/
DataFrameAggregateSuite.scala
1097 lines (948 loc) · 37.9 KB
/
DataFrameAggregateSuite.scala
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/*
* 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 scala.util.Random
import org.scalatest.matchers.must.Matchers.the
import org.apache.spark.sql.execution.WholeStageCodegenExec
import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper
import org.apache.spark.sql.execution.aggregate.{HashAggregateExec, ObjectHashAggregateExec, SortAggregateExec}
import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSparkSession
import org.apache.spark.sql.test.SQLTestData.DecimalData
import org.apache.spark.sql.types._
case class Fact(date: Int, hour: Int, minute: Int, room_name: String, temp: Double)
class DataFrameAggregateSuite extends QueryTest
with SharedSparkSession
with AdaptiveSparkPlanHelper {
import testImplicits._
val absTol = 1e-8
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($"totB")),
Row(9)
)
checkAnswer(
testData2.groupBy("a").agg(count("*")),
Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil
)
checkAnswer(
testData2.groupBy("a").agg(Map("*" -> "count")),
Row(1, 2) :: Row(2, 2) :: Row(3, 2) :: Nil
)
checkAnswer(
testData2.groupBy("a").agg(Map("b" -> "sum")),
Row(1, 3) :: Row(2, 3) :: Row(3, 3) :: Nil
)
val df1 = Seq(("a", 1, 0, "b"), ("b", 2, 4, "c"), ("a", 2, 3, "d"))
.toDF("key", "value1", "value2", "rest")
checkAnswer(
df1.groupBy("key").min(),
df1.groupBy("key").min("value1", "value2").collect()
)
checkAnswer(
df1.groupBy("key").min("value2"),
Seq(Row("a", 0), Row("b", 4))
)
checkAnswer(
decimalData.groupBy("a").agg(sum("b")),
Seq(Row(new java.math.BigDecimal(1), new java.math.BigDecimal(3)),
Row(new java.math.BigDecimal(2), new java.math.BigDecimal(3)),
Row(new java.math.BigDecimal(3), new java.math.BigDecimal(3)))
)
val decimalDataWithNulls = spark.sparkContext.parallelize(
DecimalData(1, 1) ::
DecimalData(1, null) ::
DecimalData(2, 1) ::
DecimalData(2, null) ::
DecimalData(3, 1) ::
DecimalData(3, 2) ::
DecimalData(null, 2) :: Nil).toDF()
checkAnswer(
decimalDataWithNulls.groupBy("a").agg(sum("b")),
Seq(Row(new java.math.BigDecimal(1), new java.math.BigDecimal(1)),
Row(new java.math.BigDecimal(2), new java.math.BigDecimal(1)),
Row(new java.math.BigDecimal(3), new java.math.BigDecimal(3)),
Row(null, new java.math.BigDecimal(2)))
)
}
test("SPARK-17124 agg should be ordering preserving") {
val df = spark.range(2)
val ret = df.groupBy("id").agg("id" -> "sum", "id" -> "count", "id" -> "min")
assert(ret.schema.map(_.name) == Seq("id", "sum(id)", "count(id)", "min(id)"))
checkAnswer(
ret,
Row(0, 0, 1, 0) :: Row(1, 1, 1, 1) :: Nil
)
}
test("SPARK-18952: regexes fail codegen when used as keys due to bad forward-slash escapes") {
val df = Seq(("some[thing]", "random-string")).toDF("key", "val")
checkAnswer(
df.groupBy(regexp_extract($"key", "([a-z]+)\\[", 1)).count(),
Row("some", 1) :: Nil
)
}
test("rollup") {
checkAnswer(
courseSales.rollup("course", "year").sum("earnings"),
Row("Java", 2012, 20000.0) ::
Row("Java", 2013, 30000.0) ::
Row("Java", null, 50000.0) ::
Row("dotNET", 2012, 15000.0) ::
Row("dotNET", 2013, 48000.0) ::
Row("dotNET", null, 63000.0) ::
Row(null, null, 113000.0) :: Nil
)
}
test("cube") {
checkAnswer(
courseSales.cube("course", "year").sum("earnings"),
Row("Java", 2012, 20000.0) ::
Row("Java", 2013, 30000.0) ::
Row("Java", null, 50000.0) ::
Row("dotNET", 2012, 15000.0) ::
Row("dotNET", 2013, 48000.0) ::
Row("dotNET", null, 63000.0) ::
Row(null, 2012, 35000.0) ::
Row(null, 2013, 78000.0) ::
Row(null, null, 113000.0) :: Nil
)
val df0 = spark.sparkContext.parallelize(Seq(
Fact(20151123, 18, 35, "room1", 18.6),
