Github user mallman commented on a diff in the pull request: https://github.com/apache/spark/pull/21320#discussion_r205022895 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruning.scala --- @@ -0,0 +1,153 @@ +/* + * 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.spark.sql.catalyst.expressions.{And, Attribute, Expression, NamedExpression} +import org.apache.spark.sql.catalyst.planning.{PhysicalOperation, ProjectionOverSchema, SelectedField} +import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan, Project} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.execution.datasources.{HadoopFsRelation, LogicalRelation} +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructField, StructType} + +/** + * Prunes unnecessary Parquet columns given a [[PhysicalOperation]] over a + * [[ParquetRelation]]. By "Parquet column", we mean a column as defined in the + * Parquet format. In Spark SQL, a root-level Parquet column corresponds to a + * SQL column, and a nested Parquet column corresponds to a [[StructField]]. + */ +private[sql] object ParquetSchemaPruning extends Rule[LogicalPlan] { + 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, + l @ LogicalRelation(hadoopFsRelation @ HadoopFsRelation(_, _, + dataSchema, _, parquetFormat: ParquetFileFormat, _), _, _, _)) => + val projectionRootFields = projects.flatMap(getRootFields) + val filterRootFields = filters.flatMap(getRootFields) + val requestedRootFields = (projectionRootFields ++ filterRootFields).distinct + + // If [[requestedRootFields]] includes a nested field, continue. Otherwise, + // return [[op]] + if (requestedRootFields.exists { case RootField(_, derivedFromAtt) => !derivedFromAtt }) { + val prunedSchema = requestedRootFields + .map { case RootField(field, _) => StructType(Array(field)) } + .reduceLeft(_ merge _) + val dataSchemaFieldNames = dataSchema.fieldNames.toSet + val prunedDataSchema = + StructType(prunedSchema.filter(f => dataSchemaFieldNames.contains(f.name))) + + // 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)) { --- End diff -- No comment.
--- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org