Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/21305#discussion_r207576303 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala --- @@ -336,4 +337,97 @@ object DataType { case (fromDataType, toDataType) => fromDataType == toDataType } } + + /** + * Returns true if the write data type can be read using the read data type. + * + * The write type is compatible with the read type if: + * - Both types are arrays, the array element types are compatible, and element nullability is + * compatible (read allows nulls or write does not contain nulls). + * - Both types are maps and the map key and value types are compatible, and value nullability + * is compatible (read allows nulls or write does not contain nulls). + * - Both types are structs and each field in the read struct is present in the write struct and + * compatible (including nullability), or is nullable if the write struct does not contain the + * field. Write-side structs are not compatible if they contain fields that are not present in + * the read-side struct. + * - Both types are atomic and the write type can be safely cast to the read type. + * + * Extra fields in write-side structs are not allowed to avoid accidentally writing data that + * the read schema will not read, and to ensure map key equality is not changed when data is read. + * + * @param write a write-side data type to validate against the read type + * @param read a read-side data type + * @return true if data written with the write type can be read using the read type + */ + def canWrite( + write: DataType, + read: DataType, + resolver: Resolver, + context: String, + addError: String => Unit = (_: String) => {}): Boolean = { + (write, read) match { + case (wArr: ArrayType, rArr: ArrayType) => + if (wArr.containsNull && !rArr.containsNull) { + addError(s"Cannot write nullable elements to array of non-nulls: '$context'") + false + } else { + canWrite(wArr.elementType, rArr.elementType, resolver, context + ".element", addError) + } + + case (wMap: MapType, rMap: MapType) => + // map keys cannot include data fields not in the read schema without changing equality when + // read. map keys can be missing fields as long as they are nullable in the read schema. + if (wMap.valueContainsNull && !rMap.valueContainsNull) { + addError(s"Cannot write nullable values to map of non-nulls: '$context'") + false + } else { + canWrite(wMap.keyType, rMap.keyType, resolver, context + ".key", addError) && + canWrite(wMap.valueType, rMap.valueType, resolver, context + ".value", addError) + } + + case (StructType(writeFields), StructType(readFields)) => + lazy val extraFields = writeFields.map(_.name).toSet -- readFields.map(_.name) + + var result = readFields.forall { readField => + val fieldContext = context + "." + readField.name + writeFields.find(writeField => resolver(writeField.name, readField.name)) match { --- End diff -- is it safe to match struct fields by name? How do we reorder the nested fields in `ResolveOutputRelation`?
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