On Tue, Feb 27, 2024 at 7:44 AM Robert Burke <rob...@frantil.com> wrote:
>
> An "as fast as it can runner" with dynamic splits, would ultimately split to
> the systems maximum available parallelism (for stateful DoFns, this is the
> number of keys; for SplittableDoFns, this is the maximum sharding of each
> input element's restriction. That's what would happen with a "normal" sleep.
>
> WRT Portability, this means adding a current ProcessingTime field to the
> ProcessBundleRequest, and likely also to the ProgressRequest so the runner
> could coordinate. ProgressResponse may then need a "asleepUntil" field to
> communicate back the state of the bundle, which the runner could then use to
> better time its next ProgressRequest, and potentially arrest dynamic
> splitting for that bundle. After all, the sleeping bundle is blocked until
> processing time has advanced anyway; no progress can be made.
>
> I like moving the abstraction out of the timer space, as it better aligns
> with user intent for the throttle case, and it doesn't require a Stateful
> DoFn to operate (orthogonal!), meaning it's useful for It also solves the
> testing issue WRT ProcessingTime timers using an absolute time, rather than a
> relative time, as the SDK can rebuild it's relative setters for output time
> on the new canonical processing time, without user code changing.
>
> The sleeping inprogress bundle naturally holds back the watermark too.
>
> I suspect this mechanism would end up tending to over throttle as Reuven
> described earlier, since the user is only pushing back on immediate
> processing for the current element, not necessarily all elements. This is
> particularly likely if there's a long gap between ProgressRequests for the
> bundle and the runner doesn't adapt it's cadence.
>
> An external source of rate doesn't really exist, other than some external
> source that can provide throttle information. There would remain time skew
> between the runner system and the external system though, but for a throttle
> that's likely fine.
>
> A central notion of ProcessingTime also allows the runner to "smear"
> processing time so if there's a particularly long delay, it doesn't need to
> catch up at once. I don't think that's relevant for the throttle case though,
> since with the described clock mechanism and the communication back to the
> runner, the unblocking notion is probably fine.
On this note, I have become skeptical that a global throttling rate
can be done well with local information.
For streaming dataflow, we can have an approximate solution by knowing
the number of keys and doing per-key throttling because keys (at least
up to hundreds per worker) are all processed concurrently. This
solution doesn't even require state + timers and would best be done by
standard sleeps.
For most other systems, including dataflow batch, this would massively
under throttle. Here we need to either add something to the model, or
do something outside the model, to discover, dynamically, how many
siblings are being concurrently run. (This could be done at a
worker/process level, rather than bundle level, as well.) The ability
to broadcast, aggregate, and read dynamic, provisional from all
workers could help in other cases too (e.g. a more efficient top N),
but this is a whole new thread...
So while I think the semantics of processing timers in batch is worth
solving, this probably isn't the best application.
> We'd need a discussion of what an SDK must do if the runner doesn't support
> the central clock for completeness, and consistency.
>
>
> On Tue, Feb 27, 2024, 6:58 AM Jan Lukavský <je...@seznam.cz> wrote:
>>
>> On 2/27/24 14:51, Kenneth Knowles wrote:
>>
>> I very much like the idea of processing time clock as a parameter to
>> @ProcessElement. That will be obviously useful and remove a source of
>> inconsistency, in addition to letting the runner/SDK harness control it. I
>> also like the idea of passing a Sleeper or to @ProcessElement. These are
>> both good practices for testing and flexibility and runner/SDK language
>> differences.
>>
>> In your (a) (b) (c) can you be more specific about which watermarks you are
>> referring to? Are they the same as in my opening email? If so, then what you
>> describe is what we already have.
>>
>> Yes, we have that for streaming, but it does not work this way in batch. In
>> my understanding we violate (a), we ignore (b) because we fire timers at GC
>> time only and (c) is currently relevant only immediately preceding window GC
>> time, but can be defined more generally. But essentially yes, I was just
>> trying to restate the streaming processing time semantics in the limited
>> batch case.