Fact(20151123, 18, 35, "room2", 22.4),
Fact(20151123, 18, 36, "room1", 17.4),
Fact(20151123, 18, 36, "room2", 25.6))).toDF()
val cube0 = df0.cube("date", "hour", "minute", "room_name").agg(Map("temp" -> "avg"))
assert(cube0.where("date IS NULL").count > 0)
}
test("grouping and grouping_id") {
checkAnswer(
courseSales.cube("course", "year")
.agg(grouping("course"), grouping("year"), grouping_id("course", "year")),
Row("Java", 2012, 0, 0, 0) ::
Row("Java", 2013, 0, 0, 0) ::
Row("Java", null, 0, 1, 1) ::
Row("dotNET", 2012, 0, 0, 0) ::
Row("dotNET", 2013, 0, 0, 0) ::
Row("dotNET", null, 0, 1, 1) ::
Row(null, 2012, 1, 0, 2) ::
Row(null, 2013, 1, 0, 2) ::
Row(null, null, 1, 1, 3) :: Nil
)
// use column reference in `grouping_id` instead of column name
checkAnswer(
courseSales.cube("course", "year")
.agg(grouping_id(courseSales("course"), courseSales("year"))),
Row("Java", 2012, 0) ::
Row("Java", 2013, 0) ::
Row("Java", null, 1) ::
Row("dotNET", 2012, 0) ::
Row("dotNET", 2013, 0) ::
Row("dotNET", null, 1) ::
Row(null, 2012, 2) ::
Row(null, 2013, 2) ::
Row(null, null, 3) :: Nil
)
intercept[AnalysisException] {
courseSales.groupBy().agg(grouping("course")).explain()
}
intercept[AnalysisException] {
courseSales.groupBy().agg(grouping_id("course")).explain()
}
}
test("grouping/grouping_id inside window function") {
val w = Window.orderBy(sum("earnings"))
checkAnswer(
courseSales.cube("course", "year")
.agg(sum("earnings"),
grouping_id("course", "year"),
rank().over(Window.partitionBy(grouping_id("course", "year")).orderBy(sum("earnings")))),
Row("Java", 2012, 20000.0, 0, 2) ::
Row("Java", 2013, 30000.0, 0, 3) ::
Row("Java", null, 50000.0, 1, 1) ::
Row("dotNET", 2012, 15000.0, 0, 1) ::
Row("dotNET", 2013, 48000.0, 0, 4) ::
Row("dotNET", null, 63000.0, 1, 2) ::
Row(null, 2012, 35000.0, 2, 1) ::
Row(null, 2013, 78000.0, 2, 2) ::
Row(null, null, 113000.0, 3, 1) :: Nil
)
}
test("SPARK-21980: References in grouping functions should be indexed with semanticEquals") {
checkAnswer(
courseSales.cube("course", "year")
.agg(grouping("CouRse"), grouping("year")),
Row("Java", 2012, 0, 0) ::
Row("Java", 2013, 0, 0) ::
Row("Java", null, 0, 1) ::
Row("dotNET", 2012, 0, 0) ::
Row("dotNET", 2013, 0, 0) ::
Row("dotNET", null, 0, 1) ::
Row(null, 2012, 1, 0) ::
Row(null, 2013, 1, 0) ::
Row(null, null, 1, 1) :: Nil
)
}
test("rollup overlapping columns") {
checkAnswer(
testData2.rollup($"a" + $"b" as "foo", $"b" as "bar").agg(sum($"a" - $"b") as "foo"),
Row(2, 1, 0) :: Row(3, 2, -1) :: Row(3, 1, 1) :: Row(4, 2, 0) :: Row(4, 1, 2) :: Row(5, 2, 1)
:: Row(2, null, 0) :: Row(3, null, 0) :: Row(4, null, 2) :: Row(5, null, 1)
:: Row(null, null, 3) :: Nil
)
checkAnswer(
testData2.rollup("a", "b").agg(sum("b")),
Row(1, 1, 1) :: Row(1, 2, 2) :: Row(2, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 1) :: Row(3, 2, 2)
:: Row(1, null, 3) :: Row(2, null, 3) :: Row(3, null, 3)
:: Row(null, null, 9) :: Nil
)
}
test("cube overlapping columns") {
checkAnswer(
testData2.cube($"a" + $"b", $"b").agg(sum($"a" - $"b")),
Row(2, 1, 0) :: Row(3, 2, -1) :: Row(3, 1, 1) :: Row(4, 2, 0) :: Row(4, 1, 2) :: Row(5, 2, 1)
:: Row(2, null, 0) :: Row(3, null, 0) :: Row(4, null, 2) :: Row(5, null, 1)
:: Row(null, 1, 3) :: Row(null, 2, 0)
:: Row(null, null, 3) :: Nil
)
checkAnswer(
testData2.cube("a", "b").agg(sum("b")),
Row(1, 1, 1) :: Row(1, 2, 2) :: Row(2, 1, 1) :: Row(2, 2, 2) :: Row(3, 1, 1) :: Row(3, 2, 2)
:: Row(1, null, 3) :: Row(2, null, 3) :: Row(3, null, 3)
:: Row(null, 1, 3) :: Row(null, 2, 6)
:: Row(null, null, 9) :: Nil
)
}
test("spark.sql.retainGroupColumns config") {
checkAnswer(
testData2.groupBy("a").agg(sum($"b")),
Seq(Row(1, 3), Row(2, 3), Row(3, 3))
