[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-21 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15594989#comment-15594989
 ] 

Ashish Shrowty commented on SPARK-17709:


[~sowen], [~smilegator] - I confirmed that this is not a problem in 2.0.1. 
Sorry .. forgot to come back and post my finding. Thanks for your help guys!

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-15 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15578200#comment-15578200
 ] 

Ashish Shrowty commented on SPARK-17709:


Oh .. sorry .. I misread. Will try with 2.0.1 later

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15576763#comment-15576763
 ] 

Xiao Li commented on SPARK-17709:
-

That is what I said above. The deduplication is not triggered. It looks weird 
to me. Please try the 2.0.1. We fixed a lot of bugs in 2.0.1

Thanks!

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15576143#comment-15576143
 ] 

Ashish Shrowty commented on SPARK-17709:


There is a slight difference .. in my case its companyid#121 in both relations 
whereas in yours its different. Perhaps that is 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15575999#comment-15575999
 ] 

Xiao Li commented on SPARK-17709:
-

Below is the statements I used to recreate the problem

{noformat}
sql("CREATE TABLE testext2(companyid int, productid int, price int, count 
int) using parquet")
sql("insert into testext2 values (1, 1, 1, 1)")
val d1 = spark.sql("select * from testext2")
val df1 = d1.groupBy("companyid","productid").agg(sum("price").as("price"))
val df2 = d1.groupBy("companyid","productid").agg(sum("count").as("count"))
df1.join(df2, Seq("companyid", "productid")).show
{noformat}

Can you try it?

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15575995#comment-15575995
 ] 

Xiao Li commented on SPARK-17709:
-

Still works well in 2.0.1

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-14 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15575938#comment-15575938
 ] 

Xiao Li commented on SPARK-17709:
-

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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-13 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15573512#comment-15573512
 ] 

Ashish Shrowty commented on SPARK-17709:


[~smilegator] I compiled with the added debug information and here is the 
output -

{code:java}
scala> val d1 = spark.sql("select * from testext2")
d1: org.apache.spark.sql.DataFrame = [productid: int, price: float ... 2 more 
fields]

scala> val df1 = 
d1.groupBy("companyid","productid").agg(sum("price").as("price"))
df1: org.apache.spark.sql.DataFrame = [companyid: int, productid: int ... 1 
more field]

scala> val df2 = 
d1.groupBy("companyid","productid").agg(sum("count").as("count"))
df2: org.apache.spark.sql.DataFrame = [companyid: int, productid: int ... 1 
more field]

scala> df1.join(df2, Seq("companyid", "productid")).show
org.apache.spark.sql.AnalysisException: using columns ['companyid,'productid] 
can not be resolved given input columns: [companyid, productid, price, count] ;;
'Join UsingJoin(Inner,List('companyid, 'productid))
:- Aggregate [companyid#121, productid#122], [companyid#121, productid#122, 
sum(cast(price#123 as double)) AS price#166]
:  +- Project [productid#122, price#123, count#124, companyid#121]
: +- SubqueryAlias testext2
:+- Relation[productid#122,price#123,count#124,companyid#121] parquet
+- Aggregate [companyid#121, productid#122], [companyid#121, productid#122, 
sum(cast(count#124 as bigint)) AS count#177L]
   +- Project [productid#122, price#123, count#124, companyid#121]
  +- SubqueryAlias testext2
 +- Relation[productid#122,price#123,count#124,companyid#121] parquet

  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:174)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
  at 
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2589)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:641)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:614)
  ... 48 elided

{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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-11 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566160#comment-15566160
 ] 

Ashish Shrowty commented on SPARK-17709:


Cool.. thanks. Will do this in next day or two.

