I also had a different understanding of the lifecycle of a DoFn.

My understanding of the use case for every method in the DoFn was clear and
perfectly aligned with Thomas explanation, but what I understood was that in a
general terms ‘@Setup was where I got resources/prepare connections and
@Teardown where I free them’, so calling Teardown seemed essential to have a
complete lifecycle:
Setup → StartBundle* → ProcessElement* → FinishBundle* → Teardown

The fact that @Teardown could not be called is a new detail for me too, and I
also find weird to have a method that may or not be called as part of an API,
why would users implement teardown if it will not be called? In that case
probably a cleaner approach would be to get rid of that method altogether, no?

But well maybe that’s not so easy too, there was another point: Some user
reported an issue with leaking resources using KafkaIO in the Spark runner, for

In that moment my understanding was that there was something fishy because we
should be calling Teardown to close correctly the connections and free the
resources in case of exceptions on start/process/finish, so I filled a JIRA and
fixed this by enforcing the call of teardown for the Spark runner and the Flink

As you can see not calling this method does have consequences at least for
non-containerized runners. Of course a runner that uses containers could not
care about cleaning the resources this way, but a long living JVM in a Hadoop
environment probably won’t have the same luck. So I am not sure that having a
loose semantic there is the right option, I mean, runners could simply guarantee
that they call teardown and if teardown takes too long they can decide to send a
signal or kill the process/container/etc and go ahead, that way at least users
would have a motivation to implement the teardown method, otherwise it doesn’t
make any sense to have it (API wise).

