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From "Ashish Shrowty (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error
Date Sat, 01 Oct 2016 02:59:21 GMT

    [ https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15537740#comment-15537740
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Ashish Shrowty commented on SPARK-17709:
----------------------------------------

Join keys are both companyid and loyaltycardnumber. Wonder why you are not seeing it. I tried
it on a few other tables I have and its the same behavior.

> 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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