)
spark.conf.set(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS.key, false)
checkAnswer(
testData2.groupBy("a").agg(sum($"b")),
Seq(Row(3), Row(3), Row(3))
)
spark.conf.set(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS.key, true)
}
test("agg without groups") {
checkAnswer(
testData2.agg(sum($"b")),
Row(9)
)
}
test("agg without groups and functions") {
checkAnswer(
testData2.agg(lit(1)),
Row(1)
)
}
test("average") {
checkAnswer(
testData2.agg(avg($"a"), mean($"a")),
Row(2.0, 2.0))
checkAnswer(
testData2.agg(avg($"a"), sumDistinct($"a")), // non-partial and test deprecated version
Row(2.0, 6.0) :: Nil)
checkAnswer(
decimalData.agg(avg($"a")),
Row(new java.math.BigDecimal(2)))
checkAnswer(
decimalData.agg(avg($"a"), sum_distinct($"a")), // non-partial
Row(new java.math.BigDecimal(2), new java.math.BigDecimal(6)) :: Nil)
checkAnswer(
decimalData.agg(avg($"a" cast DecimalType(10, 2))),
Row(new java.math.BigDecimal(2)))
// non-partial
checkAnswer(
decimalData.agg(
avg($"a" cast DecimalType(10, 2)), sum_distinct($"a" cast DecimalType(10, 2))),
Row(new java.math.BigDecimal(2), new java.math.BigDecimal(6)) :: Nil)
}
test("null average") {
checkAnswer(
testData3.agg(avg($"b")),
Row(2.0))
checkAnswer(
testData3.agg(avg($"b"), count_distinct($"b")),
Row(2.0, 1))
checkAnswer(
testData3.agg(avg($"b"), sum_distinct($"b")), // non-partial
Row(2.0, 2.0))
}
test("zero average") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(
emptyTableData.agg(avg($"a")),
Row(null))
checkAnswer(
emptyTableData.agg(avg($"a"), sum_distinct($"b")), // non-partial
Row(null, null))
}
test("count") {
assert(testData2.count() === testData2.rdd.map(_ => 1).count())
checkAnswer(
testData2.agg(count($"a"), sum_distinct($"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($"b"), count(lit(1)), count_distinct($"a"), count_distinct($"b")),
Row(2, 1, 2, 2, 1)
)
checkAnswer(
testData3.agg(count($"b"), count_distinct($"b"), sum_distinct($"b")), // non-partial
Row(1, 1, 2)
)
}
test("multiple column distinct count") {
val df1 = Seq(
("a", "b", "c"),
("a", "b", "c"),
("a", "b", "d"),
("x", "y", "z"),
("x", "q", null.asInstanceOf[String]))
.toDF("key1", "key2", "key3")
checkAnswer(
df1.agg(count_distinct($"key1", $"key2")),
Row(3)
)
checkAnswer(
df1.agg(count_distinct($"key1", $"key2", $"key3")),
Row(3)
)
checkAnswer(
df1.groupBy($"key1").agg(count_distinct($"key2", $"key3")),
Seq(Row("a", 2), Row("x", 1))
)
}
test("zero count") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(
emptyTableData.agg(count($"a"), sum_distinct($"a")), // non-partial
Row(0, null))
}
test("stddev") {
val testData2ADev = math.sqrt(4.0 / 5.0)
checkAnswer(
testData2.agg(stddev($"a"), stddev_pop($"a"), stddev_samp($"a")),
Row(testData2ADev, math.sqrt(4 / 6.0), testData2ADev))
checkAnswer(
testData2.agg(stddev("a"), stddev_pop("a"), stddev_samp("a")),
Row(testData2ADev, math.sqrt(4 / 6.0), testData2ADev))
}
test("zero stddev") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(
emptyTableData.agg(stddev($"a"), stddev_pop($"a"), stddev_samp($"a")),
Row(null, null, null))
}
test("zero sum") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(
emptyTableData.agg(sum($"a")),
Row(null))
}
test("zero sum distinct") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(
emptyTableData.agg(sum_distinct($"a")),
Row(null))
}
test("moments") {
val sparkVariance = testData2.agg(variance($"a"))
checkAggregatesWithTol(sparkVariance, Row(4.0 / 5.0), absTol)
val sparkVariancePop = testData2.agg(var_pop($"a"))
checkAggregatesWithTol(sparkVariancePop, Row(4.0 / 6.0), absTol)
val sparkVarianceSamp = testData2.agg(var_samp($"a"))
checkAggregatesWithTol(sparkVarianceSamp, Row(4.0 / 5.0), absTol)
