Hi, Here is the ticket https://issues.apache.org/jira/browse/IGNITE-3084 <https://issues.apache.org/jira/browse/IGNITE-3084>
Feel free to paste your questions there as well so that the implementer takes them into account. — Denis > On Nov 14, 2016, at 6:14 AM, pragmaticbigdata <[email protected]> wrote: > > 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. > > > > -- > View this message in context: > http://apache-ignite-users.70518.x6.nabble.com/Apache-Spark-Ignite-Integration-tp8556p8951.html > Sent from the Apache Ignite Users mailing list archive at Nabble.com.
