[
https://issues.apache.org/jira/browse/SPARK-31854?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Paul Finkelshteyn updated SPARK-31854:
--------------------------------------
Description:
Preface: I'm creating Kotlin API for spark to take best parts from three worlds
— spark scala, spark java and kotlin.
What is nice — it works in most scenarios.
But i've hit following cornercase:
{code:scala}
withSpark(props = mapOf("spark.sql.codegen.wholeStage" to true)) {
dsOf(1, null, 2)
.map { c(it) }
.debugCodegen()
.show()
}
{code}
c(it) is creation of unnamed tuple
It fails with exception
{code}
java.lang.NullPointerException: Null value appeared in non-nullable field:
top level Product or row object
If the schema is inferred from a Scala tuple/case class, or a Java bean, please
try to use scala.Option[_] or other nullable types (e.g. java.lang.Integer
instead of int/scala.Int).
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.serializefromobject_doConsume_0$(Unknown
Source)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown
Source)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown
Source)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
Source)
…
{code}
I know, in Scala it won't work, so I could stop here. But it works in Kotlin if
I turn wholestage codegen off!
Moreover, if we will dig into generated code (when wholestage codegen is on),
we'll see that basically flow is following:
If one of elements in source dataset was null we wil throw NPE no matter what.
Flow is as follows:
{code}
private void serializefromobject_doConsume_0(org.jetbrains.spark.api.Arity1
serializefromobject_expr_0_0, boolean serializefromobject_exprIsNull_0_0)
throws java.io.IOException {
serializefromobject_doConsume_0(mapelements_value_1, mapelements_isNull_1);
mapelements_isNull_1 = mapelements_resultIsNull_0;
mapelements_resultIsNull_0 = mapelements_exprIsNull_0_0;
private void mapelements_doConsume_0(java.lang.Integer
mapelements_expr_0_0, boolean mapelements_exprIsNull_0_0) throws
java.io.IOException {
mapelements_doConsume_0(deserializetoobject_value_0,
deserializetoobject_isNull_0);
deserializetoobject_resultIsNull_0 =
deserializetoobject_exprIsNull_0_0;
private void
deserializetoobject_doConsume_0(InternalRow localtablescan_row_0, int
deserializetoobject_expr_0_0, boolean deserializetoobject_exprIsNull_0_0)
throws java.io.IOException {
deserializetoobject_doConsume_0(localtablescan_row_0, localtablescan_value_0,
localtablescan_isNull_0);
boolean localtablescan_isNull_0 =
localtablescan_row_0.isNullAt(0);
mapelements_isNull_1 = true;
{code}
You can find generated code in it's original view and slightly simplified and
refacored version
[here|https://gist.github.com/asm0dey/5c0fa4c985ab999b383d16257b515100]
I believe that Spark should not behave differently when wholestage codegen is
on and off and differences in behavior look like a bug.
My Spark version is 3.0.0-preview2
was:
Preface: I'm creating Kotlin API for spark to take best parts from three worlds
— spark scala, spark java and kotlin.
What is nice — it works in most scenarios.
But i've hit following cornercase:
{code:scala}
withSpark(props = mapOf("spark.sql.codegen.wholeStage" to true)) {
dsOf(1, null, 2)
.map { c(it) }
.debugCodegen()
.show()
}
{code}
c(it) is creation of unnamed tuple
It fails with exception
{code}
java.lang.NullPointerException: Null value appeared in non-nullable field:
top level Product or row object
If the schema is inferred from a Scala tuple/case class, or a Java bean, please
try to use scala.Option[_] or other nullable types (e.g. java.lang.Integer
instead of int/scala.Int).
{code}
I know, in Scala it won't work, so I could stop here. But it works in Kotlin if
I turn wholestage codegen off!
Moreover, if we will dig into generated code (when wholestage codegen is on),
we'll see that basically flow is following:
If one of elements in source dataset was null we wil throw NPE no matter what.
