At risk of devolving the discussion, is it worth calling the method addInstances as opposed to scaleOut? I find the former more descriptive.
On Tue, Jan 19, 2016 at 11:12 AM, Maxim Khutornenko <ma...@apache.org> wrote: > "Of course, the scaler could manually health check that all instances > have come up and are being used as expected, but I guess that is what > Aurora is for." > > I'd argue the updater "watch_secs" health checking isn't enough to > ensure graceful rollout as instances may start flapping right after > the updater signs off. Instances outside of update window may also > flap (e.g. due to backend pressure) and updater will not be able to > catch that. That's why a robust autoscaler has to rely on external > monitoring tools and overall job health instead. > > A very basic approach, as you mentioned above, could be querying job > status repeatedly and count the ratio of tasks in RUNNING vs active > (ASSIGNED, PENDING, THROTTLED, STARTING, etc.) states in order to make > a scaleOut decision. The more reliable approach though would also rely > on external monitoring stats exposed by user processes. That would be > a much higher fidelity signal than a decision based on task status > alone. Scheduler does not (and should not for scalability reasons) > have visibility into those stats, so the autoscaler would be in a much > better position to make an executive decision there. > > On Sun, Jan 17, 2016 at 9:00 AM, Erb, Stephan > <stephan....@blue-yonder.com> wrote: > > I believe the operation is not that simple when you look at the > end-to-end scenario. > > > > For example, the implementation of an auto-scaler using the new > scaleOut() API could look like: > > > > 1) check some KPI > > 2) Infer an action based on this KPI such as scaleUp() or scaleDown() > > 3) wait until the effects of the adjusted instance count is reflected in > the KPI. Go to 1 and repeat. > > > > The health checking capabilities of the existing updater (in particular > together with [1]) would be really helpful here. Still, the simplified > scaleOut() API would offer the great benefit that the auto-scaler would not > need to know about the used aurora configuration. > > > > We even had an incident with a sub-optimal implementation of step 3): An > overloaded package backend lead to slow service startups. The service > startup took longer than the grace-period of our auto-scaler. It therefore > decided to add more and more instances, because the KPI wasn't improving as > expected. It had no way of knowing that these instances were not even > 'running'. The additionally added instances aggravated the overload > situation of the package backend. Of course, the scaler could manually > health check that all instances have come up and are being used as > expected, but I guess that is what Aurora is for. > > > > [1] > https://docs.google.com/document/d/1ZdgW8S4xMhvKW7iQUX99xZm10NXSxEWR0a-21FP5d94/edit?pref=2&pli=1#heading=h.n0kb37aiy8ua > > > > Best Regards, > > Stephan > > ________________________________________ > > From: Maxim Khutornenko <ma...@apache.org> > > Sent: Friday, January 15, 2016 7:06 PM > > To: dev@aurora.apache.org > > Subject: Re: [PROPOSAL] Job instance scaling APIs > > > > I wasn't planning on using the rolling updater functionality given the > > simplicity of the operation. I'd second Steve's earlier concerns about > > scaleOut() looking more like startJobUpdate() if we keep adding > > features. If health watching, throttling (batch_size) or rollback on > > failure is required then I believe the startJobUpdate() should be used > > instead of scaleOut(). > > > > On Fri, Jan 15, 2016 at 1:09 AM, Erb, Stephan > > <stephan....@blue-yonder.com> wrote: > >> I really like the proposal. The gain in simplicity on the client-side > by not having to provide an aurora config is quite significant. > >> > >> The implementation on the scheduler side is probably rather straight > forward as the update can be reused. That would also provide us with the > update UI, which has shown to be quite useful when tracing autoscaler > events. > >> > >> Regards, > >> Stephan > >> ________________________________________ > >> From: Maxim Khutornenko <ma...@apache.org> > >> Sent: Thursday, January 14, 2016 9:50 PM > >> To: dev@aurora.apache.org > >> Subject: Re: [PROPOSAL] Job instance scaling APIs > >> > >> "I'd be concerned that any > >> scaling API to be powerful enough to fit all (most) use cases would just > >> end up looking like the update API." > >> > >> There is a big difference between scaleOut and startJobUpdate APIs > >> that justifies the inclusion of the former. Namely, scaleOut may only > >> replicate the existing instances without changing/introducing any new > >> scheduling requirements or performing instance rollout/rollback. I > >> don't see scaleOut ever becoming more powerful to threaten > >> startJobUpdate. At the same time, the absence of aurora config > >> requirement is a huge boost to autoscaling client simplification. > >> > >> "For example, when scaling down we don't just kill the last N > instances, we > >> actually look at the least loaded hosts (globally) and kill tasks from > >> those." > >> > >> I don't quite see why the same wouldn't be possible with a scaleIn > >> API. Isn't it always external process responsibility to pay due > >> diligence before killing instances? > >> > >> > >> On Thu, Jan 14, 2016 at 12:35 PM, Steve Niemitz <sniem...