Sort columns (#236)

dataset/src/main/scala/frameless/TypedColumn.scala

@@ -305,6 +305,20 @@ abstract class AbstractTypedColumn[T, U]
     */
   def /(u: U)(implicit n: CatalystNumeric[U]): ThisType[T, Double] = typed(self.untyped.divide(u))
 
+  /** Returns a descending ordering used in sorting
+    *
+    * apache/spark
+    */
+  def desc(implicit catalystOrdered: CatalystOrdered[U]): SortedTypedColumn[T, U] =
+    new SortedTypedColumn[T, U](untyped.desc)
+
+  /** Returns an ascending ordering used in sorting
+    *
+    * apache/spark
+    */
+  def asc(implicit catalystOrdered: CatalystOrdered[U]): SortedTypedColumn[T, U] =
+    new SortedTypedColumn[T, U](untyped.asc)
+
   /**
     * Bitwise AND this expression and another expression.
     * {{{
@@ -602,6 +616,29 @@ abstract class AbstractTypedColumn[T, U]
 }
 
 
+sealed class SortedTypedColumn[T, U](val expr: Expression)(
+  implicit
+  val uencoder: TypedEncoder[U]
+) extends UntypedExpression[T] {
+
+  def this(column: Column)(implicit e: TypedEncoder[U]) {
+    this(FramelessInternals.expr(column))
+  }
+
+  def untyped: Column = new Column(expr)
+}
+
+object SortedTypedColumn {
+  implicit def defaultAscending[T, U : CatalystOrdered](typedColumn: TypedColumn[T, U]): SortedTypedColumn[T, U] =
+    new SortedTypedColumn[T, U](typedColumn.untyped.asc)(typedColumn.uencoder)
+
+    object defaultAscendingPoly extends Poly1 {
+      implicit def caseTypedColumn[T, U : CatalystOrdered] = at[TypedColumn[T, U]](c => defaultAscending(c))
+      implicit def caseTypeSortedColumn[T, U] = at[SortedTypedColumn[T, U]](identity)
+    }
+  }
+
+
 object TypedColumn {
   /**
     * Evidence that type `T` has column `K` with type `V`.

dataset/src/main/scala/frameless/TypedDataset.scala

@@ -11,7 +11,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter}
 import org.apache.spark.sql.types.StructType
 import shapeless._
 import shapeless.labelled.FieldType
-import shapeless.ops.hlist.{Diff, IsHCons, Prepend, ToTraversable, Tupler}
+import shapeless.ops.hlist.{Diff, IsHCons, Mapper, Prepend, ToTraversable, Tupler}
 import shapeless.ops.record.{Keys, Remover, Values}
 
 /** [[TypedDataset]] is a safer interface for working with `Dataset`.
@@ -710,6 +710,80 @@ class TypedDataset[T] protected[frameless](val dataset: Dataset[T])(implicit val
       }
   }
 
+  /** Sort each partition in the dataset using the columns selected. */
+  def sortWithinPartitions[A: CatalystOrdered](ca: SortedTypedColumn[T, A]): TypedDataset[T] =
+    sortWithinPartitionsMany(ca)
+
+  /** Sort each partition in the dataset using the columns selected. */
+  def sortWithinPartitions[A: CatalystOrdered, B: CatalystOrdered](
+    ca: SortedTypedColumn[T, A],
+    cb: SortedTypedColumn[T, B]
+  ): TypedDataset[T] = sortWithinPartitionsMany(ca, cb)
+
+  /** Sort each partition in the dataset using the columns selected. */
+  def sortWithinPartitions[A: CatalystOrdered, B: CatalystOrdered, C: CatalystOrdered](
+    ca: SortedTypedColumn[T, A],
+    cb: SortedTypedColumn[T, B],
+    cc: SortedTypedColumn[T, C]
+  ): TypedDataset[T] = sortWithinPartitionsMany(ca, cb, cc)
+
+  /** Sort each partition in the dataset by the given column expressions
+    * Default sort order is ascending.
+    * {{{
+    *   d.sortWithinPartitionsMany(d('a), d('b).desc, d('c).asc)
+    * }}}
+    */
+  object sortWithinPartitionsMany extends ProductArgs {
+    def applyProduct[U <: HList, O <: HList](columns: U)
+      (implicit
+        i0: Mapper.Aux[SortedTypedColumn.defaultAscendingPoly.type, U, O],
+        i1: ToTraversable.Aux[O, List, SortedTypedColumn[T, _]]
+      ): TypedDataset[T] = {
+      val sorted = dataset.toDF()
+        .sortWithinPartitions(i0(columns).toList[SortedTypedColumn[T, _]].map(_.untyped):_*)
+        .as[T](TypedExpressionEncoder[T])
+
+      TypedDataset.create[T](sorted)
+    }
+  }
+
+  /** Orders the TypedDataset using the column selected. */
+  def orderBy[A: CatalystOrdered](ca: SortedTypedColumn[T, A]): TypedDataset[T] =
+    orderByMany(ca)
+
+  /** Orders the TypedDataset using the columns selected. */
+  def orderBy[A: CatalystOrdered, B: CatalystOrdered](
+    ca: SortedTypedColumn[T, A],
+    cb: SortedTypedColumn[T, B]
+  ): TypedDataset[T] = orderByMany(ca, cb)
+
+ /** Orders the TypedDataset using the columns selected. */
+ def orderBy[A: CatalystOrdered, B: CatalystOrdered, C: CatalystOrdered](
+   ca: SortedTypedColumn[T, A],
+   cb: SortedTypedColumn[T, B],
+   cc: SortedTypedColumn[T, C]
+ ): TypedDataset[T] = orderByMany(ca, cb, cc)
+
+  /** Sort the dataset by any number of column expressions.
+    * Default sort order is ascending.
+    * {{{
+    *   d.orderByMany(d('a), d('b).desc, d('c).asc)
+    * }}}
+    */
+  object orderByMany extends ProductArgs {
+    def applyProduct[U <: HList, O <: HList](columns: U)
+      (implicit
+        i0: Mapper.Aux[SortedTypedColumn.defaultAscendingPoly.type, U, O],
+        i1: ToTraversable.Aux[O, List, SortedTypedColumn[T, _]]
+      ): TypedDataset[T] = {
+      val sorted = dataset.toDF()
+        .orderBy(i0(columns).toList[SortedTypedColumn[T, _]].map(_.untyped):_*)
+        .as[T](TypedExpressionEncoder[T])
+
+      TypedDataset.create[T](sorted)
+    }
+  }
+
   /** Returns a new Dataset as a tuple with the specified
     * column dropped.
     * Does not allow for dropping from a single column TypedDataset

