[SPARK-8992][SQL] Add pivot to dataframe api

This adds a pivot method to the dataframe api.

Following the lead of cube and rollup this adds a Pivot operator that is translated into an Aggregate by the analyzer.

Currently the syntax is like:
~~courseSales.pivot(Seq($"year"), $"course", Seq("dotNET", "Java"), sum($"earnings"))~~

~~Would we be interested in the following syntax also/alternatively? and~~

    courseSales.groupBy($"year").pivot($"course", "dotNET", "Java").agg(sum($"earnings"))
    //or
    courseSales.groupBy($"year").pivot($"course").agg(sum($"earnings"))

Later we can add it to `SQLParser`, but as Hive doesn't support it we cant add it there, right?

~~Also what would be the suggested Java friendly method signature for this?~~

Author: Andrew Ray <ray.andrew@gmail.com>

Closes #7841 from aray/sql-pivot.

4 files changed, 199 insertions, 10 deletions

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index 5babf2cc0c..63dd7fbcbe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -24,8 +24,8 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, UnresolvedAlias, UnresolvedAttribute, Star}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.logical.{Rollup, Cube, Aggregate}
-import org.apache.spark.sql.types.NumericType
+import org.apache.spark.sql.catalyst.plans.logical.{Pivot, Rollup, Cube, Aggregate}
+import org.apache.spark.sql.types.{StringType, NumericType}
 
 
 /**
@@ -50,14 +50,8 @@ class GroupedData protected[sql](
       aggExprs
     }
 
-    val aliasedAgg = aggregates.map {
-      // Wrap UnresolvedAttribute with UnresolvedAlias, as when we resolve UnresolvedAttribute, we
-      // will remove intermediate Alias for ExtractValue chain, and we need to alias it again to
-      // make it a NamedExpression.
-      case u: UnresolvedAttribute => UnresolvedAlias(u)
-      case expr: NamedExpression => expr
-      case expr: Expression => Alias(expr, expr.prettyString)()
-    }
+    val aliasedAgg = aggregates.map(alias)
+
     groupType match {
       case GroupedData.GroupByType =>
         DataFrame(
@@ -68,9 +62,22 @@ class GroupedData protected[sql](
       case GroupedData.CubeType =>
         DataFrame(
           df.sqlContext, Cube(groupingExprs, df.logicalPlan, aliasedAgg))
+      case GroupedData.PivotType(pivotCol, values) =>
+        val aliasedGrps = groupingExprs.map(alias)
+        DataFrame(
+          df.sqlContext, Pivot(aliasedGrps, pivotCol, values, aggExprs, df.logicalPlan))
     }
   }
 
+  // Wrap UnresolvedAttribute with UnresolvedAlias, as when we resolve UnresolvedAttribute, we
+  // will remove intermediate Alias for ExtractValue chain, and we need to alias it again to
+  // make it a NamedExpression.
+  private[this] def alias(expr: Expression): NamedExpression = expr match {
+    case u: UnresolvedAttribute => UnresolvedAlias(u)
+    case expr: NamedExpression => expr
+    case expr: Expression => Alias(expr, expr.prettyString)()
+  }
+
   private[this] def aggregateNumericColumns(colNames: String*)(f: Expression => AggregateFunction)
     : DataFrame = {
 
@@ -273,6 +280,77 @@ class GroupedData protected[sql](
   def sum(colNames: String*): DataFrame = {
     aggregateNumericColumns(colNames : _*)(Sum)
   }
+
+  /**
+    * (Scala-specific) Pivots a column of the current [[DataFrame]] and preform the specified
+    * aggregation.
+    * {{{
+    *   // Compute the sum of earnings for each year by course with each course as a separate column
+    *   df.groupBy($"year").pivot($"course", "dotNET", "Java").agg(sum($"earnings"))
+    *   // Or without specifying column values
+    *   df.groupBy($"year").pivot($"course").agg(sum($"earnings"))
+    * }}}
+    * @param pivotColumn Column to pivot
+    * @param values Optional list of values of pivotColumn that will be translated to columns in the
+    *               output data frame. If values are not provided the method with do an immediate
+    *               call to .distinct() on the pivot column.
+    * @since 1.6.0
+    */
+  @scala.annotation.varargs
+  def pivot(pivotColumn: Column, values: Column*): GroupedData = groupType match {
+    case _: GroupedData.PivotType =>
+      throw new UnsupportedOperationException("repeated pivots are not supported")
+    case GroupedData.GroupByType =>
+      val pivotValues = if (values.nonEmpty) {
+        values.map {
+          case Column(literal: Literal) => literal
+          case other =>
+            throw new UnsupportedOperationException(
+              s"The values of a pivot must be literals, found $other")
+        }
+      } else {
+        // This is to prevent unintended OOM errors when the number of distinct values is large
+        val maxValues = df.sqlContext.conf.getConf(SQLConf.DATAFRAME_PIVOT_MAX_VALUES)
+        // Get the distinct values of the column and sort them so its consistent
+        val values = df.select(pivotColumn)
+          .distinct()
+          .sort(pivotColumn)
+          .map(_.get(0))
+          .take(maxValues + 1)
+          .map(Literal(_)).toSeq
+        if (values.length > maxValues) {
+          throw new RuntimeException(
+            s"The pivot column $pivotColumn has more than $maxValues distinct values, " +
+              "this could indicate an error. " +
+              "If this was intended, set \"" + SQLConf.DATAFRAME_PIVOT_MAX_VALUES.key + "\" " +
+              s"to at least the number of distinct values of the pivot column.")
+        }
+        values
+      }
+      new GroupedData(df, groupingExprs, GroupedData.PivotType(pivotColumn.expr, pivotValues))
+    case _ =>
+      throw new UnsupportedOperationException("pivot is only supported after a groupBy")
+  }
+
+  /**
+    * Pivots a column of the current [[DataFrame]] and preform the specified aggregation.
+    * {{{
+    *   // Compute the sum of earnings for each year by course with each course as a separate column
+    *   df.groupBy("year").pivot("course", "dotNET", "Java").sum("earnings")
+    *   // Or without specifying column values
+    *   df.groupBy("year").pivot("course").sum("earnings")
+    * }}}
+    * @param pivotColumn Column to pivot
+    * @param values Optional list of values of pivotColumn that will be translated to columns in the
+    *               output data frame. If values are not provided the method with do an immediate
+    *               call to .distinct() on the pivot column.
+    * @since 1.6.0
+    */
+  @scala.annotation.varargs
+  def pivot(pivotColumn: String, values: Any*): GroupedData = {
+    val resolvedPivotColumn = Column(df.resolve(pivotColumn))
+    pivot(resolvedPivotColumn, values.map(functions.lit): _*)
+  }
 }
 
