e the first one.
b. Ignite works in concurrent fashion without locking the world. While you
save new data, you can still read it and run queries.
-Val
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going to be updated
and lock only those?
Thanks.
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Hi,
To be honest, I don't quite understand these diagrams :) Can you give some
comments?
-Val
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ers.70518.x6.nabble.com/file/n9393/q7e83SI.png>
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lose the
whole purpose of the shared RDD.
-Val
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. Kindly let me know.
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in context:
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e runs in standalone mode, right? i.e When spark fetches the
ignite cache through the IgniteRDD api (val igniteRDD =
igniteContext.fromCache("") a copy of data is created in the
spark worker's memory.
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APIs, you can always combine - use Ignite to update the data in
transactional manner and Spark for analytics.
-Val
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te is to easily share data across spark jobs
where ignite is used for in-memory data storage and spark for its high speed
data processing capabilities.
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with Spark?
-Val
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e flow correct? If so, how do I maintain
transaction isolation when other spark jobs try to read the data from ignite
in parallel?
Thanks.
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/query/annotations/QuerySqlFunction.html
-Val
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executor). This should
minimize data movement.
2. For this you will have to use Ignite API directly. You can use
IgniteContext.ignite() to get the local client on the driver.
-Val
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any.
>
> 2. Since spark does not have transaction support, how I can use the ACID
> transaction support that Ignite provides when updating RDD's? A code example
> would be helpful if possible.
>
> Thanks.
>
>
>
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ction support that Ignite provides when updating RDD's? A code example
would be helpful if possible.
Thanks.
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on IgniteRDD.
-Val
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spark dataset take the advantage of these indexes when
queries are executed on it? Do I need to do anything explicit for that other
than enabling the indexes while defining IgniteConfiguration?
Thanks!
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