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Posted to issues@ignite.apache.org by "Manish Mishra (JIRA)" <ji...@apache.org> on 2017/01/08 16:44:58 UTC

[jira] [Comment Edited] (IGNITE-4526) Add Spark Shared RDD examples

    [ https://issues.apache.org/jira/browse/IGNITE-4526?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15809663#comment-15809663 ] 

Manish Mishra edited comment on IGNITE-4526 at 1/8/17 4:44 PM:
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The Above PR contains a very basic example of instantiating the IgniteRDD and filling values into it. The above PR was necessary to check if the build is passing for the added code so far, and for test-running the Teamcity. I will continue to add examples w.r.t above mentioned task description. [~dmagda] Does it look ok ? I need some feedback on current work. 


was (Author: manish__mishra):
The Above PR contains a very basic example of instantiating the IgniteRDD and filling values into it. The above PR was necessary to check if the build is passing for the added code so far, and for test-running the Teamcity. I will continue to add examples w.r.t above mentioned task description. 

> Add Spark Shared RDD examples
> -----------------------------
>
>                 Key: IGNITE-4526
>                 URL: https://issues.apache.org/jira/browse/IGNITE-4526
>             Project: Ignite
>          Issue Type: Task
>            Reporter: Denis Magda
>            Assignee: Manish Mishra
>             Fix For: 2.0
>
>
> Spark Shared RDD functionality doesn't have its own examples. We need to add an example that will do the following:
> - First Spark Worker: creation of a shared RDD and filling it in with data.
> - First Spark Worker: performing some native spark transformation with the RDD.
> - Second Spark Worker: connecting to the same shared RDD.
> - Second Spark Worker: execution of SQL query using Spark API and Ignite API. Show that Ignite's query executes faster.
> The reason why the example should consist of two workers is to showcase one of the main benefits of Ignite's RDDs - ability to share the state (RDD) amid different Spark workers and processes.



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