@apache.org> > wrote: > >>> As some background, we handle scale up / down purely from the client > side, > >>> using the update API for both directions. I'd be concerned that any > >>> scaling API to be powerful enough to fit all (most) use cases would > just > >>> end up looking like the update API. > >>> > >>> For example, when scaling down we don't just kill the last N > instances, we > >>> actually look at the least loaded hosts (globally) and kill tasks from > >>> those. > >>> > >>> > >>> On Thu, Jan 14, 2016 at 3:28 PM, Maxim Khutornenko <ma...@apache.org> > wrote: > >>> > >>>> "How is scaling down different from killing instances?" > >>>> > >>>> I found 'killTasks' syntax too different and way much more powerful to > >>>> be used for scaling in. The TaskQuery allows killing instances across > >>>> jobs/roles, whereas 'scaleIn' is narrowed down to just a single job. > >>>> Additional benefit: it can be ACLed independently by allowing external > >>>> process kill tasks only within a given job. We may also add rate > >>>> limiting or backoff to it later. > >>>> > >>>> As for Joshua's question, I feel it should be an operator's > >>>> responsibility to diff a job with its aurora config before applying an > >>>> update. That said, if there is enough demand we can definitely > >>>> consider adding something similar to what George suggested or > >>>> resurrecting a 'large change' warning message we used to have in > >>>> client updater. > >>>> > >>>> On Thu, Jan 14, 2016 at 12:06 PM, George Sirois <geo...@tellapart.com > > > >>>> wrote: > >>>> > As a point of reference, we solved this problem by adding a binding > >>>> helper > >>>> > that queries the scheduler for the current number of instances and > uses > >>>> > that number instead of a hardcoded config: > >>>> > > >>>> > instances='{{scaling_instances[60]}}' > >>>> > > >>>> > In this example, instances will be set to the currently running > number > >>>> > (unless there are none, in which case 60 instances will be created). > >>>> > > >>>> > On Thu, Jan 14, 2016 at 2:44 PM, Joshua Cohen <jco...@apache.org> > wrote: > >>>> > > >>>> >> What happens if a job has been scaled out, but the underlying > config is > >>>> not > >>>> >> updated to take that scaling into account? Would the next update > on that > >>>> >> job revert the number of instances (presumably, because what else > could > >>>> we > >>>> >> do)? Is there anything we can do, tooling-wise, to improve upon > this? > >>>> >> > >>>> >> On Thu, Jan 14, 2016 at 1:40 PM, Maxim Khutornenko < > ma...@apache.org> > >>>> >> wrote: > >>>> >> > >>>> >> > Our rolling update APIs can be quite inconvenient to work with > when it > >>>> >> > comes to instance scaling [1]. It's especially frustrating when > >>>> >> > adding/removing instances has to be done in an automated fashion > >>>> (e.g.: > >>>> >> by > >>>> >> > an external autoscaling process) as it requires holding on to the > >>>> >> original > >>>> >> > aurora config at all times. > >>>> >> > > >>>> >> > I propose we add simple instance scaling APIs to address the > above. > >>>> Since > >>>> >> > Aurora job may have instances at different configs at any > moment, I > >>>> >> propose > >>>> >> > we accept an InstanceKey as a reference point when scaling out. > For > >>>> >> > example: > >>>> >> > > >>>> >> > /** Scales out a given job by adding more instances with the > task > >>>> >> > config of the templateKey. */ > >>>> >> > Response scaleOut(1: InstanceKey templateKey, 2: i32 > >>>> incrementCount) > >>>> >> > > >>>> >> > /** Scales in a given job by removing existing instances. */ > >>>> >> > Response scaleIn(1: JobKey job, 2: i32 decrementCount) > >>>> >> > > >>>> >> > A correspondent client command could then look like: > >>>> >> > > >>>> >> > aurora job scale-out devcluster/vagrant/test/hello/1 10 > >>>> >> > > >>>> >> > For the above command, a scheduler would take task config of > instance > >>>> 1 > >>>> >> of > >>>> >> > the 'hello' job and replicate it 10 more times thus adding 10 > >>>> additional > >>>> >> > instances to the job. > >>>> >> > > >>>> >> > There are, of course, some details to work out like making sure > no > >>>> active > >>>> >> > update is in flight, scale out does not violate quota and etc. I > >>>> intend > >>>> >> to > >>>> >> > address those during the implementation as things progress. > >>>> >> > > >>>> >> > Does the above make sense? Any concerns/suggestions? > >>>> >> > > >>>> >> > Thanks, > >>>> >> > Maxim > >>>> >> > > >>>> >> > [1] - https://issues.apache.org/jira/browse/AURORA-1258 > >>>> >> > > >>>> >> > >>>> >
