[ https://issues.apache.org/jira/browse/SPARK-36874?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Vincent Doba updated SPARK-36874: --------------------------------- Description: 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"),(3,4, "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) {code} h2. Actual result Empty dataframe: {code:java} +----+----+-----+----+----+-----+ |key1|key2|value|key1|key2|value| +----+----+-----+----+----+-----+ +----+----+-----+----+----+-----+ {code} h1. Related issue https://issues.apache.org/jira/browse/SPARK-28344 was: 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"),(3,4, "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) {code} h2. Actual result Empty dataframe: {code:java} +----+----+-----+----+----+-----+ |key1|key2|value|key1|key2|value| +----+----+-----+----+----+-----+ +----+----+-----+----+----+-----+ {code} h2. Related issue https://issues.apache.org/jira/browse/SPARK-28344 > 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 > Priority: Major > Labels: correctness > > 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"),(3,4, "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) > {code} > h2. Actual result > Empty dataframe: > {code:java} > +----+----+-----+----+----+-----+ > |key1|key2|value|key1|key2|value| > +----+----+-----+----+----+-----+ > +----+----+-----+----+----+-----+ > {code} > h1. Related issue > https://issues.apache.org/jira/browse/SPARK-28344 -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org