[jira] [Commented] (SPARK-25943) Corruption when writing data into a catalog table with a different struct schema

["James Baker (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-25943?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16674924#comment-16674924 ] 

James Baker commented on SPARK-25943:
-------------------------------------

I think this also conflicts with [https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala#L369|https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala#L369;]. There's a thing where at the top level it's easy to fix this by reordering the columns, but Spark doesn't currently make it easy to permute the column names of a struct.

As a snippet of how I could fix it:
{code:java}
private def reorderStructFields(expected: DataType, actual: Expression): Expression = {
  if (expected.equals(actual.dataType)) {
    actual
  } else {
    expected match {
      case ArrayType(elementType, containsNull) =>
        val lambdaVariable = NamedLambdaVariable.apply("x", elementType, containsNull)
        val reordered = reorderStructFields(elementType, lambdaVariable)
        if (reordered.equals(lambdaVariable)) {
          actual
        } else {
          If(
            IsNull(actual),
            Cast(Literal(null), expected),
            ArrayTransform(actual, LambdaFunction(reordered, Seq(lambdaVariable))))
        }
      case StructType(elementTypes) =>
        If(
          IsNull(actual),
          Cast(Literal(null), expected),
          CreateStruct(
            elementTypes.toSeq.map(structField => Alias(reorderStructFields(
                structField.dataType,
                GetStructField(
                  actual,
                  findStructIndex(actual.dataType, structField.name),
                  Option(structField.name))),
                structField.name)(explicitMetadata = Option(structField.metadata)))))
      case _ => actual
    }
  }
}

private def findStructIndex(actual: DataType, column: String): Int = {
   actual match {
     case StructType(elementTypes) => elementTypes.map(field => field.name).indexOf(column)
   }
}
{code}
but this clearly isn't ideal.

It's probably reasonable to just throw.

 

> Corruption when writing data into a catalog table with a different struct schema
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-25943
>                 URL: https://issues.apache.org/jira/browse/SPARK-25943
>             Project: Spark
>          Issue Type: Bug
>          Components: Optimizer, SQL
>    Affects Versions: 2.3.2, 2.4.1, 2.5.0, 3.0.0
>            Reporter: James Baker
>            Priority: Major
>
> Suppose I have a catalog table with schema StructType(Seq(StructField("a", StructType(Seq(StructField("b", DataTypes.StringType), StructField("c", DataTypes.StringType))).
> Suppose I now try to append a record to it:
> {code:java}
> {"a": {"c": "data1", "b": "data2"}}
> {code}
> That record will actually be appended as:
> {code:java}
> {"a": {"b": "data1", "c": "data2"}}
> {code}
> which is obviously not close to what the user wanted or expected (for me it silently corrupted my data).
> It turns out that the user could provide a totally different record,
> {code:java}
> {"a": {"this column": "is totally different", "but": "the types match up"}}
> {code}
> and it'd still get written out, but as
> {code:java}
> {"a": {"b": "is totally different", "c": "the types match up"}}
> {code}
> This is because [in DDLPreprocessingUtils.castAndRenameOutput|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala#L500] [,|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala#L500],] and for DSV2 in [in Analyzer.ResolveOutputRelation|https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2239] Spark puts effort in to reordering column names in line with what the output expects, but merely casts any other types. This works nicely in a case where you try to e.g. write an int into a double field, but goes wrong on complex datatypes if the types match up but the field names do not.



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