[GitHub] [spark] albertusk95 commented on a change in pull request #25107: [SPARK-28344][SQL] detect ambiguous self-join and fail the query

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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
 
 

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 File path: sql/core/src/main/scala/org/apache/spark/sql/execution/analysis/DetectAmbiguousSelfJoin.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.
+ */
+
+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.

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