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From "Ray (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (IGNITE-10314) Spark dataframe will get wrong schema if user executes add/drop column DDL
Date Fri, 07 Dec 2018 04:01:00 GMT

    [ https://issues.apache.org/jira/browse/IGNITE-10314?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16712316#comment-16712316
] 

Ray commented on IGNITE-10314:
------------------------------

[~NIzhikov]

I have implemented the fix for this issue, please review and comment.

Some of the tests fail because currently there're bugs like IGNITE-10585 and IGNITE-10569
causing wrong table schema returned by thin JDBC driver.

 

> Spark dataframe will get wrong schema if user executes add/drop column DDL
> --------------------------------------------------------------------------
>
>                 Key: IGNITE-10314
>                 URL: https://issues.apache.org/jira/browse/IGNITE-10314
>             Project: Ignite
>          Issue Type: Bug
>          Components: spark
>    Affects Versions: 2.3, 2.4, 2.5, 2.6, 2.7
>            Reporter: Ray
>            Assignee: Ray
>            Priority: Critical
>             Fix For: 2.8
>
>
> When user performs add/remove column in DDL,  Spark will get the old/wrong schema.
>  
> Analyse 
> Currently Spark data frame API relies on QueryEntity to construct schema, but QueryEntity
in QuerySchema is a local copy of the original QueryEntity, so the original QueryEntity is
not updated when modification happens.
>  
> Solution
> Get the latest schema using JDBC thin driver's column metadata call, then update fields
in QueryEntity.



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