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https://issues.apache.org/jira/browse/SPARK-10925?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14958054#comment-14958054
]
Xiao Li commented on SPARK-10925:
---------------------------------
I have not tried Spark 1.4, but inner joining 2 tables with the same column
names will not automatically merge them in most commercial RDBMS. They are
treated as separate columns, even if the column names are the same. However,
based on SQL standard, natural join combines the columns with the same names.
For example, in your test case, you can try this:
val df = sqlContext.createDataFrame(rdd)
val df1 = df;
val df2 = df1;
val df3 = df1.join(df2, df1("name") === df2("name"))
val df4 = df3.join(df2, df3("name") === df2("name"))
df4.show()
The exception you should get is like
Exception in thread "main" org.apache.spark.sql.AnalysisException: Reference
'name' is ambiguous, could be: name#1, name#5.;
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
at
org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:191)
at org.apache.spark.sql.DataFrame.resolve(DataFrame.scala:158)
at org.apache.spark.sql.DataFrame.col(DataFrame.scala:672)
at org.apache.spark.sql.DataFrame.apply(DataFrame.scala:660)
at SimpleApp$.main(SimpleApp.scala:49)
at SimpleApp.main(SimpleApp.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at
org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:680)
at
org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> Exception when joining DataFrames
> ---------------------------------
>
> Key: SPARK-10925
> URL: https://issues.apache.org/jira/browse/SPARK-10925
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0, 1.5.1
> Environment: Tested with Spark 1.5.0 and Spark 1.5.1
> Reporter: Alexis Seigneurin
> Attachments: Photo 05-10-2015 14 31 16.jpg, TestCase2.scala
>
>
> I get an exception when joining a DataFrame with another DataFrame. The
> second DataFrame was created by performing an aggregation on the first
> DataFrame.
> My complete workflow is:
> # read the DataFrame
> # apply an UDF on column "name"
> # apply an UDF on column "surname"
> # apply an UDF on column "birthDate"
> # aggregate on "name" and re-join with the DF
> # aggregate on "surname" and re-join with the DF
> If I remove one step, the process completes normally.
> Here is the exception:
> {code}
> Exception in thread "main" org.apache.spark.sql.AnalysisException: resolved
> attribute(s) surname#20 missing from id#0,birthDate#3,name#10,surname#7 in
> operator !Project [id#0,birthDate#3,name#10,surname#20,UDF(birthDate#3) AS
> birthDate_cleaned#8];
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:37)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:44)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:154)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:49)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:103)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$foreachUp$1.apply(TreeNode.scala:102)
> at scala.collection.immutable.List.foreach(List.scala:318)
> at
> org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:102)
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:49)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:44)
> at
> org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:914)
> at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:132)
> at
> org.apache.spark.sql.DataFrame.org$apache$spark$sql$DataFrame$$logicalPlanToDataFrame(DataFrame.scala:154)
> at org.apache.spark.sql.DataFrame.join(DataFrame.scala:553)
> at org.apache.spark.sql.DataFrame.join(DataFrame.scala:520)
> at TestCase2$.main(TestCase2.scala:51)
> at TestCase2.main(TestCase2.scala)
> 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:497)
> at com.intellij.rt.execution.application.AppMain.main(AppMain.java:140)
> {code}
> I'm attaching a test case that I tried with Spark 1.5.0 and 1.5.1. Please
> note it used to work with version 1.4.1
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