Maryann Xue created SPARK-25690:
-----------------------------------
Summary: Analyzer rule "HandleNullInputsForUDF" does not stabilize
and can be applied infinitely
Key: SPARK-25690
URL: https://issues.apache.org/jira/browse/SPARK-25690
Project: Spark
Issue Type: Sub-task
Components: Spark Core, SQL
Affects Versions: 2.4.0
Reporter: Maryann Xue
Assignee: Sean Owen
Fix For: 2.4.0
A few SQL-related tests fail in Scala 2.12, such as UDFSuite's "SPARK-24891 Fix
HandleNullInputsForUDF rule":
{code:java}
- SPARK-24891 Fix HandleNullInputsForUDF rule *** FAILED ***
Results do not match for query:
...
== Results ==
== Results ==
!== Correct Answer - 3 == == Spark Answer - 3 ==
!struct<> struct<a:bigint,b:int,c:int>
![0,10,null] [0,10,0]
![1,12,null] [1,12,1]
![2,14,null] [2,14,2] (QueryTest.scala:163){code}
You can kind of get what's going on reading the test:
{code:java}
test("SPARK-24891 Fix HandleNullInputsForUDF rule") {
// assume(!ClosureCleanerSuite2.supportsLMFs)
// This test won't test what it intends to in 2.12, as lambda metafactory
closures
// have arg types that are not primitive, but Object
val udf1 = udf({(x: Int, y: Int) => x + y})
val df = spark.range(0, 3).toDF("a")
.withColumn("b", udf1($"a", udf1($"a", lit(10))))
.withColumn("c", udf1($"a", lit(null)))
val plan = spark.sessionState.executePlan(df.logicalPlan).analyzed
comparePlans(df.logicalPlan, plan)
checkAnswer(
df,
Seq(
Row(0, 10, null),
Row(1, 12, null),
Row(2, 14, null)))
}{code}
It seems that the closure that is fed in as a UDF changes behavior, in a way
that primitive-type arguments are handled differently. For example an Int
argument, when fed 'null', acts like 0.
I'm sure it's a difference in the LMF closure and how its types are understood,
but not exactly sure of the cause yet.
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