frameless: Compilation errors when SmartProjecting to a case class of 1 field

s2.scala

package tags

import frameless.{TypedColumn, TypedDataset}
import frameless.ops.SmartProject
import frameless.{CatalystCast, Injection}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

import scala.{App,Int}
import scala.Predef.{require,String}
// Tagged types version
object s2 extends App {

  /**
    * This is a covariant version of Shapeless 2.3.2 tag.
    * @see https://github.com/milessabin/shapeless/blob/shapeless-2.3.2/core/src/main/scala/shapeless/typeoperators.scala#L25
    */
  object covariantTag {
    def apply[U] = new Tagger[U]
    trait Tagged[+U]
    class Tagger[U] {
      def apply[T](t : T) : T @@ U = t.asInstanceOf[T @@ U]
    }
    type @@[+T, +U] = T with Tagged[U]
  }

  import covariantTag.@@

  implicit def intTagInjection[Tag] = new Injection[Int @@ Tag, Int] {
    def apply(t: Int @@ Tag): Int = t
    def invert(s: Int): Int @@ Tag = covariantTag[Tag][Int](s)
  }

  implicit def stringTagInjection[Tag] = new Injection[String @@ Tag, String] {
    def apply(t: String @@ Tag): String = t
    def invert(s: String): String @@ Tag = covariantTag[Tag][String](s)
  }

  sealed trait NameTag
  type Name = String @@ NameTag
  def name(id: String): Name = covariantTag[NameTag][String](id)

  sealed trait SizeTag
  type Size = Int @@ SizeTag
  def size(n: Int): Size = covariantTag[SizeTag][Int](n)

  sealed trait LengthTag
  type Length = Int @@ LengthTag
  def length(n: Int): Length = covariantTag[LengthTag][Int](n)

  // Hierarchy of tags
  sealed abstract trait ATag
  sealed trait BTag extends ATag
  sealed trait CTag extends BTag
  sealed trait DTag extends ATag

  type AId = String @@ ATag
  def aid(id: String): AId = covariantTag[ATag][String](id)

  type BId = String @@ BTag
  def bid(id: String): BId = covariantTag[BTag][String](id)

  type CId = String @@ CTag
  def cid(id: String): CId = covariantTag[CTag][String](id)

  type DId = String @@ DTag
  def did(id: String): DId = covariantTag[DTag][String](id)

  // CatalystCast instances to make the following work when Nothing <: U <: V <: Any|Object
  // x.col[T @@ U]('field).cast[T @@ V]

  private val theInstance = new CatalystCast[Any, Any] {}
  implicit val BId2AId: CatalystCast[BId, AId] = theInstance.asInstanceOf[CatalystCast[BId, AId]]
  implicit val CId2BId: CatalystCast[CId, BId] = theInstance.asInstanceOf[CatalystCast[CId, BId]]
  implicit val DId2AId: CatalystCast[DId, AId] = theInstance.asInstanceOf[CatalystCast[DId, AId]]

  // Case classes for TypedDatasets
  case class A(id: AId)
  case class B(id: BId, name: Name)
  case class B1(id: BId)
  case class C(id: CId, name: Name, size: Size)
  case class D(id: DId, length: Length)

  implicit val C2BSmartProject: SmartProject[C, B] = SmartProject[C, B]( (x: TypedDataset[C]) => {
    val x_id: TypedColumn[C, BId] = x.col[CId]('id).cast[BId]
    val x_name: TypedColumn[C, Name] = x.col[Name]('name)
    x.select[BId, Name](x_id, x_name).as[B]
  })

  val conf = new SparkConf().setMaster("local").setAppName(this.getClass.getSimpleName)

  implicit val spark = SparkSession.builder().config(conf).getOrCreate()
  implicit val sqlContext = spark.sqlContext
  import frameless.syntax.DefaultSparkDelay

  val bs: TypedDataset[B] = TypedDataset.create[B](Seq(
    B(bid("12345"), name("Mark")),
    B(bid("23456"), name("Jane"))
  ))

  val cs: TypedDataset[C] = TypedDataset.create[C](Seq(
    C(cid("02468"), name("Table"), size(8)),
    C(cid("13579"), name("Chair"), size(4))
  ))

  val ds: TypedDataset[D] = TypedDataset.create[D](Seq(
    D(did("03846"), length(2)),
    D(did("19384"), length(3))
  ))

  val bcs: TypedDataset[B] = bs.union(cs.project[B])
  val bcs_collection = bcs.collect().run()
  require(bcs_collection.size == 4)

  // Compilation errors if uncommented.

