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[SPARK-51580][SQL] Throw proper user facing error message when lambda function is out of place in HigherOrderFunction #50345

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[SPARK-51580][SQL] Throw proper user facing error message when lambda function is out of place in HigherOrderFunction #50345

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5 changes: 5 additions & 0 deletions common/utils/src/main/resources/error/error-conditions.json
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Expand Up @@ -2954,6 +2954,11 @@
"message" : [
"A higher order function expects <expectedNumArgs> arguments, but got <actualNumArgs>."
]
},
"PARAMETER_DOES_NOT_ACCEPT_LAMBDA_FUNCTION" : {
"message" : [
"You passed a lambda function to a parameter that does not accept it. Please check if lambda function argument is in the correct position."
]
}
},
"sqlState" : "42K0D"
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10 changes: 10 additions & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
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Expand Up @@ -345,6 +345,16 @@ trait CheckAnalysis extends LookupCatalog with QueryErrorsBase with PlanToString

case operator: LogicalPlan =>
operator transformExpressionsDown {
case hof: HigherOrderFunction if hof.arguments.exists {
case LambdaFunction(_, _, _) => true
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I need a bit more context. So ideally the analyzer should rewrite LambdaFunction if it's in the right place?

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The issue is in the following query pattern: select transform(x -> x + 1, array(1,2,3)). By reversing the parameter order the user sees the error: call to dataType on unresolved object, because the lambda function will not be bound properly. This is because HigherOrderFunction expects a lambda in a certain place but we never enforce this check anywhere. We can't really place it in checkInputDataTypes because we are not going to hit that codepath before erroring out, so we do it in CheckAnalysis instead.

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So after binding, LambdaFunction becomes something different?

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No, we are never going to bind LambdaFunction because in order to bind it, we need to have arguments resolved, but because lambda is an argument we get into a recursive cycle of it never being resolved: https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/higherOrderFunctions.scala#L116

case _ => false
} =>
throw new AnalysisException(
errorClass =
"INVALID_LAMBDA_FUNCTION_CALL.PARAMETER_DOES_NOT_ACCEPT_LAMBDA_FUNCTION",
messageParameters = Map.empty,
origin = hof.origin
)
// Check argument data types of higher-order functions downwards first.
// If the arguments of the higher-order functions are resolved but the type check fails,
// the argument functions will not get resolved, but we should report the argument type
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23 changes: 23 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/errors/QueryCompilationErrorsSuite.scala
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Expand Up @@ -1069,6 +1069,29 @@ class QueryCompilationErrorsSuite
context = ExpectedContext(fragment = "in (select map(1,2))", start = 16, stop = 35)
)
}

test("SPARK-51580: Throw proper user facing error message when lambda function is out of " +
"place in HigherOrderFunction") {
checkError(
exception = intercept[AnalysisException] {
sql("select transform(x -> x + 1, array(1,2,3))")
},
condition = "INVALID_LAMBDA_FUNCTION_CALL.PARAMETER_DOES_NOT_ACCEPT_LAMBDA_FUNCTION",
parameters = Map(),
context =
ExpectedContext(fragment = "transform(x -> x + 1, array(1,2,3))", start = 7, stop = 41)
)

checkError(
exception = intercept[AnalysisException] {
sql("select aggregate(array(1,2,3), x -> x + 1, 0)")
},
condition = "INVALID_LAMBDA_FUNCTION_CALL.PARAMETER_DOES_NOT_ACCEPT_LAMBDA_FUNCTION",
parameters = Map(),
context =
ExpectedContext(fragment = "aggregate(array(1,2,3), x -> x + 1, 0)", start = 7, stop = 44)
)
}
}

class MyCastToString extends SparkUserDefinedFunction(
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