yihua commented on code in PR #20077: URL: https://github.com/apache/hudi/pull/20077#discussion_r4169894931
########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieFileGroupReaderFunction.scala: ########## @@ -0,0 +1,374 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.parquet + +import org.apache.hudi.{HoodiePartitionCDCFileGroupMapping, HoodiePartitionFileSliceMapping, HoodieTableSchema, SparkAdapterSupport, SparkFileFormatInternalRowReaderContext} +import org.apache.hudi.cdc.{CDCFileGroupIterator, HoodieCDCFileGroupSplit, HoodieCDCFileIndex} +import org.apache.hudi.common.config.{HoodieReaderConfig, TypedProperties} +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.schema.HoodieSchema +import org.apache.hudi.common.schema.internal.InternalSchema +import org.apache.hudi.common.table.{HoodieTableMetaClient, ParquetTableSchemaResolver} +import org.apache.hudi.common.table.log.InstantRange +import org.apache.hudi.common.table.read.{HoodieFileGroupReader, HoodieRecordReader} +import org.apache.hudi.common.table.read.lsm.{HoodieLsmFileGroupReader, LsmReaderUtils} +import org.apache.hudi.common.util.{ConfigUtils, Option => HOption} +import org.apache.hudi.common.util.collection.ClosableIterator +import org.apache.hudi.data.CloseableIteratorListener +import org.apache.hudi.io.storage.HoodieSparkParquetReader.ENABLE_LOGICAL_TIMESTAMP_REPAIR +import org.apache.hudi.io.storage.VectorConversionUtils +import org.apache.hudi.storage.StorageConfiguration +import org.apache.hudi.storage.hadoop.HadoopStorageConfiguration + +import org.apache.hadoop.conf.Configuration +import org.apache.parquet.schema.MessageType +import org.apache.spark.SparkEnv +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.sql.HoodieCatalystExpressionUtils.generateUnsafeProjection +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeProjection} +import org.apache.spark.sql.execution.datasources.{PartitionedFile, SparkColumnarFileReader, SparkSchemaTransformUtils} +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnarBatchUtils} +import org.apache.spark.util.{SerializableConfiguration, Utils} + +import java.io.Closeable +import java.nio.ByteBuffer + +import scala.collection.JavaConverters.mapAsJavaMapConverter +import scala.reflect.ClassTag + +/** + * Read-only state of one scan of [[HoodieFileGroupReaderBasedFileFormat]], built on the driver and shared by every + * task of an executor through [[HoodieFileGroupReaderFunction]]. Executors only fill thread-safe lazy caches in it; + * per-file state such as reader properties is copied before use. + */ +private[parquet] class HoodieFileGroupReadState(val metaClient: HoodieTableMetaClient, + val tableSchema: HoodieTableSchema, + val queryTimestamp: String, + val readerProps: TypedProperties, + val cdcProps: TypedProperties, + val dataSchema: HoodieSchema, + val requestedSchema: HoodieSchema, + val internalSchemaOpt: HOption[InternalSchema], + val instantRangeOpt: HOption[InstantRange], + val shouldUseRecordPosition: Boolean, + val isCount: Boolean, + val filters: Seq[Filter], + val requiredFilters: Seq[Filter], + val requiredSchema: StructType, + val partitionSchema: StructType, + val remainingPartitionSchema: StructType, + val fixedPartitionIndexes: Set[Int], + val outputSchema: StructType, + val projectionInputSchema: StructType, + val baseFileReadSchemas: BaseFileReadSchemas) extends Serializable { + + /** + * Parquet form of the table schema for base file reads. The conversion reads Hadoop's default resources, so it is + * done once per executor rather than per task or file. + */ + @transient lazy val tableSchemaAsMessageType: HOption[MessageType] = + HOption.ofNullable(ParquetTableSchemaResolver.convertAvroSchemaToParquet(tableSchema.schema, new Configuration())) +} + +/** + * Base file read schemas with VECTOR columns rewritten to BinaryType, plus the ordinals of those columns. + */ +private[parquet] case class BaseFileReadSchemas(readRequiredSchema: StructType, + readVectorColumns: Map[Int, HoodieSchema.Vector], + outputSchema: StructType, + outputVectorColumns: Map[Int, HoodieSchema.Vector], + requestedSchema: StructType) + +/** + * Holds a value as Java-serialized bytes and