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https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15534417#comment-15534417
]
Dilip Biswal commented on SPARK-17709:
--------------------------------------
[~smilegator] Hi Sean, I tried it on my master branch and don't see the
exception.
{code}
test("join issue") {
withTable("tbl") {
sql("CREATE TABLE tbl(key1 int, key2 int, totalprice int, itemcount int)")
sql("insert into tbl values (1, 1, 1, 1)")
val d1 = sql("select * from tbl")
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")).show()
}
}
Output
+----+----+-------------+------+
|key1|key2|avgtotalprice|avgqty|
+----+----+-------------+------+
| 1| 1| 1.0| 1.0|
+----+----+-------------+------+
{code}
> 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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