Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/8509#discussion_r38760227
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
---
@@ -179,31 +154,7 @@ private[parquet] class CatalystRowConverter(
// Converters for each field.
private val fieldConverters: Array[Converter with
HasParentContainerUpdater] = {
- // In case of schema merging, `parquetType` can be a subset of
`catalystType`. We need to pad
- // those missing fields and create converters for them, although
values of these fields are
- // always null.
- val paddedParquetFields = {
- val parquetFields = parquetType.getFields.asScala
- val parquetFieldNames = parquetFields.map(_.getName).toSet
- val missingFields = catalystType.filterNot(f =>
parquetFieldNames.contains(f.name))
-
- // We don't need to worry about feature flag arguments like
`assumeBinaryIsString` when
- // creating the schema converter here, since values of missing
fields are always null.
- val toParquet = new CatalystSchemaConverter()
-
- (parquetFields ++ missingFields.map(toParquet.convertField)).sortBy
{ f =>
- catalystType.indexWhere(_.name == f.getName)
- }
- }
-
- if (paddedParquetFields.length != catalystType.length) {
- throw new UnsupportedOperationException(
- "A Parquet file's schema has different number of fields with the
table schema. " +
- "Please enable schema merging by setting \"mergeSchema\" to true
when load " +
- "a Parquet dataset or set spark.sql.parquet.mergeSchema to true
in SQLConf.")
- }
-
- paddedParquetFields.zip(catalystType).zipWithIndex.map {
+ parquetType.getFields.asScala.zip(catalystType).zipWithIndex.map {
--- End diff --
Good point, should add an assertion. `parquetType` and `catalystType`
should guarantee to have the same number of fields here.
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