Defining a UDF that accepts an Array of objects in a Spark DataFrame?

When working with Spark's DataFrames, User Defined Functions (UDFs) are required for mapping data in columns. UDFs require that argument types are explicitly specified. In my case, I need to manipulate a column that is made up of arrays of objects, and I do not know what type to use. Here's an example:

import sqlContext.implicits._

// Start with some data. Each row (here, there's only one row) 
// is a topic and a bunch of subjects
val data = sqlContext.read.json(sc.parallelize(Seq(
  """
  |{
  |  "topic" : "pets",
  |  "subjects" : [
  |    {"type" : "cat", "score" : 10},
  |    {"type" : "dog", "score" : 1}
  |  ]
  |}
  """)))

It's relatively straightforward to use the built-in org.apache.spark.sql.functions to perform basic operations on the data in the columns

import org.apache.spark.sql.functions.size
data.select($"topic", size($"subjects")).show

+-----+--------------+
|topic|size(subjects)|
+-----+--------------+
| pets|             2|
+-----+--------------+

and it's generally easy to write custom UDFs to perform arbitrary operations

import org.apache.spark.sql.functions.udf
val enhance = udf { topic : String => topic.toUpperCase() }
data.select(enhance($"topic"), size($"subjects")).show 

+----------+--------------+
|UDF(topic)|size(subjects)|
+----------+--------------+
|      PETS|             2|
+----------+--------------+

But what if I want to use a UDF to manipulate the array of objects in the "subjects" column? What type do I use for the argument in the UDF? For example, if I want to reimplement the size function, instead of using the one provided by spark:

val my_size = udf { subjects: Array[Something] => subjects.size }
data.select($"topic", my_size($"subjects")).show

Clearly Array[Something] does not work... what type should I use!? Should I ditch Array[] altogether? Poking around tells me scala.collection.mutable.WrappedArray may have something to do with it, but still there's another type I need to provide.

What you're looking for is Seq[o.a.s.sql.Row]:

import org.apache.spark.sql.Row

val my_size = udf { subjects: Seq[Row] => subjects.size }

Explanation:

• Current representation of ArrayType is, as you already know, WrappedArray so Array won't work and it is better to stay on the safe side.
• According to the official specification, the local (external) type for StructType is Row. Unfortunately it means that access to the individual fields is not type safe.

Notes:

• To create struct in Spark < 2.3, function passed to udf has to return Product type (Tuple* or case class), not Row. That's because corresponding udf variants depend on Scala reflection:

  Defines a Scala closure of n arguments as user-defined function (UDF). The data types are automatically inferred based on the Scala closure's signature.

• In Spark >= 2.3 it is possible to return Row directly, as long as the schema is provided.

  def udf(f: AnyRef, dataType: DataType): UserDefinedFunction Defines a deterministic user-defined function (UDF) using a Scala closure. For this variant, the caller must specify the output data type, and there is no automatic input type coercion.

  See for example How to create a Spark UDF in Java / Kotlin which returns a complex type?.