val sparkSkewness = testData2.agg(skewness($"a"))
checkAggregatesWithTol(sparkSkewness, Row(0.0), absTol)
val sparkKurtosis = testData2.agg(kurtosis($"a"))
checkAggregatesWithTol(sparkKurtosis, Row(-1.5), absTol)
}
test("zero moments") {
withSQLConf(SQLConf.LEGACY_STATISTICAL_AGGREGATE.key -> "true") {
val input = Seq((1, 2)).toDF("a", "b")
checkAnswer(
input.agg(stddev($"a"), stddev_samp($"a"), stddev_pop($"a"), variance($"a"),
var_samp($"a"), var_pop($"a"), skewness($"a"), kurtosis($"a")),
Row(Double.NaN, Double.NaN, 0.0, Double.NaN, Double.NaN, 0.0,
Double.NaN, Double.NaN))
checkAnswer(
input.agg(
expr("stddev(a)"),
expr("stddev_samp(a)"),
expr("stddev_pop(a)"),
expr("variance(a)"),
expr("var_samp(a)"),
expr("var_pop(a)"),
expr("skewness(a)"),
expr("kurtosis(a)")),
Row(Double.NaN, Double.NaN, 0.0, Double.NaN, Double.NaN, 0.0,
Double.NaN, Double.NaN))
}
}
test("SPARK-13860: zero moments LEGACY_STATISTICAL_AGGREGATE off") {
withSQLConf(SQLConf.LEGACY_STATISTICAL_AGGREGATE.key -> "false") {
val input = Seq((1, 2)).toDF("a", "b")
checkAnswer(
input.agg(stddev($"a"), stddev_samp($"a"), stddev_pop($"a"), variance($"a"),
var_samp($"a"), var_pop($"a"), skewness($"a"), kurtosis($"a")),
Row(null, null, 0.0, null, null, 0.0,
null, null))
checkAnswer(
input.agg(
expr("stddev(a)"),
expr("stddev_samp(a)"),
expr("stddev_pop(a)"),
expr("variance(a)"),
expr("var_samp(a)"),
expr("var_pop(a)"),
expr("skewness(a)"),
expr("kurtosis(a)")),
Row(null, null, 0.0, null, null, 0.0,
null, null))
}
}
test("null moments") {
val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
checkAnswer(emptyTableData.agg(
variance($"a"), var_samp($"a"), var_pop($"a"), skewness($"a"), kurtosis($"a")),
Row(null, null, null, null, null))
checkAnswer(
emptyTableData.agg(
expr("variance(a)"),
expr("var_samp(a)"),
expr("var_pop(a)"),
expr("skewness(a)"),
expr("kurtosis(a)")),
Row(null, null, null, null, null))
}
test("collect functions") {
val df = Seq((1, 2), (2, 2), (3, 4)).toDF("a", "b")
checkAnswer(
df.select(collect_list($"a"), collect_list($"b")),
Seq(Row(Seq(1, 2, 3), Seq(2, 2, 4)))
)
checkAnswer(
df.select(collect_set($"a"), collect_set($"b")),
Seq(Row(Seq(1, 2, 3), Seq(2, 4)))
)
checkDataset(
df.select(collect_set($"a").as("aSet")).as[Set[Int]],
Set(1, 2, 3))
checkDataset(
df.select(collect_set($"b").as("bSet")).as[Set[Int]],
Set(2, 4))
checkDataset(
df.select(collect_set($"a"), collect_set($"b")).as[(Set[Int], Set[Int])],
Seq(Set(1, 2, 3) -> Set(2, 4)): _*)
}
test("collect functions structs") {
val df = Seq((1, 2, 2), (2, 2, 2), (3, 4, 1))
.toDF("a", "x", "y")
.select($"a", struct($"x", $"y").as("b"))
checkAnswer(
df.select(collect_list($"a"), sort_array(collect_list($"b"))),
Seq(Row(Seq(1, 2, 3), Seq(Row(2, 2), Row(2, 2), Row(4, 1))))
)
checkAnswer(
df.select(collect_set($"a"), sort_array(collect_set($"b"))),
Seq(Row(Seq(1, 2, 3), Seq(Row(2, 2), Row(4, 1))))
)
}
test("SPARK-31500: collect_set() of BinaryType returns duplicate elements") {
val bytesTest1 = "test1".getBytes
val bytesTest2 = "test2".getBytes
val df = Seq(bytesTest1, bytesTest1, bytesTest2).toDF("a")
checkAnswer(df.select(size(collect_set($"a"))), Row(2) :: Nil)
val a = "aa".getBytes
val b = "bb".getBytes
val c = "cc".getBytes
val d = "dd".getBytes
val df1 = Seq((a, b), (a, b), (c, d))
.toDF("x", "y")
.select(struct($"x", $"y").as("a"))
checkAnswer(df1.select(size(collect_set($"a"))), Row(2) :: Nil)
}
test("collect_set functions cannot have maps") {
val df = Seq((1, 3, 0), (2, 3, 0), (3, 4, 1))
.toDF("a", "x", "y")
.select($"a", map($"x", $"y").as("b"))
val error = intercept[AnalysisException] {
df.select(collect_set($"a"), collect_set($"b"))
}
