Ashish Shrowty commented on SPARK-17709:

There is a slight difference, in my case the IDs generated are the same for 
e.g. companyid#121 in both aggregates, whereas in your plan the ids are 
difference companyid#5 and companyid#46. This is probably causing the 
resolution error?

> spark 2.0 join - column resolution error
> ----------------------------------------
>                 Key: SPARK-17709
>                 URL: https://issues.apache.org/jira/browse/SPARK-17709
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Ashish Shrowty
>            Priority: Critical
> 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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