albertusk95 commented on a change in pull request #25107: [SPARK-28344][SQL] detect ambiguous self-join and fail the query URL: https://github.com/apache/spark/pull/25107#discussion_r305573260
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/analysis/DetectAmbiguousSelfJoin.scala ########## @@ -0,0 +1,159 @@ +/* + * 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.execution.analysis + +import scala.collection.mutable + +import org.apache.spark.sql.{AnalysisException, Column, Dataset} +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Cast, Equality, Expression, ExprId} +import org.apache.spark.sql.catalyst.plans.logical.{Join, LogicalPlan} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.internal.SQLConf + +/** + * Detects ambiguous self-joins, so that we can fail the query instead of returning confusing + * results. + * + * Dataset column reference is simply an [[AttributeReference]] that is returned by `Dataset#col`. + * Most of time we don't need to do anything special, as [[AttributeReference]] can point to + * the column precisely. However, in case of self-join, the analyzer generates + * [[AttributeReference]] with new expr IDs for the right side plan of the join. If the Dataset + * column reference points to a column in the right side plan of a self-join, users will get + * unexpected result because the column reference can't match the newly generated + * [[AttributeReference]]. + */ +class DetectAmbiguousSelfJoin(conf: SQLConf) extends Rule[LogicalPlan] { + + // Dataset column reference is an `AttributeReference` with 2 special metadata. + private def isColumnReference(a: AttributeReference): Boolean = { + a.metadata.contains(Dataset.ID_PREFIX) && a.metadata.contains(Dataset.COL_POS_PREFIX) + } + + private case class ColumnReference(datasetId: Long, colPos: Int, exprId: ExprId) + + private def toColumnReference(a: AttributeReference): ColumnReference = { + ColumnReference( + a.metadata.getLong(Dataset.ID_PREFIX), + a.metadata.getLong(Dataset.COL_POS_PREFIX).toInt, + a.exprId) + } + + object LogicalPlanWithDatasetId { + def unapply(p: LogicalPlan): Option[(LogicalPlan, Long)] = { + p.getTagValue(Dataset.DATASET_ID_TAG).map(id => p -> id) + } + } + + object AttrWithCast { + def unapply(expr: Expression): Option[AttributeReference] = expr match { + case Cast(child, _, _) => unapply(child) + case a: AttributeReference => Some(a) + case _ => None + } + } + + override def apply(plan: LogicalPlan): LogicalPlan = { + if (!conf.getConf(SQLConf.FAIL_AMBIGUOUS_SELF_JOIN)) return plan + + // We always remove the special metadata from `AttributeReference` at the end of this rule, so + // Dataset column reference only exists in the root node via Dataset transformations like + // `Dataset#select`. + val colRefAttrs = plan.expressions.flatMap(_.collect { + case a: AttributeReference if isColumnReference(a) => a + }) + + if (colRefAttrs.nonEmpty) { + val colRefs = colRefAttrs.map(toColumnReference).distinct + val ambiguousColRefs = mutable.HashSet.empty[ColumnReference] + val dsIdSet = colRefs.map(_.datasetId).toSet + + plan.foreach { + case LogicalPlanWithDatasetId(p, id) if dsIdSet.contains(id) => + colRefs.foreach { ref => + if (id == ref.datasetId) { + if (ref.colPos < 0 || ref.colPos >= p.output.length) { + throw new IllegalStateException("[BUG] Hit an invalid Dataset column reference: " + + s"$ref. Please open a JIRA ticket to report it.") + } else { + // When self-join happens, the analyzer asks the right side plan to generate + // attributes with new exprIds. If a plan of a Dataset outputs an attribute which + // is referred by a column reference, and this attribute has different exprId than + // the attribute of column reference, then the column reference is ambiguous, as it + // refers to a column that gets regenerated by self-join. + val actualAttr = p.output(ref.colPos).asInstanceOf[AttributeReference] + if (actualAttr.exprId != ref.exprId) { + ambiguousColRefs += ref + } + } + } + } + + case _ => + } + + val ambiguousAttrs: Seq[AttributeReference] = plan match { + case Join( + LogicalPlanWithDatasetId(_, leftId), + LogicalPlanWithDatasetId(_, rightId), + _, condition, _) => + // If we are dealing with root join node, we need to take care of SPARK-6231: + // 1. We can de-ambiguous `df("col") === df("col")` in the join condition. + // 2. There is no ambiguity in direct self join like + // `df.join(df, df("col") === 1)`, because it doesn't matter which side the + // column comes from. + def getAmbiguousAttrs(expr: Expression): Seq[AttributeReference] = expr match { + case Equality(AttrWithCast(a), AttrWithCast(b)) if a.sameRef(b) => + Nil + case Equality(AttrWithCast(a), b) if leftId == rightId && b.foldable => + Nil + case Equality(a, AttrWithCast(b)) if leftId == rightId && a.foldable => + Nil + case a: AttributeReference => + if (isColumnReference(a)) { + val colRef = toColumnReference(a) + if (ambiguousColRefs.contains(colRef)) Seq(a) else Nil + } else { + Nil + } + case _ => expr.children.flatMap(getAmbiguousAttrs) + } + condition.toSeq.flatMap(getAmbiguousAttrs) + + case _ => ambiguousColRefs.toSeq.map { ref => + colRefAttrs.find(attr => toColumnReference(attr) == ref).get + } + } + + if (ambiguousAttrs.nonEmpty) { + throw new AnalysisException(s"Column ${ambiguousAttrs.mkString(", ")} are ambiguous. " + + "It's probably because you joined several Datasets together, and some of these " + + "Datasets are the same. This column points to one of the Datasets but Spark is unable " + + "to figure out which one. Please alias the Datasets with different names via " + + "`Dataset.as` before joining them, and specify the column using qualified name, e.g. " + + """`df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set """ + + s"${SQLConf.FAIL_AMBIGUOUS_SELF_JOIN} to false to disable this check.") Review comment: it seems that based on my experience, aliasing the dataset before joining still results in an `ambiguous reference exception` when a certain column is selected. For instance, `joined_df = df.as("a").join(df.as("b"), $"a.id" > $"b.id")` and then `joined_df.select('certain_column')` gave an exception. Providing the alias name didn't help as well -> `joined_df.select(a.certain_column)`. However, by deep copying the dataframes gave the correct result. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
