在Spark数据帧中爆炸嵌套的Struct

Question

我正在研究Databricks 示例.数据框架的架构如下所示:

> 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)

在该示例中,他们将展示如何将employees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)

我如何使用department列执行类似的操作(即在数据框中添加两个名为"id"和"name"的列)？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

你可以使用这样的东西:

var explodeDF = explodeDF.withColumn("id", explodeDF("department.id"))
explodeDeptDF = explodeDeptDF.withColumn("name", explodeDeptDF("department.name"))

你帮助过我和这些问题:

• 在Spark中展平行
• Spark 1.4.1 DataFrame爆炸JSON对象列表

Answer 2

我认为，最优雅的解决方案是使用select运算符对Struct进行星标展开，如下所示：

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

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