On Sun, Feb 18, 2018 at 10:50 AM, Romain Manni-Bucau <rmannibu...@gmail.com> wrote:
> > > Le 18 févr. 2018 19:28, "Ben Chambers" <bchamb...@apache.org> a écrit : > > It feels like his thread may be a bit off-track. Rather than focusing on > the semantics of the existing methods -- which have been noted to be meet > many existing use cases -- it would be helpful to focus on more on the > reason you are looking for something with different semantics. > > Some possibilities (I'm not sure which one you are trying to do): > > 1. Clean-up some external, global resource, that was initialized once > during the startup of the pipeline. If this is the case, how are you > ensuring it was really only initialized once (and not once per worker, per > thread, per instance, etc.)? How do you know when the pipeline should > release it? If the answer is "when it reaches step X", then what about a > streaming pipeline? > > > When the dofn is no more needed logically ie when the batch is done or > stream is stopped (manually or by a jvm shutdown) > I'm really not following what this means. Let's say that a pipeline is running 1000 workers, and each worker is running 1000 threads (each running a copy of the same DoFn). How many cleanups do you want (do you want 1000 * 1000 = 1M cleanups) and when do you want it called? When the entire pipeline is shut down? When an individual worker is about to shut down (which may be temporary - may be about to start back up)? Something else? > > > > 2. Finalize some resources that are used within some region of the > pipeline. While, the DoFn lifecycle methods are not a good fit for this > (they are focused on managing resources within the DoFn), you could model > this on how FileIO finalizes the files that it produced. For instance: > a) ParDo generates "resource IDs" (or some token that stores > information about resources) > b) "Require Deterministic Input" (to prevent retries from changing > resource IDs) > c) ParDo that initializes the resources > d) Pipeline segments that use the resources, and eventually output the > fact they're done > e) "Require Deterministic Input" > f) ParDo that frees the resources > > By making the use of the resource part of the data it is possible to > "checkpoint" which resources may be in use or have been finished by using > the require deterministic input. This is important to ensuring everything > is actually cleaned up. > > > I nees that but generic and not case by case to industrialize some api on > top of beam. > > > > 3. Some other use case that I may be missing? If it is this case, could > you elaborate on what you are trying to accomplish? That would help me > understand both the problems with existing options and possibly what could > be done to help. > > > I understand there are sorkaround for almost all cases but means each > transform is different in its lifecycle handling except i dislike it a lot > at a scale and as a user since you cant put any unified practise on top of > beam, it also makes beam very hard to integrate or to use to build higher > level libraries or softwares. > > This is why i tried to not start the workaround discussions and just stay > at API level. > > > > -- Ben > > > On Sun, Feb 18, 2018 at 9:56 AM Romain Manni-Bucau <rmannibu...@gmail.com> > wrote: > >> 2018-02-18 18:36 GMT+01:00 Eugene Kirpichov <kirpic...@google.com>: >> >>> "Machine state" is overly low-level because many of the possible reasons >>> can happen on a perfectly fine machine. >>> If you'd like to rephrase it to "it will be called except in various >>> situations where it's logically impossible or impractical to guarantee that >>> it's called", that's fine. Or you can list some of the examples above. >>> >> >> Sounds ok to me >> >> >>> >>> The main point for the user is, you *will* see non-preventable >>> situations where it couldn't be called - it's not just intergalactic >>> crashes - so if the logic is very important (e.g. cleaning up a large >>> amount of temporary files, shutting down a large number of VMs you started >>> etc), you have to express it using one of the other methods that have >>> stricter guarantees (which obviously come at a cost, e.g. no >>> pass-by-reference). >>> >> >> FinishBundle has the exact same guarantee sadly so not which which other >> method you speak about. Concretely if you make it really unreliable - this >> is what best effort sounds to me - then users can use it to clean anything >> but if you make it "can happen but it is unexpected and means something >> happent" then it is fine to have a manual - or auto if fancy - recovery >> procedure. This is where it makes all the difference and impacts the >> developpers, ops (all users basically). >> >> >>> >>> On Sun, Feb 18, 2018 at 9:16 AM Romain Manni-Bucau < >>> rmannibu...@gmail.com> wrote: >>> >>>> Agree Eugene except that "best effort" means that. It is also often >>>> used to say "at will" and this is what triggered this thread. >>>> >>>> I'm fine using "except if the machine state prevents it" but "best >>>> effort" is too open and can be very badly and wrongly perceived by users >>>> (like I did). >>>> >>>> >>>> Romain Manni-Bucau >>>> @rmannibucau <https://twitter.com/rmannibucau> | Blog >>>> <https://rmannibucau.metawerx.net/> | Old Blog >>>> <http://rmannibucau.wordpress.com> | Github >>>> <https://github.com/rmannibucau> | LinkedIn >>>> <https://www.linkedin.com/in/rmannibucau> | Book >>>> <https://www.packtpub.com/application-development/java-ee-8-high-performance> >>>> >>>> 2018-02-18 18:13 GMT+01:00 Eugene Kirpichov <kirpic...