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-11 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566126#comment-15566126
 ] 

Xiao Li commented on SPARK-17709:
-

Below is the link: http://spark.apache.org/docs/latest/building-spark.html

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-11 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566124#comment-15566124
 ] 

Xiao Li commented on SPARK-17709:
-

Below is the link: http://spark.apache.org/docs/latest/building-spark.html

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-11 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566114#comment-15566114
 ] 

Ashish Shrowty commented on SPARK-17709:


I assume I would need to modify the Spark code and build Spark libraries 
locally? Haven't done that before, but willing to try. Is there some docs/links 
you can point me to that show the best way to go about this?

Thanks

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-10 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15562826#comment-15562826
 ] 

Xiao Li commented on SPARK-17709:
-

It is pretty hard to reproduce it in our environment. Are you able to make some 
source changes? https://github.com/apache/spark/pull/15316 Then, it might help 
us identify the root cause. 

Thanks!

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-10 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15562368#comment-15562368
 ] 

Ashish Shrowty commented on SPARK-17709:


[~dkbiswal], [~smilegator] - Hi guys .. any thoughts? I exchanged notes with 
AWS guys and they were able to replicate the same issue and believe that it 
might be a Spark issue.

Thanks,
Ashish

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-03 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15543921#comment-15543921
 ] 

Ashish Shrowty commented on SPARK-17709:


[~dkbiswal] Sorry Dilip .. I keep making typos .. the join was on companyid and 
product id -

scala> df1.join(df2, Seq("companyid","productid"))
org.apache.spark.sql.AnalysisException: using columns ['companyid,'productid] 
can not be resolved given input columns: [companyid, productid, avgprice, 
avgitemcount] ;
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:174)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
  at 
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2589)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:641)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:614)
  ... 48 elided

Attached is explain outputs for df1 and df2 -

scala> df1.explain
== Physical Plan ==
*HashAggregate(keys=[companyid#53, productid#54], functions=[avg(price#56)])
+- Exchange hashpartitioning(companyid#53, productid#54, 200)
   +- *HashAggregate(keys=[companyid#53, productid#54], 
functions=[partial_avg(price#56)])
  +- *Sample 0.0, 0.5, false, 2419324063718201506
 +- *Project [companyid#53, productid#54, price#56]
+- *BatchedScan parquet 
referencedata.testproduct[productid#54,price#56,companyid#53] Format: 
ParquetFormat, InputPaths: 
s3://com.birdzi.datalake.test/testtable/companyid=100, 
s3://com.birdzi.datalake.test/testtable/co..., PushedFilters: [], ReadSchema: 
struct

scala> df2.explain
== Physical Plan ==
*HashAggregate(keys=[companyid#53, productid#54], 
functions=[avg(cast(itemcount#57 as bigint))])
+- Exchange hashpartitioning(companyid#53, productid#54, 200)
   +- *HashAggregate(keys=[companyid#53, productid#54], 
functions=[partial_avg(cast(itemcount#57 as bigint))])
  +- *Sample 0.0, 0.5, false, -7492644014085475670
 +- *Project [companyid#53, productid#54, itemcount#57]
+- *BatchedScan parquet 
referencedata.testproduct[productid#54,itemcount#57,companyid#53] Format: 
ParquetFormat, InputPaths: 
s3://com.birdzi.datalake.test/testtable/companyid=100, 
s3://com.birdzi.datalake.test/testtable/co..., PushedFilters: [], ReadSchema: 
struct


> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-03 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15543141#comment-15543141
 ] 

Dilip Biswal commented on SPARK-17709:
--

@ashrowty Hi Ashish, in your example, the column loyalitycardnumber is not in 
the outputset and that is why we see the exception. I tried using productid 
instead and got
the correct result.

