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[SPARK-51259][SQL] Refactor natural and using join keys computation
### What changes were proposed in this pull request? Refactor natural and using join key computation to a separate component so that it can be reused in single-pass resolver. ### Why are the changes needed? To reuse code in single-pass resolver. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. ### Was this patch authored or co-authored using generative AI tooling? No Closes #50009 from mihailotim-db/mihailotim-db/join_refactor. Authored-by: Mihailo Timotic <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
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...src/main/scala/org/apache/spark/sql/catalyst/analysis/NaturalAndUsingJoinResolution.scala
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.catalyst.analysis | ||
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| import org.apache.spark.sql.catalyst.SQLConfHelper | ||
| import org.apache.spark.sql.catalyst.expressions.{ | ||
| Alias, | ||
| And, | ||
| Attribute, | ||
| Coalesce, | ||
| EqualTo, | ||
| Expression, | ||
| NamedExpression | ||
| } | ||
| import org.apache.spark.sql.catalyst.plans.{ | ||
| FullOuter, | ||
| InnerLike, | ||
| JoinType, | ||
| LeftExistence, | ||
| LeftOuter, | ||
| RightOuter | ||
| } | ||
| import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan | ||
| import org.apache.spark.sql.errors.{ | ||
| DataTypeErrorsBase, | ||
| QueryCompilationErrors, | ||
| QueryExecutionErrors | ||
| } | ||
|
|
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| object NaturalAndUsingJoinResolution extends DataTypeErrorsBase with SQLConfHelper { | ||
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| /** | ||
| * For a given [[Join]], computes output, hidden output and new condition, if such exists. | ||
| */ | ||
| def computeJoinOutputsAndNewCondition( | ||
| left: LogicalPlan, | ||
| leftOutput: Seq[Attribute], | ||
| right: LogicalPlan, | ||
| rightOutput: Seq[Attribute], | ||
| joinType: JoinType, | ||
| joinNames: Seq[String], | ||
| condition: Option[Expression], | ||
| resolveName: (String, String) => Boolean) | ||
| : (Seq[NamedExpression], Seq[Attribute], Option[Expression]) = { | ||
| val (leftKeys, rightKeys) = resolveKeysForNaturalAndUsingJoin( | ||
| left, | ||
| leftOutput, | ||
| right, | ||
| rightOutput, | ||
| joinNames, | ||
| resolveName | ||
| ) | ||
| val joinPairs = leftKeys.zip(rightKeys) | ||
|
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| val newCondition = (condition ++ joinPairs.map(EqualTo.tupled)).reduceOption(And) | ||
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| // the output list looks like: join keys, columns from left, columns from right | ||
| val (output, hiddenOutput) = computeOutputAndHiddenOutput( | ||
| leftOutput, | ||
| leftKeys, | ||
| rightOutput, | ||
| rightKeys, | ||
| joinPairs, | ||
| joinType | ||
| ) | ||
| (output, hiddenOutput, newCondition) | ||
| } | ||
|
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| /** | ||
| * Returns resolved keys for joining based on the output of [[Join]]'s children or throws and | ||
| * error if a key name doesn't exist. | ||
| */ | ||
| private def resolveKeysForNaturalAndUsingJoin( | ||
| left: LogicalPlan, | ||
| leftOutput: Seq[Attribute], | ||
| right: LogicalPlan, | ||
| rightOutput: Seq[Attribute], | ||
| joinNames: Seq[String], | ||
| resolveName: (String, String) => Boolean): (Seq[Attribute], Seq[Attribute]) = { | ||
| val leftKeys = joinNames.map { keyName => | ||
| leftOutput.find(attribute => resolveName(attribute.name, keyName)).getOrElse { | ||
| throw QueryCompilationErrors.unresolvedUsingColForJoinError( | ||
| keyName, | ||
| left.schema.fieldNames.sorted.map(toSQLId).mkString(", "), | ||
| "left" | ||
| ) | ||
| } | ||
| } | ||
| val rightKeys = joinNames.map { keyName => | ||
| rightOutput.find(attribute => resolveName(attribute.name, keyName)).getOrElse { | ||
| throw QueryCompilationErrors.unresolvedUsingColForJoinError( | ||
| keyName, | ||
| right.schema.fieldNames.sorted.map(toSQLId).mkString(", "), | ||
| "right" | ||
| ) | ||
| } | ||
| } | ||
| (leftKeys, rightKeys) | ||
| } | ||
|
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||
| /** | ||
| * Computes the output and hidden output for a given [[Join]], based on the output of its | ||
| * children. | ||
| */ | ||
| private def computeOutputAndHiddenOutput( | ||
| leftOutput: Seq[Attribute], | ||
| leftKeys: Seq[Attribute], | ||
| rightOutput: Seq[Attribute], | ||
| rightKeys: Seq[Attribute], | ||
| joinPairs: Seq[(Attribute, Attribute)], | ||
| joinType: JoinType): (Seq[NamedExpression], Seq[Attribute]) = { | ||
| // columns not in joinPairs | ||
| val lUniqueOutput = leftOutput.filterNot(att => leftKeys.contains(att)) | ||
| val rUniqueOutput = rightOutput.filterNot(att => rightKeys.contains(att)) | ||
| joinType match { | ||
| case LeftOuter => | ||
| ( | ||
| leftKeys ++ lUniqueOutput ++ rUniqueOutput.map(_.withNullability(true)), | ||
| rightKeys.map(_.withNullability(true)) | ||
| ) | ||
| case LeftExistence(_) => | ||
| (leftKeys ++ lUniqueOutput, Seq.empty) | ||
| case RightOuter => | ||
| ( | ||
| rightKeys ++ lUniqueOutput.map(_.withNullability(true)) ++ rUniqueOutput, | ||
| leftKeys.map(_.withNullability(true)) | ||
| ) | ||
| case FullOuter => | ||
| // In full outer join, we should return non-null values for the join columns | ||
| // if either side has non-null values for those columns. Therefore, for each | ||
| // join column pair, add a coalesce to return the non-null value, if it exists. | ||
| val joinedCols = joinPairs.map { | ||
| case (l, r) => | ||
| // Since this is a full outer join, either side could be null, so we explicitly | ||
| // set the nullability to true for both sides. | ||
| Alias(Coalesce(Seq(l.withNullability(true), r.withNullability(true))), l.name)() | ||
| } | ||
| ( | ||
| joinedCols ++ | ||
| lUniqueOutput.map(_.withNullability(true)) ++ | ||
| rUniqueOutput.map(_.withNullability(true)), | ||
| leftKeys.map(_.withNullability(true)) ++ | ||
| rightKeys.map(_.withNullability(true)) | ||
| ) | ||
| case _: InnerLike => | ||
| (leftKeys ++ lUniqueOutput ++ rUniqueOutput, rightKeys) | ||
| case _ => | ||
| throw QueryExecutionErrors.unsupportedNaturalJoinTypeError(joinType) | ||
| } | ||
| } | ||
| } |