dataset/src/test/scala/frameless/OrderByTests.scala

@@ -0,0 +1,161 @@
+package frameless
+
+import org.scalacheck.Prop
+import org.scalacheck.Prop._
+import org.scalatest.Matchers
+import shapeless.test.illTyped
+import org.apache.spark.sql.Column
+
+class OrderByTests extends TypedDatasetSuite with Matchers {
+  def sortings[A : CatalystOrdered, T]: Seq[(TypedColumn[T, A] => SortedTypedColumn[T, A], Column => Column)] = Seq(
+    (_.desc, _.desc),
+    (_.asc, _.asc),
+    (t => t, t => t) //default ascending
+  )
+
+  test("single column non nullable orderBy") {
+    def prop[A: TypedEncoder : CatalystOrdered](data: Vector[X1[A]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X1[A]].map { case (typ, untyp) =>
+        ds.dataset.orderBy(untyp(ds.dataset.col("a"))).collect().toVector.?=(
+          ds.orderBy(typ(ds('a))).collect().run().toVector)
+      }.reduce(_ && _)
+    }
+
+    check(forAll(prop[Int] _))
+    check(forAll(prop[Boolean] _))
+    check(forAll(prop[Byte] _))
+    check(forAll(prop[Short] _))
+    check(forAll(prop[Long] _))
+    check(forAll(prop[Float] _))
+    check(forAll(prop[Double] _))
+    check(forAll(prop[SQLDate] _))
+    check(forAll(prop[SQLTimestamp] _))
+    check(forAll(prop[String] _))
+  }
+
+  test("single column non nullable partition sorting") {
+    def prop[A: TypedEncoder : CatalystOrdered](data: Vector[X1[A]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X1[A]].map { case (typ, untyp) =>
+        ds.dataset.sortWithinPartitions(untyp(ds.dataset.col("a"))).collect().toVector.?=(
+          ds.sortWithinPartitions(typ(ds('a))).collect().run().toVector)
+      }.reduce(_ && _)
+    }
+
+    check(forAll(prop[Int] _))
+    check(forAll(prop[Boolean] _))
+    check(forAll(prop[Byte] _))
+    check(forAll(prop[Short] _))
+    check(forAll(prop[Long] _))
+    check(forAll(prop[Float] _))
+    check(forAll(prop[Double] _))
+    check(forAll(prop[SQLDate] _))
+    check(forAll(prop[SQLTimestamp] _))
+    check(forAll(prop[String] _))
+  }
+
+  test("two columns non nullable orderBy") {
+    def prop[A: TypedEncoder : CatalystOrdered, B: TypedEncoder : CatalystOrdered](data: Vector[X2[A,B]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X2[A, B]].reverse.zip(sortings[B, X2[A, B]]).map { case ((typA, untypA), (typB, untypB)) =>
+        val vanillaSpark = ds.dataset.orderBy(untypA(ds.dataset.col("a")), untypB(ds.dataset.col("b"))).collect().toVector
+        vanillaSpark.?=(ds.orderBy(typA(ds('a)), typB(ds('b))).collect().run().toVector).&&(
+          vanillaSpark ?= ds.orderByMany(typA(ds('a)), typB(ds('b))).collect().run().toVector
+        )
+      }.reduce(_ && _)
+    }
+
+    check(forAll(prop[SQLDate, Long] _))
+    check(forAll(prop[String, Boolean] _))
+    check(forAll(prop[SQLTimestamp, Long] _))
+  }
+
+  test("two columns non nullable partition sorting") {
+    def prop[A: TypedEncoder : CatalystOrdered, B: TypedEncoder : CatalystOrdered](data: Vector[X2[A,B]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X2[A, B]].reverse.zip(sortings[B, X2[A, B]]).map { case ((typA, untypA), (typB, untypB)) =>
+        val vanillaSpark = ds.dataset.sortWithinPartitions(untypA(ds.dataset.col("a")), untypB(ds.dataset.col("b"))).collect().toVector
+        vanillaSpark.?=(ds.sortWithinPartitions(typA(ds('a)), typB(ds('b))).collect().run().toVector).&&(
