[
https://issues.apache.org/jira/browse/SPARK-51356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated SPARK-51356:
-----------------------------------
Labels: pull-request-available (was: )
> FilterExec incorrectly reorders IsNotNull predicates for nested access
> ----------------------------------------------------------------------
>
> Key: SPARK-51356
> URL: https://issues.apache.org/jira/browse/SPARK-51356
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.5.5, 4.0.0
> Reporter: Herman van Hövell
> Priority: Major
> Labels: pull-request-available
>
> In whole stage code generation FilterExec reorders IsNotNull to make sure we
> only materialize fields when we have to. This unfortunately does not seem to
> work in cases where we access nested data.
> For example:
> {noformat}
> case class A(b: B = null)
> case class B(c: C = null)
> case class C(d: Int)
> val data = Seq(
> A(null),
> A(B(null)), // This causes the failure
> A(B(C(0))),
> A(B(C(1))))
> val aValues = spark.createDataset(data).map(i => i) // Map is needed to avoid
> the local relation from being optimized away.
> val bValues = aValues.where(col("b").isNotNull).select(col("b").as[B])
> val isDZero = udf((c: C) => c.d == 0)
> // Select all from b where c.d == 0
> val result = bValues
> .filter(col("c").isNotNull)
> .filter(not(isDZero(col("c"))))
> // KABOOM!
> result.show(){noformat}
>
> This yields the following plan:
> {noformat}
> == Parsed Logical Plan ==
> 'Filter '`!`(UDF('c))
> +- Filter isnotnull(c#18)
> +- Project [b#11.c AS c#18]
> +- Project [b#11]
> +- Filter isnotnull(b#11)
> +- SerializeFromObject [if
> (isnull(invoke(knownnotnull(assertnotnull(input[0, $line15.$read$$iw$A,
> true])).b()))) null else named_struct(c, if
> (isnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c()))) null else named_struct(d,
> invoke(knownnotnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c())).d()))) AS b#11]
> +- MapElements
> $line15.$read$$iw$$Lambda$3174/0x000000c801ba7518@70b62924, class
> $line15.$read$$iw$A,
> [StructField(b,StructType(StructField(c,StructType(StructField(d,IntegerType,false)),true)),true)],
> obj#9: $line15.$read$$iw$A
> +- DeserializeToObject newInstance(class
> $line15.$read$$iw$A), obj#7: $line15.$read$$iw$A
> +- LocalRelation [b#1]
> == Analyzed Logical Plan ==
> c: struct<d:int>
> Filter NOT UDF(c#18)
> +- Filter isnotnull(c#18)
> +- Project [b#11.c AS c#18]
> +- Project [b#11]
> +- Filter isnotnull(b#11)
> +- SerializeFromObject [if
> (isnull(invoke(knownnotnull(assertnotnull(input[0, $line15.$read$$iw$A,
> true])).b()))) null else named_struct(c, if
> (isnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c()))) null else named_struct(d,
> invoke(knownnotnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c())).d()))) AS b#11]
> +- MapElements
> $line15.$read$$iw$$Lambda$3174/0x000000c801ba7518@70b62924, class
> $line15.$read$$iw$A,
> [StructField(b,StructType(StructField(c,StructType(StructField(d,IntegerType,false)),true)),true)],
> obj#9: $line15.$read$$iw$A
> +- DeserializeToObject newInstance(class
> $line15.$read$$iw$A), obj#7: $line15.$read$$iw$A
> +- LocalRelation [b#1]
> == Optimized Logical Plan ==
> Project [b#11.c AS c#18]
> +- Filter (isnotnull(b#11) AND (isnotnull(b#11.c) AND NOT UDF(b#11.c)))
> +- SerializeFromObject [if
> (isnull(invoke(knownnotnull(assertnotnull(input[0, $line15.$read$$iw$A,
> true])).b()))) null else named_struct(c, if
> (isnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c()))) null else named_struct(d,
> invoke(knownnotnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c())).d()))) AS b#11]
> +- MapElements
> $line15.$read$$iw$$Lambda$3174/0x000000c801ba7518@70b62924, class
> $line15.$read$$iw$A,
> [StructField(b,StructType(StructField(c,StructType(StructField(d,IntegerType,false)),true)),true)],
> obj#9: $line15.$read$$iw$A
> +- DeserializeToObject newInstance(class $line15.$read$$iw$A),
> obj#7: $line15.$read$$iw$A
> +- LocalRelation [b#1]
> == Physical Plan ==
> *(1) Project [b#11.c AS c#18]
> +- *(1) Filter (isnotnull(b#11) AND (isnotnull(b#11.c) AND NOT UDF(b#11.c)))
> +- *(1) SerializeFromObject [if
> (isnull(invoke(knownnotnull(assertnotnull(input[0, $line15.$read$$iw$A,
> true])).b()))) null else named_struct(c, if
> (isnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c()))) null else named_struct(d,
> invoke(knownnotnull(invoke(knownnotnull(invoke(knownnotnull(assertnotnull(input[0,
> $line15.$read$$iw$A, true])).b())).c())).d()))) AS b#11]
> +- *(1) MapElements
> $line15.$read$$iw$$Lambda$3174/0x000000c801ba7518@70b62924, obj#9:
> $line15.$read$$iw$A
> +- *(1) DeserializeToObject newInstance(class $line15.$read$$iw$A),
> obj#7: $line15.$read$$iw$A
> +- *(1) LocalTableScan [b#1]{noformat}
> The optimizer has collapsed the filters and moved the project. Now we have a
> filter {{{}isnotnull(b#11) AND (isnotnull(b#11.c) AND NOT UDF(b#11.c)){}}}.
> In WholeStageCodegen the second filter is moved after the UDF invocation. The
> problem with that is that we now pass a null into a UDF which it should be
> guarded from. In this case we don't even invoke the UDF because the encoder
> will fail (which is also a bug). The IsNotNull reordering (and this bug) has
> been there since we introduces whole stage code generation.
>
> This particular case triggers in Spark 4, and not in earlier versions because
> we improved typed select. Typed select used to use a map to flatten results
> if the typed column returned a struct, now we use star expansion (which is
> cheaper). This allows the optimizer to be more aggressive and fuse the two
> filters. Reverting that change would fix this particular scenario, however it
> is easy to trigger this in older versions by using {{{}val bValues =
> aValues.where(col("b").isNotNull).select(col("b.*")).as[B]{}}}.
>
> This problem can be mitigated by either disabling whole stage code generation
> ({{{}spark.conf.set("spark.sql.codegen.wholeStage", false){}}}), or by
> introducing a barrier for optimization (e.g. val {{{}bValues =
> aValues.where(col("b").isNotNull).map(_.b){}}})
>
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