更新-从 Spark 1.6 开始，您可以简单地使用内置的csv数据源:

Update - as of Spark 1.6, you can simply use the built-in csv data source:

spark: SparkSession = // create the Spark Session
val df = spark.read.csv("file.txt")

您还可以使用各种选项来控制CSV解析，例如:

You can also use various options to control the CSV parsing, e.g.:

val df = spark.read.option("header", "false").csv("file.txt")

对于Spark版本< 1.6 : 最简单的方法是使用 spark-csv -将其包含在依赖项中并遵循自述文件，允许设置自定义定界符(;)，可以读取CSV标头(如果有)，并且可以推断模式 types (这需要额外扫描数据).

For Spark version < 1.6: The easiest way is to use spark-csv - include it in your dependencies and follow the README, it allows setting a custom delimiter (;), can read CSV headers (if you have them), and it can infer the schema types (with the cost of an extra scan of the data).

或者，如果您知道该模式，则可以创建一个表示该模式的案例类，然后将RDD元素映射到该类的实例中，然后再转换为DataFrame，例如:

Alternatively, if you know the schema you can create a case-class that represents it and map your RDD elements into instances of this class before transforming into a DataFrame, e.g.:

case class Record(id: Int, name: String)

val myFile1 = myFile.map(x=>x.split(";")).map {
  case Array(id, name) => Record(id.toInt, name)
} 

myFile1.toDF() // DataFrame will have columns "id" and "name"