Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14676#discussion_r75248289
--- 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 {
+ table.failAnalysis(s"incompatible types found in column $name for
inline table")
+ }
+ }
+ assert(targetTypes.size == table.names.size)
--- End diff --
it's duplicated, `validateInputDimension` already guarantees
`table.names.size` is equal to number of column
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