jonvex commented on code in PR #11770:
URL: https://github.com/apache/hudi/pull/11770#discussion_r1715649002
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieFileGroupReaderBasedParquetFileFormat.scala:
##########
@@ -108,8 +108,8 @@ class
HoodieFileGroupReaderBasedParquetFileFormat(tableState: HoodieTableState,
//dataSchema is not always right due to spark bugs
val partitionColumns = partitionSchema.fieldNames
val preCombineField =
options.getOrElse(HoodieTableConfig.PRECOMBINE_FIELD.key, "")
- val dataSchema = StructType(tableSchema.structTypeSchema.fields.filter(f
=> !partitionColumns.contains(f.name)
- || preCombineField.equals(f.name)))
+ val dataSchema = StructType(tableSchema.structTypeSchema.fields.filter(f
=> mandatoryFields.contains(f.name)
+ || !partitionColumns.contains(f.name) || preCombineField.equals(f.name)))
Review Comment:
tbh we might want to just use tableSchema.structTypeSchema instead of
computing the data schema? Are there any downsides to doing that?
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