//  val b1s: TypedDataset[B1] = bcs.project[B1]
//  val b1s_collection = b1s.collect().run()
//  require(b1s_collection.size == 4)
//
//  val ab1s: TypedDataset[A] = b1s.project[A] // line 121
//  val ab1s_collection = ab1s.collect().run()
//  require(ab1s_collection.size == 4)
//
//  implicit val B2ASmartProject: SmartProject[B, A] = SmartProject[B, A]( (x: TypedDataset[B]) => {
//    val x_id: TypedColumn[B, AId] = x.col[BId]('id).cast[AId]
//    x.select[AId](x_id).as[A] // line 127
//  })
//
//  implicit val D2ASmartProject: SmartProject[D, A] = SmartProject[D, A]( (x: TypedDataset[D]) => {
//    val x_id: TypedColumn[D, AId] = x.col[DId]('id).cast[AId]
//    x.select[AId](x_id).as[A] // line 132
//  })
//
//  val all: TypedDataset[A] = bcs.project[A].union(ds.project[A])
//  val all_collection = all.collect().run()
//  require(all_collection.size == 6)

  // Compilation errors if the above lines are all uncommented:

//  Error:(121, 42) Cannot prove that tags.s2.B1 can be projected to tags.s2.A. Perhaps not all member names and types of tags.s2.A are the same in tags.s2.B1?
//  val ab1s: TypedDataset[A] = b1s.project[A] // line 121
//  Error:(121, 42) not enough arguments for method project: (implicit projector: frameless.ops.SmartProject[tags.s2.B1,tags.s2.A])frameless.TypedDataset[tags.s2.A].
//    Unspecified value parameter projector.
//  val ab1s: TypedDataset[A] = b1s.project[A] // line 121

//  Error:(127, 27) could not find implicit value for parameter as: frameless.ops.As[tags.s2.AId,tags.s2.A]
//  x.select[AId](x_id).as[A] // line 127
//  Error:(127, 27) not enough arguments for method as: (implicit as: frameless.ops.As[tags.s2.AId,tags.s2.A])frameless.TypedDataset[tags.s2.A].
//    Unspecified value parameter as.
//  x.select[AId](x_id).as[A] // line 127

//  Error:(132, 27) could not find implicit value for parameter as: frameless.ops.As[tags.s2.AId,tags.s2.A]
//  x.select[AId](x_id).as[A] // line 132
//  Error:(132, 27) not enough arguments for method as: (implicit as: frameless.ops.As[tags.s2.AId,tags.s2.A])frameless.TypedDataset[tags.s2.A].
//    Unspecified value parameter as.
//  x.select[AId](x_id).as[A] // line 132
}

s3.scala

package tags

import frameless.ops.SmartProject
import frameless.{TypedColumn, TypedDataset}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

import scala.{App,Int}
import scala.Predef.{require,String}
// Untagged types with SmartProject for unary case class
object s3 extends App {

  type Name = String
  def name(id: String): Name = id

  type Size = Int
  def size(n: Int): Size = n

  type Length = Int
  def length(n: Int): Length = n

  type AId = String
  def aid(id: String): AId = id

  type BId = String
  def bid(id: String): BId = id

  type CId = String
  def cid(id: String): CId = id

  type DId = String
  def did(id: String): DId = id

  // Case classes for TypedDatasets
  case class A(id: AId)
  case class B(id: BId, name: Name)
  case class B1(id: BId)
  case class C(id: CId, name: Name, size: Size)
  case class D(id: DId, length: Length)

  implicit val C2BSmartProject: SmartProject[C, B] = SmartProject[C, B]( (x: TypedDataset[C]) => {
    val x_id: TypedColumn[C, BId] = x.col[CId]('id).cast[BId]
    val x_name: TypedColumn[C, Name] = x.col[Name]('name)
    x.select[BId, Name](x_id, x_name).as[B]
  })

  val conf = new SparkConf().setMaster("local").setAppName(this.getClass.getSimpleName)

  implicit val spark = SparkSession.builder().config(conf).getOrCreate()
  implicit val sqlContext = spark.sqlContext
  import frameless.syntax.DefaultSparkDelay

  val bs: TypedDataset[B] = TypedDataset.create[B](Seq(
    B(bid("12345"), name("Mark")),
    B(bid("23456"), name("Jane"))
  ))

  val cs: TypedDataset[C] = TypedDataset.create[C](Seq(
    C(cid("02468"), name("Table"), size(8)),
    C(cid("13579"), name("Chair"), size(4))
  ))

  val ds: TypedDataset[D] = TypedDataset.create[D](Seq(
    D(did("03846"), length(2)),
    D(did("19384"), length(3))
  ))

  val bcs: TypedDataset[B] = bs.union(cs.project[B])
  val bcs_collection = bcs.collect().run()
  require(bcs_collection.size == 4)

  // Compilation errors if uncommented.

  val b1s: TypedDataset[B1] = bcs.project[B1]
  val b1s_collection = b1s.collect().run()
  require(b1s_collection.size == 4)

  val ab1s: TypedDataset[A] = b1s.project[A]
  val ab1s_collection = ab1s.collect().run()
  require(ab1s_collection.size == 4)

  // Compilation errors if uncommented.

