[SPARK-7462] By default retain group by columns in aggregate

Updated Java, Scala, Python, and R.

Author: Reynold Xin <[email protected]>
Author: Shivaram Venkataraman <[email protected]>

Closes apache#5996 from rxin/groupby-retain and squashes the following commits:

aac7119 [Reynold Xin] Merge branch 'groupby-retain' of github.com:rxin/spark into groupby-retain
f6858f6 [Reynold Xin] Merge branch 'master' into groupby-retain
5f923c0 [Reynold Xin] Merge pull request #15 from shivaram/sparkr-groupby-retrain
c1de670 [Shivaram Venkataraman] Revert workaround in SparkR to retain grouped cols Based on reverting code added in commit amplab-extras@9a6be74
b8b87e1 [Reynold Xin] Fixed DataFrameJoinSuite.
d910141 [Reynold Xin] Updated rest of the files
1e6e666 [Reynold Xin] [SPARK-7462] By default retain group by columns in aggregate

R/pkg/R/group.R

@@ -102,9 +102,7 @@ setMethod("agg",
                 }
               }
               jcols <- lapply(cols, function(c) { c@jc })
-              # the GroupedData.agg(col, cols*) API does not contain grouping Column
-              sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "aggWithGrouping",
-                                 x@sgd, listToSeq(jcols))
+              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], listToSeq(jcols[-1]))
             } else {
               stop("agg can only support Column or character")
             }

python/pyspark/sql/dataframe.py

@@ -1069,7 +1069,7 @@ def agg(self, *exprs):
 
         >>> from pyspark.sql import functions as F
         >>> gdf.agg(F.min(df.age)).collect()
-        [Row(MIN(age)=2), Row(MIN(age)=5)]
+        [Row(name=u'Alice', MIN(age)=2), Row(name=u'Bob', MIN(age)=5)]
         """
         assert exprs, "exprs should not be empty"
         if len(exprs) == 1 and isinstance(exprs[0], dict):

sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala

@@ -135,8 +135,9 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression])
   }
 
   /**
-   * Compute aggregates by specifying a series of aggregate columns. Unlike other methods in this
-   * class, the resulting [[DataFrame]] won't automatically include the grouping columns.
+   * Compute aggregates by specifying a series of aggregate columns. Note that this function by
+   * default retains the grouping columns in its output. To not retain grouping columns, set
+   * `spark.sql.retainGroupColumns` to false.
    *
    * The available aggregate methods are defined in [[org.apache.spark.sql.functions]].
    *
@@ -158,7 +159,15 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression])
       case expr: NamedExpression => expr
       case expr: Expression => Alias(expr, expr.prettyString)()
     }
-    DataFrame(df.sqlContext, Aggregate(groupingExprs, aggExprs, df.logicalPlan))
+    if (df.sqlContext.conf.dataFrameRetainGroupColumns) {
+      val retainedExprs = groupingExprs.map {
+        case expr: NamedExpression => expr
+        case expr: Expression => Alias(expr, expr.prettyString)()
+      }
+      DataFrame(df.sqlContext, Aggregate(groupingExprs, retainedExprs ++ aggExprs, df.logicalPlan))
+    } else {
+      DataFrame(df.sqlContext, Aggregate(groupingExprs, aggExprs, df.logicalPlan))
+    }
   }
 
   /**

sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala

@@ -74,6 +74,9 @@ private[spark] object SQLConf {
   // See SPARK-6231.
   val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY = "spark.sql.selfJoinAutoResolveAmbiguity"
 
+  // Whether to retain group by columns or not in GroupedData.agg.
+  val DATAFRAME_RETAIN_GROUP_COLUMNS = "spark.sql.retainGroupColumns"
+
   val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2"
 
   val USE_JACKSON_STREAMING_API = "spark.sql.json.useJacksonStreamingAPI"
@@ -242,6 +245,9 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def dataFrameSelfJoinAutoResolveAmbiguity: Boolean =
     getConf(DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY, "true").toBoolean
+
+  private[spark] def dataFrameRetainGroupColumns: Boolean =
+    getConf(DATAFRAME_RETAIN_GROUP_COLUMNS, "true").toBoolean
 
   /** ********************** SQLConf functionality methods ************ */
 

sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala

@@ -72,17 +72,6 @@ private[r] object SQLUtils {
     sqlContext.createDataFrame(rowRDD, schema)
   }
 
-  // A helper to include grouping columns in Agg()
-  def aggWithGrouping(gd: GroupedData, exprs: Column*): DataFrame = {
-    val aggExprs = exprs.map { col =>
-      col.expr match {
-        case expr: NamedExpression => expr
-        case expr: Expression => Alias(expr, expr.simpleString)()
-      }
-    }
-    gd.toDF(aggExprs)
-  }
-
   def dfToRowRDD(df: DataFrame): JavaRDD[Array[Byte]] = {
     df.map(r => rowToRBytes(r))
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala

@@ -104,7 +104,7 @@ private[sql] object StatFunctions extends Logging {
   /** Generate a table of frequencies for the elements of two columns. */
   private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = {
     val tableName = s"${col1}_$col2"
-    val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt)
+    val counts = df.groupBy(col1, col2).agg(count("*")).take(1e6.toInt)
     if (counts.length == 1e6.toInt) {
       logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
         "the pairs. Please try reducing the amount of distinct items in your columns.")

sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala

@@ -0,0 +1,193 @@
+/*
+ * 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.TestData._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.TestSQLContext.implicits._
+import org.apache.spark.sql.types.DecimalType
+
+
+class DataFrameAggregateSuite extends QueryTest {
+
+  test("groupBy") {
+    checkAnswer(
+      testData2.groupBy("a").agg(sum($"b")),
+      Seq(Row(1, 3), Row(2, 3), Row(3, 3))
+    )
+    checkAnswer(
+      testData2.groupBy("a").agg(sum($"b").as("totB")).agg(sum('totB)),
+      Row(9)
+    )
+    checkAnswer(
+      testData2.groupBy("a").agg(count("*")),
+      Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil
+    )
+    checkAnswer(
+      testData2.groupBy("a").agg(Map("*" -> "count")),
+      Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil
+    )
+    checkAnswer(
+      testData2.groupBy("a").agg(Map("b" -> "sum")),
+      Row(1, 3) :: Row(2, 3) :: Row(3, 3) :: Nil
+    )
+
+    val df1 = Seq(("a", 1, 0, "b"), ("b", 2, 4, "c"), ("a", 2, 3, "d"))
+      .toDF("key", "value1", "value2", "rest")
+
+    checkAnswer(
+      df1.groupBy("key").min(),
+      df1.groupBy("key").min("value1", "value2").collect()
+    )
+    checkAnswer(
+      df1.groupBy("key").min("value2"),
+      Seq(Row("a", 0), Row("b", 4))
+    )
+  }
+
+  test("spark.sql.retainGroupColumns config") {
+    checkAnswer(
+      testData2.groupBy("a").agg(sum($"b")),
+      Seq(Row(1, 3), Row(2, 3), Row(3, 3))
+    )
+
+    TestSQLContext.conf.setConf("spark.sql.retainGroupColumns", "false")
+    checkAnswer(
+      testData2.groupBy("a").agg(sum($"b")),
+      Seq(Row(3), Row(3), Row(3))
+    )
+    TestSQLContext.conf.setConf("spark.sql.retainGroupColumns", "true")
+  }
+
+  test("agg without groups") {
+    checkAnswer(
+      testData2.agg(sum('b)),
+      Row(9)
+    )
+  }
+
+  test("average") {
+    checkAnswer(
+      testData2.agg(avg('a)),
+      Row(2.0))
+
+    // Also check mean
+    checkAnswer(
+      testData2.agg(mean('a)),
+      Row(2.0))
+
+    checkAnswer(
+      testData2.agg(avg('a), sumDistinct('a)), // non-partial
+      Row(2.0, 6.0) :: Nil)
+
+    checkAnswer(
+      decimalData.agg(avg('a)),
+      Row(new java.math.BigDecimal(2.0)))
+    checkAnswer(
+      decimalData.agg(avg('a), sumDistinct('a)), // non-partial
+      Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil)
+
+    checkAnswer(
+      decimalData.agg(avg('a cast DecimalType(10, 2))),
+      Row(new java.math.BigDecimal(2.0)))
+    // non-partial
+    checkAnswer(
+      decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))),
+      Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil)
+  }
+
+  test("null average") {
+    checkAnswer(
+      testData3.agg(avg('b)),
+      Row(2.0))
+
+    checkAnswer(
+      testData3.agg(avg('b), countDistinct('b)),
+      Row(2.0, 1))
+
+    checkAnswer(
+      testData3.agg(avg('b), sumDistinct('b)), // non-partial
+      Row(2.0, 2.0))
+  }
+
+  test("zero average") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    checkAnswer(
+      emptyTableData.agg(avg('a)),
+      Row(null))
+
+    checkAnswer(
+      emptyTableData.agg(avg('a), sumDistinct('b)), // non-partial
+      Row(null, null))
+  }
+
+  test("count") {
+    assert(testData2.count() === testData2.map(_ => 1).count())
+
+    checkAnswer(
+      testData2.agg(count('a), sumDistinct('a)), // non-partial
+      Row(6, 6.0))
+  }
+
+  test("null count") {
+    checkAnswer(
+      testData3.groupBy('a).agg(count('b)),
+      Seq(Row(1,0), Row(2, 1))
+    )
+
+    checkAnswer(
+      testData3.groupBy('a).agg(count('a + 'b)),
+      Seq(Row(1,0), Row(2, 1))
+    )
+
+    checkAnswer(
+      testData3.agg(count('a), count('b), count(lit(1)), countDistinct('a), countDistinct('b)),
+      Row(2, 1, 2, 2, 1)
+    )
+
+    checkAnswer(
+      testData3.agg(count('b), countDistinct('b), sumDistinct('b)), // non-partial
+      Row(1, 1, 2)
+    )
+  }
+
+  test("zero count") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    assert(emptyTableData.count() === 0)
+
+    checkAnswer(
+      emptyTableData.agg(count('a), sumDistinct('a)), // non-partial
+      Row(0, null))
+  }
+
+  test("zero sum") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    checkAnswer(
+      emptyTableData.agg(sum('a)),
+      Row(null))
+  }
+
+  test("zero sum distinct") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    checkAnswer(
+      emptyTableData.agg(sumDistinct('a)),
+      Row(null))
+  }
+
+}

sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala

@@ -77,8 +77,8 @@ class DataFrameJoinSuite extends QueryTest {
       df.join(df, df("key") === df("key") && df("value") === 1),
       Row(1, "1", 1, "1") :: Nil)
 
-    val left = df.groupBy("key").agg($"key", count("*"))
-    val right = df.groupBy("key").agg($"key", sum("key"))
+    val left = df.groupBy("key").agg(count("*"))
+    val right = df.groupBy("key").agg(sum("key"))
     checkAnswer(
       left.join(right, left("key") === right("key")),
       Row(1, 1, 1, 1) :: Row(2, 1, 2, 2) :: Nil)