>>
>>
>> Kenn
>>
>> On Tue, Feb 27, 2024 at 2:40 AM Jan Lukavský <je...@seznam.cz> wrote:
>>>
>>> I think that before we introduce a possibly somewhat duplicate new feature
>>> we should be certain that it is really semantically different. I'll
>>> rephrase the two cases:
>>>
>>> a) need to wait and block data (delay) - the use case is the motivating
>>> example of Throttle transform
>>>
>>> b) act without data, not block
>>>
>>> Provided we align processing time with local machine clock (or better,
>>> because of testing, make current processing time available via context to
>>> @ProcessElement) it seems to possble to unify both cases under slightly
>>> updated semantics of processing time timer in batch:
>>>
>>> a) processing time timers fire with best-effort, i.e. trying to minimize
>>> delay between firing timestamp and timer's timestamp
>>> b) timer is valid only in the context of current key-window, once
>>> watermark passes window GC time for the particular window that created the
>>> timer, it is ignored
>>> c) if timer has output timestamp, this timestamp holds watermark (but this
>>> is currently probably noop, because runners currently do no propagate
>>> (per-key) watermark in batch, I assume)
>>>
>>> In case b) there might be needed to distinguish cases when timer has output
>>> timestamp, if so, it probably should be taken into account.
>>>
>>> Now, such semantics should be quite aligned with what we do in streaming
>>> case and what users generally expect. The blocking part can be implemented
>>> in @ProcessElement using buffer & timer, once there is need to wait, it can
>>> be implemented in user code using plain sleep(). That is due to the
>>> alignment between local time and definition of processing time. If we had
>>> some reason to be able to run faster-than-wall-clock (as I'm still not in
>>> favor of that), we could do that using ProcessContext.sleep(). Delaying
>>> processing in the @ProcessElement should result in backpressuring and
>>> backpropagation of this backpressure from the Throttle transform to the
>>> sources as mentioned (of course this is only for the streaming case).
>>>
>>> Is there anything missing in such definition that would still require
>>> splitting the timers into two distinct features?
>>>
>>> Jan
>>>
>>> On 2/26/24 21:22, Kenneth Knowles wrote:
>>>
>>> Yea I like DelayTimer, or SleepTimer, or WaitTimer or some such.
>>>
>>> OutputTime is always an event time timestamp so it isn't even allowed to be
>>> set outside the window (or you'd end up with an element assigned to a
>>> window that it isn't within, since OutputTime essentially represents
>>> reserving the right to output an element with that timestamp)
>>>
>>> Kenn
>>>
>>> On Mon, Feb 26, 2024 at 3:19 PM Robert Burke <rob...@frantil.com> wrote:
>>>>
>>>> Agreed that a retroactive behavior change would be bad, even if tied to a
>>>> beam version change. I agree that it meshes well with the general theme of
>>>> State & Timers exposing underlying primitives for implementing Windowing
>>>> and similar. I'd say the distinction between the two might be additional
>>>> complexity for users to grok, and would need to be documented well, as
>>>> both operate in the ProcessingTime domain, but differently.
>>>>
>>>> What to call this new timer then? DelayTimer?
>>>>
>>>> "A DelayTimer sets an instant in ProcessingTime at which point
>>>> computations can continue. Runners will prevent the EventTimer watermark
>>>> from advancing past the set OutputTime until Processing Time has advanced
>>>> to at least the provided instant to execute the timers callback. This can
>>>> be used to allow the runner to constrain pipeline throughput with user
>>>> guidance."
>>>>
>>>> I'd probably add that a timer with an output time outside of the window
>>>> would not be guaranteed to fire, and that OnWindowExpiry is the correct
>>>> way to ensure cleanup occurs.
>>>>
>>>> No solution to the Looping Timers on Drain problem here, but i think
>>>> that's ultimately an orthogonal discussion, and will restrain my thoughts
>>>> on that for now.
>>>>
>>>> This isn't a proposal, but exploring the solution space within our
>>>> problem. We'd want to break down exactly what different and the same for
>>>> the 3 kinds of timers...
>>>>
>>>>
>>>>
>>>>
>>>> On Mon, Feb 26, 2024, 11:45 AM Kenneth Knowles <k...@apache.org> wrote:
>>>>>
>>>>> Pulling out focus points:
>>>>>
>>>>> On Fri, Feb 23, 2024 at 7:21 PM Robert Bradshaw via dev
>>>>> <dev@beam.apache.org> wrote:
>>>>> > I can't act on something yet [...] but I expect to be able to [...] at
>>>>> > some time in the processing-time future.
