARN")

    import spark.implicits._
    val sourceFile: Dataframe = Seq(
      (1, "张三_24_男_上海市"),
      (2, "李四_33_女_北京市"),
      (2, "王四_23_女_广州市"),
      (2, "赵四_53_女_西安市"),
      (2, "高四_32_女_成都市")
    ).toDF("id", "message")
    sourceFile.show()

    val dataRDD: RDD[Row] = sourceFile.rdd.map(
      row => {
        val message = row.getAs[String]("message")

        var testSeq: Seq[Any] = Seq()

        message.split("_").foreach(dv => {
          testSeq = testSeq.:+(dv)
        })
        Row.fromSeq(testSeq)
      })
    dataRDD.foreach(row => println(row))

    val fieldsConf = new mutable.linkedHashMap[String, DataType]()
    fieldsConf.put("name", StringType)
    fieldsConf.put("age", StringType)
    fieldsConf.put("gender", StringType)
    fieldsConf.put("address", StringType)

    val fields = fieldsConf.map(fieldName => {
      StructField(fieldName._1, fieldName._2)
    }).toArray[StructField]
    val schema = StructType(fields)

    spark.createDataframe(dataRDD, schema).show()


  }
}

 上面有三次输出数据，格式如下：

+---+-----------------+
| id|          message|
+---+-----------------+
|  1|张三_24_男_上海市|
|  2|李四_33_女_北京市|
|  2|王四_23_女_广州市|
|  2|赵四_53_女_西安市|
|  2|高四_32_女_成都市|
+---+-----------------+

[张三,24,男,上海市]
[李四,33,女,北京市]
[王四,23,女,广州市]
[赵四,53,女,西安市]
[高四,32,女,成都市]
+----+---+------+-------+
|name|age|gender|address|
+----+---+------+-------+
|张三| 24|    男| 上海市|
|李四| 33|    女| 北京市|
|王四| 23|    女| 广州市|
|赵四| 53|    女| 西安市|
|高四| 32|    女| 成都市|
+----+---+------+-------+


Process finished with exit code 0