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https://issues.apache.org/jira/browse/IGNITE-3084?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16290644#comment-16290644
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Nikolay Izhikov commented on IGNITE-3084:
-----------------------------------------

9.

> IgniteCacheRelation is questionable. Main problem is that it works with 
> classes which are not always available. 

With BinaryMarshaller Key and Values classes should be available only on master 
node.
I think it a very common case.

> Also what if schema is dynamic, how are we going to support it? 

If we can't divide objects based on common field(type, class or something 
similar) we can query only common fields from all cache classes.

> I think it's better to support data frames only via Ignite SQL, unless we 
> come up with a cleaner solution. Let me know what you think.

As you know, I think Ignite should provide an ability to query key-value cache 
via generic, widely used SQL interface.
I think that limitation you mentioned is natural to a user and can be just 
documented.

But, without a doubt, you are more experienced Ignite developer than me.
So If your decision is to exclude support of DataFrames for key-value - let's 
exclude it.

If we exclude that feature for now, should I create a separate ticket to 
discuss it in future?

> Spark Data Frames Support in Apache Ignite
> ------------------------------------------
>
>                 Key: IGNITE-3084
>                 URL: https://issues.apache.org/jira/browse/IGNITE-3084
>             Project: Ignite
>          Issue Type: Task
>          Components: spark
>    Affects Versions: 1.5.0.final
>            Reporter: Vladimir Ozerov
>            Assignee: Nikolay Izhikov
>            Priority: Critical
>              Labels: bigdata, important
>             Fix For: 2.4
>
>
> Apache Spark already benefits from integration with Apache Ignite. The latter 
> provides shared RDDs, an implementation of Spark RDD, that help Spark to 
> share a state between Spark workers and execute SQL queries much faster. The 
> next logical step is to enable support for modern Spark Data Frames API in a 
> similar way.
> As a contributor, you will be fully in charge of the integration of Spark 
> Data Frame API and Apache Ignite.



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