+          vanillaSpark ?= ds.sortWithinPartitionsMany(typA(ds('a)), typB(ds('b))).collect().run().toVector
+        )
+      }.reduce(_ && _)
+    }
+
+    check(forAll(prop[SQLDate, Long] _))
+    check(forAll(prop[String, Boolean] _))
+    check(forAll(prop[SQLTimestamp, Long] _))
+  }
+
+  test("three columns non nullable orderBy") {
+    def prop[A: TypedEncoder : CatalystOrdered, B: TypedEncoder : CatalystOrdered](data: Vector[X3[A,B,A]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X3[A, B, A]].reverse
+        .zip(sortings[B, X3[A, B, A]])
+        .zip(sortings[A, X3[A, B, A]])
+        .map { case (((typA, untypA), (typB, untypB)), (typA2, untypA2)) =>
+          val vanillaSpark = ds.dataset
+            .orderBy(untypA(ds.dataset.col("a")), untypB(ds.dataset.col("b")), untypA2(ds.dataset.col("c")))
+            .collect().toVector
+
+          vanillaSpark.?=(ds.orderBy(typA(ds('a)), typB(ds('b)), typA2(ds('c))).collect().run().toVector).&&(
+            vanillaSpark ?= ds.orderByMany(typA(ds('a)), typB(ds('b)), typA2(ds('c))).collect().run().toVector
+          )
+        }.reduce(_ && _)
+    }
+
+    check(forAll(prop[SQLDate, Long] _))
+    check(forAll(prop[String, Boolean] _))
+    check(forAll(prop[SQLTimestamp, Long] _))
+  }
+
+  test("three columns non nullable partition sorting") {
+    def prop[A: TypedEncoder : CatalystOrdered, B: TypedEncoder : CatalystOrdered](data: Vector[X3[A,B,A]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      sortings[A, X3[A, B, A]].reverse
+        .zip(sortings[B, X3[A, B, A]])
+        .zip(sortings[A, X3[A, B, A]])
+        .map { case (((typA, untypA), (typB, untypB)), (typA2, untypA2)) =>
+          val vanillaSpark = ds.dataset
+            .sortWithinPartitions(untypA(ds.dataset.col("a")), untypB(ds.dataset.col("b")), untypA2(ds.dataset.col("c")))
+            .collect().toVector
+
+          vanillaSpark.?=(ds.sortWithinPartitions(typA(ds('a)), typB(ds('b)), typA2(ds('c))).collect().run().toVector).&&(
+            vanillaSpark ?= ds.sortWithinPartitionsMany(typA(ds('a)), typB(ds('b)), typA2(ds('c))).collect().run().toVector
+          )
+        }.reduce(_ && _)
+    }
+
+    check(forAll(prop[SQLDate, Long] _))
+    check(forAll(prop[String, Boolean] _))
+    check(forAll(prop[SQLTimestamp, Long] _))
+  }
+
+  test("sort support for mixed default and explicit ordering") {
+    def prop[A: TypedEncoder : CatalystOrdered, B: TypedEncoder : CatalystOrdered](data: Vector[X2[A, B]]): Prop = {
+      val ds = TypedDataset.create(data)
+
+      ds.dataset.orderBy(ds.dataset.col("a"), ds.dataset.col("b").desc).collect().toVector.?=(
+        ds.orderByMany(ds('a), ds('b).desc).collect().run().toVector) &&
+      ds.dataset.sortWithinPartitions(ds.dataset.col("a"), ds.dataset.col("b").desc).collect().toVector.?=(
+        ds.sortWithinPartitionsMany(ds('a), ds('b).desc).collect().run().toVector)
+    }
+
+    check(forAll(prop[SQLDate, Long] _))
+    check(forAll(prop[String, Boolean] _))
+    check(forAll(prop[SQLTimestamp, Long] _))
+  }
+
+  test("fail when selected column is not sortable") {
+    val d = TypedDataset.create(X2(1, Map(1 -> 2)) :: X2(2, Map(2 -> 2)) :: Nil)
+    d.orderBy(d('a).desc)
+    illTyped("""d.orderBy(d('b).desc)""")
+    illTyped("""d.sortWithinPartitions(d('b).desc)""")
+  }
+}