zhangyue19921010 commented on code in PR #8714: URL: https://github.com/apache/hudi/pull/8714#discussion_r1198715332
########## hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/avro/AvroDeserializer.scala: ########## @@ -0,0 +1,492 @@ +/* + * 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.avro + +import org.apache.avro.Conversions.DecimalConversion +import org.apache.avro.LogicalTypes.{TimestampMicros, TimestampMillis} +import org.apache.avro.Schema.Type._ +import org.apache.avro.generic._ +import org.apache.avro.util.Utf8 +import org.apache.avro.{LogicalTypes, Schema, SchemaBuilder} +import org.apache.spark.sql.avro.AvroDeserializer.{createDateRebaseFuncInRead, createTimestampRebaseFuncInRead} +import org.apache.spark.sql.catalyst.expressions.{SpecificInternalRow, UnsafeArrayData} +import org.apache.spark.sql.catalyst.util.DateTimeConstants.MILLIS_PER_DAY +import org.apache.spark.sql.catalyst.util._ +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.execution.datasources.DataSourceUtils +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.internal.SQLConf.LegacyBehaviorPolicy +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.types.UTF8String + +import java.math.BigDecimal +import java.nio.ByteBuffer +import scala.collection.JavaConverters._ +import scala.collection.mutable.ArrayBuffer + +/** + * A deserializer to deserialize data in avro format to data in catalyst format. + * + * NOTE: This code is borrowed from Spark 3.1.2 + * This code is borrowed, so that we can better control compatibility w/in Spark minor + * branches (3.2.x, 3.1.x, etc) + * + * PLEASE REFRAIN MAKING ANY CHANGES TO THIS CODE UNLESS ABSOLUTELY NECESSARY + */ +private[sql] class AvroDeserializer(rootAvroType: Schema, Review Comment: we need to make AvroDeserializer, AvroSerializer and SchemaConverters consistent. For example make UT `TestAvroSerDe#testAvroUnionSerDe` happy. ########## hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/execution/datasources/Spark30NestedSchemaPruning.scala: ########## @@ -0,0 +1,270 @@ +/* + * 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.hudi.{HoodieBaseRelation, SparkAdapterSupport} +import org.apache.spark.sql.HoodieSpark3CatalystPlanUtils +import org.apache.spark.sql.catalyst.expressions.{Alias, And, Attribute, AttributeReference, AttributeSet, Expression, NamedExpression, ProjectionOverSchema} +import org.apache.spark.sql.catalyst.planning.PhysicalOperation +import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan, Project} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources.BaseRelation +import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructField, StructType} + +/** + * Prunes unnecessary physical columns given a [[PhysicalOperation]] over a data source relation. + * By "physical column", we mean a column as defined in the data source format like Parquet format + * or ORC format. For example, in Spark SQL, a root-level Parquet column corresponds to a SQL + * column, and a nested Parquet column corresponds to a [[StructField]]. + * + * NOTE: This class is borrowed from Spark 3.2.1, with modifications adapting it to handle [[HoodieBaseRelation]], + * instead of [[HadoopFsRelation]] + */ +class Spark30NestedSchemaPruning extends Rule[LogicalPlan] { + import org.apache.spark.sql.catalyst.expressions.SchemaPruning._ + override def apply(plan: LogicalPlan): LogicalPlan = + if (SQLConf.get.nestedSchemaPruningEnabled) { + apply0(plan) + } else { + plan + } + + private def apply0(plan: LogicalPlan): LogicalPlan = + plan transformDown { + case op @ PhysicalOperation(projects, filters, + // NOTE: This is modified to accommodate for Hudi's custom relations, given that original + // [[NestedSchemaPruning]] rule is tightly coupled w/ [[HadoopFsRelation]] + // TODO generalize to any file-based relation + l @ LogicalRelation(relation: HoodieBaseRelation, _, _, _)) + if relation.canPruneRelationSchema => + + prunePhysicalColumns(l.output, projects, filters, relation.dataSchema, + prunedDataSchema => { + val prunedRelation = + relation.updatePrunedDataSchema(prunedSchema = prunedDataSchema) + buildPrunedRelation(l, prunedRelation) + }).getOrElse(op) + } + + /** + * This method returns optional logical plan. `None` is returned if no nested field is required or + * all nested fields are required. + */ + private def prunePhysicalColumns(output: Seq[AttributeReference], + projects: Seq[NamedExpression], + filters: Seq[Expression], + dataSchema: StructType, + outputRelationBuilder: StructType => LogicalRelation): Option[LogicalPlan] = { + val (normalizedProjects, normalizedFilters) = + normalizeAttributeRefNames(output, projects, filters) + val requestedRootFields = identifyRootFields(normalizedProjects, normalizedFilters) + + // If requestedRootFields includes a nested field, continue. Otherwise, + // return op + if (requestedRootFields.exists { root: RootField => !root.derivedFromAtt }) { + val prunedDataSchema = pruneDataSchema(dataSchema, requestedRootFields) + + // If the data schema is different from the pruned data schema, continue. Otherwise, + // return op. We effect this comparison by counting the number of "leaf" fields in + // each schemata, assuming the fields in prunedDataSchema are a subset of the fields + // in dataSchema. + if (countLeaves(dataSchema) > countLeaves(prunedDataSchema)) { + val planUtils = SparkAdapterSupport.sparkAdapter.getCatalystPlanUtils.asInstanceOf[HoodieSpark3CatalystPlanUtils] + + val prunedRelation = outputRelationBuilder(prunedDataSchema) + val projectionOverSchema = planUtils.projectOverSchema(prunedDataSchema, AttributeSet(output)) + + Some(buildNewProjection(projects, normalizedProjects, normalizedFilters, + prunedRelation, projectionOverSchema)) + } else { + None + } + } else { + None + } + } + + /** + * Normalizes the names of the attribute references in the given projects and filters to reflect + * the names in the given logical relation. This makes it possible to compare attributes and + * fields by name. Returns a tuple with the normalized projects and filters, respectively. + */ + private def normalizeAttributeRefNames(output: Seq[AttributeReference], + projects: Seq[NamedExpression], + filters: Seq[Expression]): (Seq[NamedExpression], Seq[Expression]) = { + val normalizedAttNameMap = output.map(att => (att.exprId, att.name)).toMap + val normalizedProjects = projects.map(_.transform { + case att: AttributeReference if normalizedAttNameMap.contains(att.exprId) => + att.withName(normalizedAttNameMap(att.exprId)) + }).map { case expr: NamedExpression => expr } + val normalizedFilters = filters.map(_.transform { + case att: AttributeReference if normalizedAttNameMap.contains(att.exprId) => + att.withName(normalizedAttNameMap(att.exprId)) + }) + (normalizedProjects, normalizedFilters) + } + + /** + * Builds the new output [[Project]] Spark SQL operator that has the `leafNode`. + */ + private def buildNewProjection(projects: Seq[NamedExpression], + normalizedProjects: Seq[NamedExpression], + filters: Seq[Expression], + prunedRelation: LogicalRelation, + projectionOverSchema: ProjectionOverSchema): Project = { + // Construct a new target for our projection by rewriting and + // including the original filters where available + val projectionChild = + if (filters.nonEmpty) { + val projectedFilters = filters.map(_.transformDown { + case projectionOverSchema(expr) => expr + }) + val newFilterCondition = projectedFilters.reduce(And) + Filter(newFilterCondition, prunedRelation) + } else { + prunedRelation + } + + // Construct the new projections of our Project by + // rewriting the original projections + val newProjects = normalizedProjects.map(_.transformDown { + case projectionOverSchema(expr) => expr + }).map { case expr: NamedExpression => expr } + + if (log.isDebugEnabled) { + logDebug(s"New projects:\n${newProjects.map(_.treeString).mkString("\n")}") + } + + Project(restoreOriginalOutputNames(newProjects, projects.map(_.name)), projectionChild) + } + + /** + * Builds a pruned logical relation from the output of the output relation and the schema of the + * pruned base relation. + */ + private def buildPrunedRelation(outputRelation: LogicalRelation, + prunedBaseRelation: BaseRelation): LogicalRelation = { + val prunedOutput = getPrunedOutput(outputRelation.output, prunedBaseRelation.schema) + outputRelation.copy(relation = prunedBaseRelation, output = prunedOutput) + } + + // Prune the given output to make it consistent with `requiredSchema`. + private def getPrunedOutput(output: Seq[AttributeReference], + requiredSchema: StructType): Seq[AttributeReference] = { + // We need to replace the expression ids of the pruned relation output attributes + // with the expression ids of the original relation output attributes so that + // references to the original relation's output are not broken + val outputIdMap = output.map(att => (att.name, att.exprId)).toMap + requiredSchema + .toAttributes + .map { + case att if outputIdMap.contains(att.name) => + att.withExprId(outputIdMap(att.name)) + case att => att + } + } + + /** + * Counts the "leaf" fields of the given dataType. Informally, this is the + * number of fields of non-complex data type in the tree representation of + * [[DataType]]. + */ + private def countLeaves(dataType: DataType): Int = { + dataType match { + case array: ArrayType => countLeaves(array.elementType) + case map: MapType => countLeaves(map.keyType) + countLeaves(map.valueType) + case struct: StructType => + struct.map(field => countLeaves(field.dataType)).sum + case _ => 1 + } + } + + private def restoreOriginalOutputNames( + projectList: Seq[NamedExpression], + originalNames: Seq[String]): Seq[NamedExpression] = { + projectList.zip(originalNames).map { + case (attr: Attribute, name) => attr.withName(name) + case (alias: Alias, name) => if (name == alias.name) { + alias + } else { + AttributeReference(name, alias.dataType, alias.nullable, alias.metadata)(alias.exprId, alias.qualifier) + } + case (other, _) => other + } + } + + Review Comment: changed. ########## hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/hudi/Spark30ResolveHudiAlterTableCommand.scala: ########## @@ -0,0 +1,297 @@ +/* + * 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.hudi + +import org.apache.hudi.common.config.HoodieCommonConfig +import org.apache.hudi.internal.schema.action.TableChange.ColumnChangeID +import org.apache.spark.sql.catalyst.TableIdentifier +import org.apache.spark.sql.catalyst.catalog.CatalogTable +import org.apache.spark.sql.catalyst.plans.logical._ +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.connector.catalog.TableChange._ +import org.apache.spark.sql.connector.catalog.TableChange +import org.apache.spark.sql.hudi.command.Spark30AlterTableCommand +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{ArrayType, DataType, MapType, NullType, StructType} +import org.apache.spark.sql.{AnalysisException, SparkSession} + +import java.util.Locale +import scala.collection.mutable + +/** + * Rule to mostly resolve, normalize and rewrite column names based on case sensitivity + * for alter table column commands. + * TODO: we should remove this file when we support datasourceV2 for hoodie on spark3.0x Review Comment: changed. -- This is an automated message from the Apache Git Service. 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