[GitHub] dongjoon-hyun commented on a change in pull request #23740: [SPARK-26837][SQL] Pruning nested fields from object serializers

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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_r254566381
 
 

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 File path: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SchemaPruning.scala
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 @@ -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 =
 
 Review comment:
   `def` -> `private def`.

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