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https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15575938#comment-15575938
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Xiao Li commented on SPARK-17709:
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I can get an exactly same plan in the master branch, but my job can pass.
{noformat}
'Join UsingJoin(Inner,List('companyid, 'productid))
:- Aggregate [companyid#5, productid#6], [companyid#5, productid#6,
sum(cast(price#7 as bigint)) AS price#30L]
: +- Project [companyid#5, productid#6, price#7, count#8]
: +- SubqueryAlias testext2
: +- Relation[companyid#5,productid#6,price#7,count#8] parquet
+- Aggregate [companyid#46, productid#47], [companyid#46, productid#47,
sum(cast(count#49 as bigint)) AS count#41L]
+- Project [companyid#46, productid#47, price#48, count#49]
+- SubqueryAlias testext2
+- Relation[companyid#46,productid#47,price#48,count#49] parquet
{noformat}
The only difference is yours does not trigger deduplication of expression ids.
Let me try it in the 2.0.1 branch.
> 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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