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https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15537205#comment-15537205
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Dilip Biswal commented on SPARK-17709:
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@ashrowty Hi Ashish, is it possible for you to post explain output for both the
legs of the join.
So if we are joining two dataframes df1 and df2 , can we get the output of
df1.explain(true)
df2.explain(true)
>From the error, it seems like key1 and key2 are not present in one leg of join
>output attribute set.
So if i were to change your test program to the following :
val df1 = d1.groupBy("key1", "key2")
.agg(avg("totalprice").as("avgtotalprice"))
df1.explain(true)
val df2 = d1.agg(avg("itemcount").as("avgqty"))
df2.explain(true)
df1.join(df2, Seq("key1", "key2"))
I am able to see the same error you are seeing.
> spark 2.0 join - column resolution error
> ----------------------------------------
>
> Key: SPARK-17709
> URL: https://issues.apache.org/jira/browse/SPARK-17709
> Project: Spark
> Issue Type: Bug
> Affects Versions: 2.0.0
> Reporter: Ashish Shrowty
> Labels: easyfix
>
> If I try to inner-join two dataframes which originated from the same initial
> dataframe that was loaded using spark.sql() call, it results in an error -
> // reading from Hive .. the data is stored in Parquet format in Amazon S3
> val d1 = spark.sql("select * from <hivetable>")
> val df1 = d1.groupBy("key1","key2")
> .agg(avg("totalprice").as("avgtotalprice"))
> val df2 = d1.groupBy("key1","key2")
> .agg(avg("itemcount").as("avgqty"))
> df1.join(df2, Seq("key1","key2")) gives error -
> org.apache.spark.sql.AnalysisException: using columns ['key1,'key2] can
> not be resolved given input columns: [key1, key2, avgtotalprice, avgqty];
> If the same Dataframe is initialized via spark.read.parquet(), the above code
> works. This same code above worked with Spark 1.6.2
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