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.