deserializes it at most once per JVM instance of the holder. + * + * <p>Broadcasting this holder instead of the value keeps the broadcast independent of `spark.serializer`: Kryo + * would otherwise serialize the value field by field and ignore the custom Java serialization of types such as + * [[HoodieSchema]] and [[HadoopStorageConfiguration]]. + */ +private[parquet] class JavaSerializedValue[T: ClassTag] private(bytes: Array[Byte]) extends Serializable { + + @transient lazy val value: T = SparkEnv.get.closureSerializer.newInstance() + .deserialize[T](ByteBuffer.wrap(bytes), Utils.getContextOrSparkClassLoader) +} + +private[parquet] object JavaSerializedValue { + + def apply[T: ClassTag](value: T): JavaSerializedValue[T] = { + val buffer = SparkEnv.get.closureSerializer.newInstance().serialize(value) + val bytes = new Array[Byte](buffer.remaining()) + buffer.get(bytes) + new JavaSerializedValue[T](bytes) + } +} + +/** + * The per-file read function returned by [[HoodieFileGroupReaderBasedFileFormat.buildReaderWithPartitionValues]]. + * + * <p>Spark deserializes this function once per task, so it holds only broadcast handles. The scan state behind + * `state` is deserialized once per executor. + */ +private[parquet] class HoodieFileGroupReaderFunction(baseFileReader: Broadcast[SparkColumnarFileReader], + fileGroupBaseFileReader: Broadcast[SparkColumnarFileReader], + storageConf: Broadcast[SerializableConfiguration], + state: Broadcast[JavaSerializedValue[HoodieFileGroupReadState]]) Review Comment: Done, renamed to broadcastedState. ########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieFileGroupReaderFunction.scala: ########## @@ -0,0 +1,374 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.parquet + +import org.apache.hudi.{HoodiePartitionCDCFileGroupMapping, HoodiePartitionFileSliceMapping, HoodieTableSchema, SparkAdapterSupport, SparkFileFormatInternalRowReaderContext} +import org.apache.hudi.cdc.{CDCFileGroupIterator, HoodieCDCFileGroupSplit, HoodieCDCFileIndex} +import org.apache.hudi.common.config.{HoodieReaderConfig, TypedProperties} +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.schema.HoodieSchema +import org.apache.hudi.common.schema.internal.InternalSchema +import org.apache.hudi.common.table.{HoodieTableMetaClient, ParquetTableSchemaResolver} +import org.apache.hudi.common.table.log.InstantRange +import org.apache.hudi.common.table.read.{HoodieFileGroupReader, HoodieRecordReader} +import org.apache.hudi.common.table.read.lsm.{HoodieLsmFileGroupReader, LsmReaderUtils} +import org.apache.hudi.common.util.{ConfigUtils, Option => HOption} +import org.apache.hudi.common.util.collection.ClosableIterator +import org.apache.hudi.data.CloseableIteratorListener +import org.apache.hudi.io.storage.HoodieSparkParquetReader.ENABLE_LOGICAL_TIMESTAMP_REPAIR +import org.apache.hudi.io.storage.VectorConversionUtils +import org.apache.hudi.storage.StorageConfiguration +import org.apache.hudi.storage.hadoop.HadoopStorageConfiguration + +import org.apache.hadoop.conf.Configuration +import org.apache.parquet.schema.MessageType +import org.apache.spark.SparkEnv +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.sql.HoodieCatalystExpressionUtils.generateUnsafeProjection +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeProjection} +import org.apache.spark.sql.execution.datasources.{PartitionedFile, SparkColumnarFileReader, SparkSchemaTransformUtils} +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnarBatchUtils} +import org.apache.spark.util.{SerializableConfiguration, Utils} + +import java.io.Closeable +import java.nio.ByteBuffer + +import scala.collection.JavaConverters.mapAsJavaMapConverter +import scala.reflect.ClassTag + +/** + * Read-only state of one scan of [[HoodieFileGroupReaderBasedFileFormat]], built on the driver and shared by every + * task of an executor through [[HoodieFileGroupReaderFunction]]. Executors only fill thread-safe lazy caches in it; + * per-file state such as reader properties is copied before use. + */ +private[parquet] class HoodieFileGroupReadState(val metaClient: HoodieTableMetaClient, + val tableSchema: HoodieTableSchema, + val queryTimestamp: String, + val readerProps: