Encoders.product[of a scala trait].schema in spark

Question

How to create a schema for spark from a trait? Considering a trait:

trait A{
val name:String
val size:String
}

As:

Encoders.product[A].schema

gives:

Error:type arguments do not conform to method product's type parameter bounds [T <: Product]

Also the number of fields will be more then the limit of case class parameters > 200

Answer 1

Case class do supports more than 22 columns, try creating outside all other class/object. If your need is to create a dataframe schema with large number of fields, this should work.

val schema: StructType = StructType(
    Array(
      StructField(name = "name", StringType),
      StructField(name = "size", StringType)
    )
 )
val data = Seq(Row("Ramanan","29"))
spark.createDataFrame(spark.sparkContext.parallelize(data),schema).show()

Answer 2

I cannot give you all the details why this is not working but I am proposing a slightly alternative solution that we frequently use in our Scala Spark projects.

The signature of Encoders.product looks like

product[T <: scala.Product](implicit evidence$5 : scala.reflect.runtime.universe.TypeTag[T])

which means tt expects a class that extends Product trait and an implicit TypeTag.

Instead of a trait, you could create a case class as case classes are extending Product (and Serializable ) automatically.

In order to get a schema you could do:

case class A (
  val name: String,
  val size: String
)

def createSchema[T <: Product]()(implicit tag: scala.reflect.runtime.universe.TypeTag[T]) = Encoders.product[T].schema
val schema = createSchema[A]()
schema.printTreeString()

/*
root
 |-- name: string (nullable = true)
 |-- size: string (nullable = true)
*/

As said in the beginning, I can't explain all the details, just provide a working solution and hoping it fit your needs.