 
@@ -307,4 +385,9 @@ private[sql] object GroupedData {
    * To indicate it's the ROLLUP
    */
   private[sql] object RollupType extends GroupType
+
+  /**
+    * To indicate it's the PIVOT
+    */
+  private[sql] case class PivotType(pivotCol: Expression, values: Seq[Literal]) extends GroupType
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index e02b502b7b..41d28d448c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -437,6 +437,13 @@ private[spark] object SQLConf {
     defaultValue = Some(true),
     isPublic = false)
 
+  val DATAFRAME_PIVOT_MAX_VALUES = intConf(
+    "spark.sql.pivotMaxValues",
+    defaultValue = Some(10000),
+    doc = "When doing a pivot without specifying values for the pivot column this is the maximum " +
+      "number of (distinct) values that will be collected without error."
+  )
+
   val RUN_SQL_ON_FILES = booleanConf("spark.sql.runSQLOnFiles",
     defaultValue = Some(true),
     isPublic = false,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFramePivotSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFramePivotSuite.scala
new file mode 100644
index 0000000000..0c23d14267
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFramePivotSuite.scala
@@ -0,0 +1,87 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
+
+class DataFramePivotSuite extends QueryTest with SharedSQLContext{
+  import testImplicits._
+
+  test("pivot courses with literals") {
+    checkAnswer(
+      courseSales.groupBy($"year").pivot($"course", lit("dotNET"), lit("Java"))
+        .agg(sum($"earnings")),
+      Row(2012, 15000.0, 20000.0) :: Row(2013, 48000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot year with literals") {
+    checkAnswer(
+      courseSales.groupBy($"course").pivot($"year", lit(2012), lit(2013)).agg(sum($"earnings")),
+      Row("dotNET", 15000.0, 48000.0) :: Row("Java", 20000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot courses with literals and multiple aggregations") {
+    checkAnswer(
+      courseSales.groupBy($"year").pivot($"course", lit("dotNET"), lit("Java"))
+        .agg(sum($"earnings"), avg($"earnings")),
+      Row(2012, 15000.0, 7500.0, 20000.0, 20000.0) ::
+        Row(2013, 48000.0, 48000.0, 30000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot year with string values (cast)") {
+    checkAnswer(
+      courseSales.groupBy("course").pivot("year", "2012", "2013").sum("earnings"),
+      Row("dotNET", 15000.0, 48000.0) :: Row("Java", 20000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot year with int values") {
+    checkAnswer(
+      courseSales.groupBy("course").pivot("year", 2012, 2013).sum("earnings"),
+      Row("dotNET", 15000.0, 48000.0) :: Row("Java", 20000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot courses with no values") {
+    // Note Java comes before dotNet in sorted order
+    checkAnswer(
+      courseSales.groupBy($"year").pivot($"course").agg(sum($"earnings")),
+      Row(2012, 20000.0, 15000.0) :: Row(2013, 30000.0, 48000.0) :: Nil
+    )
+  }
+
+  test("pivot year with no values") {
+    checkAnswer(
+      courseSales.groupBy($"course").pivot($"year").agg(sum($"earnings")),
+      Row("dotNET", 15000.0, 48000.0) :: Row("Java", 20000.0, 30000.0) :: Nil
+    )
+  }
+
+  test("pivot max values inforced") {
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_PIVOT_MAX_VALUES, 1)
+    intercept[RuntimeException](
+      courseSales.groupBy($"year").pivot($"course")
+    )
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_PIVOT_MAX_VALUES,
+      SQLConf.DATAFRAME_PIVOT_MAX_VALUES.defaultValue.get)
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
index 520dea7f7d..abad0d7eaa 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -242,6 +242,17 @@ private[sql] trait SQLTestData { self =>
     df
   }
 
+  protected lazy val courseSales: DataFrame = {
+    val df = sqlContext.sparkContext.parallelize(
+      CourseSales("dotNET", 2012, 10000) ::
+        CourseSales("Java", 2012, 20000) ::
+        CourseSales("dotNET", 2012, 5000) ::
+        CourseSales("dotNET", 2013, 48000) ::
+        CourseSales("Java", 2013, 30000) :: Nil).toDF()
+    df.registerTempTable("courseSales")
+    df
+  }
+
   /**
    * Initialize all test data such that all temp tables are properly registered.
    */
@@ -295,4 +306,5 @@ private[sql] object SQLTestData {
   case class Person(id: Int, name: String, age: Int)
   case class Salary(personId: Int, salary: Double)
   case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
+  case class CourseSales(course: String, year: Int, earnings: Double)
 }