如果列的数量和列的名称是固定的，则可以按以下方式执行：

val columns = rdd.collect.slice(5,15).mkString(",") //  it will hold columnnames
val data = rdd.collect.slice(17,21)
val d = data.mkString("\n").split('\n').toSeq.toDF()
import org.apache.spark.sql.functions._
val dd = d.withColumn("columnX",split($"value","\\|")).withColumn("column1",$"columnx".getItem(0)).withColumn("column2",$"columnx".getItem(1)).withColumn("column3",$"columnx".getItem(2)).withColumn("column4",$"columnx".getItem(3)).withColumn("column5",$"columnx".getItem(4)).withColumn("column6",$"columnx".getItem(5)).withColumn("column8",$"columnx".getItem(7)).withColumn("column10",$"columnx".getItem(8)).withColumn("column11",$"columnx".getItem(9)).drop("columnX","value")
display(dd)

您可以看到如下输出：

你能试试这个吗？

import spark.implicits._ // Add to use `toDS()` and `toDF()`
val rdd = sc.textFile("the above text file")
val columns = rdd.collect.slice(5,16) // `.mkString(",")` is not needed
val dataDS = rdd.collect.slice(5,16)
  .map(_.trim())                           // to remove whitespaces
  .map(s => s.substring(0, s.length - 1))  // to remove last pipe '|'
  .toSeq
  .toDS
val df = spark.read
  .option("header", false)
  .option("delimiter", "|")
  .csv(dataDS)
  .toDF(columns: _*)
df.show(false)

+--------+--------+--------+--------+--------+--------+--------+--------+---------+--------+
|column01|column02|column03|column04|column05|column06|column07|column08|column010|column11|
+--------+--------+--------+--------+--------+--------+--------+--------+---------+--------+
|G0002B  |0       |13      |IS      |LS      |Xys     |Xyz     |12      |23       |48      |
|G0002A  |0       |13      |IS      |LS      |Xys     |Xyz     |12      |23       |45      |
|G0002x  |0       |13      |IS      |LS      |Xys     |Xyz     |12      |23       |48      |
|G0002C  |0       |13      |IS      |LS      |Xys     |Xyz     |12      |23       |48      |
+--------+--------+--------+--------+--------+--------+--------+--------+---------+--------+

打电话 spark.read...csv() 没有模式的方法，可能需要很长时间处理大量数据，因为模式推理（例如。附加读数）。
在这种情况下，您可以如下所示指定模式。

/*
  column01 STRING,
  column02 STRING,
  column03 STRING,
  ...
* /
val schema = columns
  .map(c => s"$c STRING")
  .mkString(",\n")
val df = spark.read
  .option("header", false)
  .option("delimiter", "|")
  .schema(schema)  // schema inferences not occurred
  .csv(dataDS)
// .toDF(columns: _*) => unnecessary when schema is specified