> The design of the job system decides how we are able to utilize it, and the old design (in use since Arma 2) worked, but it was quite primitive. It only allow

> The design of the job system decides how we are able to utilize it, and the old design (in use since Arma 2) worked, but it was quite primitive. It only allowed submitting jobs as one block, and there could only be one block active at a time. This means all jobs had to be submitted at once, after which we needed to wait for the jobs to be finished, before we could continue doing other things. > This is also called "Fork-Join". I removed one of these from a batch processing system and got about 3x the throughput in the process. As it was written, the system was trying not to be a noisy neighbor for online (consumer-facing) traffic and was not succeeding. It could still cause brownouts if too many other things were running at the same time. So the authors thought to have the users run Splunk queries, looking for traffic from these other jobs, to see if it was safe to run the CI/CD pipeline. That's ridiculous. It's the sort of thing someone Ops-facing forgets that nobody else on the team gives a shit about most days. If we wanted people to be able to run it without consequence, it needed to go quite a bit slower, but it was already more than half of the wall clock time being spent in a runbook as it was. So I replaced it with a queue instead, that would have n tasks running all the time, instead of up to n and as few as 1. I ended up being able to tune it to about 75-80% of the original number with little problem. Well, one problem. There was one group of customers whose assets generated about 10x the load of any other customer, and the way we queued the work clustered by group. Once I sorted by customer instead of group we stopped getting a clustering of excess workload, and it also made it a hell of a lot easier to eyeball the status messages and estimate how far along you were. Spiky processes cause problems for both load-shedding and autoscaling schemes. And starting too many tasks at once causes memory pressure on your end, since most data processing tasks take more memory in the middle than at the beginning or the end. You are better off self-throttling your traffic so you make the memory load more sequential and you allow compensatory systems the time they need to adapt appropriately to your sudden peak of traffic.