Details
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Bug
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Status: Closed
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Major
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Resolution: Not A Problem
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2.1.0, 2.1.1
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None
Description
While this seemed to work in Spark 2.0.2, it fails in 2.1.0 and 2.1.1.
Even after aliasing both the table names and all the columns, joining Datasets using a criteria assembled from Columns rather than the with the join(.... usingColumns) method variants errors complaining that a join is a cross join / cartesian product even when it isn't.
Example:
Dataset<Row> left = spark.sql("select 'bob' as name, 23 as age");
left = left
.alias("l")
.select(
left.col("name").as("l_name"),
left.col("age").as("l_age"));
Dataset<Row> right = spark.sql("select 'bob' as name, 'bobco' as company");
right = right
.alias("r")
.select(
right.col("name").as("r_name"),
right.col("company").as("r_age"));
Dataset<Row> result = left.join(
right,
left.col("l_name").equalTo(right.col("r_name")),
"inner");
result.show();
Results in
org.apache.spark.sql.AnalysisException: Detected cartesian product for INNER join between logical plans Project [bob AS l_name#22, 23 AS l_age#23] +- OneRowRelation$ and Project [bob AS r_name#33, bobco AS r_age#34] +- OneRowRelation$ Join condition is missing or trivial. Use the CROSS JOIN syntax to allow cartesian products between these relations.; at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$21.applyOrElse(Optimizer.scala:1067) at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts$$anonfun$apply$21.applyOrElse(Optimizer.scala:1064) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:268) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:305) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:273) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:257) at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:1064) at org.apache.spark.sql.catalyst.optimizer.CheckCartesianProducts.apply(Optimizer.scala:1049) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:35) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78) at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84) at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89) at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89) at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2814) at org.apache.spark.sql.Dataset.head(Dataset.scala:2127) at org.apache.spark.sql.Dataset.take(Dataset.scala:2342) at org.apache.spark.sql.Dataset.showString(Dataset.scala:248) at org.apache.spark.sql.Dataset.show(Dataset.scala:638) at org.apache.spark.sql.Dataset.show(Dataset.scala:597) at org.apache.spark.sql.Dataset.show(Dataset.scala:606) at com.nuna.platform.common.spark.util.JoinBuilderIntegrationTest.testSimpleJoin(JoinBuilderIntegrationTest.java:129) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50) at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12) at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47) at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17) at org.junit.rules.ExpectedException$ExpectedExceptionStatement.evaluate(ExpectedException.java:239) at org.junit.rules.RunRules.evaluate(RunRules.java:20) at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78) at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57) at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290) at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71) at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288) at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58) at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268) at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26) at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27) at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:48) at org.junit.rules.RunRules.evaluate(RunRules.java:20) at org.junit.runners.ParentRunner.run(ParentRunner.java:363) at org.junit.runner.JUnitCore.run(JUnitCore.java:137) at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:68) at com.intellij.rt.execution.junit.IdeaTestRunner$Repeater.startRunnerWithArgs(IdeaTestRunner.java:51) at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:242) at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:70)
This is related to other issues like SPARK-14854.
I feel like in many of these cases, Spark shouldn't be considering these joins as Cartesian products. Usually, I run across this when one table is derived from another, but in this case it happens even with the two tables have fully distinct lineages.
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