linliu-code commented on code in PR #12622: URL: https://github.com/apache/hudi/pull/12622#discussion_r1950104524
########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/HoodieFileGroupReaderBasedFileFormat.scala: ########## @@ -0,0 +1,366 @@ +/* + * 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 + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileStatus, Path} +import org.apache.hadoop.mapreduce.Job +import org.apache.hudi.avro.AvroSchemaUtils +import org.apache.hudi.cdc.{CDCFileGroupIterator, CDCRelation, HoodieCDCFileGroupSplit} +import org.apache.hudi.client.utils.SparkInternalSchemaConverter +import org.apache.hudi.common.config.TypedProperties +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.table.read.HoodieFileGroupReader +import org.apache.hudi.common.table.{HoodieTableConfig, HoodieTableMetaClient} +import org.apache.hudi.internal.schema.InternalSchema +import org.apache.hudi.storage.StorageConfiguration +import org.apache.hudi.storage.hadoop.{HadoopStorageConfiguration, HoodieHadoopStorage} +import org.apache.hudi.{AvroConversionUtils, HoodieFileIndex, HoodiePartitionCDCFileGroupMapping, HoodiePartitionFileSliceMapping, HoodieTableSchema, HoodieTableState, SparkAdapterSupport, SparkFileFormatInternalRowReaderContext} +import org.apache.spark.sql.HoodieCatalystExpressionUtils.generateUnsafeProjection +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.JoinedRow +import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat +import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat, SparkFileReader} +import org.apache.spark.sql.execution.vectorized.{OffHeapColumnVector, OnHeapColumnVector} +import org.apache.spark.sql.internal.SQLConf +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 + +import java.io.Closeable +import scala.collection.mutable + +trait HoodieFormatTrait { + + // Used so that the planner only projects once and does not stack overflow + var isProjected: Boolean = false + def getRequiredFilters: Seq[Filter] +} + +/** + * This class utilizes {@link HoodieFileGroupReader} and its related classes to support reading + * from Parquet formatted base files and their log files. + */ +class HoodieFileGroupReaderBasedFileFormat(tableState: HoodieTableState, + tableSchema: HoodieTableSchema, + tableName: String, + mergeType: String, + mandatoryFields: Seq[String], + isMOR: Boolean, + isBootstrap: Boolean, + isIncremental: Boolean, + isCDC: Boolean, + validCommits: String, + shouldUseRecordPosition: Boolean, + requiredFilters: Seq[Filter]) + extends FileFormat with SparkAdapterSupport with HoodieFormatTrait with Serializable { + /** + * Support batch needs to remain consistent, even if one side of a bootstrap merge can support + * while the other side can't + */ + private var supportBatchCalled = false + private var supportBatchResult = false + + def getRequiredFilters: Seq[Filter] = requiredFilters + + override def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean = { + if (!supportBatchCalled || supportBatchResult) { + supportBatchCalled = true + supportBatchResult = !isMOR && !isIncremental && !isBootstrap && super.supportBatch(sparkSession, schema) + } + supportBatchResult + } + + override def isSplitable(sparkSession: SparkSession, + options: Map[String, String], + path: Path): Boolean = false + + //for partition columns that we read from the file, we don't want them to be constant column vectors so we + //modify the vector types in this scenario + override def vectorTypes(requiredSchema: StructType, + partitionSchema: StructType, + sqlConf: SQLConf): Option[Seq[String]] = { + val originalVectorTypes = super.vectorTypes(requiredSchema, partitionSchema, sqlConf) + if (mandatoryFields.isEmpty) { + originalVectorTypes + } else { + val regularVectorType = if (!sqlConf.offHeapColumnVectorEnabled) { + classOf[OnHeapColumnVector].getName + } else { + classOf[OffHeapColumnVector].getName + } + originalVectorTypes.map { + o: Seq[String] => o.zipWithIndex.map(a => { + if (a._2 >= requiredSchema.length && mandatoryFields.contains(partitionSchema.fields(a._2 - requiredSchema.length).name)) { + regularVectorType + } else { + a._1 + } + }) + } + } + } + + private val sanitizedTableName = AvroSchemaUtils.getAvroRecordQualifiedName(tableName) + + private lazy val internalSchemaOpt: org.apache.hudi.common.util.Option[InternalSchema] = if (tableSchema.internalSchema.isEmpty) { + org.apache.hudi.common.util.Option.empty() + } else { + org.apache.hudi.common.util.Option.of(tableSchema.internalSchema.get) + } + + override def buildReaderWithPartitionValues(spark: SparkSession, + dataSchema: StructType, + partitionSchema: StructType, + requiredSchema: StructType, + filters: Seq[Filter], + options: Map[String, String], + hadoopConf: Configuration): PartitionedFile => Iterator[InternalRow] = { + val outputSchema = StructType(requiredSchema.fields ++ partitionSchema.fields) + val isCount = requiredSchema.isEmpty && !isMOR && !isIncremental + val augmentedStorageConf = new HadoopStorageConfiguration(hadoopConf).getInline + setSchemaEvolutionConfigs(augmentedStorageConf) + val (remainingPartitionSchemaArr, fixedPartitionIndexesArr) = partitionSchema.fields.toSeq.zipWithIndex.filter(p => !mandatoryFields.contains(p._1.name)).unzip + + // The schema of the partition cols we want to append the value instead of reading from the file + val remainingPartitionSchema = StructType(remainingPartitionSchemaArr) + + // index positions of the remainingPartitionSchema fields in partitionSchema + val fixedPartitionIndexes = fixedPartitionIndexesArr.toSet + + // schema that we want fg reader to output to us + val requestedSchema = StructType(requiredSchema.fields ++ partitionSchema.fields.filter(f => mandatoryFields.contains(f.name))) + val requestedAvroSchema = AvroConversionUtils.convertStructTypeToAvroSchema(requestedSchema, sanitizedTableName) + val dataAvroSchema = AvroConversionUtils.convertStructTypeToAvroSchema(dataSchema, sanitizedTableName) + val broadcastedParquetFileReader = spark.sparkContext.broadcast( + sparkAdapter.createParquetFileReader(supportBatchResult, spark.sessionState.conf, options, augmentedStorageConf.unwrap())) + val broadcastedOrcFileReader = spark.sparkContext.broadcast( + sparkAdapter.createOrcFileReader(supportBatchCalled, spark.sessionState.conf, options, augmentedStorageConf.unwrap())) Review Comment: Will do. -- This is an automated message from the Apache Git Service. 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