assert(error.message.contains("collect_set() cannot have map type data"))
}
test("SPARK-17641: collect functions should not collect null values") {
val df = Seq(("1", 2), (null, 2), ("1", 4)).toDF("a", "b")
checkAnswer(
df.select(collect_list($"a"), collect_list($"b")),
Seq(Row(Seq("1", "1"), Seq(2, 2, 4)))
)
checkAnswer(
df.select(collect_set($"a"), collect_set($"b")),
Seq(Row(Seq("1"), Seq(2, 4)))
)
}
test("collect functions should be able to cast to array type with no null values") {
val df = Seq(1, 2).toDF("a")
checkAnswer(df.select(collect_list("a") cast ArrayType(IntegerType, false)),
Seq(Row(Seq(1, 2))))
checkAnswer(df.select(collect_set("a") cast ArrayType(FloatType, false)),
Seq(Row(Seq(1.0, 2.0))))
}
test("SPARK-14664: Decimal sum/avg over window should work.") {
checkAnswer(
spark.sql("select sum(a) over () from values 1.0, 2.0, 3.0 T(a)"),
Row(6.0) :: Row(6.0) :: Row(6.0) :: Nil)
checkAnswer(
spark.sql("select avg(a) over () from values 1.0, 2.0, 3.0 T(a)"),
Row(2.0) :: Row(2.0) :: Row(2.0) :: Nil)
}
test("SQL decimal test (used for catching certain decimal handling bugs in aggregates)") {
checkAnswer(
decimalData.groupBy($"a" cast DecimalType(10, 2)).agg(avg($"b" cast DecimalType(10, 2))),
Seq(Row(new java.math.BigDecimal(1), new java.math.BigDecimal("1.5")),
Row(new java.math.BigDecimal(2), new java.math.BigDecimal("1.5")),
Row(new java.math.BigDecimal(3), new java.math.BigDecimal("1.5"))))
}
test("SPARK-17616: distinct aggregate combined with a non-partial aggregate") {
val df = Seq((1, 3, "a"), (1, 2, "b"), (3, 4, "c"), (3, 4, "c"), (3, 5, "d"))
.toDF("x", "y", "z")
checkAnswer(
df.groupBy($"x").agg(count_distinct($"y"), sort_array(collect_list($"z"))),
Seq(Row(1, 2, Seq("a", "b")), Row(3, 2, Seq("c", "c", "d"))))
}
test("SPARK-18004 limit + aggregates") {
val df = Seq(("a", 1), ("b", 2), ("c", 1), ("d", 5)).toDF("id", "value")
val limit2Df = df.limit(2)
checkAnswer(
limit2Df.groupBy("id").count().select($"id"),
limit2Df.select($"id"))
}
test("SPARK-17237 remove backticks in a pivot result schema") {
val df = Seq((2, 3, 4), (3, 4, 5)).toDF("a", "x", "y")
withSQLConf(SQLConf.SUPPORT_QUOTED_REGEX_COLUMN_NAME.key -> "false") {
checkAnswer(
df.groupBy("a").pivot("x").agg(count("y"), avg("y")).na.fill(0),
Seq(Row(3, 0, 0.0, 1, 5.0), Row(2, 1, 4.0, 0, 0.0))
)
}
}
test("aggregate function in GROUP BY") {
val e = intercept[AnalysisException] {
testData.groupBy(sum($"key")).count()
}
assert(e.message.contains("aggregate functions are not allowed in GROUP BY"))
}
private def assertNoExceptions(c: Column): Unit = {
for ((wholeStage, useObjectHashAgg) <-
Seq((true, true), (true, false), (false, true), (false, false))) {
withSQLConf(
(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, wholeStage.toString),
(SQLConf.USE_OBJECT_HASH_AGG.key, useObjectHashAgg.toString)) {
val df = Seq(("1", 1), ("1", 2), ("2", 3), ("2", 4)).toDF("x", "y")
// test case for HashAggregate
val hashAggDF = df.groupBy("x").agg(c, sum("y"))
hashAggDF.collect()
val hashAggPlan = hashAggDF.queryExecution.executedPlan
if (wholeStage) {
assert(find(hashAggPlan) {
case WholeStageCodegenExec(_: HashAggregateExec) => true
case _ => false
}.isDefined)
} else {
assert(stripAQEPlan(hashAggPlan).isInstanceOf[HashAggregateExec])
}
// test case for ObjectHashAggregate and SortAggregate
val objHashAggOrSortAggDF = df.groupBy("x").agg(c, collect_list("y"))
objHashAggOrSortAggDF.collect()
val objHashAggOrSortAggPlan =
stripAQEPlan(objHashAggOrSortAggDF.queryExecution.executedPlan)
if (useObjectHashAgg) {
assert(objHashAggOrSortAggPlan.isInstanceOf[ObjectHashAggregateExec])
} else {
assert(objHashAggOrSortAggPlan.isInstanceOf[SortAggregateExec])
