Github user liancheng commented on a diff in the pull request: https://github.com/apache/spark/pull/13269#discussion_r65492813 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala --- @@ -1884,10 +1884,62 @@ class Analyzer( } else { inputAttributes } - val unbound = deserializer transform { - case b: BoundReference => inputs(b.ordinal) + + validateTupleColumns(deserializer, inputs) + val ordinalResolved = deserializer transform { + case GetColumnByOrdinal(ordinal, _) => inputs(ordinal) + } + val attrResolved = resolveExpression( + ordinalResolved, LocalRelation(inputs), throws = true) + validateInnerTupleFields(attrResolved) + attrResolved + } + } + + private def fail(schema: StructType, maxOrdinal: Int): Unit = { + throw new AnalysisException(s"Try to map ${schema.simpleString} to Tuple${maxOrdinal + 1}, " + + "but failed as the number of fields does not line up.") + } + + /** + * For each Tuple field, we use [[GetColumnByOrdinal]] to get its corresponding column by + * position. However, the actual number of columns may be different from the number of Tuple + * fields. This method is used to check the number of columns and fields, and throw an + * exception if they do not match. + */ + private def validateTupleColumns(deserializer: Expression, inputs: Seq[Attribute]): Unit = { + var maxOrdinal = -1 + deserializer.foreach { + case GetColumnByOrdinal(ordinal, _) => if (ordinal > maxOrdinal) maxOrdinal = ordinal + case _ => + } + if (maxOrdinal >= 0 && maxOrdinal != inputs.length - 1) { + fail(inputs.toStructType, maxOrdinal) + } --- End diff -- Actually we should also check that each ordinal from 0 to `inputs.length - 1` appears in deserializer expression: ```scala val ordinals = deserializer.collect { case GetColumnByOrdinal(ordinal, _) => ordinal }.distinct.sorted if (ordinals.nonEmpty && ordinals != (0 until inputs.length)) { fail(inputs.toStructType, ordinals.max) } ```
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