taiyang-li commented on code in PR #7268: URL: https://github.com/apache/incubator-gluten/pull/7268#discussion_r1837347319
########## gluten-substrait/src/main/scala/org/apache/spark/sql/hive/HiveTableScanNestedColumnPruning.scala: ########## @@ -0,0 +1,259 @@ +/* + * 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.hive + +import org.apache.gluten.GlutenConfig +import org.apache.gluten.backendsapi.BackendsApiManager +import org.apache.gluten.execution.ProjectExecTransformer + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.catalyst.catalog.HiveTableRelation +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.execution.{FilterExec, LeafExecNode, ProjectExec, SparkPlan} +import org.apache.spark.sql.hive.HiveTableScanExecTransformer.{ORC_INPUT_FORMAT_CLASS, PARQUET_INPUT_FORMAT_CLASS, TEXT_INPUT_FORMAT_CLASS} +import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructType} +import org.apache.spark.sql.util.SchemaUtils._ +import org.apache.spark.util.Utils + +object HiveTableScanNestedColumnPruning extends Logging { + import org.apache.spark.sql.catalyst.expressions.SchemaPruning._ + + def supportNestedColumnPruning(projectExec: ProjectExec): Boolean = { + if ( + GlutenConfig.getConf.enableColumnarHiveTableScanNestedColumnPruning + && BackendsApiManager.getSettings.supportHiveTableScanNestedColumnPruning() + ) { + projectExec.child match { + case HiveTableScanExecTransformer(_, relation, _, _) => + relation.tableMeta.storage.inputFormat match { + case Some(inputFormat) + if TEXT_INPUT_FORMAT_CLASS.isAssignableFrom(Utils.classForName(inputFormat)) => + relation.tableMeta.storage.serde match { + case Some("org.openx.data.jsonserde.JsonSerDe") | Some( + "org.apache.hive.hcatalog.data.JsonSerDe") => + return true + case _ => + } + case Some(inputFormat) + if ORC_INPUT_FORMAT_CLASS.isAssignableFrom(Utils.classForName(inputFormat)) => + return true + case Some(inputFormat) + if PARQUET_INPUT_FORMAT_CLASS.isAssignableFrom(Utils.classForName(inputFormat)) => + return true + case _ => + } + case _ => + } + } + false + } + + def apply(plan: SparkPlan): SparkPlan = { + plan match { + case ProjectExec(projectList, child) => + child match { + case h: HiveTableScanExecTransformer => + val newPlan = prunePhysicalColumns( + h.relation, + projectList, + Seq.empty[Expression], + (prunedDataSchema, prunedMetadataSchema) => { + buildNewHiveTableScan(h, prunedDataSchema, prunedMetadataSchema) + }, + (schema, requestFields) => { + h.pruneSchema(schema, requestFields) + } + ) + if (newPlan.nonEmpty) { + return newPlan.get + } else { + return ProjectExecTransformer(projectList, child) + } + case _ => + return ProjectExecTransformer(projectList, child) + } + case _ => + } + plan + } + + private def prunePhysicalColumns( + relation: HiveTableRelation, + projects: Seq[NamedExpression], + filters: Seq[Expression], + leafNodeBuilder: (StructType, StructType) => LeafExecNode, + pruneSchemaFunc: (StructType, Seq[SchemaPruning.RootField]) => StructType) + : Option[SparkPlan] = { + val (normalizedProjects, normalizedFilters) = + normalizeAttributeRefNames(relation.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 = pruneSchemaFunc(relation.tableMeta.dataSchema, requestedRootFields) + val metaFieldNames = relation.tableMeta.schema.fieldNames + val metadataSchema = relation.output.collect { + case attr: AttributeReference if metaFieldNames.contains(attr.name) => attr + }.toStructType + val prunedMetadataSchema = if (metadataSchema.nonEmpty) { + pruneSchemaFunc(metadataSchema, requestedRootFields) + } else { + metadataSchema + } + // If the data schema is different from the pruned data schema + // OR + // the metadata schema is different from the pruned metadata schema, continue. + // Otherwise, return None. + if ( + countLeaves(relation.tableMeta.dataSchema) > countLeaves(prunedDataSchema) || + countLeaves(metadataSchema) > countLeaves(prunedMetadataSchema) + ) { + val leafNode = leafNodeBuilder(prunedDataSchema, prunedMetadataSchema) + val projectionOverSchema = ProjectionOverSchema( + prunedDataSchema.merge(prunedMetadataSchema), + AttributeSet(relation.output)) + Some( + buildNewProjection( + projects, + normalizedProjects, + normalizedFilters, + leafNode, + 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], + leafNode: LeafExecNode, + projectionOverSchema: ProjectionOverSchema): ProjectExecTransformer = { + // 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) + FilterExec(newFilterCondition, leafNode) + } else { + leafNode + } + + // 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) { Review Comment: remove. -- This is an automated message from the Apache Git Service. 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