[SPARK-16714][SQL] array should create a decimal array from decimals with different precisions and scales
#14353
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Test build #62848 has finished for PR 14353 at commit
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| TypeUtils.checkForSameTypeInputExpr(children.map(_.dataType), "function array") | ||
| override def checkInputDataTypes(): TypeCheckResult = { | ||
| if (children.map(_.dataType).forall(_.isInstanceOf[DecimalType])) { | ||
| TypeCheckResult.TypeCheckSuccess |
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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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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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Thank you for review, @yhuai .
I see. I'll check that more.
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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$There was a problem hiding this comment.
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|
+--------------+--------------+----+---+There was a problem hiding this comment.
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 */ }There was a problem hiding this comment.
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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Is there anything to check more?
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Hi, @yhuai .
Could you give me some advice?
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Hi, @rxin . |
…ng different inferred precessions and scales
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Rebased. |
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Test build #62954 has finished for PR 14353 at commit
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| 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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i think this suffers from the same issue as the map pr.
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Thank you, @rxin .
Yep. I've read you comment about the lose.
I'll check that and revise.
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@dongjoon-hyun I created a patch here: #14389 |
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Close this for the better PR #14439 |
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What changes were proposed in this pull request?
In Spark 2.0, we will parse float literals as decimals. However, it introduces a side-effect, which is described below.
Before
After
How was this patch tested?
Pass the Jenkins tests with a new test case.