CTTY commented on code in PR #8885: URL: https://github.com/apache/hudi/pull/8885#discussion_r1223566158
########## hudi-spark-datasource/hudi-spark3.4.x/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/Spark34HoodieParquetFileFormat.scala: ########## @@ -0,0 +1,532 @@ +/* + * 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.hadoop.conf.Configuration +import org.apache.hadoop.mapred.FileSplit +import org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl +import org.apache.hadoop.mapreduce.{JobID, TaskAttemptID, TaskID, TaskType} +import org.apache.hudi.HoodieSparkUtils +import org.apache.hudi.client.utils.SparkInternalSchemaConverter +import org.apache.hudi.common.fs.FSUtils +import org.apache.hudi.common.util.InternalSchemaCache +import org.apache.hudi.common.util.StringUtils.isNullOrEmpty +import org.apache.hudi.common.util.collection.Pair +import org.apache.hudi.internal.schema.InternalSchema +import org.apache.hudi.internal.schema.action.InternalSchemaMerger +import org.apache.hudi.internal.schema.utils.{InternalSchemaUtils, SerDeHelper} +import org.apache.parquet.filter2.compat.FilterCompat +import org.apache.parquet.filter2.predicate.FilterApi +import org.apache.parquet.format.converter.ParquetMetadataConverter.SKIP_ROW_GROUPS +import org.apache.parquet.hadoop.{ParquetInputFormat, ParquetRecordReader} +import org.apache.spark.TaskContext +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection +import org.apache.spark.sql.catalyst.expressions.{Cast, JoinedRow} +import org.apache.spark.sql.catalyst.util.DateTimeUtils +import org.apache.spark.sql.execution.WholeStageCodegenExec +import org.apache.spark.sql.execution.datasources.parquet.Spark34HoodieParquetFileFormat._ +import org.apache.spark.sql.execution.datasources.{DataSourceUtils, PartitionedFile, RecordReaderIterator} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources._ +import org.apache.spark.sql.types.{AtomicType, DataType, StructField, StructType} +import org.apache.spark.util.SerializableConfiguration +/** + * This class is an extension of [[ParquetFileFormat]] overriding Spark-specific behavior + * that's not possible to customize in any other way + * + * NOTE: This is a version of [[AvroDeserializer]] impl from Spark 3.2.1 w/ w/ the following changes applied to it: + * <ol> + * <li>Avoiding appending partition values to the rows read from the data file</li> + * <li>Schema on-read</li> + * </ol> + */ +class Spark34HoodieParquetFileFormat(private val shouldAppendPartitionValues: Boolean) extends ParquetFileFormat { + + override def supportBatch(sparkSession: SparkSession, schema: StructType): Boolean = { + val conf = sparkSession.sessionState.conf + conf.parquetVectorizedReaderEnabled && schema.forall(_.dataType.isInstanceOf[AtomicType]) + } + + def supportsColumnar(sparkSession: SparkSession, schema: StructType): Boolean = { + val conf = sparkSession.sessionState.conf + // Only output columnar if there is WSCG to read it. + val requiredWholeStageCodegenSettings = + conf.wholeStageEnabled && !WholeStageCodegenExec.isTooManyFields(conf, schema) + requiredWholeStageCodegenSettings && + supportBatch(sparkSession, schema) + } + + override def buildReaderWithPartitionValues(sparkSession: SparkSession, + dataSchema: StructType, + partitionSchema: StructType, + requiredSchema: StructType, + filters: Seq[Filter], + options: Map[String, String], + hadoopConf: Configuration): PartitionedFile => Iterator[InternalRow] = { + hadoopConf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[ParquetReadSupport].getName) + hadoopConf.set( + ParquetReadSupport.SPARK_ROW_REQUESTED_SCHEMA, + requiredSchema.json) + hadoopConf.set( + ParquetWriteSupport.SPARK_ROW_SCHEMA, + requiredSchema.json) + hadoopConf.set( + SQLConf.SESSION_LOCAL_TIMEZONE.key, + sparkSession.sessionState.conf.sessionLocalTimeZone) + hadoopConf.setBoolean( + SQLConf.NESTED_SCHEMA_PRUNING_ENABLED.key, + sparkSession.sessionState.conf.nestedSchemaPruningEnabled) + hadoopConf.setBoolean( + SQLConf.CASE_SENSITIVE.key, + sparkSession.sessionState.conf.caseSensitiveAnalysis) + + ParquetWriteSupport.setSchema(requiredSchema, hadoopConf) + + // Sets flags for `ParquetToSparkSchemaConverter` + hadoopConf.setBoolean( + SQLConf.PARQUET_BINARY_AS_STRING.key, + sparkSession.sessionState.conf.isParquetBinaryAsString) + hadoopConf.setBoolean( + SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, + sparkSession.sessionState.conf.isParquetINT96AsTimestamp) + // Using string value of this conf to preserve compatibility across spark versions. + hadoopConf.setBoolean( + "spark.sql.legacy.parquet.nanosAsLong", + sparkSession.sessionState.conf.getConfString("spark.sql.legacy.parquet.nanosAsLong", "false").toBoolean Review Comment: If we are splitting `SparkXXHoodieParquetFileFormat` for each spark version then it should be fine to use config object instead of plain string. This applies to other versions of `SparkXXHoodieParquetFileFormat` as well -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@hudi.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
