dongjoon-hyun commented on a change in pull request #23740: [SPARK-26837][SQL] Pruning nested fields from object serializers URL: https://github.com/apache/spark/pull/23740#discussion_r254566530
########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SchemaPruning.scala ########## @@ -0,0 +1,212 @@ +/* + * 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.catalyst.expressions + +import scala.collection.mutable.ArrayBuffer + +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ + +private[sql] object SchemaPruning { + + /** + * Collects all struct types from given data type object, recursively. Supports struct and array + * types for now. + * TODO: support map type. + */ + def collectStructType(dt: DataType, structs: ArrayBuffer[StructType]): ArrayBuffer[StructType] = { + dt match { + case s @ StructType(fields) => + structs += s + fields.map(f => collectStructType(f.dataType, structs)) + case ArrayType(elementType, _) => + collectStructType(elementType, structs) + case _ => + } + structs + } + + /** + * This method prunes given serializer expression by given pruned data type. For example, + * given a serializer creating struct(a int, b int) and pruned data type struct(a int), + * this method returns pruned serializer creating struct(a int). For now it supports to + * prune nested fields in struct and array of struct. + * TODO: support to prune nested fields in key and value of map type. + */ + def pruneSerializer( + serializer: NamedExpression, + prunedDataType: DataType): NamedExpression = { + val prunedStructTypes = collectStructType(prunedDataType, ArrayBuffer.empty[StructType]) + var structTypeIndex = 0 + + val prunedSerializer = serializer.transformDown { + case s: CreateNamedStruct if structTypeIndex < prunedStructTypes.size => + val prunedType = prunedStructTypes(structTypeIndex) + + // Filters out the pruned fields. + val prunedFields = s.nameExprs.zip(s.valExprs).filter { case (nameExpr, _) => + val name = nameExpr.eval(EmptyRow).toString + prunedType.fieldNames.exists { fieldName => + if (SQLConf.get.caseSensitiveAnalysis) { + fieldName.equals(name) + } else { + fieldName.equalsIgnoreCase(name) + } + } + }.flatMap(pair => Seq(pair._1, pair._2)) + + structTypeIndex += 1 + CreateNamedStruct(prunedFields) + }.transformUp { + // When we change nested serializer data type, `If` expression will be unresolved because + // literal null's data type doesn't match now. We need to align it with new data type. + // Note: we should do `transformUp` explicitly to change data types. + case i @ If(_: IsNull, Literal(null, dt), ser) if !dt.sameType(ser.dataType) => + i.copy(trueValue = Literal(null, ser.dataType)) + }.asInstanceOf[NamedExpression] + + if (prunedSerializer.dataType.sameType(prunedDataType)) { + prunedSerializer + } else { + serializer + } + } + + /** + * Filters the schema from the given file by the requested fields. + * Schema field ordering from the file is preserved. + */ + def pruneDataSchema( + fileDataSchema: StructType, + requestedRootFields: Seq[RootField]): StructType = { + // Merge the requested root fields into a single schema. Note the ordering of the fields + // in the resulting schema may differ from their ordering in the logical relation's + // original schema + val mergedSchema = requestedRootFields + .map { case root: RootField => StructType(Array(root.field)) } + .reduceLeft(_ merge _) + val dataSchemaFieldNames = fileDataSchema.fieldNames.toSet + val mergedDataSchema = + StructType(mergedSchema.filter(f => dataSchemaFieldNames.contains(f.name))) + // Sort the fields of mergedDataSchema according to their order in dataSchema, + // recursively. This makes mergedDataSchema a pruned schema of dataSchema + sortLeftFieldsByRight(mergedDataSchema, fileDataSchema).asInstanceOf[StructType] + } + + /** + * Sorts the fields and descendant fields of structs in left according to their order in + * right. This function assumes that the fields of left are a subset of the fields of + * right, recursively. That is, left is a "subschema" of right, ignoring order of + * fields. + */ + def sortLeftFieldsByRight(left: DataType, right: DataType): DataType = + (left, right) match { + case (ArrayType(leftElementType, containsNull), ArrayType(rightElementType, _)) => + ArrayType( + sortLeftFieldsByRight(leftElementType, rightElementType), + containsNull) + case (MapType(leftKeyType, leftValueType, containsNull), + MapType(rightKeyType, rightValueType, _)) => + MapType( + sortLeftFieldsByRight(leftKeyType, rightKeyType), + sortLeftFieldsByRight(leftValueType, rightValueType), + containsNull) + case (leftStruct: StructType, rightStruct: StructType) => + val filteredRightFieldNames = rightStruct.fieldNames.filter(leftStruct.fieldNames.contains) + val sortedLeftFields = filteredRightFieldNames.map { fieldName => + val leftFieldType = leftStruct(fieldName).dataType + val rightFieldType = rightStruct(fieldName).dataType + val sortedLeftFieldType = sortLeftFieldsByRight(leftFieldType, rightFieldType) + StructField(fieldName, sortedLeftFieldType, nullable = leftStruct(fieldName).nullable) + } + StructType(sortedLeftFields) + case _ => left + } + + /** + * Returns the set of fields from the Parquet file that the query plan needs. + */ + def identifyRootFields(projects: Seq[NamedExpression], + filters: Seq[Expression]): Seq[RootField] = { Review comment: Shall we indent like the following? ```scala def identifyRootFields( projects: Seq[NamedExpression], filters: Seq[Expression]): Seq[RootField] = { ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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