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[SPARK-16714][SQL] array should create a decimal array from decimals with different precisions and scales #14353

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[SPARK-16714][SQL] array should create a decimal array from decimals with different precisions and scales #14353

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23 changes: 17 additions & 6 deletions ...atalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, TypeCoercion}
import org.apache.spark.sql.catalyst.expressions.codegen._
import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, GenericArrayData, MapData, TypeUtils}
import org.apache.spark.sql.types._
Expand All @@ -33,13 +33,24 @@ case class CreateArray(children: Seq[Expression]) extends Expression {

override def foldable: Boolean = children.forall(_.foldable)

override def checkInputDataTypes(): TypeCheckResult =
TypeUtils.checkForSameTypeInputExpr(children.map(_.dataType), "function array")
override def checkInputDataTypes(): TypeCheckResult = {
if (children.map(_.dataType).forall(_.isInstanceOf[DecimalType])) {
TypeCheckResult.TypeCheckSuccess
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@yhuai yhuai Jul 26, 2016

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I think we cannot just make the check pass. We need to need to actually cast those element to the same prevision and scale.

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@yhuai yhuai Jul 26, 2016

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For example, if we access a single element, its data type actually may not be the one shown as the array's datatype.

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016

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Thank you for review, @yhuai .
I see. I'll check that more.

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016 •
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Hi, @yhuai . I checked the following.

scala> sql("select a[0], a[1] from (select array(0.001, 0.02) a) T")
res4: org.apache.spark.sql.DataFrame = [a[0]: decimal(3,3), a[1]: decimal(3,3)]

scala> sql("select a[0], a[1] from (select array(0.001, 0.02) a) T").show()
+-----+-----+
| a[0]| a[1]|
+-----+-----+
|0.001|0.020|
+-----+-----+

scala> sql("select a[0], a[1] from (select array(0.001, 0.02) a) T").explain(true)
== Parsed Logical Plan ==
'Project [unresolvedalias('a[0], None), unresolvedalias('a[1], None)]
+- 'SubqueryAlias T
   +- 'Project ['array(0.001, 0.02) AS a#54]
      +- OneRowRelation$

== Analyzed Logical Plan ==
a[0]: decimal(3,3), a[1]: decimal(3,3)
Project [a#54[0] AS a[0]#61, a#54[1] AS a[1]#62]
+- SubqueryAlias T
   +- Project [array(0.001, 0.02) AS a#54]
      +- OneRowRelation$

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016 •
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scala> sql("create table d1(a DECIMAL(3,2))")
scala> sql("create table d2(a DECIMAL(2,1))")
scala> sql("insert into d1 values(1.0)")
scala> sql("insert into d2 values(1.0)")
scala> sql("select * from d1, d2").show()
+----+---+
|   a|  a|
+----+---+
|1.00|1.0|
+----+---+

scala> sql("select array(d1.a,d2.a),array(d2.a,d1.a),* from d1, d2")
res5: org.apache.spark.sql.DataFrame = [array(a, a): array<decimal(3,2)>, array(a, a): array<decimal(3,2)> ... 2 more fields]

scala> sql("select array(d1.a,d2.a),array(d2.a,d1.a),* from d1, d2").show()
+------------+------------+----+---+
| array(a, a)| array(a, a)|   a|  a|
+------------+------------+----+---+
|[1.00, 1.00]|[1.00, 1.00]|1.00|1.0|
+------------+------------+----+---+

scala> sql("select array(d1.a,d2.a)[0],array(d2.a,d1.a)[0],* from d1, d2").show()
+--------------+--------------+----+---+
|array(a, a)[0]|array(a, a)[0]|   a|  a|
+--------------+--------------+----+---+
|          1.00|          1.00|1.00|1.0|
+--------------+--------------+----+---+

scala> sql("select array(d1.a,d2.a)[1],array(d2.a,d1.a)[1],* from d1, d2").show()
+--------------+--------------+----+---+
|array(a, a)[1]|array(a, a)[1]|   a|  a|
+--------------+--------------+----+---+
|          1.00|          1.00|1.00|1.0|
+--------------+--------------+----+---+

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016

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And Finally, the following is the codegen result. Please see the line 29.