Flow is as follows:
{code}
private void serializefromobject_doConsume_0(org.jetbrains.spark.api.Arity1
serializefromobject_expr_0_0, boolean serializefromobject_exprIsNull_0_0)
throws java.io.IOException {
serializefromobject_doConsume_0(mapelements_value_1, mapelements_isNull_1);
mapelements_isNull_1 = mapelements_resultIsNull_0;
mapelements_resultIsNull_0 = mapelements_exprIsNull_0_0;
private void mapelements_doConsume_0(java.lang.Integer
mapelements_expr_0_0, boolean mapelements_exprIsNull_0_0) throws
java.io.IOException {
mapelements_doConsume_0(deserializetoobject_value_0,
deserializetoobject_isNull_0);
deserializetoobject_resultIsNull_0 =
deserializetoobject_exprIsNull_0_0;
private void
deserializetoobject_doConsume_0(InternalRow localtablescan_row_0, int
deserializetoobject_expr_0_0, boolean deserializetoobject_exprIsNull_0_0)
throws java.io.IOException {
deserializetoobject_doConsume_0(localtablescan_row_0, localtablescan_value_0,
localtablescan_isNull_0);
boolean localtablescan_isNull_0 =
localtablescan_row_0.isNullAt(0);
mapelements_isNull_1 = true;
{code}
You can find generated code in it's original view and slightly simplified and
refacored version
[here|https://gist.github.com/asm0dey/5c0fa4c985ab999b383d16257b515100]
I believe that Spark should not behave differently when wholestage codegen is
on and off and differences in behavior look like a bug.
My Spark version is 3.0.0-preview2
> Different results of query execution with wholestage codegen on and off
> -----------------------------------------------------------------------
>
> Key: SPARK-31854
> URL: https://issues.apache.org/jira/browse/SPARK-31854
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.0.0
> Reporter: Paul Finkelshteyn
> Priority: Major
>
> Preface: I'm creating Kotlin API for spark to take best parts from three
> worlds — spark scala, spark java and kotlin.
> What is nice — it works in most scenarios.
> But i've hit following cornercase:
> {code:scala}
> withSpark(props = mapOf("spark.sql.codegen.wholeStage" to true)) {
> dsOf(1, null, 2)
> .map { c(it) }
> .debugCodegen()
> .show()
> }
> {code}
> c(it) is creation of unnamed tuple
> It fails with exception
> {code}
> java.lang.NullPointerException: Null value appeared in non-nullable field:
> top level Product or row object
> If the schema is inferred from a Scala tuple/case class, or a Java bean,
> please try to use scala.Option[_] or other nullable types (e.g.
> java.lang.Integer instead of int/scala.Int).
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.serializefromobject_doConsume_0$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.mapelements_doConsume_0$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.deserializetoobject_doConsume_0$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown
> Source)
> …
> {code}
> I know, in Scala it won't work, so I could stop here. But it works in Kotlin
> if I turn wholestage codegen off!
> Moreover, if we will dig into generated code (when wholestage codegen is on),
> we'll see that basically flow is following:
> If one of elements in source dataset was null we wil throw NPE no matter what.
> Flow is as follows:
> {code}
> private void serializefromobject_doConsume_0(org.jetbrains.spark.api.Arity1
> serializefromobject_expr_0_0, boolean serializefromobject_exprIsNull_0_0)
> throws java.io.IOException {
> serializefromobject_doConsume_0(mapelements_value_1,
> mapelements_isNull_1);
> mapelements_isNull_1 = mapelements_resultIsNull_0;
> mapelements_resultIsNull_0 = mapelements_exprIsNull_0_0;
> private void mapelements_doConsume_0(java.lang.Integer
> mapelements_expr_0_0, boolean mapelements_exprIsNull_0_0) throws
> java.io.IOException {
> mapelements_doConsume_0(deserializetoobject_value_0,
> deserializetoobject_isNull_0);
> deserializetoobject_resultIsNull_0 =
> deserializetoobject_exprIsNull_0_0;
> private void
> deserializetoobject_doConsume_0(InternalRow localtablescan_row_0, int
> deserializetoobject_expr_0_0, boolean deserializetoobject_exprIsNull_0_0)
> throws java.io.IOException {
>
> deserializetoobject_doConsume_0(localtablescan_row_0, localtablescan_value_0,
> localtablescan_isNull_0);
> boolean localtablescan_isNull_0 =
> localtablescan_row_0.isNullAt(0);
> mapelements_isNull_1 = true;
> {code}
> You can find generated code in it's original view and slightly simplified and
> refacored version
> [here|https://gist.github.com/asm0dey/5c0fa4c985ab999b383d16257b515100]
> I believe that Spark should not behave differently when wholestage codegen is
> on and off and differences in behavior look like a bug.
> My Spark version is 3.0.0-preview2
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