I have a Spark cluster of 3 servers (1 worker per server = 3 workers
). The resources are very much the same across servers (70 cores, 386GB of RAM each
).
I also have an application that I spark-submit
, with 120 cores
and 200GB ram
(24 executors).
When I submit the aforementioned app, my cluster manager (standalone) assign all executors to the first two workers and leave the third worker alone without any executor being occupied there.
I want to assign a specific number of executors at each worker and not let the cluster manager (yarn, mesos, or standalone) decide, as with this setup the load of the 2 workers (servers) is extremely high, leading to disk utilization 100%, disk I/O issues, etc.
- Spark version: 2.4.4
- Cluster Manager: Standalone (Will yarn solve my issue?)
I searched everywhere without any luck.