scala - 分解 Spark 数据框中的嵌套结构

Question

我正在研究 Databricks 示例。数据框的架构如下所示:

> parquetDF.printSchema
root
|-- department: struct (nullable = true)
|    |-- id: string (nullable = true)
|    |-- name: string (nullable = true)
|-- employees: array (nullable = true)
|    |-- element: struct (containsNull = true)
|    |    |-- firstName: string (nullable = true)
|    |    |-- lastName: string (nullable = true)
|    |    |-- email: string (nullable = true)
|    |    |-- salary: integer (nullable = true)

在示例中，他们展示了如何将员工列分解为 4 个附加列:

val explodeDF = parquetDF.explode($"employees") { 
case Row(employee: Seq[Row]) => employee.map{ employee =>
  val firstName = employee(0).asInstanceOf[String]
  val lastName = employee(1).asInstanceOf[String]
  val email = employee(2).asInstanceOf[String]
  val salary = employee(3).asInstanceOf[Int]
  Employee(firstName, lastName, email, salary)
 }
}.cache()
display(explodeDF)

我将如何对部门列执行类似的操作(即向数据框中添加两个名为“id”和“name”的附加列)？这些方法并不完全相同，我只能弄清楚如何使用以下方法创建一个全新的数据框:

val explodeDF = parquetDF.select("department.id","department.name")
display(explodeDF)

如果我尝试:

val explodeDF = parquetDF.explode($"department") { 
  case Row(dept: Seq[String]) => dept.map{dept => 
  val id = dept(0) 
  val name = dept(1)
  } 
}.cache()
display(explodeDF)

我收到警告和错误:

<console>:38: warning: non-variable type argument String in type pattern Seq[String] is unchecked since it is eliminated by erasure
            case Row(dept: Seq[String]) => dept.map{dept => 
                           ^
<console>:37: error: inferred type arguments [Unit] do not conform to    method explode's type parameter bounds [A <: Product]
  val explodeDF = parquetDF.explode($"department") { 
                                   ^

Answer 1

在我看来，最优雅的解决方案是使用选择运算符星形扩展结构，如下所示:

var explodedDf2 = explodedDf.select("department.*","*")

https://docs.databricks.com/spark/latest/spark-sql/complex-types.html