Github user petermaxlee commented on a diff in the pull request: https://github.com/apache/spark/pull/14676#discussion_r75248833 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveInlineTables.scala --- @@ -0,0 +1,105 @@ +/* + * 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.analysis + +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Cast, InterpretedProjection, Unevaluable} +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types.{StructField, StructType} + +/** + * An analyzer rule that replaces [[UnresolvedInlineTable]] with [[LocalRelation]]. + */ +object ResolveInlineTables extends Rule[LogicalPlan] { + override def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { + case table: UnresolvedInlineTable if table.expressionsResolved => + validateInputDimension(table) + validateInputEvaluable(table) + convert(table) + } + + /** + * Validates that all inline table data are foldable expressions. + * + * This is package visible for unit testing. + */ + private[analysis] def validateInputEvaluable(table: UnresolvedInlineTable): Unit = { + table.rows.foreach { row => + row.foreach { e => + if (!e.resolved || e.isInstanceOf[Unevaluable]) { + e.failAnalysis(s"cannot evaluate expression ${e.sql} in inline table definition") + } + } + } + } + + /** + * Validates the input data dimension: + * 1. All rows have the same cardinality. + * 2. The number of column aliases defined is consistent with the number of columns in data. + * + * This is package visible for unit testing. + */ + private[analysis] def validateInputDimension(table: UnresolvedInlineTable): Unit = { + if (table.rows.nonEmpty) { + val numCols = table.rows.head.size + table.rows.zipWithIndex.foreach { case (row, ri) => + if (row.size != numCols) { + table.failAnalysis(s"expected $numCols columns but found ${row.size} columns in row $ri") + } + } + + if (table.names.size != numCols) { + table.failAnalysis(s"expected ${table.names.size} columns but found $numCols in first row") + } + } + } + + /** + * Convert a valid (with right shape and foldable inputs) [[UnresolvedInlineTable]] + * into a [[LocalRelation]]. + * + * This function attempts to coerce inputs into consistent types. + * + * This is package visible for unit testing. + */ + private[analysis] def convert(table: UnresolvedInlineTable): LocalRelation = { + val numCols = table.rows.head.size + + // For each column, traverse all the values and find a common data type. + val targetTypes = table.rows.transpose.zip(table.names).map { case (column, name) => + val inputTypes = column.map(_.dataType) + TypeCoercion.findWiderTypeWithoutStringPromotion(inputTypes).getOrElse { --- End diff -- Postgres doesn't allow it. We can choose to be consistent with union though.
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