On Mon, Feb 19, 2018 at 11:30 PM, Eugene Kirpichov <kirpic...@google.com> wrote:
> Romain, would it be fair to say that currently the goal of your
> participation in this discussion is to identify situations where @Teardown
> in principle could have been called, but some of the current runners don't
> make a good enough effort to call it? If yes - as I said before, please, by
> all means, file bugs of the form "Runner X doesn't call @Teardown in
> situation Y" if you're aware of any, and feel free to send PRs fixing runner
> X to reliably call @Teardown in situation Y. I think we all agree that this
> would be a good improvement.
> On Mon, Feb 19, 2018 at 2:03 PM Romain Manni-Bucau <rmannibu...@gmail.com>
> wrote:
>> Le 19 févr. 2018 22:56, "Reuven Lax" <re...@google.com> a écrit :
>> On Mon, Feb 19, 2018 at 1:51 PM, Romain Manni-Bucau
>> <rmannibu...@gmail.com> wrote:
>>> Le 19 févr. 2018 21:28, "Reuven Lax" <re...@google.com> a écrit :
>>> How do you call teardown? There are cases in which the Java code gets no
>>> indication that the restart is happening (e.g. cases where the machine
>>> itself is taken down)
>>> This is a bug, 0 downtime maintenance is very doable in 2018 ;). Crashes
>>> are bugs, kill -9 to shutdown is a bug too. Other cases let call shutdown
>>> with a hook worse case.
>> What you say here is simply not true.
>> There are many scenarios in which workers shutdown with no opportunity for
>> any sort of shutdown hook. Sometimes the entire machine gets shutdown, and
>> not even the OS will have much of a chance to do anything. At scale this
>> will happen with some regularity, and a distributed system that assumes this
>> will not happen is a poor distributed system.
>> This is part of the infra and there is no reason the machine is shutdown
>> without shutting down what runs on it before except if it is a bug in the
>> software or setup. I can hear you maybe dont do it everywhere but there is
>> no blocker to do it. Means you can shutdown the machines and guarantee
>> teardown is called.
>> Where i go is simply that it is doable and beam sdk core can assume setup
>> is well done. If there is a best effort downside due to that - with the
>> meaning you defined - it is an impl bug or a user installation issue.
>> Technically all is true.
>> What can prevent teardown is a hardware failure or so. This is fine and
>> doesnt need to be in doc since it is life in IT and obvious or must be very
>> explicit to avoid current ambiguity.
>>> On Mon, Feb 19, 2018, 12:24 PM Romain Manni-Bucau <rmannibu...@gmail.com>
>>> wrote:
>>>> Restarting doesnt mean you dont call teardown. Except a bug there is no
>>>> reason - technically - it happens, no reason.
>>>> Le 19 févr. 2018 21:14, "Reuven Lax" <re...@google.com> a écrit :
>>>>> Workers restarting is not a bug, it's standard often expected.
>>>>> On Mon, Feb 19, 2018, 12:03 PM Romain Manni-Bucau
>>>>> <rmannibu...@gmail.com> wrote:
>>>>>> Nothing, as mentionned it is a bug so recovery is a bug recovery
>>>>>> (procedure)
>>>>>> Le 19 févr. 2018 19:42, "Eugene Kirpichov" <kirpic...@google.com> a
>>>>>> écrit :
>>>>>>> So what would you like to happen if there is a crash? The DoFn
>>>>>>> instance no longer exists because the JVM it ran on no longer exists. 
>>>>>>> What
>>>>>>> should Teardown be called on?
>>>>>>> On Mon, Feb 19, 2018, 10:20 AM Romain Manni-Bucau
>>>>>>> <rmannibu...@gmail.com> wrote:
>>>>>>>> This is what i want and not 999999 teardowns for 1000000 setups
>>>>>>>> until there is an unexpected crash (= a bug).
>>>>>>>> Le 19 févr. 2018 18:57, "Reuven Lax" <re...@google.com> a écrit :
>>>>>>>>> On Mon, Feb 19, 2018 at 7:11 AM, Romain Manni-Bucau
>>>>>>>>> <rmannibu...@gmail.com> wrote:
>>>>>>>>>> 2018-02-19 15:57 GMT+01:00 Reuven Lax <re...@google.com>:
>>>>>>>>>>> On Mon, Feb 19, 2018 at 12:35 AM, Romain Manni-Bucau
>>>>>>>>>>> <rmannibu...@gmail.com> wrote:
>>>>>>>>>>>> @Reuven: in practise it is created by pool of 256 but leads to
