Are you sure that focusing on the cleanup of specific DoFn's is appropriate? Many cases where cleanup is necessary, it is around an entire composite PTransform. I think there have been discussions/proposals around a more methodical "cleanup" option, but those haven't been implemented, to the best of my knowledge.
For instance, consider the steps of a FileIO: 1. Write to a bunch (N shards) of temporary files 2. When all temporary files are complete, attempt to do a bulk copy to put them in the final destination. 3. Cleanup all the temporary files. (This is often desirable because it minimizes the chance of seeing partial/incomplete results in the final destination). In the above, you'd want step 1 to execute on many workers, likely using a ParDo (say N different workers). The move step should only happen once, so on one worker. This means it will be a different DoFn, likely with some stuff done to ensure it runs on one worker. In such a case, cleanup / @TearDown of the DoFn is not enough. We need an API for a PTransform to schedule some cleanup work for when the transform is "done". In batch this is relatively straightforward, but doesn't exist. This is the source of some problems, such as BigQuery sink leaving files around that have failed to import into BigQuery. In streaming this is less straightforward -- do you want to wait until the end of the pipeline? Or do you want to wait until the end of the window? In practice, you just want to wait until you know nobody will need the resource anymore. This led to some discussions around a "cleanup" API, where you could have a transform that output resource objects. Each resource object would have logic for cleaning it up. And there would be something that indicated what parts of the pipeline needed that resource, and what kind of temporal lifetime those objects had. As soon as that part of the pipeline had advanced far enough that it would no longer need the resources, they would get cleaned up. This can be done at pipeline shutdown, or incrementally during a streaming pipeline, etc. Would something like this be a better fit for your use case? If not, why is handling teardown within a single DoFn sufficient? On Sun, Feb 18, 2018 at 11:53 AM Romain Manni-Bucau <rmannibu...@gmail.com> wrote: > Yes 1M. Lets try to explain you simplifying the overall execution. Each > instance - one fn so likely in a thread of a worker - has its lifecycle. > Caricaturally: "new" and garbage collection. > > In practise, new is often an unsafe allocate (deserialization) but it > doesnt matter here. > > What i want is any "new" to have a following setup before any process or > stattbundle and the last time beam has the instance before it is gc-ed and > after last finishbundle it calls teardown. > > It is as simple as it. > This way no need to comibe fn in a way making a fn not self contained to > implement basic transforms. > > Le 18 févr. 2018 20:07, "Reuven Lax" <re...@google.com> a écrit : > >> >> >> 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 >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>> >>> >>
