[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-04-04 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1496311839

   Thanks a lot @cloud-fan for the guidance and support in getting this issue 
fixed.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-04-03 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1495354346

   Gentle ping @cloud-fan 


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-31 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1492817996

   @cloud-fan I have made the change. All Tests have passed. Can you please 
review?


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-31 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1491954910

   > If you really worry about regression, we can add a legacy config to fall 
back to the old code. I don't agree to make code changes that only fix the 
problem in one particular code path, while we know other code paths have the 
same problem as well.
   
   Ok, I will update the PR with suggested change.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-31 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1491795763

   
   
   
   
   > according to the [code in 
2.3](https://github.com/apache/spark/blob/branch-2.3/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala#L190),
 I think we should call `distinct` in line 345
   
   @cloud-fan 
   Yes, that should also work, but making it there will increase the impact of 
change to lot more other scenarios.
   Whereas the place where I have made distinct keeps the scope very limited.
   


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-27 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1486232865

   @cloud-fan Can you please check my last comments.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-24 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1482819187

   FWIW Both the use cases were working fine in Spark 2.3


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-24 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1482792058

   > I think case 1 works by accident. It's not an intentional design. I don't 
think it's a bug that case 2 doesn't work.
   
   As I had said in previous comment :
   Not sure about the deduplication before, but even if it was doing it at some 
stage, in the second use case it might not have converted the column name to 
lowercase by that time, that's why that would still treat the two id and ID 
columns as different.
   Only at end result of column match, we see that both column matches are same 
id#17.
   The speculation was right. Dedup is happening in unique method.
   
   For case 1: 
   unique before:: Map(col3 -> Vector(col3#18571), col2 -> Vector(col2#18570), 
id -> Vector(id#18569, id#18569), col5 -> Vector(col5#18573), col4 -> 
Vector(col4#18572))
   after before:: Map(col3 -> Vector(col3#18571), col2 -> Vector(col2#18570), 
id -> Vector(id#18569), col5 -> Vector(col5#18573), col4 -> Vector(col4#18572))
   
   For Case 2: 
   unique before:: Map(col3 -> Vector(col3#18610), col2 -> Vector(col2#18609), 
id -> Vector(id#18608, ID#18608), col5 -> Vector(col5#18612), col4 -> 
Vector(col4#18611))
   after before:: Map(col3 -> Vector(col3#18610), col2 -> Vector(col2#18609), 
id -> Vector(id#18608, ID#18608), col5 -> Vector(col5#18612), col4 -> 
Vector(col4#18611))
   
   Most of the places we are calling unique before returning the result. So 
what' the negative impact you think it will have if we return unique results 
for the column match also?
   
   One positive use case is it will fix this wrong ambiguous error being thrown 
just because the result of match has two duplicate values.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-24 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1482368131

   > > It works because the resolved column has just one match
   > 
   > But there are two id columns. Does Spark already do deduplication 
somewhere?
   
   Not sure about the deduplication before, but even if it was doing it at some 
stage, in the second use case it might not have converted the column name to 
lowercase by that time, that's why that would still treat the two id and ID 
columns as different.
   Only at end result of column match, we see that both column matches are same 
id#17. 


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-22 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1480595916

   > @shrprasa do you know how the case 1 works?
   
   yes. It works because the resolved column has just one match 
   attributes: Vector(id#17)
   
   but for second case, the match result is
   attributes: Vector(id#17, id#17)
   Since, there are more than one value although both are exactly same, it 
fails. This fix proposes to fix this by taking distinct values of match result.
   
   
   


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-22 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1480574606

   > df3.select("id").show()
   
   @cloud-fan The example you have shared will behave the same even after this 
fix. It will give ambiguous error. 
   The use case which the fix is trying to solve is different. Can you please 
try these two cases:
   Case 1: which works fine
   val df1 = 
sc.parallelize(List((1,2,3,4,5),(1,2,3,4,5))).toDF("id","col2","col3","col4", 
"col5")
   val op_cols_same_case = List("id","col2","col3","col4", "col5", "id")
   val df3 = df1.select(op_cols_same_case.head, op_cols_same_case.tail: _*)
   df3.select("id").show()  
   
   Case 2: which doesn't work fine and the fix is to solve this issue
   val df2 = 
sc.parallelize(List((1,2,3,4,5),(1,2,3,4,5))).toDF("id","col2","col3","col4", 
"col5")
   val op_cols_mixed_case = List("id","col2","col3","col4", "col5", "ID")
   val df4 = df2.select(op_cols_mixed_case.head, op_cols_mixed_case.tail: _*)
   df4.select("id").show()


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-22 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1480532873