}
}
}
}
test("SPARK-19471: AggregationIterator does not initialize the generated result projection" +
" before using it") {
Seq(
monotonically_increasing_id(), spark_partition_id(),
rand(Random.nextLong()), randn(Random.nextLong())
).foreach(assertNoExceptions)
}
test("SPARK-21580 ints in aggregation expressions are taken as group-by ordinal.") {
checkAnswer(
testData2.groupBy(lit(3), lit(4)).agg(lit(6), lit(7), sum("b")),
Seq(Row(3, 4, 6, 7, 9)))
checkAnswer(
testData2.groupBy(lit(3), lit(4)).agg(lit(6), $"b", sum("b")),
Seq(Row(3, 4, 6, 1, 3), Row(3, 4, 6, 2, 6)))
checkAnswer(
spark.sql("SELECT 3, 4, SUM(b) FROM testData2 GROUP BY 1, 2"),
Seq(Row(3, 4, 9)))
checkAnswer(
spark.sql("SELECT 3 AS c, 4 AS d, SUM(b) FROM testData2 GROUP BY c, d"),
Seq(Row(3, 4, 9)))
}
test("SPARK-22223: ObjectHashAggregate should not introduce unnecessary shuffle") {
withSQLConf(SQLConf.USE_OBJECT_HASH_AGG.key -> "true") {
val df = Seq(("1", "2", 1), ("1", "2", 2), ("2", "3", 3), ("2", "3", 4)).toDF("a", "b", "c")
.repartition(col("a"))
val objHashAggDF = df
.withColumn("d", expr("(a, b, c)"))
.groupBy("a", "b").agg(collect_list("d").as("e"))
.withColumn("f", expr("(b, e)"))
.groupBy("a").agg(collect_list("f").as("g"))
val aggPlan = objHashAggDF.queryExecution.executedPlan
val sortAggPlans = collect(aggPlan) {
case sortAgg: SortAggregateExec => sortAgg
}
assert(sortAggPlans.isEmpty)
val objHashAggPlans = collect(aggPlan) {
case objHashAgg: ObjectHashAggregateExec => objHashAgg
}
assert(objHashAggPlans.nonEmpty)
val exchangePlans = collect(aggPlan) {
case shuffle: ShuffleExchangeExec => shuffle
}
assert(exchangePlans.length == 1)
}
}
testWithWholeStageCodegenOnAndOff("SPARK-22951: dropDuplicates on empty dataFrames " +
"should produce correct aggregate") { _ =>
// explicit global aggregations
val emptyAgg = Map.empty[String, String]
checkAnswer(spark.emptyDataFrame.agg(emptyAgg), Seq(Row()))
checkAnswer(spark.emptyDataFrame.groupBy().agg(emptyAgg), Seq(Row()))
checkAnswer(spark.emptyDataFrame.groupBy().agg(count("*")), Seq(Row(0)))
checkAnswer(spark.emptyDataFrame.dropDuplicates().agg(emptyAgg), Seq(Row()))
checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(emptyAgg), Seq(Row()))
checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(count("*")), Seq(Row(0)))
// global aggregation is converted to grouping aggregation:
assert(spark.emptyDataFrame.dropDuplicates().count() == 0)
}
test("SPARK-21896: Window functions inside aggregate functions") {
def checkWindowError(df: => DataFrame): Unit = {
val thrownException = the [AnalysisException] thrownBy {
df.queryExecution.analyzed
}
assert(thrownException.message.contains("not allowed to use a window function"))
}
checkWindowError(testData2.select(min(avg($"b").over(Window.partitionBy($"a")))))
checkWindowError(testData2.agg(sum($"b"), max(rank().over(Window.orderBy($"a")))))
checkWindowError(testData2.groupBy($"a").agg(sum($"b"), max(rank().over(Window.orderBy($"b")))))
checkWindowError(testData2.groupBy($"a").agg(max(sum(sum($"b")).over(Window.orderBy($"a")))))
checkWindowError(testData2.groupBy($"a").agg(
sum($"b").as("s"), max(count("*").over())).where($"s" === 3))
checkAnswer(testData2.groupBy($"a").agg(
max($"b"), sum($"b").as("s"), count("*").over()).where($"s" === 3),
Row(1, 2, 3, 3) :: Row(2, 2, 3, 3) :: Row(3, 2, 3, 3) :: Nil)
checkWindowError(sql("SELECT MIN(AVG(b) OVER(PARTITION BY a)) FROM testData2"))
checkWindowError(sql("SELECT SUM(b), MAX(RANK() OVER(ORDER BY a)) FROM testData2"))
checkWindowError(sql("SELECT SUM(b), MAX(RANK() OVER(ORDER BY b)) FROM testData2 GROUP BY a"))
checkWindowError(sql("SELECT MAX(SUM(SUM(b)) OVER(ORDER BY a)) FROM testData2 GROUP BY a"))
