Ok. Is there a jira task that I can track for the dataframes and datasets
support?
I do have a couple of follow up questions to understand the memory
representation of the shared RDD support that ignite brings with the spark
integration.
1. Could you detail on how are shared RDD's implemented when ignite is
deployed in a standalone mode? Assuming we have a ignite cluster consisting
a cached named "partitioned" would creating a IgniteRDD through val
sharedRDD: IgniteRDD[Int,Int] = ic.fromCache("partitioned") create another
copy of the cache on the spark executor jvm or would the spark executor
operate on the original copy of the cache that is present on the ignite
nodes? I am more interested in understanding the performance impact of data
shuffling or movement if there is 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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