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https://issues.apache.org/jira/browse/SPARK-29682?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16967174#comment-16967174
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Terry Kim edited comment on SPARK-29682 at 11/5/19 2:58 AM:
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[~sandeshyapuram] Glad that you found the workaround. I am still looking into
this. I will update this thread when I find more about it. Thanks for the
patience.
was (Author: imback82):
[~sandeshyapuram] Glad that you found the workaround. I am still looking into
this. I will update this thread when I find more about the root cause. Thanks
for the patience.
> Failure when resolving conflicting references in Join:
> ------------------------------------------------------
>
> Key: SPARK-29682
> URL: https://issues.apache.org/jira/browse/SPARK-29682
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, Spark Shell, Spark Submit
> Affects Versions: 2.4.3
> Reporter: sandeshyapuram
> Priority: Major
>
> When I try to self join a parentDf with multiple childDf say childDf1 ... ...
> where childDfs are derived after a cube or rollup and are filtered based on
> group bys,
> I get and error
> {{Failure when resolving conflicting references in Join: }}
> This shows a long error message which is quite unreadable. On the other hand,
> if I replace cube or rollup with old groupBy, it works without issues.
>
> *Sample code:*
> {code:java}
> val numsDF = sc.parallelize(Seq(1,2,3,4,5,6)).toDF("nums")
> val cubeDF = numsDF
> .cube("nums")
> .agg(
> max(lit(0)).as("agcol"),
> grouping_id().as("gid")
> )
>
> val group0 = cubeDF.filter(col("gid") <=> lit(0))
> val group1 = cubeDF.filter(col("gid") <=> lit(1))
> cubeDF.printSchema
> group0.printSchema
> group1.printSchema
> //Recreating cubeDf
> cubeDF.select("nums").distinct
> .join(group0, Seq("nums"), "inner")
> .join(group1, Seq("nums"), "inner")
> .show
> {code}
> *Sample output:*
> {code:java}
> numsDF: org.apache.spark.sql.DataFrame = [nums: int]
> cubeDF: org.apache.spark.sql.DataFrame = [nums: int, agcol: int ... 1 more
> field]
> group0: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [nums: int,
> agcol: int ... 1 more field]
> group1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [nums: int,
> agcol: int ... 1 more field]
> root
> |-- nums: integer (nullable = true)
> |-- agcol: integer (nullable = true)
> |-- gid: integer (nullable = false)
> root
> |-- nums: integer (nullable = true)
> |-- agcol: integer (nullable = true)
> |-- gid: integer (nullable = false)
> root
> |-- nums: integer (nullable = true)
> |-- agcol: integer (nullable = true)
> |-- gid: integer (nullable = false)
> org.apache.spark.sql.AnalysisException:
> Failure when resolving conflicting references in Join:
> 'Join Inner
> :- Deduplicate [nums#220]
> : +- Project [nums#220]
> : +- Aggregate [nums#220, spark_grouping_id#218], [nums#220, max(0) AS
> agcol#216, spark_grouping_id#218 AS gid#217]
> : +- Expand [List(nums#212, nums#219, 0), List(nums#212, null, 1)],
> [nums#212, nums#220, spark_grouping_id#218]
> : +- Project [nums#212, nums#212 AS nums#219]
> : +- Project [value#210 AS nums#212]
> : +- SerializeFromObject [input[0, int, false] AS value#210]
> : +- ExternalRDD [obj#209]
> +- Filter (gid#217 <=> 0)
> +- Aggregate [nums#220, spark_grouping_id#218], [nums#220, max(0) AS
> agcol#216, spark_grouping_id#218 AS gid#217]
> +- Expand [List(nums#212, nums#219, 0), List(nums#212, null, 1)],
> [nums#212, nums#220, spark_grouping_id#218]
> +- Project [nums#212, nums#212 AS nums#219]
> +- Project [value#210 AS nums#212]
> +- SerializeFromObject [input[0, int, false] AS value#210]
> +- ExternalRDD [obj#209]
> Conflicting attributes: nums#220
> ;;
> 'Join Inner
> :- Deduplicate [nums#220]
> : +- Project [nums#220]
> : +- Aggregate [nums#220, spark_grouping_id#218], [nums#220, max(0) AS
> agcol#216, spark_grouping_id#218 AS gid#217]
> : +- Expand [List(nums#212, nums#219, 0), List(nums#212, null, 1)],
> [nums#212, nums#220, spark_grouping_id#218]
> : +- Project [nums#212, nums#212 AS nums#219]
> : +- Project [value#210 AS nums#212]
> : +- SerializeFromObject [input[0, int, false] AS value#210]
> : +- ExternalRDD [obj#209]
> +- Filter (gid#217 <=> 0)
> +- Aggregate [nums#220, spark_grouping_id#218], [nums#220, max(0) AS
> agcol#216, spark_grouping_id#218 AS gid#217]
> +- Expand [List(nums#212, nums#219, 0), List(nums#212, null, 1)],
> [nums#212, nums#220, spark_grouping_id#218]
> +- Project [nums#212, nums#212 AS nums#219]
> +- Project [value#210 AS nums#212]
> +- SerializeFromObject [input[0, int, false] AS value#210]
> +- ExternalRDD [obj#209]
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:42)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:96)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:335)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:85)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:125)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:85)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:96)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:109)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:106)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:202)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:106)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:68)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:66)
> at org.apache.spark.sql.Dataset.join(Dataset.scala:939)
> ... 46 elided
> {code}
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