checkWindowError(
sql("SELECT MAX(RANK() OVER(ORDER BY b)) FROM testData2 GROUP BY a HAVING SUM(b) = 3"))
checkAnswer(
sql("SELECT a, MAX(b), RANK() OVER(ORDER BY a) FROM testData2 GROUP BY a HAVING SUM(b) = 3"),
Row(1, 2, 1) :: Row(2, 2, 2) :: Row(3, 2, 3) :: Nil)
}
test("SPARK-24788: RelationalGroupedDataset.toString with unresolved exprs should not fail") {
// Checks if these raise no exception
assert(testData.groupBy($"key").toString.contains(
"[grouping expressions: [key], value: [key: int, value: string], type: GroupBy]"))
assert(testData.groupBy(col("key")).toString.contains(
"[grouping expressions: [key], value: [key: int, value: string], type: GroupBy]"))
assert(testData.groupBy(current_date()).toString.contains(
"grouping expressions: [current_date(None)], value: [key: int, value: string], " +
"type: GroupBy]"))
}
test("SPARK-26021: NaN and -0.0 in grouping expressions") {
checkAnswer(
Seq(0.0f, -0.0f, 0.0f/0.0f, Float.NaN).toDF("f").groupBy("f").count(),
Row(0.0f, 2) :: Row(Float.NaN, 2) :: Nil)
checkAnswer(
Seq(0.0d, -0.0d, 0.0d/0.0d, Double.NaN).toDF("d").groupBy("d").count(),
Row(0.0d, 2) :: Row(Double.NaN, 2) :: Nil)
// test with complicated type grouping expressions
checkAnswer(
Seq(0.0f, -0.0f, 0.0f/0.0f, Float.NaN).toDF("f")
.groupBy(array("f"), struct("f")).count(),
Row(Seq(0.0f), Row(0.0f), 2) ::
Row(Seq(Float.NaN), Row(Float.NaN), 2) :: Nil)
checkAnswer(
Seq(0.0d, -0.0d, 0.0d/0.0d, Double.NaN).toDF("d")
.groupBy(array("d"), struct("d")).count(),
Row(Seq(0.0d), Row(0.0d), 2) ::
Row(Seq(Double.NaN), Row(Double.NaN), 2) :: Nil)
checkAnswer(
Seq(0.0f, -0.0f, 0.0f/0.0f, Float.NaN).toDF("f")
.groupBy(array(struct("f")), struct(array("f"))).count(),
Row(Seq(Row(0.0f)), Row(Seq(0.0f)), 2) ::
Row(Seq(Row(Float.NaN)), Row(Seq(Float.NaN)), 2) :: Nil)
checkAnswer(
Seq(0.0d, -0.0d, 0.0d/0.0d, Double.NaN).toDF("d")
.groupBy(array(struct("d")), struct(array("d"))).count(),
Row(Seq(Row(0.0d)), Row(Seq(0.0d)), 2) ::
Row(Seq(Row(Double.NaN)), Row(Seq(Double.NaN)), 2) :: Nil)
// test with complicated type grouping columns
val df = Seq(
(Array(-0.0f, 0.0f), Tuple2(-0.0d, Double.NaN), Seq(Tuple2(-0.0d, Double.NaN))),
(Array(0.0f, -0.0f), Tuple2(0.0d, Double.NaN), Seq(Tuple2(0.0d, 0.0/0.0)))
).toDF("arr", "stru", "arrOfStru")
checkAnswer(
df.groupBy("arr", "stru", "arrOfStru").count(),
Row(Seq(0.0f, 0.0f), Row(0.0d, Double.NaN), Seq(Row(0.0d, Double.NaN)), 2)
)
}
test("SPARK-27581: DataFrame count_distinct(\"*\") shouldn't fail with AnalysisException") {
val df = sql("select id % 100 from range(100000)")
val distinctCount1 = df.select(expr("count(distinct(*))"))
val distinctCount2 = df.select(countDistinct("*"))
checkAnswer(distinctCount1, distinctCount2)
val countAndDistinct = df.select(count("*"), countDistinct("*"))
checkAnswer(countAndDistinct, Row(100000, 100))
}
test("max_by") {
val yearOfMaxEarnings =
sql("SELECT course, max_by(year, earnings) FROM courseSales GROUP BY course")
checkAnswer(yearOfMaxEarnings, Row("dotNET", 2013) :: Row("Java", 2013) :: Nil)
checkAnswer(
sql("SELECT max_by(x, y) FROM VALUES (('a', 10)), (('b', 50)), (('c', 20)) AS tab(x, y)"),
Row("b") :: Nil
)
checkAnswer(
sql("SELECT max_by(x, y) FROM VALUES (('a', 10)), (('b', null)), (('c', 20)) AS tab(x, y)"),
Row("c") :: Nil
)
checkAnswer(
sql("SELECT max_by(x, y) FROM VALUES (('a', null)), (('b', null)), (('c', 20)) AS tab(x, y)"),
Row("c") :: Nil
)
checkAnswer(
sql("SELECT max_by(x, y) FROM VALUES (('a', 10)), (('b', 50)), (('c', null)) AS tab(x, y)"),
Row("b") :: Nil
)
checkAnswer(
sql("SELECT max_by(x, y) FROM VALUES (('a', null)), (('b', null)) AS tab(x, y)"),
Row(null) :: Nil
)
// structs as ordering value.
