[GitHub] [hudi] YannByron commented on a diff in pull request #5708: [HUDI-4420][Stacked on 5430] Fixing table schema delineation on partition/data schema for Spark relations

[GitBox <gi...@apache.org>]

YannByron commented on code in PR #5708:
URL: https://github.com/apache/hudi/pull/5708#discussion_r928123957


##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -564,42 +538,56 @@ abstract class HoodieBaseRelation(val sqlContext: SQLContext,
     //       we have to eagerly initialize all of the readers even though only one specific to the type
     //       of the file being read will be used. This is required to avoid serialization of the whole
     //       relation (containing file-index for ex) and passing it to the executor
-    val reader = tableBaseFileFormat match {
-      case HoodieFileFormat.PARQUET =>
-        HoodieDataSourceHelper.buildHoodieParquetReader(
-          sparkSession = spark,
-          dataSchema = dataSchema.structTypeSchema,
-          partitionSchema = partitionSchema,
-          requiredSchema = requiredSchema.structTypeSchema,
-          filters = filters,
-          options = options,
-          hadoopConf = hadoopConf,
-          // We're delegating to Spark to append partition values to every row only in cases
-          // when these corresponding partition-values are not persisted w/in the data file itself
-          appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
-        )
+    val (read: (PartitionedFile => Iterator[InternalRow]), schema: StructType) =
+      tableBaseFileFormat match {
+        case HoodieFileFormat.PARQUET =>
+          val parquetReader = HoodieDataSourceHelper.buildHoodieParquetReader(
+            sparkSession = spark,
+            dataSchema = dataSchema.structTypeSchema,
+            partitionSchema = partitionSchema,
+            requiredSchema = requiredDataSchema.structTypeSchema,
+            filters = filters,
+            options = options,
+            hadoopConf = hadoopConf,
+            // We're delegating to Spark to append partition values to every row only in cases
+            // when these corresponding partition-values are not persisted w/in the data file itself
+            appendPartitionValues = shouldExtractPartitionValuesFromPartitionPath
+          )
+          // Since partition values by default are omitted, and not persisted w/in data-files by Spark,
+          // data-file readers (such as [[ParquetFileFormat]]) have to inject partition values while reading
+          // the data. As such, actual full schema produced by such reader is composed of
+          //    a) Data-file schema (projected or not)
+          //    b) Appended partition column values
+          val readerSchema = StructType(requiredDataSchema.structTypeSchema.fields ++ partitionSchema.fields)
+
+          (parquetReader, readerSchema)
 
       case HoodieFileFormat.HFILE =>
-        createHFileReader(
+        val hfileReader = createHFileReader(
           spark = spark,
           dataSchema = dataSchema,
-          requiredSchema = requiredSchema,
+          requiredDataSchema = requiredDataSchema,
           filters = filters,
           options = options,
           hadoopConf = hadoopConf
         )
 
+        (hfileReader, requiredDataSchema.structTypeSchema)
+
       case _ => throw new UnsupportedOperationException(s"Base file format is not currently supported ($tableBaseFileFormat)")
     }
 
-    partitionedFile => {
-      val extension = FSUtils.getFileExtension(partitionedFile.filePath)
-      if (tableBaseFileFormat.getFileExtension.equals(extension)) {
-        reader.apply(partitionedFile)
-      } else {
-        throw new UnsupportedOperationException(s"Invalid base-file format ($extension), expected ($tableBaseFileFormat)")

Review Comment:
   can we move the judgement into `HoodieDataSourceHelper.buildHoodieParquetReader` and `createHFileReader` separately?



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