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[SPARK-29140][SQL] Handle parameters having "array" of javaType properly in splitAggregateExpressions #25830

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[SPARK-29140][SQL] Handle parameters having "array" of javaType properly in splitAggregateExpressions #25830

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[SPARK-29140][SQL] Handle BinaryType of parameter properly in HashAgg…
HeartSaVioR Sep 18, 2019
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Reflect review comments
HeartSaVioR Sep 18, 2019
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Additional review comment for nits
HeartSaVioR Sep 18, 2019
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HeartSaVioR Sep 20, 2019
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Roll back unrelated change
HeartSaVioR Sep 20, 2019
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8 changes: 8 additions & 0 deletions ...lyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
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Expand Up @@ -1811,6 +1811,14 @@ object CodeGenerator extends Logging {

def boxedType(dt: DataType): String = boxedType(javaType(dt))

def typeName(clazz: Class[_]): String = {
if (clazz.isArray) {
typeName(clazz.getComponentType) + "[]"
} else {
clazz.getName
}
}

/**
* Returns the representation of default value for a given Java Type.
* @param jt the string name of the Java type
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4 changes: 3 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
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Expand Up @@ -299,7 +299,9 @@ case class HashAggregateExec(
if (inputVars.forall(_.isDefined)) {
val splitCodes = inputVars.flatten.zipWithIndex.map { case (args, i) =>
val doAggFunc = ctx.freshName(s"doAggregate_${aggNames(i)}")
val argList = args.map(v => s"${v.javaType.getName} ${v.variableName}").mkString(", ")
val argList = args.map { v =>
s"${CodeGenerator.typeName(v.javaType)} ${v.variableName}"
}.mkString(", ")
val doAggFuncName = ctx.addNewFunction(doAggFunc,
s"""
|private void $doAggFunc($argList) throws java.io.IOException {
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15 changes: 15 additions & 0 deletions sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
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Expand Up @@ -1028,6 +1028,21 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Te
}
}
}

test("SPARK-29140: HashAggregateExec aggregating binary type doesn't break codegen compilation") {
val schema = new StructType().add("id", IntegerType, nullable = false)
.add("c1", BinaryType, nullable = true)

withSQLConf(
SQLConf.CODEGEN_SPLIT_AGGREGATE_FUNC.key -> "true",
SQLConf.CODEGEN_METHOD_SPLIT_THRESHOLD.key -> "1") {
val emptyRows = spark.sparkContext.parallelize(Seq.empty[Row], 1)
val aggDf = spark.createDataFrame(emptyRows, schema)
.groupBy($"id" % 10 as "group")
.agg(countDistinct($"c1"))
checkAnswer(aggDf, Seq.empty[Row])
}
}
}


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