Sorry for the post again. I guess I'm not understanding this… The question is how to scale up/increase the execution of a problem. What I'm trying to do, is get the best out of the available processors for a given node count and compare this against spark, using KMeans.
For spark, one method is to increase the executors and RDD partitions - for Flink I can increase the number of task slots (taskmanager.numberOfTaskSlots). My empirical evidence suggests that just increasing the slots does not increase processing of the data. Is there something I'm missing? Much like spark with re-partitioning your datasets, is there an equivalent option for flink? What about the parallelism argument The referring document seems to be broken… This seems to be a dead link: https://github.com/apache/flink/blob/master/docs/setup/%7B%7Bsite.baseurl%7D%7D/apis/programming_guide.html#parallel-execution If I do increase the parallelism to be (taskManagers*slots) I hit the "Insufficient number of network buffers…" I have 16 nodes (64 HT cores), and have run TaskSlots from 1, 4, 8, 16 and still the execution time is always around 5-6 minutes, using the default parallelism. Regards, Bill -- Jonathan (Bill) Sparks Software Architecture Cray Inc.
