[jira] [Updated] (SPARK-47217) De-duplication of Relations in Joins, can result in plan resolution failure
[
https://issues.apache.org/jira/browse/SPARK-47217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Peter Toth updated SPARK-47217:
---
Shepherd: (was: Peter Toth)
> De-duplication of Relations in Joins, can result in plan resolution failure
> ---
>
> Key: SPARK-47217
> URL: https://issues.apache.org/jira/browse/SPARK-47217
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.5.1
>Reporter: Asif
>Priority: Major
> Labels: Spark-SQL, pull-request-available
>
> In case of some flavours of nested joins involving repetition of relation,
> the projected columns when passed to the DataFrame.select API , as form of
> df.column , can result in plan resolution failure due to attribute resolution
> not happening.
> A scenario in which this happens is
> {noformat}
>
> Project ( dataframe A.column("col-a") )
> |
> Join2
> ||
>Join1 DataFrame A
> |
> DataFrame ADataFrame B
> {noformat}
> In such cases, If it so happens that Join2 - right leg DataFrame A gets
> re-aliased due to De-Duplication of relations, and if the project uses Column
> definition obtained from DataFrame A, its exprId will not match the
> re-aliased Join2 - right Leg- DataFrame A , causing resolution failure.
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[jira] [Updated] (SPARK-47217) De-duplication of Relations in Joins, can result in plan resolution failure
[
https://issues.apache.org/jira/browse/SPARK-47217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ASF GitHub Bot updated SPARK-47217:
---
Labels: Spark-SQL pull-request-available (was: Spark-SQL)
> De-duplication of Relations in Joins, can result in plan resolution failure
> ---
>
> Key: SPARK-47217
> URL: https://issues.apache.org/jira/browse/SPARK-47217
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.5.1
>Reporter: Asif
>Priority: Major
> Labels: Spark-SQL, pull-request-available
>
> In case of some flavours of nested joins involving repetition of relation,
> the projected columns when passed to the DataFrame.select API , as form of
> df.column , can result in plan resolution failure due to attribute resolution
> not happening.
> A scenario in which this happens is
> {noformat}
>
> Project ( dataframe A.column("col-a") )
> |
> Join2
> ||
>Join1 DataFrame A
> |
> DataFrame ADataFrame B
> {noformat}
> In such cases, If it so happens that Join2 - right leg DataFrame A gets
> re-aliased due to De-Duplication of relations, and if the project uses Column
> definition obtained from DataFrame A, its exprId will not match the
> re-aliased Join2 - right Leg- DataFrame A , causing resolution failure.
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[jira] [Updated] (SPARK-47217) De-duplication of Relations in Joins, can result in plan resolution failure
[
https://issues.apache.org/jira/browse/SPARK-47217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Asif updated SPARK-47217:
-
Description:
In case of some flavours of nested joins involving repetition of relation, the
projected columns when passed to the DataFrame.select API , as form of
df.column , can result in plan resolution failure due to attribute resolution
not happening.
A scenario in which this happens is
{noformat}
Project ( dataframe A.column("col-a") )
|
Join2
||
Join1 DataFrame A
|
DataFrame ADataFrame B
{noformat}
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
was:
In case of some flavours of self join queries or nested joins involving
repetition of relation, the projected columns when passed to the
DataFrame.select API , as form of df.column , can result in plan resolution
failure due to attribute resolution not happening.
A scenario in which this happens is
{noformat}
Project ( dataframe A.column("col-a") )
|
Join2
||
Join1 DataFrame A
|
DataFrame ADataFrame B
{noformat}
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
> De-duplication of Relations in Joins, can result in plan resolution failure
> ---
>
> Key: SPARK-47217
> URL: https://issues.apache.org/jira/browse/SPARK-47217
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.5.1
>Reporter: Asif
>Priority: Major
> Labels: Spark-SQL
>
> In case of some flavours of nested joins involving repetition of relation,
> the projected columns when passed to the DataFrame.select API , as form of
> df.column , can result in plan resolution failure due to attribute resolution
> not happening.
> A scenario in which this happens is
> {noformat}
>
> Project ( dataframe A.column("col-a") )
> |
> Join2
> ||
>Join1 DataFrame A
> |
> DataFrame ADataFrame B
> {noformat}
> In such cases, If it so happens that Join2 - right leg DataFrame A gets
> re-aliased due to De-Duplication of relations, and if the project uses Column
> definition obtained from DataFrame A, its exprId will not match the
> re-aliased Join2 - right Leg- DataFrame A , causing resolution failure.