TypedProperties, + val cdcProps: TypedProperties, + val dataSchema: HoodieSchema, + val requestedSchema: HoodieSchema, + val internalSchemaOpt: HOption[InternalSchema], + val instantRangeOpt: HOption[InstantRange], + val shouldUseRecordPosition: Boolean, + val isCount: Boolean, + val filters: Seq[Filter], + val requiredFilters: Seq[Filter], + val requiredSchema: StructType, + val partitionSchema: StructType, + val remainingPartitionSchema: StructType, + val fixedPartitionIndexes: Set[Int], + val outputSchema: StructType, + val projectionInputSchema: StructType, + val baseFileReadSchemas: BaseFileReadSchemas) extends Serializable { + + /** + * Parquet form of the table schema for base file reads. The conversion reads Hadoop's default resources, so it is + * done once per executor rather than per task or file. + */ + @transient lazy val tableSchemaAsMessageType: HOption[MessageType] = + HOption.ofNullable(ParquetTableSchemaResolver.convertAvroSchemaToParquet(tableSchema.schema, new Configuration())) +} + +/** + * Base file read schemas with VECTOR columns rewritten to BinaryType, plus the ordinals of those columns. + */ +private[parquet] case class BaseFileReadSchemas(readRequiredSchema: StructType, + readVectorColumns: Map[Int, HoodieSchema.Vector], + outputSchema: StructType, + outputVectorColumns: Map[Int, HoodieSchema.Vector], + requestedSchema: StructType) + +/** + * Holds a value as Java-serialized bytes and deserializes it at most once per JVM instance of the holder. + * + * <p>Broadcasting this holder instead of the value keeps the broadcast independent of `spark.serializer`: Kryo + * would otherwise serialize the value field by field and ignore the custom Java serialization of types such as + * [[HoodieSchema]] and [[HadoopStorageConfiguration]]. + */ +private[parquet] class JavaSerializedValue[T: ClassTag] private(bytes: Array[Byte]) extends Serializable { + + @transient lazy val value: T = SparkEnv.get.closureSerializer.newInstance() + .deserialize[T](ByteBuffer.wrap(bytes), Utils.getContextOrSparkClassLoader) +} + +private[parquet] object JavaSerializedValue { + + def apply[T: ClassTag](value: T): JavaSerializedValue[T] = { + val buffer = SparkEnv.get.closureSerializer.newInstance().serialize(value) + val bytes = new Array[Byte](buffer.remaining()) + buffer.get(bytes) + new JavaSerializedValue[T](bytes) + } +} + +/** + * The per-file read function returned by [[HoodieFileGroupReaderBasedFileFormat.buildReaderWithPartitionValues]]. + * + * <p>Spark deserializes this function once per task, so it holds only broadcast handles. The scan state behind + * `state` is deserialized once per executor. + */ +private[parquet] class HoodieFileGroupReaderFunction(baseFileReader: Broadcast[SparkColumnarFileReader], + fileGroupBaseFileReader: Broadcast[SparkColumnarFileReader], + storageConf: Broadcast[SerializableConfiguration], + state: Broadcast[JavaSerializedValue[HoodieFileGroupReadState]]) + extends (PartitionedFile => Iterator[InternalRow]) with Serializable { + + import HoodieFileGroupReaderFunction._ + + private def sparkAdapter = SparkAdapterSupport.sparkAdapter + + override def apply(file: PartitionedFile): Iterator[InternalRow] = { + val s = state.value.value Review Comment: Done, the local is now state. ########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieFileGroupReaderFunction.scala: ########## @@ -0,0 +1,374 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.datasources.parquet + +import org.apache.hudi.{HoodiePartitionCDCFileGroupMapping, HoodiePartitionFileSliceMapping, HoodieTableSchema, SparkAdapterSupport, SparkFileFormatInternalRowReaderContext} +import org.apache.hudi.cdc.{CDCFileGroupIterator, HoodieCDCFileGroupSplit, HoodieCDCFileIndex} +import org.apache.hudi.common.config.{HoodieReaderConfig, TypedProperties} +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.schema.HoodieSchema +import org.apache.hudi.common.schema.internal.InternalSchema +import org.apache.hudi.common.table.{HoodieTableMetaClient, ParquetTableSchemaResolver} +import org.apache.hudi.common.table.log.InstantRange +import org.apache.hudi.common.table.read.{HoodieFileGroupReader, HoodieRecordReader} +import org.apache.hudi.common.table.read.lsm.