hudi-agent commented on code in PR #20183:
URL: https://github.com/apache/hudi/pull/20183#discussion_r4152860611
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hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieFileGroupReaderBasedFileFormat.scala:
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@@ -346,10 +337,11 @@ class HoodieFileGroupReaderBasedFileFormat(tablePath:
String,
val dataStructTypeWithMandatoryPartitionFields =
StructType(dataStructType.fields ++ partitionSchema.fields.filter(f =>
mandatoryFields.contains(f.name) && !isNestedPartitionField(f.name)))
val dataSchema = HoodieSchemaUtils.pruneDataSchema(schema,
HoodieSchemaConversionUtils.convertStructTypeToHoodieSchema(dataStructTypeWithMandatoryPartitionFields,
sanitizedTableName), exclusionFields)
-
spark.sessionState.conf.setConfString("spark.sql.parquet.enableVectorizedReader",
supportVectorizedRead.toString)
+ // Decide per scan: Spark skips supportBatch for scans wider than
spark.sql.codegen.maxFields
+ val vectorizedRead = canReadVectorized(spark, outputSchema)
Review Comment:
🤖 `canReadVectorized` throws `HoodieNotSupportedException` for any format
other than PARQUET/ORC/LANCE, so calling it here makes every Vortex scan fail.
Before, only narrow scans hit it through `supportBatch`. `buildBaseFileReader`
already has a Vortex branch. Could the fallback return `false` for unknown or
row-only formats instead of throwing?
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