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[jira] [Updated] (SPARK-47217) De-duplication of Relations in Joins, can result in plan resolution failure
[
https://issues.apache.org/jira/browse/SPARK-47217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Asif updated SPARK-47217:
-
Description:
In case of some flavours of self join queries or nested joins involving
repetition of relation, the projected columns when passed to the
DataFrame.select API , as form of df.column , can result in plan resolution
failure due to attribute resolution not happening.
A scenario in which this happens is
{noformat}
Project ( dataframe A.column("col-a") )
|
Join2
||
Join1 DataFrame A
|
DataFrame ADataFrame B
{noformat}
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
was:
In case of some flavours of nested self join queries, the projected columns
when passed to the DataFrame.select API , as form of df.column , can result
in plan resolution failure due to attribute resolution not happening.
A scenario in which this happens is
{noformat}
Project ( dataframe A.column("col-a") )
|
Join2
||
Join1 DataFrame A
|
DataFrame ADataFrame B
{noformat}
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
> De-duplication of Relations in Joins, can result in plan resolution failure
> ---
>
> Key: SPARK-47217
> URL: https://issues.apache.org/jira/browse/SPARK-47217
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.5.1
>Reporter: Asif
>Priority: Major
> Labels: Spark-SQL
>
> In case of some flavours of self join queries or nested joins involving
> repetition of relation, the projected columns when passed to the
> DataFrame.select API , as form of df.column , can result in plan resolution
> failure due to attribute resolution not happening.
> A scenario in which this happens is
> {noformat}
>
> Project ( dataframe A.column("col-a") )
> |
> Join2
> ||
>Join1 DataFrame A
> |
> DataFrame ADataFrame B
> {noformat}
> In such cases, If it so happens that Join2 - right leg DataFrame A gets
> re-aliased due to De-Duplication of relations, and if the project uses Column
> definition obtained from DataFrame A, its exprId will not match the
> re-aliased Join2 - right Leg- DataFrame A , causing resolution failure.
--
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[jira] [Updated] (SPARK-47217) De-duplication of Relations in Joins, can result in plan resolution failure
[
https://issues.apache.org/jira/browse/SPARK-47217?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Asif updated SPARK-47217:
-
Description:
In case of some flavours of nested self join queries, the projected columns
when passed to the DataFrame.select API , as form of df.column , can result
in plan resolution failure due to attribute resolution not happening.
A scenario in which this happens is
{noformat}
Project ( dataframe A.column("col-a") )
|
Join2
||
Join1 DataFrame A
|
DataFrame ADataFrame B
{noformat}
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
was:
In case of some flavours of nested self join queries, the projected columns
when passed to the DataFrame.select API , as form of df.column , can result
in plan resolution failure due to attribute resolution not happening.
A scenario in which this happens is
Project ( dataframe A.column("col-a") )
|
Join2
|DataFrame A
Join1
|
DataFrame ADataFrame B
In such cases, If it so happens that Join2 - right leg DataFrame A gets
re-aliased due to De-Duplication of relations, and if the project uses Column
definition obtained from DataFrame A, its exprId will not match the re-aliased
Join2 - right Leg- DataFrame A , causing resolution failure.
> De-duplication of Relations in Joins, can result in plan resolution failure
> ---
>
> Key: SPARK-47217
> URL: https://issues.apache.org/jira/browse/SPARK-47217
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 3.5.1
>Reporter: Asif
>Priority: Major
> Labels: Spark-SQL
>
> In case of some flavours of nested self join queries, the projected columns
> when passed to the DataFrame.select API , as form of df.column , can result
> in plan resolution failure due to attribute resolution not happening.
> A scenario in which this happens is
>
> {noformat}
>
> Project ( dataframe A.column("col-a") )
> |
> Join2
> ||
>Join1 DataFrame A
> |
> DataFrame ADataFrame B
> {noformat}
> In such cases, If it so happens that Join2 - right leg DataFrame A gets
> re-aliased due to De-Duplication of relations, and if the project uses
> Column definition obtained from DataFrame A, its exprId will not match the
> re-aliased Join2 - right Leg- DataFrame A , causing resolution failure.
--
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