{HoodieLsmFileGroupReader, LsmReaderUtils} +import org.apache.hudi.common.util.{ConfigUtils, Option => HOption} +import org.apache.hudi.common.util.collection.ClosableIterator +import org.apache.hudi.data.CloseableIteratorListener +import org.apache.hudi.io.storage.HoodieSparkParquetReader.ENABLE_LOGICAL_TIMESTAMP_REPAIR +import org.apache.hudi.io.storage.VectorConversionUtils +import org.apache.hudi.storage.StorageConfiguration +import org.apache.hudi.storage.hadoop.HadoopStorageConfiguration + +import org.apache.hadoop.conf.Configuration +import org.apache.parquet.schema.MessageType +import org.apache.spark.SparkEnv +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.sql.HoodieCatalystExpressionUtils.generateUnsafeProjection +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{JoinedRow, UnsafeProjection} +import org.apache.spark.sql.execution.datasources.{PartitionedFile, SparkColumnarFileReader, SparkSchemaTransformUtils} +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnarBatchUtils} +import org.apache.spark.util.{SerializableConfiguration, Utils} + +import java.io.Closeable +import java.nio.ByteBuffer + +import scala.collection.JavaConverters.mapAsJavaMapConverter +import scala.reflect.ClassTag + +/** + * Read-only state of one scan of [[HoodieFileGroupReaderBasedFileFormat]], built on the driver and shared by every + * task of an executor through [[HoodieFileGroupReaderFunction]]. Executors only fill thread-safe lazy caches in it; + * per-file state such as reader properties is copied before use. + */ +private[parquet] class HoodieFileGroupReadState(val metaClient: HoodieTableMetaClient, + val tableSchema: HoodieTableSchema, + val queryTimestamp: String, + val readerProps: TypedProperties, + val cdcProps: TypedProperties, + val dataSchema: HoodieSchema, + val requestedSchema: HoodieSchema, + val internalSchemaOpt: HOption[InternalSchema], + val instantRangeOpt: HOption[InstantRange], + val shouldUseRecordPosition: Boolean, + val isCount: Boolean, + val filters: Seq[Filter], + val requiredFilters: Seq[Filter], + val requiredSchema: StructType, + val partitionSchema: StructType, + val remainingPartitionSchema: StructType, + val fixedPartitionIndexes: Set[Int], + val outputSchema: StructType, + val projectionInputSchema: StructType, + val baseFileReadSchemas: BaseFileReadSchemas) extends Serializable { + + /** + * Parquet form of the table schema for base file reads. The conversion reads Hadoop's default resources, so it is + * done once per executor rather than per task or file. + */ + @transient lazy val tableSchemaAsMessageType: HOption[MessageType] = + HOption.ofNullable(ParquetTableSchemaResolver.convertAvroSchemaToParquet(tableSchema.schema, new Configuration())) +} + +/** + * Base file read schemas with VECTOR columns rewritten to BinaryType, plus the ordinals of those columns. + */ +private[parquet] case class BaseFileReadSchemas(readRequiredSchema: StructType, + readVectorColumns: Map[Int, HoodieSchema.Vector], + outputSchema: StructType, + outputVectorColumns: Map[Int, HoodieSchema.Vector], + requestedSchema: StructType) + +/** + * Holds a value as Java-serialized bytes and deserializes it at most once per JVM instance of the holder. + * + * <p>Broadcasting this holder instead of the value keeps the broadcast independent of `spark.serializer`: Kryo + * would otherwise serialize the value field by field and ignore the custom Java serialization of types such as + * [[HoodieSchema]] and [[HadoopStorageConfiguration]]. + */ +private[parquet] class JavaSerializedValue[T: ClassTag] private(bytes: Array[Byte]) extends Serializable { + + @transient lazy val value: T = SparkEnv.get.closureSerializer.newInstance() + .deserialize[T](ByteBuffer.wrap(bytes), Utils.getContextOrSparkClassLoader) +} + +private[parquet] object JavaSerializedValue { + + def apply[T: ClassTag](value: T): JavaSerializedValue[T] = { + val buffer = SparkEnv.get.closureSerializer.newInstance().serialize(value) + val bytes = new Array[Byte](buffer.remaining()) + buffer.get(bytes) + new JavaSerializedValue[T](bytes) + } +} + +/** + * The per-file read function returned by [[HoodieFileGroupReaderBasedFileFormat.buildReaderWithPartitionValues]]. + * + * <p>Spark deserializes this function once per task, so it holds only broadcast handles. The scan state behind + * `state` is deserialized once per executor. + */ +private[parquet] class HoodieFileGroupReaderFunction(baseFileReader: Broadcast[SparkColumnarFileReader], + fileGroupBaseFileReader: Broadcast[SparkColumnarFileReader], + storageConf: Broadcast[SerializableConfiguration], + state: Broadcast[JavaSerializedValue[HoodieFileGroupReadState]]) + extends (PartitionedFile => Iterator[InternalRow]) with Serializable { + + import HoodieFileGroupReaderFunction._ + + private def sparkAdapter = SparkAdapterSupport.sparkAdapter + + override def apply(file: PartitionedFile): Iterator[InternalRow] = { + val s = state.value.value + val conf = new HadoopStorageConfiguration(storageConf.value.value) + val iter = file.partitionValues match { + // Snapshot or incremental queries. + case fileSliceMapping: HoodiePartitionFileSliceMapping => + val fileGroupName = FSUtils.getFileIdFromFilePath(sparkAdapter + .getSparkPartitionedFileUtils.getPathFromPartitionedFile(file)) + fileSliceMapping.getSlice(fileGroupName) match { + case Some(fileSlice) if !s.isCount && (s.requiredSchema.nonEmpty || fileSlice.getLogFiles.findAny().isPresent) => + val tableConfig = s.metaClient.getTableConfig + // requiredFilters preserve Spark's row-level filtering semantics, while instantRangeOpt + // keeps out-of-range records from participating in the file-group merge itself. + val readerContext = new SparkFileFormatInternalRowReaderContext( + fileGroupBaseFileReader.value, s.filters, s.requiredFilters, conf, tableConfig, + sparkRequiredSchema = Some(s.requiredSchema), instantRangeOpt = s.instantRangeOpt) + readerContext.enableLogicalTimestampFieldRepair(conf.getBoolean(ENABLE_LOGICAL_TIMESTAMP_REPAIR, true)) + val props = TypedProperties.copy(s.readerProps) + val baseFileLength = if (fileSlice.getBaseFile.isPresent) { + fileSlice.getBaseFile.get.getFileSize + } else { + 0 + } + val reader: HoodieRecordReader[InternalRow] = + if (LsmReaderUtils.shouldUseLsmReader( + tableConfig, + ConfigUtils.getStringWithAltKeys(props, HoodieReaderConfig.MERGE_TYPE, true))) { + HoodieLsmFileGroupReader.builder[InternalRow]() + .withReaderContext(readerContext) + .withHoodieTableMetaClient(s.metaClient) + .withLatestCommitTime(s.queryTimestamp) + .withBaseFileOption(fileSlice.getBaseFile) + .withLogFiles(fileSlice.getLogFiles) + .withPartitionPath(fileSlice.getPartitionPath) + .withDataSchema(s.dataSchema) + .withRequestedSchema(s.requestedSchema) + .withInternalSchemaOpt(s.internalSchemaOpt) + .withProps(props) + .withStart(file.start) + .withLength(baseFileLength) + .build() + } else { + HoodieFileGroupReader.builder[InternalRow]() + .withReaderContext(readerContext) + .withHoodieTableMetaClient(s.metaClient) + .withLatestCommitTime(s.queryTimestamp) + .withBaseFileOption(fileSlice.getBaseFile) + .withLogFiles(fileSlice.getLogFiles) + .withPartitionPath(fileSlice.getPartitionPath) + .withDataSchema(s.dataSchema) + .withRequestedSchema(s.requestedSchema) + .withInternalSchemaOpt(s.internalSchemaOpt) + .withProps(props) + .withStart(file.start) + .withLength(baseFileLength) + .withShouldUseRecordPosition(s.shouldUseRecordPosition) + .build() + } + // Append partition values to rows and project to output schema + appendPartitionAndProject( + reader.getClosableIterator, + s.projectionInputSchema, + s.remainingPartitionSchema, + s.outputSchema, + fileSliceMapping.getPartitionValues, + s.fixedPartitionIndexes) + + case _ => + readBaseFile(file, s, conf) + } + // CDC queries. + case cdcFileGroupMapping: HoodiePartitionCDCFileGroupMapping => + new CDCFileGroupIterator( + HoodieCDCFileGroupSplit(cdcFileGroupMapping.getFileSplits().toArray), + s.metaClient, + conf, + fileGroupBaseFileReader.value, + s.tableSchema, + HoodieCDCFileIndex.FULL_CDC_SPARK_SCHEMA, + s.requiredSchema, + TypedProperties.copy(s.cdcProps)) Review Comment: Yes, kept: CDCFileGroupIterator hands these props to BufferedRecordMergerFactory, whose CustomPayloadRecordMerger writes the payload class into them. 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