{code}
scala> df1.join(df2, Seq("companyid","loyaltycardnumber"));
org.apache.spark.sql.AnalysisException: using columns 
['companyid,'loyaltycardnumber] can not be resolved given input columns: 
[productid, companyid, avgprice, avgitemcount, companyid, productid] ;
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:57)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:174)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:132)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:57)
  at 
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:61)
  at 
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2651)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:679)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:652)
  ... 48 elided

scala> df1.join(df2, Seq("companyid","productid"));
res1: org.apache.spark.sql.DataFrame = [companyid: int, productid: int ... 2 
more fields]

scala> df1.join(df2, Seq("companyid","productid")).show
+-+-+++ 
|companyid|productid|avgprice|avgitemcount|
+-+-+++
|  101|3|13.0|12.0|
|  100|1|10.0|10.0|
+-+-+++
{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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-03 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15543046#comment-15543046
 ] 

Dilip Biswal commented on SPARK-17709:
--

Hi Ashish, Thanks a lot.. will try and get back.

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-10-01 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15538927#comment-15538927
 ] 

Ashish Shrowty commented on SPARK-17709:


[~dkbiswal] I just went through manual steps of creating the table in Hive 
(using EMR 5.0.0), inserting data into it, and then querying using spark .. and 
got the exception .. steps I followed - 
Step 1 - 
hive> create external table referencedata.testproduct (
hive> create external table referencedata.testproduct (
> productid int,
> name string,
> price double,
> itemcount int
> ) PARTITIONED BY (companyid int)
> STORED AS PARQUET
> LOCATION 's3://com.birdzi.datalake.test/testtable'
> ;
Step 2 - Insert data -
set hive.exec.dynamic.partition.mode=nonstrict
insert into referencedata.testproduct partition(companyid) 
values(1,"p1",10.0,10,100);
insert into referencedata.testproduct partition(companyid) 
values(2,"p1",12.0,12,100);
insert into referencedata.testproduct partition(companyid) 
values(3,"p3",13.0,12,101);

Step 3 - query using spark-shell -
val d1 = spark.sql("select * from referencedata.testproduct")
val df1 = 
d1.sample(false,0.5).select("companyid","productid","price").groupBy("companyid","productid").agg(avg("price").as("avgprice"))
val df2 = 
d1.sample(false,0.5).select("companyid","productid","itemcount").groupBy("companyid","productid").agg(avg("itemcount").as("avgitemcount"))
df1.join(df2, Seq("companyid","loyaltycardnumber")) .. throws exception -
org.apache.spark.sql.AnalysisException: using columns 
['companyid,'loyaltycardnumber] can not be resolved given input columns: 
[companyid, productid, price, avgitemcount] ;
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:174)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
  at 
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2589)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:641)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:614)
  ... 49 elided


> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15537740#comment-15537740
 ] 

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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15537336#comment-15537336
 ] 

Dilip Biswal commented on SPARK-17709:
--

[~ashrowty] Hmmn.. and your join keys are companyid or loyalitycardnumber or 
both ? If so, i have the exact same scenario but not seeing the error you are 
seeing.

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15537264#comment-15537264
 ] 

Ashish Shrowty commented on SPARK-17709:


[~dkbiswal] Attached are the explain() outputs -

df1.explain
== Physical Plan ==
*HashAggregate(keys=[companyid#3364, loyaltycardnumber#3370], 
functions=[avg(cast(itemcount#3372 as bigint))])
+- Exchange hashpartitioning(companyid#3364, loyaltycardnumber#3370, 200)
   +- *HashAggregate(keys=[companyid#3364, loyaltycardnumber#3370], 
functions=[partial_avg(cast(itemcount#3372 as bigint))])
  +- *Project [loyaltycardnumber#3370, itemcount#3372, companyid#3364]
 +- *BatchedScan parquet 
facts.storetransaction[loyaltycardnumber#3370,itemcount#3372,year#3362,month#3363,companyid#3364]
 Format: ParquetFormat, InputPaths: 
s3://com.birdzi.datalake.test/basedatasets/facts/storetransaction/2016-09-15-2012/year=2002/month...,
 PushedFilters: [], ReadSchema: struct

