codope commented on code in PR #9761: URL: https://github.com/apache/hudi/pull/9761#discussion_r1372534307
########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/HoodieMultipleBaseFileFormat.scala: ########## @@ -0,0 +1,278 @@ +/* + * 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.DataSourceReadOptions.{REALTIME_PAYLOAD_COMBINE_OPT_VAL, REALTIME_SKIP_MERGE_OPT_VAL} +import org.apache.hudi.MergeOnReadSnapshotRelation.createPartitionedFile +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.model.{FileSlice, HoodieLogFile} +import org.apache.hudi.{HoodieBaseRelation, HoodieTableSchema, HoodieTableState, LogFileIterator, MergeOnReadSnapshotRelation, PartitionFileSliceMapping, RecordMergingFileIterator, SparkAdapterSupport} +import org.apache.spark.broadcast.Broadcast +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 +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.types.{StructField, StructType} +import org.apache.spark.util.SerializableConfiguration + +import scala.collection.mutable +import scala.jdk.CollectionConverters.asScalaIteratorConverter + +/** + * File format that supports reading multiple base file formats in a table. + */ +class HoodieMultipleBaseFileFormat(tableState: Broadcast[HoodieTableState], + tableSchema: Broadcast[HoodieTableSchema], + tableName: String, + mergeType: String, + mandatoryFields: Seq[String], + isMOR: Boolean) extends FileFormat with SparkAdapterSupport { + private val parquetFormat = new ParquetFileFormat() + private val orcFormat = new OrcFileFormat() + + override def inferSchema(sparkSession: SparkSession, + options: Map[String, String], + files: Seq[FileStatus]): Option[StructType] = { + // This is a simple heuristic assuming all files have the same extension. + val fileFormat = detectFileFormat(files.head.getPath.toString) + + fileFormat match { + case "parquet" => parquetFormat.inferSchema(sparkSession, options, files) + case "orc" => orcFormat.inferSchema(sparkSession, options, files) + case _ => throw new UnsupportedOperationException(s"File format $fileFormat is not supported.") + } + } + + override def isSplitable(sparkSession: SparkSession, options: Map[String, String], path: Path): Boolean = { + false + } + + // Used so that the planner only projects once and does not stack overflow + var isProjected = false + + /** + * 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 + + override def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean = { + if (!supportBatchCalled) { + supportBatchCalled = true + supportBatchResult = + !isMOR && parquetFormat.supportBatch(sparkSession, schema) && orcFormat.supportBatch(sparkSession, schema) + } + supportBatchResult + } + + override def prepareWrite(sparkSession: SparkSession, + job: Job, + options: Map[String, String], + dataSchema: StructType): OutputWriterFactory = { + throw new UnsupportedOperationException("Write operations are not supported in this example.") + } + + override def buildReaderWithPartitionValues(sparkSession: 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 requiredSchemaWithMandatory = if (!isMOR || MergeOnReadSnapshotRelation.isProjectionCompatible(tableState.value)) { + // add mandatory fields to required schema + val added: mutable.Buffer[StructField] = mutable.Buffer[StructField]() + for (field <- mandatoryFields) { + if (requiredSchema.getFieldIndex(field).isEmpty) { + val fieldToAdd = dataSchema.fields(dataSchema.getFieldIndex(field).get) + added.append(fieldToAdd) + } + } + val addedFields = StructType(added.toArray) + StructType(requiredSchema.toArray ++ addedFields.fields) + } else { + dataSchema + } + + val (parquetBaseFileReader, orcBaseFileReader, preMergeParquetBaseFileReader, preMergeOrcBaseFileReader) = buildFileReaders( + sparkSession, dataSchema, partitionSchema, requiredSchema, filters, options, hadoopConf, requiredSchemaWithMandatory) + + val broadcastedHadoopConf = sparkSession.sparkContext.broadcast(new SerializableConfiguration(hadoopConf)) + (file: PartitionedFile) => { + val filePath = sparkAdapter.getSparkPartitionedFileUtils.getPathFromPartitionedFile(file) + val fileFormat = detectFileFormat(filePath.toString) + file.partitionValues match { + case fileSliceMapping: PartitionFileSliceMapping => + if (FSUtils.isLogFile(filePath)) { + // no base file + val fileSlice = fileSliceMapping.getSlice(FSUtils.getFileId(filePath.getName).substring(1)).get + val logFiles = getLogFilesFromSlice(fileSlice) + val outputAvroSchema = HoodieBaseRelation.convertToAvroSchema(outputSchema, tableName) + new LogFileIterator(logFiles, filePath.getParent, tableSchema.value, outputSchema, outputAvroSchema, + tableState.value, broadcastedHadoopConf.value.value) + } else { + // We do not broadcast the slice if it has no log files + fileSliceMapping.getSlice(FSUtils.getFileId(filePath.getName)) match { + case Some(fileSlice) => + val hoodieBaseFile = fileSlice.getBaseFile.get() + val baseFileFormat = detectFileFormat(hoodieBaseFile.getFileName) + val partitionValues = fileSliceMapping.getInternalRow + val logFiles = getLogFilesFromSlice(fileSlice) + if (requiredSchemaWithMandatory.isEmpty) { + val baseFile = createPartitionedFile(partitionValues, hoodieBaseFile.getHadoopPath, 0, hoodieBaseFile.getFileLen) + baseFileFormat match { + case "parquet" => parquetBaseFileReader(baseFile) + case "orc" => orcBaseFileReader(baseFile) Review Comment: No, it will ultimately fallback to using the table config because the `HoodieSyncConfig.META_SYNC_BASE_FILE_FORMAT` has infer function - https://github.com/apache/hudi/blob/0ad4560f2a4de00e43814b0d6cef2886a8a38155/hudi-sync/hudi-sync-common/src/main/java/org/apache/hudi/sync/common/HoodieSyncConfig.java#L103 -- This is an automated message from the Apache Git Service. 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