据我所知，从1.6开始，没有什么改变，而如何在Spark 1.6中的数据集中存储自定义对象是唯一可用的选项，但是您的当前代码应该适用于产品类型的默认编码器。

为了获得一些洞察力，为什么你的代码在1.x中工作，可能在2.0.0中不起作用，你必须检查签名1.x DataFrame.map 是一种方法，它采用函数 Row =>将 RDD [Row] 转换为 RDD [T] 。

在2.0.0中 DataFrame.map 采用类型为 Row =>也可以将 Dataset [Row] （aka DataFrame ）转换为数据集[T] 因此 T 需要一个 Encoder 。如果你想获得旧行为，你应该使用 RDD 显式地：

  df.rdd.map（row => ???）
 

Spark Datasets move away from Row's to Encoder's for Pojo's/primitives. The Catalyst engine uses an ExpressionEncoder to convert columns in a SQL expression. However there do not appear to be other subclasses of Encoder available to use as a template for our own implementations.

Here is an example of code that is happy in Spark 1.X / DataFrames that does not compile in the new regime:

//mapping each row to RDD tuple
df.map(row => {
    var id: String = if (!has_id) "" else row.getAs[String]("id")
    var label: String = row.getAs[String]("label")
    val channels  : Int = if (!has_channels) 0 else row.getAs[Int]("channels")
    val height  : Int = if (!has_height) 0 else row.getAs[Int]("height")
    val width : Int = if (!has_width) 0 else row.getAs[Int]("width")
    val data : Array[Byte] = row.getAs[Any]("data") match {
      case str: String => str.getBytes
      case arr: Array[Byte@unchecked] => arr
      case _ => {
        log.error("Unsupport value type")
        null
      }
    }
    (id, label, channels, height, width, data)
  }).persist(StorageLevel.DISK_ONLY)

}

We get a compiler error of

Error:(56, 11) Unable to find encoder for type stored in a Dataset.
Primitive types (Int, String, etc) and Product types (case classes) are supported 
by importing spark.implicits._  Support for serializing other types will be added in future releases.
    df.map(row => {
          ^

So then somehow/somewhere there should be a means to

• Define/implement our custom Encoder
• Apply it when performing a mapping on the DataFrame (which is now a Dataset of type Row)
• Register the Encoder for use by other custom code

I am looking for code that successfully performs these steps.

As far as I am aware nothing really changed since 1.6 and the solutions described in How to store custom objects in a Dataset in Spark 1.6 are the only available options. Nevertheless your current code should work just fine with default encoders for product types.

To get some insight why your code worked in 1.x and may not work in 2.0.0 you'll have to check the signatures. In 1.x DataFrame.map is a method which takes function Row => T and transforms RDD[Row] into RDD[T].

In 2.0.0 DataFrame.map takes a function of type Row => T as well, but transforms Dataset[Row] (a.k.a DataFrame) into Dataset[T] hence T requires an Encoder. If you want to get the "old" behavior you should use RDD explicitly:

df.rdd.map(row => ???)