df2.explain
== Physical Plan ==
*HashAggregate(keys=[companyid#3364, loyaltycardnumber#3370], 
functions=[avg(totalprice#3373)])
+- Exchange hashpartitioning(companyid#3364, loyaltycardnumber#3370, 200)
   +- *HashAggregate(keys=[companyid#3364, loyaltycardnumber#3370], 
functions=[partial_avg(totalprice#3373)])
  +- *Project [loyaltycardnumber#3370, totalprice#3373, companyid#3364]
 +- *BatchedScan parquet 
facts.storetransaction[loyaltycardnumber#3370,totalprice#3373,year#3362,month#3363,companyid#3364]
 Format: ParquetFormat, InputPaths: 
s3://com.birdzi.datalake.test/basedatasets/facts/storetransaction/2016-09-15-2012/year=2002/month...,
 PushedFilters: [], ReadSchema: 
struct


> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15537205#comment-15537205
 ] 

Dilip Biswal commented on SPARK-17709:
--

@ashrowty Hi Ashish, is it possible for you to post explain output for both the 
legs of the join. 
So if we are joining two dataframes df1 and df2 , can we get the output of
df1.explain(true)
df2.explain(true)

>From the error, it seems like key1 and key2 are not present in one leg of join 
>output attribute set.

So if i were to change your test program to the following :

 val df1 = d1.groupBy("key1", "key2")
  .agg(avg("totalprice").as("avgtotalprice"))
  df1.explain(true)
  val df2 = d1.agg(avg("itemcount").as("avgqty"))
  df2.explain(true)
df1.join(df2, Seq("key1", "key2"))

I am able to see the same error you are seeing.

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15536694#comment-15536694
 ] 

Ashish Shrowty commented on SPARK-17709:


Sorry ... its not really col1, its another column .. edited it to col8

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15536464#comment-15536464
 ] 

Dilip Biswal commented on SPARK-17709:
--

[~ashrowty] Ashish, you have the same column name as regular and partitioning 
columns ? I thought hive didn't allow it ?

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-30 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535957#comment-15535957
 ] 

Ashish Shrowty commented on SPARK-17709:


Sure .. the data is brought over into the EMR (5.0.0) HDFS cluster via sqoop. 
Once there, I issue the following commands in Hive (2.1.0) to store it in S3 -

CREATE EXTERNAL TABLE  (
   col1 bigint,
   col2 int,
   col3 string,
   
)
PARTITIONED BY (col1 int)
STORED AS PARQUET
LOCATION 's3_table_dir'

INSERT into 
SELECT col1,col2, FROM 


> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534910#comment-15534910
 ] 

Xiao Li commented on SPARK-17709:
-

[~ashrowty]Can you share the exact way how you load the external table? 

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Ashish Shrowty (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534518#comment-15534518
 ] 

Ashish Shrowty commented on SPARK-17709:


Dilip,

I tried your code and it works on my end too. It's only when I try load an 
external table stored as parquet (in my case its stored in S3). Attaching stack 
trace if that helps (this time I tried on on a different table and hence the 
difference in column names) -

org.apache.spark.sql.AnalysisException: using columns ['productid] can not be 
resolved given input columns: [productid, name1, name2] ;
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:40)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:174)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
  at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
  at 
org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
  at 
org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
  at 
org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
  at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
  at 
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2589)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:641)
  at org.apache.spark.sql.Dataset.join(Dataset.scala:614)

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534434#comment-15534434
 ] 

Dilip Biswal commented on SPARK-17709:
--

[~smilegator] Sure.

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534429#comment-15534429
 ] 

Xiao Li commented on SPARK-17709:
-

Can you try it in the latest 2.0?

> 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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Dilip Biswal (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=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 ")  
> 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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[jira] [Commented] (SPARK-17709) spark 2.0 join - column resolution error

2016-09-29 Thread Xiao Li (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-17709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534338#comment-15534338
 ] 

Xiao Li commented on SPARK-17709:
-

Let me try to reproduce it. Thanks!

> 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 ")  
> 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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