My apologies, in my excitement of finding a rather simple way to accomplish the scheduling goal I have in mind, I hastily jumped straight into a technical solution, without explaining that goal, or the problem it's attempting to solve.
You are correct that I'm looking for an additional running mode for the standalone scheduler. Perhaps you could/should classify it as a different scheduler, but I don't want to give the impression that this will be as difficult to implement as most schedulers are. Initially, from a memory perspective, we would still allocate in a FIFO manner. This new scheduling mode (or new scheduler, if you'd rather) would mostly benefit any users with small-ish clusters, both on-premise and cloud based. Essentially, my end goal is to be able to run multiple *applications* simultaneously with each application having *access* to the entire core count of the cluster. I have a very cpu intensive application that I'd like to run weekly. I have a second application that I'd like to run hourly. The hourly application is more time critical (higher priority), so I'd like it to finish in a small amount of time. If I allow the first app to run with all cores (this takes several days on my 64 core cluster), then nothing else can be executed when running with the default FIFO scheduler. All of the cores have been allocated to the first application, and it will not release them until it is finished. Dynamic allocation does not help in this case, as there is always a backlog of tasks to run until the first application is nearing the end anyway. Naturally, I could just limit the number of cores that the first application has access to, but then I have idle cpu time when the second app is not running, and that is not optimal. Secondly in that case, the second application only has access to the *leftover* cores that the first app has not allocated, and will take a considerably longer amount of time to run. You could also imagine a scenario where a developer has a spark-shell running without specifying the number of cores they want to utilize (whether intentionally or not). As I'm sure you know, the default is to allocate the entire cluster to this application. The cores allocated to this shell are unavailable to other applications, even if they are just sitting idle while a developer is getting their environment set up to run a very big job interactively. Other developers that would like to launch interactive shells are stuck waiting for the first one to exit their shell. My proposal would eliminate this static nature of core counts and allow as many simultaneous applications to be running as the cluster memory (still statically partitioned, at least initially) will allow. Applications could be configured with a "cpu shares" parameter (just an arbitrary integer relative only to other applications) which is essentially just passed through to the linux cgroup cpu.shares setting. Since each executor of an application on a given worker runs in it's own process/jvm, then that process could be easily be placed into a cgroup created and dedicated for that application. Linux cgroups cpu.shares are pretty well documented, but the gist is that processes competing for cpu time are allocated a percentage of time equal to their share count as a percentage of all shares in that level of the cgroup hierarchy. If two applications are both scheduled on the same core with the same weight, each will get to utilize 50% of the time on that core. This is all built into the kernel, and the only thing the spark worker has to do is create a cgroup for each application, set the cpu.shares parameter, and assign the executors for that application to the new cgroup. If multiple executors are running on a single worker, for a single application, the cpu time available to that application is divided among each of those executors equally. The default for cpu.shares is that they are not limiting in any way. A process can consume all available cpu time if it would otherwise be idle anyway. Another benefit to passing cpu.shares directly to the kernel (as opposed to some abstraction) is that cpu share allocations are heterogeneous to all processes running on a machine. An admin could have very fine grained control over which processes get priority access to cpu time, depending on their needs. To continue my personal example above, my long running cpu intensive application could utilize 100% of all cluster cores if they are idle. Then my time sensitive app could be launched with nine times the priority and the linux kernel would scale back the first application to 10% of all cores (completely seemlessly and automatically: no pre-emption, just fewer time slices of cpu allocated by the kernel to the first application), while the second application gets 90% of all the cores until it completes. The only downside that I can think of currently is that this scheduling mode would create an increase in context switching on each host. This issue is somewhat mitigated by still statically allocating memory however, since there wouldn't typically be an exorbitant number of applications running at once. In my opinion, this would allow the most optimal usage of cluster resources. Linux cgroups allow you to control access to more than just cpu shares. You can apply the same concept to other resources (memory, disk io). You can also set up hard limits so that an application will never get more than is allocated to it. I know that those limitations are important for some use cases involving predictability of application execution times. Eventually, this idea could be expanded to include many more of the features that cgroups provide. Thanks again for any feedback on this idea. I hope that I have explained it a bit better now. Does anyone else can see value in it? Travis ________________________________ From: Shuai Lin <linshuai2...@gmail.com> Sent: Saturday, December 3, 2016 06:52 To: Hegner, Travis Cc: dev@spark.apache.org Subject: Re: SPARK-18689: A proposal for priority based app scheduling utilizing linux cgroups. Sorry but I don't get the scope of the problem from your description. Seems it's an improvement for spark standalone scheduler (i.e. not for yarn or mesos)? On Sat, Dec 3, 2016 at 4:27 AM, Hegner, Travis <theg...@trilliumit.com<mailto:theg...@trilliumit.com>> wrote: Hello, I've just created a JIRA to open up discussion of a new feature that I'd like to propose. https://issues.apache.org/jira/browse/SPARK-18689 I'd love to get some feedback on the idea. I know that normally anything related to scheduling or queuing automatically throws up the "hard to implement" red flags, but the proposal contains a rather simple way to implement the concept, which delegates the scheduling logic to the actual kernel of each worker, rather than in any spark core code. I believe this to be more flexible and simpler to set up and maintain than dynamic allocation, and avoids the need for any preemption type of logic. The proposal does not contain any code. I am not (yet) familiar enough with the core spark code to confidently create an implementation. I appreciate your time and am looking forward to your feedback! Thanks, Travis
