[ https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15576141#comment-15576141 ]
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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org