>>>>>>>>>>>> the same pattern, the teardown is just a "if (iCreatedThem) 
>>>>>>>>>>>> releaseThem();"
>>>>>>>>>>> How do you control "256?" Even if you have a pool of 256 workers,
>>>>>>>>>>> nothing in Beam guarantees how many threads and DoFns are created 
>>>>>>>>>>> per
>>>>>>>>>>> worker. In theory the runner might decide to create 1000 threads on 
>>>>>>>>>>> each
>>>>>>>>>>> worker.
>>>>>>>>>> Nop was the other way around, in this case on AWS you can get 256
>>>>>>>>>> instances at once but not 512 (which will be 2x256). So when you 
>>>>>>>>>> compute the
>>>>>>>>>> distribution you allocate to some fn the role to own the instance 
>>>>>>>>>> lookup and
>>>>>>>>>> releasing.
>>>>>>>>> I still don't understand. Let's be more precise. If you write the
>>>>>>>>> following code:
>>>>>>>>>    pCollection.apply(ParDo.of(new MyDoFn()));
>>>>>>>>> There is no way to control how many instances of MyDoFn are
>>>>>>>>> created. The runner might decided to create a million instances of 
>>>>>>>>> this
>>>>>>>>> class across your worker pool, which means that you will get a 
>>>>>>>>> million Setup
>>>>>>>>> and Teardown calls.
>>>>>>>>>> Anyway this was just an example of an external resource you must
>>>>>>>>>> release. Real topic is that beam should define asap a guaranteed 
>>>>>>>>>> generic
>>>>>>>>>> lifecycle to let user embrace its programming model.
>>>>>>>>>>>> @Eugene:
>>>>>>>>>>>> 1. wait logic is about passing the value which is not always
>>>>>>>>>>>> possible (like 15% of cases from my raw estimate)
>>>>>>>>>>>> 2. sdf: i'll try to detail why i mention SDF more here
>>>>>>>>>>>> Concretely beam exposes a portable API (included in the SDK
>>>>>>>>>>>> core). This API defines a *container* API and therefore implies 
>>>>>>>>>>>> bean
>>>>>>>>>>>> lifecycles. I'll not detail them all but just use the sources and 
>>>>>>>>>>>> dofn (not
>>>>>>>>>>>> sdf) to illustrate the idea I'm trying to develop.
>>>>>>>>>>>> A. Source
>>>>>>>>>>>> A source computes a partition plan with 2 primitives:
>>>>>>>>>>>> estimateSize and split. As an user you can expect both to be 
>>>>>>>>>>>> called on the
>>>>>>>>>>>> same bean instance to avoid to pay the same connection cost(s) 
>>>>>>>>>>>> twice.
>>>>>>>>>>>> Concretely:
>>>>>>>>>>>> connect()
>>>>>>>>>>>> try {
>>>>>>>>>>>>   estimateSize()
>>>>>>>>>>>>   split()
>>>>>>>>>>>> } finally {
>>>>>>>>>>>>   disconnect()
>>>>>>>>>>>> }
>>>>>>>>>>>> this is not guaranteed by the API so you must do:
>>>>>>>>>>>> connect()
>>>>>>>>>>>> try {
>>>>>>>>>>>>   estimateSize()
>>>>>>>>>>>> } finally {
>>>>>>>>>>>>   disconnect()
>>>>>>>>>>>> }
>>>>>>>>>>>> connect()
>>>>>>>>>>>> try {
>>>>>>>>>>>>   split()
>>>>>>>>>>>> } finally {
>>>>>>>>>>>>   disconnect()
>>>>>>>>>>>> }
>>>>>>>>>>>> + a workaround with an internal estimate size since this
>>>>>>>>>>>> primitive is often called in split but you dont want to connect 
>>>>>>>>>>>> twice in the
>>>>>>>>>>>> second phase.
>>>>>>>>>>>> Why do you need that? Simply cause you want to define an API to
>>>>>>>>>>>> implement sources which initializes the source bean and destroys 
>>>>>>>>>>>> it.
>>>>>>>>>>>> I insists it is a very very basic concern for such API. However
>>>>>>>>>>>> beam doesn't embraces it and doesn't assume it so building any API 
>>>>>>>>>>>> on top of
>>>>>>>>>>>> beam is very hurtful today and for direct beam users you hit the 
>>>>>>>>>>>> exact same
>>>>>>>>>>>> issues - check how IO are implemented, the static utilities which 
>>>>>>>>>>>> create
>>>>>>>>>>>> volatile connections preventing to reuse existing connection in a 
>>>>>>>>>>>> single
>>>>>>>>>>>> method
>>>>>>>>>>>> (https://github.com/apache/beam/blob/master/sdks/java/io/elasticsearch/src/main/java/org/apache/beam/sdk/io/elasticsearch/ElasticsearchIO.java#L862).
>>>>>>>>>>>> Same logic applies to the reader which is then created.
>>>>>>>>>>>> B. DoFn & SDF
>>>>>>>>>>>> As a fn dev you expect the same from the beam runtime: init();
>>>>>>>>>>>> try { while (...) process(); } finally { destroy(); } and that it 