scala> sql("explain codegen select array(0.001, 0.02)[1]").collect().foreach(println)
[Found 1 WholeStageCodegen subtrees.
== Subtree 1 / 1 ==
*Project [0.02 AS array(0.001, 0.02)[1]#75]
+- Scan OneRowRelation[]

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */   private Object[] references;
/* 007 */   private scala.collection.Iterator inputadapter_input;
/* 008 */   private UnsafeRow project_result;
/* 009 */   private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder;
/* 010 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter;
/* 011 */
/* 012 */   public GeneratedIterator(Object[] references) {
/* 013 */     this.references = references;
/* 014 */   }
/* 015 */
/* 016 */   public void init(int index, scala.collection.Iterator inputs[]) {
/* 017 */     partitionIndex = index;
/* 018 */     inputadapter_input = inputs[0];
/* 019 */     project_result = new UnsafeRow(1);
/* 020 */     this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 0);
/* 021 */     this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 1);
/* 022 */   }
/* 023 */
/* 024 */   protected void processNext() throws java.io.IOException {
/* 025 */     while (inputadapter_input.hasNext()) {
/* 026 */       InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 027 */       Object project_obj = ((Expression) references[0]).eval(null);
/* 028 */       Decimal project_value = (Decimal) project_obj;
/* 029 */       project_rowWriter.write(0, project_value, 3, 3);
/* 030 */       append(project_result);
/* 031 */       if (shouldStop()) return;
/* 032 */     }
/* 033 */   }

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016

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In short, those are recognized correctly in the Analyzed Logical Plan. As a result, the codegen correctly writes it with the unified precision and scale.

== Analyzed Logical Plan ==
a[0]: decimal(3,3), a[1]: decimal(3,3)

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@dongjoon-hyun dongjoon-hyun Jul 26, 2016

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Is there anything to check more?

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@dongjoon-hyun dongjoon-hyun Jul 27, 2016

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Hi, @yhuai .
Could you give me some advice?

} else {
TypeUtils.checkForSameTypeInputExpr(children.map(_.dataType), "function array")
}
}

override def dataType: DataType = {
ArrayType(
children.headOption.map(_.dataType).getOrElse(NullType),
containsNull = children.exists(_.nullable))
var elementType: DataType = children.headOption.map(_.dataType).getOrElse(NullType)
if (elementType.isInstanceOf[DecimalType]) {
children.foreach { child =>
if (elementType.asInstanceOf[DecimalType].isTighterThan(child.dataType)) {
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@rxin rxin Jul 28, 2016

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i think this suffers from the same issue as the map pr.

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@dongjoon-hyun dongjoon-hyun Jul 28, 2016

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Thank you, @rxin .
Yep. I've read you comment about the lose.
I'll check that and revise.

elementType = child.dataType
}
}
}
ArrayType(elementType, containsNull = children.exists(_.nullable))
}

override def nullable: Boolean = false
Expand Down
14 changes: 13 additions & 1 deletion sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ComplexTypeSuite.scala
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Expand Up @@ -18,7 +18,7 @@
package org.apache.spark.sql.catalyst.expressions

import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.analysis.UnresolvedExtractValue
import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, UnresolvedExtractValue}
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
Expand Down Expand Up @@ -134,6 +134,18 @@ class ComplexTypeSuite extends SparkFunSuite with ExpressionEvalHelper {
checkEvaluation(CreateArray(Literal.create(null, IntegerType) :: Nil), null :: Nil)
}

test("SPARK-16714: CreateArray with Decimals") {
val array1 = CreateArray(Seq(Literal(Decimal(0.001)), Literal(Decimal(0.02))))
val array2 = CreateArray(Seq(Literal(Decimal(0.02)), Literal(Decimal(0.001))))

assert(array1.checkInputDataTypes() == TypeCheckResult.TypeCheckSuccess)
assert(array2.checkInputDataTypes() == TypeCheckResult.TypeCheckSuccess)
assert(array1.dataType == array2.dataType)

checkEvaluation(array1, Seq(Decimal(0.001), Decimal(0.02)))
checkEvaluation(array2, Seq(Decimal(0.02), Decimal(0.001)))
}

test("CreateMap") {
def interlace(keys: Seq[Literal], values: Seq[Literal]): Seq[Literal] = {
keys.zip(values).flatMap { case (k, v) => Seq(k, v) }
Expand Down