Exploded Struct in Spark

Question

I have DataFrame with following schema:

 |-- data: struct (nullable = true)
 |    |-- asin: string (nullable = true)
 |    |-- customerId: long (nullable = true)
 |    |-- eventTime: long (nullable = true)
 |    |-- marketplaceId: long (nullable = true)
 |    |-- rating: long (nullable = true)
 |    |-- region: string (nullable = true)
 |    |-- type: string (nullable = true)
 |-- uploadedDate: long (nullable = true)

I want to explode the struct such that all elements like asin, customerId, eventTime become the columns in DataFrame. I tried explode function but it works on Array not on struct type. Is it possible to convert the able data frame to below dataframe:

     |-- asin: string (nullable = true)
     |-- customerId: long (nullable = true)
     |-- eventTime: long (nullable = true)
     |-- marketplaceId: long (nullable = true)
     |-- rating: long (nullable = true)
     |-- region: string (nullable = true)
     |-- type: string (nullable = true)
     |-- uploadedDate: long (nullable = true)

Answer 1

It's quite simple:

val newDF = df.select("uploadedDate", "data.*");

You tell to select uploadedDate and then all subelements of field data

Example:

scala> case class A(a: Int, b: Double)
scala> val df = Seq((A(1, 1.0), "1"), (A(2, 2.0), "2")).toDF("data", "uploadedDate")
scala> val newDF = df.select("uploadedDate", "data.*")
scala> newDF.show()
+------------+---+---+
|uploadedDate|  a|  b|
+------------+---+---+
|           1|  1|1.0|
|           2|  2|2.0|
+------------+---+---+

scala> newDF.printSchema()
root
 |-- uploadedDate: string (nullable = true)
 |-- a: integer (nullable = true)
 |-- b: double (nullable = true)