   > I second @srowen ‘s view. cc @cloud-fan
   
   Thanks @yaooqinn for replying. Can you please explain why you think it's not 
the right fix? 
   The fix only proposes to remove duplicates from the resolved columns. As 
it's incorrect to consider the only one column match as ambiguous just because 
it occurs more than once in the resolved column list.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-22 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1480217186

   Gentle ping @dongjoon-hyun @mridulm @HyukjinKwon @yaooqinn Can you please 
review this PR or direct it to someone who can review this PR.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-20 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1476233946

   Gentle ping @dongjoon-hyun @mridulm @HyukjinKwon @yaooqinn Can you please 
review this PR or direct it to someone who can review this PR.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-13 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1466394445

   > That's a "no" from me, per the logic above
   
   Thanks @srowen But seems I am not able to explain the change to you. So it's 
better to get review from someone who is qualified to review the change and 
aware of this code.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-12 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1465517104

   Gentle ping @dongjoon-hyun @mridulm @HyukjinKwon @yaooqinn Can you please 
review this PR or direct it to someone who is aware of this code.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-09 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1463230011

   > Hm, I just don't see the logic in that. It isn't how SQL works either, as 
far as I understand. Here's maybe another example, imagine a DataFrame defined 
by `SELECT 3 as id, 3 as ID`. Would you also say selecting "id" is unambiguous? 
and it makes sense to you if I change a 3 to a 4 that this query is no longer 
semantically valid?
   
   If it's valid as per the plan then yes. 


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-08 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1461303721

   It's very much relevant as this is the only case which requires the fix. If 
they do not come from same source, the plan  will reflect that and it will 
throw the ambiguous error even after this fix.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-08 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1461288036

   > Hm, how is it not ambiguous? When case insensitive, 'id' could mean one of 
two different columns
   
   It's not ambiguous because the  when we are selecting using list of column 
names, both id and ID are getting value from same column 'id' in the source 
dataframe. 
   val **df1** = 
sc.parallelize(List((1,2,3,4,5),(1,2,3,4,5))).toDF("**id"**,"col2","col3","col4",
 "col5")
   val op_cols_mixed_case = List(**"id"**,"col2","col3","col4", "col5", 
**"ID"**)
   val df3 = **df1**.select(op_cols_mixed_case.head, op_cols_mixed_case.tail: 
_*)
   df3.select("id").show()
   
   df3.explain()
   == Physical Plan ==
   *(1) Project [**_1#6 AS id#17**, _2#7 AS col2#18, _3#8 AS col3#19, _4#9 AS 
col4#20, _5#10 AS col5#21, **_1#6 AS ID#17**]


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-08 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1461262849

   > I don't get it, it is due to case sensitivity; that's why it becomes 
ambiguous and that's what you see. The issue is that the error isn't super 
helpful because it shows the lower-cased column right? that's what I was 
saying. Or: does your change still result in an error without case sensitivity? 
it should
   
   The issue is not with the error message. Problem is that in this case error 
should not be thrown. Select query should return result.  After this change, 
ambiguous error will not be thrown as we are fixing the duplicate attribute 
match. 


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-07 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1459668923

   > You first defined a case-sensitive data set, then queried in a 
case-insensitive way, I guess the error is expected.
   
   In the physical plan, both id and ID columns are projected to the same 
column in the dataframe: _1#6
   _1#6 AS id#17,  _1#6 AS ID#17
   So, there is no ambiguity,
   
   Also, in the matched attributes, results are same: attributes: Vector(id#17, 
id#17)
   Just because, we have duplicates in the matched result, it's being 
considered as ambiguous.
   
   If the matched attribute result was Vector(id#17, ID#17) , then it would 
have been valid error.
   


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-07 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1459652942

   > Can you try `set spark.sql.caseSensitive=true`?
   
   Yes, I have tried it. With caseSensitive set to false, it will work as then 
id and ID will be treated as separate columns.
   Issue is when columns names are supposed to considered  as case insensitive.


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-07 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1459535564

   Gentle ping @dongjoon-hyun @mridulm @HyukjinKwon @yaooqinn Can you please 
review this PR?


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL] Incorrect ambiguous column reference error

2023-03-07 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1458159825

   > I'm not sure about the change, not sure I'm qualified to review it. I 
think at best the error message should change; I am not clear that the result 
is 'wrong'
   
   Thanks for replying. Can you please tag someone who should be right person 
to review this change?


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL]:Incorrect ambiguous column reference error

2023-03-06 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1457585690

   Gentle Ping @srowen  @dongjoon-hyun 


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[GitHub] [spark] shrprasa commented on pull request #40258: [SPARK-42655][SQL]:Incorrect ambiguous column reference error

2023-03-03 Thread via GitHub


shrprasa commented on PR #40258:
URL: https://github.com/apache/spark/pull/40258#issuecomment-1454348175

   @srowen @dongjoon-hyun Can you please review this PR?


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