//  implicit val B2ASmartProject: SmartProject[B, A] = SmartProject[B, A]( (x: TypedDataset[B]) => {
//    val x_id: TypedColumn[B, AId] = x.col[BId]('id).cast[AId]
//    x.select[AId](x_id).as[A] // line 86
//  })
//
//  implicit val D2ASmartProject: SmartProject[D, A] = SmartProject[D, A]( (x: TypedDataset[D]) => {
//    val x_id: TypedColumn[D, AId] = x.col[DId]('id).cast[AId]
//    x.select[AId](x_id).as[A] // line 91
//  })

  val all: TypedDataset[A] = bcs.project[A].union(ds.project[A])
  val all_collection = all.collect().run()
  require(all_collection.size == 6)

  // Compilation errors if the above lines are all uncommented:
//  Error:(86, 27) could not find implicit value for parameter as: frameless.ops.As[tags.s3.AId,tags.s3.A]
//  x.select[AId](x_id).as[A] // line 86
//  Error:(86, 27) not enough arguments for method as: (implicit as: frameless.ops.As[tags.s3.AId,tags.s3.A])frameless.TypedDataset[tags.s3.A].
//    Unspecified value parameter as.
//  x.select[AId](x_id).as[A] // line 86

//  Error:(91, 27) could not find implicit value for parameter as: frameless.ops.As[tags.s3.AId,tags.s3.A]
//  x.select[AId](x_id).as[A] // line 91
//  Error:(91, 27) not enough arguments for method as: (implicit as: frameless.ops.As[tags.s3.AId,tags.s3.A])frameless.TypedDataset[tags.s3.A].
//    Unspecified value parameter as.
//  x.select[AId](x_id).as[A] // line 91


}

s4.scala

package tags

import frameless.ops.SmartProject
import frameless.{TypedColumn, TypedDataset}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

import scala.Predef.require
// Untagged types with derived projections
object s4 extends App {

  type Name = String
  def name(id: String): Name = id

  type Size = Int
  def size(n: Int): Size = n

  type Length = Int
  def length(n: Int): Length = n

  // Hierarchy of tags
  sealed abstract trait ATag
  sealed trait BTag extends ATag
  sealed trait CTag extends BTag
  sealed trait DTag extends ATag

  type AId = String
  def aid(id: String): AId = id

  type BId = String
  def bid(id: String): BId = id

  type CId = String
  def cid(id: String): CId = id

  type DId = String
  def did(id: String): DId = id

  // Case classes for TypedDatasets
  case class A(id: AId)
  case class B(id: BId, name: Name)
  case class B1(id: BId)
  case class C(id: CId, name: Name, size: Size)
  case class D(id: DId, length: Length)

  implicit val C2BSmartProject: SmartProject[C, B] = SmartProject[C, B]( (x: TypedDataset[C]) => {
    val x_id: TypedColumn[C, BId] = x.col[CId]('id).cast[BId]
    val x_name: TypedColumn[C, Name] = x.col[Name]('name)
    x.select[BId, Name](x_id, x_name).as[B]
  })

  val conf = new SparkConf().setMaster("local").setAppName(this.getClass.getSimpleName)

  implicit val spark = SparkSession.builder().config(conf).getOrCreate()
  implicit val sqlContext = spark.sqlContext
  import frameless.syntax.DefaultSparkDelay

  val bs: TypedDataset[B] = TypedDataset.create[B](Seq(
    B(bid("12345"), name("Mark")),
    B(bid("23456"), name("Jane"))
  ))

  val cs: TypedDataset[C] = TypedDataset.create[C](Seq(
    C(cid("02468"), name("Table"), size(8)),
    C(cid("13579"), name("Chair"), size(4))
  ))

  val ds: TypedDataset[D] = TypedDataset.create[D](Seq(
    D(did("03846"), length(2)),
    D(did("19384"), length(3))
  ))

  val bcs: TypedDataset[B] = bs.union(cs.project[B])
  val bcs_collection = bcs.collect().run()
  require(bcs_collection.size == 4)

  val b1s: TypedDataset[B1] = bcs.project[B1]
  val b1s_collection = b1s.collect().run()
  require(b1s_collection.size == 4)

  val ab1s: TypedDataset[A] = b1s.project[A]
  val ab1s_collection = ab1s.collect().run()
  require(ab1s_collection.size == 4)

  val all: TypedDataset[A] = bcs.project[A].union(ds.project[A])
  val all_collection = all.collect().run()
  require(all_collection.size == 6)

}