Are you sure about the worker mem configuration? what are you setting --memory too and what does the worker UI think its memory allocation is?
On Sun, Apr 18, 2021 at 4:08 AM Mohamadreza Rostami < mohamadrezarosta...@gmail.com> wrote: > I see a bug in executer memory allocation in the standalone cluster, but I > can't find which part of the spark code causes this problem. That why's I > decided to raise this issue here. > Assume you have 3 workers with 10 CPU cores and 10 Gigabyte memories. > Assume also you have 2 spark jobs that run on this cluster of workers, and > these jobs configs set as below: > ----------------- > job-1: > executer-memory: 5g > executer-CPU: 4 > max-cores: 8 > ------------------ > job-2: > executer-memory: 6g > executer-CPU: 4 > max-cores: 8 > ------------------ > In this situation, We expect that if we submit both of these jobs, the > first job that submits get 2 executers which each of them has 4 CPU core > and 5g memory, and the second job gets only one executer on thirds worker > who has 4 CPU core and 6g memory because worker 1 and worker 2 doesn't have > enough memory to accept the second job. But surprisingly, we see that one > of the first or second workers creates an executor for job-2, and the > worker's consuming memory goes beyond what's allocated to that and gets 11g > memory from the operating system. > Is this behavior normal? I think this can cause some undefined behavior > problem in the cluster. > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > >
