Unless the behavior differs significantly, repurposing sounds like the right thing to do. We don't *need* a separate RPC to accomplish this, we can just add arguments or struct fields to accomplish dual use.
On Tue, Jan 19, 2016 at 3:53 PM, Maxim Khutornenko <ma...@apache.org> wrote: > That thought crossed my mind but due to addInstances() being reserved > for an existing RPC I filed AURORA-1581 to handle its graceful > deprecation first. > > Bill, are you suggesting re-purposing the existing 'addInstances()' > into a 'scaleOut()' equivalent? I am mostly +1 on the change but if we > do it within the same release we will not be following our deprecation > guidelines. Perhaps we can name it something like 'addTaskInstances()' > instead and let addInstances() go away naturally? > > On Tue, Jan 19, 2016 at 3:06 PM, Tony Dong <td...@twitter.com.invalid> > wrote: > > +1 to addInstances > > > > On Tue, Jan 19, 2016 at 3:00 PM, Bill Farner <wfar...@apache.org> wrote: > > > >> 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 > >> > >>>> >> > > >> > >>>> >> > >> > >>>> > >> > > >> >
