Hi, I'm using the Ignite embedded deployment to run an Ignite workload in a Spark cluster.
In my use case it's required to deploy exactly an Ignite worker for each node in the Spark cluster. However I haven't found a way to do that. Take in consideration this scenario: I'm running a 3 nodes Spark cluster on AWS (1 driver, 2 workers, each node with 3 cores). I would run 2 Ignite workers, one for each Spark worker. I'm using the following script: https://gist.github.com/pditommaso/660cbee09755b2b880099ab3bf2c609a I've set `spark.executor.instances = 2` in order to deploy two Ignite workers, indeed in the main log I can read the following: 16/07/06 18:58:02 INFO spark.IgniteContext: Will start Ignite nodes on 2 workers However what happens is that Ignite is launched only in one Spark node. Looking in the same log, the following line seems to suggest the reason: 16/07/06 18:58:05 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, *ip-10-37-175-68*.eu-west-1.compute.internal, partition 0,PROCESS_LOCAL, 2137 bytes) 16/07/06 18:58:05 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, *ip-10-37-175-68*.eu-west-1.compute.internal, partition 1,PROCESS_LOCAL, 2194 bytes) Spark is running two tasks to deploy the Ignite workers, but both of them in the same node (*ip-10-37-175-68*). Is there any workaround to avoid this? or more in general, is it possible to deploy exactly one Ignite worker for each node in the Spark cluster ? Thanks a lot. Cheers, Paolo
