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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2021/10/07 06:58:00 UTC
[jira] [Updated] (SPARK-36874) Ambiguous Self-Join detected only on
right dataframe
[ https://issues.apache.org/jira/browse/SPARK-36874?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-36874:
---------------------------------
Fix Version/s: 3.1.3
> Ambiguous Self-Join detected only on right dataframe
> ----------------------------------------------------
>
> Key: SPARK-36874
> URL: https://issues.apache.org/jira/browse/SPARK-36874
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.1.2
> Reporter: Vincent Doba
> Assignee: Kousuke Saruta
> Priority: Major
> Labels: correctness
> Fix For: 3.2.0, 3.1.3
>
>
> When joining two dataframes, if they share the same lineage and one dataframe is a transformation of the other, Ambiguous Self Join detection only works when transformed dataframe is the right dataframe.
> For instance {{df1}} and {{df2}} where {{df2}} is a filtered {{df1}}, Ambiguous Self Join detection only works when {{df2}} is the right dataframe:
> - {{df1.join(df2, ...)}} correctly fails with Ambiguous Self Join error
> - {{df2.join(df1, ...)}} returns a valid dataframe
> h1. Minimum Reproducible example
> h2. Code
> {code:scala}
> import sparkSession.implicit._
> val df1 = Seq((1, 2, "A1"),(2, 1, "A2")).toDF("key1", "key2", "value")
> val df2 = df1.filter($"value" === "A2")
> df2.join(df1, df1("key1") === df2("key2")).show()
> {code}
> h2. Expected Result
> Throw the following exception:
> {code}
> Exception in thread "main" org.apache.spark.sql.AnalysisException: Column key2#11 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" > $"b.id")`. You can also set spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.
> at org.apache.spark.sql.execution.analysis.DetectAmbiguousSelfJoin$.apply(DetectAmbiguousSelfJoin.scala:157)
> at org.apache.spark.sql.execution.analysis.DetectAmbiguousSelfJoin$.apply(DetectAmbiguousSelfJoin.scala:43)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:216)
> at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
> at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
> at scala.collection.immutable.List.foldLeft(List.scala:91)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:213)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:205)
> at scala.collection.immutable.List.foreach(List.scala:431)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:205)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:196)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:190)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:155)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:183)
> at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
> at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:183)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:174)
> at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:228)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:173)
> at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:73)
> at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
> at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:143)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:143)
> at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:73)
> at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:71)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:63)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:90)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:88)
> at org.apache.spark.sql.Dataset.withPlan(Dataset.scala:3715)
> at org.apache.spark.sql.Dataset.join(Dataset.scala:1079)
> at org.apache.spark.sql.Dataset.join(Dataset.scala:1041)
> ...
> {code}
> h2. Actual result
> Empty dataframe:
> {code:java}
> +----+----+-----+----+----+-----+
> |key1|key2|value|key1|key2|value|
> +----+----+-----+----+----+-----+
> +----+----+-----+----+----+-----+
> {code}
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