>>>>>>>>>>>> is
>>>>>>>>>>>> executed on the exact same instance to be able to be stateful at 
>>>>>>>>>>>> that level
>>>>>>>>>>>> for expensive connections/operations/flow state handling.
>>>>>>>>>>>> As you mentionned with the million example, this sequence should
>>>>>>>>>>>> happen for each single instance so 1M times for your example.
>>>>>>>>>>>> Now why did I mention SDF several times? Because SDF is a
>>>>>>>>>>>> generalisation of both cases (source and dofn). Therefore it 
>>>>>>>>>>>> creates way
>>>>>>>>>>>> more instances and requires to have a way more strict/explicit 
>>>>>>>>>>>> definition of
>>>>>>>>>>>> the exact lifecycle and which instance does what. Since beam 
>>>>>>>>>>>> handles the
>>>>>>>>>>>> full lifecycle of the bean instances it must provide init/destroy 
>>>>>>>>>>>> hooks
>>>>>>>>>>>> (setup/teardown) which can be stateful.
>>>>>>>>>>>> If you take the JDBC example which was mentionned earlier.
>>>>>>>>>>>> Today, because of the teardown issue it uses bundles. Since 
>>>>>>>>>>>> bundles size is
>>>>>>>>>>>> not defined - and will not with SDF, it must use a pool to be able 
>>>>>>>>>>>> to reuse
>>>>>>>>>>>> a connection instance to not correct performances. Now with the 
>>>>>>>>>>>> SDF and the
>>>>>>>>>>>> split increase, how do you handle the pool size? Generally in 
>>>>>>>>>>>> batch you use
>>>>>>>>>>>> a single connection per thread to avoid to consume all database 
>>>>>>>>>>>> connections.
>>>>>>>>>>>> With a pool you have 2 choices: 1. use a pool of 1, 2. use a pool 
>>>>>>>>>>>> a bit
>>>>>>>>>>>> higher but multiplied by the number of beans you will likely x2 or 
>>>>>>>>>>>> 3 the
>>>>>>>>>>>> connection count and make the execution fail with "no more 
>>>>>>>>>>>> connection
>>>>>>>>>>>> available". I you picked 1 (pool of #1), then you still have to 
>>>>>>>>>>>> have a
>>>>>>>>>>>> reliable teardown by pool instance (close() generally) to ensure 
>>>>>>>>>>>> you release
>>>>>>>>>>>> the pool and don't leak the connection information in the JVM. In 
>>>>>>>>>>>> all case
>>>>>>>>>>>> you come back to the init()/destroy() lifecycle even if you fake 
>>>>>>>>>>>> to get
>>>>>>>>>>>> connections with bundles.
>>>>>>>>>>>> Just to make it obvious: SDF mentions are just cause SDF imply
>>>>>>>>>>>> all the current issues with the loose definition of the bean 
>>>>>>>>>>>> lifecycles at
>>>>>>>>>>>> an exponential level, nothing else.
>>>>>>>>>>>> Romain Manni-Bucau
>>>>>>>>>>>> @rmannibucau |  Blog | Old Blog | Github | LinkedIn | Book
>>>>>>>>>>>> 2018-02-18 22:32 GMT+01:00 Eugene Kirpichov
>>>>>>>>>>>> <kirpic...@google.com>:
>>>>>>>>>>>>> The kind of whole-transform lifecycle you're mentioning can be
>>>>>>>>>>>>> accomplished using the Wait transform as I suggested in the 
>>>>>>>>>>>>> thread above,
>>>>>>>>>>>>> and I believe it should become the canonical way to do that.
>>>>>>>>>>>>> (Would like to reiterate one more time, as the main author of
>>>>>>>>>>>>> most design documents related to SDF and of its implementation in 
>>>>>>>>>>>>> the Java
>>>>>>>>>>>>> direct and dataflow runner that SDF is fully unrelated to the 
>>>>>>>>>>>>> topic of
>>>>>>>>>>>>> cleanup - I'm very confused as to why it keeps coming up)
>>>>>>>>>>>>> On Sun, Feb 18, 2018, 1:15 PM Romain Manni-Bucau
>>>>>>>>>>>>> <rmannibu...@gmail.com> wrote:
>>>>>>>>>>>>>> I kind of agree except transforms lack a lifecycle too. My
>>>>>>>>>>>>>> understanding is that sdf could be a way to unify it and clean 
>>>>>>>>>>>>>> the api.
>>>>>>>>>>>>>> Otherwise how to normalize - single api -  lifecycle of
>>>>>>>>>>>>>> transforms?
>>>>>>>>>>>>>> Le 18 févr. 2018 21:32, "Ben Chambers" <bchamb...@apache.org>
>>>>>>>>>>>>>> a écrit :
>>>>>>>>>>>>>>> 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 |  Blog | Old Blog | Github | LinkedIn |
>>>>>>>>>>>>>>>>>>>>> Book
>>>>>>>>>>>>>>>>>>>>> 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

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