[jira] [Commented] (SPARK-42503) Spark SQL should do further validation on join condition fields
[
https://issues.apache.org/jira/browse/SPARK-42503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17691391#comment-17691391
]
ming95 commented on SPARK-42503:
I test hive and mysql , they also do not have this validation . But I still
think this restriction should be added, because the condition of non-left and
right tables in join is meaningless.
> Spark SQL should do further validation on join condition fields
> ---
>
> Key: SPARK-42503
> URL: https://issues.apache.org/jira/browse/SPARK-42503
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.3.2
>Reporter: ming95
>Priority: Major
>
> In Spark SQL, the conditions for the join use fields that are allowed to be
> fields from a non-left table or a non-right table. In this case, the join
> will degenerate into a cross join.
> Suppose you have two tables, test1 and test2, which have the same table
> schema:
> {code:java}
> ```
> CREATE TABLE `default`.`test1` (
> `id` INT,
> `name` STRING,
> `age` INT,
> `dt` STRING)
> USING parquet
> PARTITIONED BY (dt)
> ```{code}
> The following SQL has three joins, but in the last left join, the conditions
> is `t1.name=t2.name`, and t3.name is not used. So the last left join will be
> cross join.
> {code:java}
> ```
> select *
> from
> (select * from test1 where dt="20230215" and age=1 ) t1
> left join
> (select * from test1 where dt=="20230215" and age=2) t2
> on t1.name=t2.name
> left join
> (select * from test2 where dt="20230215") t3
> on
> t1.name=t2.name;
> ```{code}
> So i think Spark SQL should do further validation on join condition, the
> fields of join condition must be a left table or right table field ,
> otherwise it is thrown `AnalysisException`.
>
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[jira] [Commented] (SPARK-42503) Spark SQL should do further validation on join condition fields
[
https://issues.apache.org/jira/browse/SPARK-42503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17691321#comment-17691321
]
Yuming Wang commented on SPARK-42503:
-
Do other databases also have this validation?
> Spark SQL should do further validation on join condition fields
> ---
>
> Key: SPARK-42503
> URL: https://issues.apache.org/jira/browse/SPARK-42503
> Project: Spark
> Issue Type: Improvement
> Components: SQL
>Affects Versions: 3.3.2
>Reporter: ming95
>Priority: Major
>
> In Spark SQL, the conditions for the join use fields that are allowed to be
> fields from a non-left table or a non-right table. In this case, the join
> will degenerate into a cross join.
> Suppose you have two tables, test1 and test2, which have the same table
> schema:
> {code:java}
> ```
> CREATE TABLE `default`.`test1` (
> `id` INT,
> `name` STRING,
> `age` INT,
> `dt` STRING)
> USING parquet
> PARTITIONED BY (dt)
> ```{code}
> The following SQL has three joins, but in the last left join, the conditions
> is `t1.name=t2.name`, and t3.name is not used. So the last left join will be
> cross join.
> {code:java}
> ```
> select *
> from
> (select * from test1 where dt="20230215" and age=1 ) t1
> left join
> (select * from test1 where dt=="20230215" and age=2) t2
> on t1.name=t2.name
> left join
> (select * from test2 where dt="20230215") t3
> on
> t1.name=t2.name;
> ```{code}
> So i think Spark SQL should do further validation on join condition, the
> fields of join condition must be a left table or right table field ,
> otherwise it is thrown `AnalysisException`.
>
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[jira] [Commented] (SPARK-42503) Spark SQL should do further validation on join condition fields
[ https://issues.apache.org/jira/browse/SPARK-42503?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17691235#comment-17691235 ] ming95 commented on SPARK-42503: [~yumwang] [~gurwls223] cc > Spark SQL should do further validation on join condition fields > --- > > Key: SPARK-42503 > URL: https://issues.apache.org/jira/browse/SPARK-42503 > Project: Spark > Issue Type: Improvement > Components: SQL >Affects Versions: 3.3.2 >Reporter: ming95 >Priority: Major > Fix For: 3.4.0 > > > In Spark SQL, the conditions for the join use fields that are allowed to be > fields from a non-left table or a non-right table. In this case, the join > will degenerate into a cross join. > Suppose you have two tables, test1 and test2, which have the same table > schema: > ``` > CREATE TABLE `default`.`test1` ( > `id` INT, > `name` STRING, > `age` INT, > `dt` STRING) > USING parquet > PARTITIONED BY (dt) > ``` > The following SQL has three joins, but in the last left join, the conditions > is `t1.name=t2.name`, and t3.name is not used. So the last left join will be > cross join. > ``` > select * > from > (select * from test1 where dt="20230215" and age=1 ) t1 > left join > (select * from test1 where dt=="20230215" and age=2) t2 > on t1.name=t2.name > left join > (select * from test2 where dt="20230215") t3 > on > t1.name=t2.name; > ``` > So i think Spark SQL should do further validation on join condition, the > fields of join condition must be a left table or right table field , > otherwise it is thrown `AnalysisException`. > -- This message was sent by Atlassian Jira (v8.20.10#820010) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
