Nit: your examples above include SessionKey, which no longer exists. Otherwise LGTM.
On Tue, Jan 19, 2016 at 4:58 PM, Maxim Khutornenko <ma...@apache.org> wrote: > I think the behavior is spot on. > > So the plan is to move the current: > > Response addInstances( > 1: AddInstancesConfig config, > 2: Lock lock, > 3: SessionKey session) > > to: > > Response addInstances( > 1: AddInstancesConfig config, > 2: Lock lock, > 3: SessionKey session, > 4: InstanceKey instanceKey, > 5: int32 instanceCount) > > In the current release we will fork the behavior depending on whether > the InstanceKey is present. In the next release, we drop 1,2 and 3 and > end up with only 4 and 5. > > Any concerns? > > On Tue, Jan 19, 2016 at 3:57 PM, Bill Farner <wfar...@apache.org> wrote: > > 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 > >> >> > >>>> >> > > >> >> > >>>> >> > >> >> > >>>> > >> >> > > >> >> > >> >