checkAnswer(
sql("select max_by(x, y) FROM VALUES (('a', (10, 20))), (('b', (10, 50))), " +
"(('c', (10, 60))) AS tab(x, y)"),
Row("c") :: Nil
)
checkAnswer(
sql("select max_by(x, y) FROM VALUES (('a', (10, 20))), (('b', (10, 50))), " +
"(('c', null)) AS tab(x, y)"),
Row("b") :: Nil
)
withTempView("tempView") {
val dfWithMap = Seq((0, "a"), (1, "b"), (2, "c"))
.toDF("x", "y")
.select($"x", map($"x", $"y").as("y"))
.createOrReplaceTempView("tempView")
val error = intercept[AnalysisException] {
sql("SELECT max_by(x, y) FROM tempView").show
}
assert(
error.message.contains("function max_by does not support ordering on type map<int,string>"))
}
}
test("min_by") {
val yearOfMinEarnings =
sql("SELECT course, min_by(year, earnings) FROM courseSales GROUP BY course")
checkAnswer(yearOfMinEarnings, Row("dotNET", 2012) :: Row("Java", 2012) :: Nil)
checkAnswer(
sql("SELECT min_by(x, y) FROM VALUES (('a', 10)), (('b', 50)), (('c', 20)) AS tab(x, y)"),
Row("a") :: Nil
)
checkAnswer(
sql("SELECT min_by(x, y) FROM VALUES (('a', 10)), (('b', null)), (('c', 20)) AS tab(x, y)"),
Row("a") :: Nil
)
checkAnswer(
sql("SELECT min_by(x, y) FROM VALUES (('a', null)), (('b', null)), (('c', 20)) AS tab(x, y)"),
Row("c") :: Nil
)
checkAnswer(
sql("SELECT min_by(x, y) FROM VALUES (('a', 10)), (('b', 50)), (('c', null)) AS tab(x, y)"),
Row("a") :: Nil
)
checkAnswer(
sql("SELECT min_by(x, y) FROM VALUES (('a', null)), (('b', null)) AS tab(x, y)"),
Row(null) :: Nil
)
// structs as ordering value.
checkAnswer(
sql("select min_by(x, y) FROM VALUES (('a', (10, 20))), (('b', (10, 50))), " +
"(('c', (10, 60))) AS tab(x, y)"),
Row("a") :: Nil
)
checkAnswer(
sql("select min_by(x, y) FROM VALUES (('a', null)), (('b', (10, 50))), " +
"(('c', (10, 60))) AS tab(x, y)"),
Row("b") :: Nil
)
withTempView("tempView") {
val dfWithMap = Seq((0, "a"), (1, "b"), (2, "c"))
.toDF("x", "y")
.select($"x", map($"x", $"y").as("y"))
.createOrReplaceTempView("tempView")
val error = intercept[AnalysisException] {
sql("SELECT min_by(x, y) FROM tempView").show
}
assert(
error.message.contains("function min_by does not support ordering on type map<int,string>"))
}
}
test("count_if") {
withTempView("tempView") {
Seq(("a", None), ("a", Some(1)), ("a", Some(2)), ("a", Some(3)),
("b", None), ("b", Some(4)), ("b", Some(5)), ("b", Some(6)))
.toDF("x", "y")
.createOrReplaceTempView("tempView")
checkAnswer(
sql("SELECT COUNT_IF(NULL), COUNT_IF(y % 2 = 0), COUNT_IF(y % 2 <> 0), " +
"COUNT_IF(y IS NULL) FROM tempView"),
Row(0L, 3L, 3L, 2L))
checkAnswer(
sql("SELECT x, COUNT_IF(NULL), COUNT_IF(y % 2 = 0), COUNT_IF(y % 2 <> 0), " +
"COUNT_IF(y IS NULL) FROM tempView GROUP BY x"),
Row("a", 0L, 1L, 2L, 1L) :: Row("b", 0L, 2L, 1L, 1L) :: Nil)
checkAnswer(
sql("SELECT x FROM tempView GROUP BY x HAVING COUNT_IF(y % 2 = 0) = 1"),
Row("a"))
checkAnswer(
sql("SELECT x FROM tempView GROUP BY x HAVING COUNT_IF(y % 2 = 0) = 2"),
Row("b"))
checkAnswer(
sql("SELECT x FROM tempView GROUP BY x HAVING COUNT_IF(y IS NULL) > 0"),
Row("a") :: Row("b") :: Nil)
checkAnswer(
sql("SELECT x FROM tempView GROUP BY x HAVING COUNT_IF(NULL) > 0"),
Nil)
val error = intercept[AnalysisException] {
sql("SELECT COUNT_IF(x) FROM tempView")
}
assert(error.message.contains("function count_if requires boolean type"))
}
}