@google.com>: >>>> >>>>> It will not be called if it's impossible to call it: in the example >>>>> situation you have (intergalactic crash), and in a number of more common >>>>> cases: eg in case the worker container has crashed (eg user code in a >>>>> different thread called a C library over JNI and it segfaulted), JVM bug, >>>>> crash due to user code OOM, in case the worker has lost network >>>>> connectivity (then it may be called but it won't be able to do anything >>>>> useful), in case this is running on a preemptible VM and it was preempted >>>>> by the underlying cluster manager without notice or if the worker was too >>>>> busy with other stuff (eg calling other Teardown functions) until the >>>>> preemption timeout elapsed, in case the underlying hardware simply failed >>>>> (which happens quite often at scale), and in many other conditions. >>>>> >>>>> "Best effort" is the commonly used term to describe such behavior. >>>>> Please feel free to file bugs for cases where you observed a runner not >>>>> call Teardown in a situation where it was possible to call it but the >>>>> runner made insufficient effort. >>>>> >>>>> On Sun, Feb 18, 2018, 9:02 AM Romain Manni-Bucau < >>>>> rmannibu...@gmail.com> wrote: >>>>> >>>>>> 2018-02-18 18:00 GMT+01:00 Eugene Kirpichov <kirpic...@google.com>: >>>>>> >>>>>>> >>>>>>> >>>>>>> On Sun, Feb 18, 2018, 2:06 AM Romain Manni-Bucau < >>>>>>> rmannibu...@gmail.com> wrote: >>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Le 18 févr. 2018 00:23, "Kenneth Knowles" <k...@google.com> a >>>>>>>> écrit : >>>>>>>> >>>>>>>> On Sat, Feb 17, 2018 at 3:09 PM, Romain Manni-Bucau < >>>>>>>> rmannibu...@gmail.com> wrote: >>>>>>>>> >>>>>>>>> If you give an example of a high-level need (e.g. "I'm trying to >>>>>>>>> write an IO for system $x and it requires the following >>>>>>>>> initialization and >>>>>>>>> the following cleanup logic and the following processing in between") >>>>>>>>> I'll >>>>>>>>> be better able to help you. >>>>>>>>> >>>>>>>>> >>>>>>>>> Take a simple example of a transform requiring a connection. Using >>>>>>>>> bundles is a perf killer since size is not controlled. Using teardown >>>>>>>>> doesnt allow you to release the connection since it is a best effort >>>>>>>>> thing. >>>>>>>>> Not releasing the connection makes you pay a lot - aws ;) - or >>>>>>>>> prevents you >>>>>>>>> to launch other processings - concurrent limit. >>>>>>>>> >>>>>>>> >>>>>>>> For this example @Teardown is an exact fit. If things die so badly >>>>>>>> that @Teardown is not called then nothing else can be called to close >>>>>>>> the >>>>>>>> connection either. What AWS service are you thinking of that stays >>>>>>>> open for >>>>>>>> a long time when everything at the other end has died? >>>>>>>> >>>>>>>> >>>>>>>> You assume connections are kind of stateless but some (proprietary) >>>>>>>> protocols requires some closing exchanges which are not only "im >>>>>>>> leaving". >>>>>>>> >>>>>>>> For aws i was thinking about starting some services - machines - on >>>>>>>> the fly in a pipeline startup and closing them at the end. If teardown >>>>>>>> is >>>>>>>> not called you leak machines and money. You can say it can be done >>>>>>>> another >>>>>>>> way...as the full pipeline ;). >>>>>>>> >>>>>>>> I dont want to be picky but if beam cant handle its components >>>>>>>> lifecycle it can be used at scale for generic pipelines and if bound to >>>>>>>> some particular IO. >>>>>>>> >>>>>>>> What does prevent to enforce teardown - ignoring the interstellar >>>>>>>> crash case which cant be handled by any human system? Nothing >>>>>>>> technically. >>>>>>>> Why do you push to not handle it? Is it due to some legacy code on >>>>>>>> dataflow >>>>>>>> or something else? >>>>>>>> >>>>>>> Teardown *is* already documented and implemented this way >>>>>>> (best-effort). So I'm not sure what kind of change you're asking for. >>>>>>> >>>>>> >>>>>> Remove "best effort" from the javadoc. If it is not call then it is a >>>>>> bug and we are done :). >>>>>> >>>>>> >>>>>>> >>>>>>> >>>>>>>> Also what does it mean for the users? Direct runner does it so if a >>>>>>>> user udes the RI in test, he will get a different behavior in prod? >>>>>>>> Also >>>>>>>> dont forget the user doesnt know what the IOs he composes use so this >>>>>>>> is so >>>>>>>> impacting for the whole product than he must be handled IMHO. >>>>>>>> >>>>>>>> I understand the portability culture is new in big data world but >>>>>>>> it is not a reason to ignore what people did for years and do it wrong >>>>>>>> before doing right ;). >>>>>>>> >>>>>>>> My proposal is to list what can prevent to guarantee - in the >>>>>>>> normal IT conditions - the execution of teardown. Then we see if we can >>>>>>>> handle it and only if there is a technical reason we cant we make it >>>>>>>> experimental/unsupported in the api. I know spark and flink can, any >>>>>>>> unknown blocker for other runners? >>>>>>>> >>>>>>>> Technical note: even a kill should go through java shutdown hooks >>>>>>>> otherwise your environment (beam enclosing software) is fully >>>>>>>> unhandled and >>>>>>>> your overall system is uncontrolled. Only case where it is not true is >>>>>>>> when >>>>>>>> the software is always owned by a vendor and never installed on >>>>>>>> customer >>>>>>>> environment. In this case it belongd to the vendor to handle beam API >>>>>>>> and >>>>>>>> not to beam to adjust its API for a vendor - otherwise all unsupported >>>>>>>> features by one runner should be made optional right? >>>>>>>> >>>>>>>> All state is not about network, even in distributed systems so this >>>>>>>> is key to have an explicit and defined lifecycle. >>>>>>>> >>>>>>>> >>>>>>>> Kenn >>>>>>>> >>>>>>>> >>>>>>>> >>>> >