>>>>>
>>>>> I like this as a clear and internally-consistent feature description. It
>>>>> describes ProcessContinuation and those timers which serve the same
>>>>> purpose as ProcessContinuation.
>>>>>
>>>>> On Fri, Feb 23, 2024 at 7:21 PM Robert Bradshaw via dev
>>>>> <dev@beam.apache.org> wrote:
>>>>> > I can't think of a batch or streaming scenario where it would be
>>>>> > correct to not wait at least that long
>>>>>
>>>>> The main reason we created timers: to take action in the absence of data.
>>>>> The archetypal use case for processing time timers was/is "flush data
>>>>> from state if it has been sitting there too long". For this use case, the
>>>>> right behavior for batch is to skip the timer. It is actually basically
>>>>> incorrect to wait.
>>>>>
>>>>> On Fri, Feb 23, 2024 at 3:54 PM Robert Burke <lostl...@apache.org> wrote:
>>>>> > It doesn't require a new primitive.
>>>>>
>>>>> IMO what's being proposed *is* a new primitive. I think it is a good
>>>>> primitive. It is the underlying primitive to ProcessContinuation. It
>>>>> would be user-friendly as a kind of timer. But if we made this the
>>>>> behavior of processing time timers retroactively, it would break everyone
>>>>> using them to flush data who is also reprocessing data.
>>>>>
>>>>> There's two very different use cases ("I need to wait, and block data" vs
>>>>> "I want to act without data, aka NOT wait for data") and I think we
>>>>> should serve both of them, but it doesn't have to be with the same
>>>>> low-level feature.
>>>>>
>>>>> Kenn
>>>>>
>>>>>
>>>>> On Fri, Feb 23, 2024 at 7:21 PM Robert Bradshaw via dev
>>>>> <dev@beam.apache.org> wrote:
>>>>>>
>>>>>> On Fri, Feb 23, 2024 at 3:54 PM Robert Burke <lostl...@apache.org> wrote:
>>>>>> >
>>>>>> > While I'm currently on the other side of the fence, I would not be
>>>>>> > against changing/requiring the semantics of ProcessingTime constructs
>>>>>> > to be "must wait and execute" as such a solution, and enables the
>>>>>> > Proposed "batch" process continuation throttling mechanism to work as
>>>>>> > hypothesized for both "batch" and "streaming" execution.
>>>>>> >
>>>>>> > There's a lot to like, as it leans Beam further into the unification
>>>>>> > of Batch and Stream, with one fewer exception (eg. unifies timer
>>>>>> > experience further). It doesn't require a new primitive. It probably
>>>>>> > matches more with user expectations anyway.
>>>>>> >
>>>>>> > It does cause looping timer execution with processing time to be a
>>>>>> > problem for Drains however.
>>>>>>
>>>>>> I think we have a problem with looping timers plus drain (a mostly
>>>>>> streaming idea anyway) regardless.
>>>>>>
>>>>>> > I'd argue though that in the case of a drain, we could updated the
>>>>>> > semantics as "move watermark to infinity" "existing timers are
>>>>>> > executed, but new timers are ignored",
>>>>>>
>>>>>> I don't like the idea of dropping timers for drain. I think correct
>>>>>> handling here requires user visibility into whether a pipeline is
>>>>>> draining or not.
>>>>>>
>>>>>> > and ensure/and update the requirements around OnWindowExpiration
>>>>>> > callbacks to be a bit more insistent on being implemented for correct
>>>>>> > execution, which is currently the only "hard" signal to the SDK side
>>>>>> > that the window's work is guaranteed to be over, and remaining state
>>>>>> > needs to be addressed by the transform or be garbage collected. This
>>>>>> > remains critical for developing a good pattern for ProcessingTime
>>>>>> > timers within a Global Window too.
>>>>>>
>>>>>> +1
>>>>>>
>>>>>> >
>>>>>> > On 2024/02/23 19:48:22 Robert Bradshaw via dev wrote:
>>>>>> > > Thanks for bringing this up.
>>>>>> > >
>>>>>> > > My position is that both batch and streaming should wait for
>>>>>> > > processing time timers, according to local time (with the exception
>>>>>> > > of
>>>>>> > > tests that can accelerate this via faked clocks).
>>>>>> > >
>>>>>> > > Both ProcessContinuations delays and ProcessingTimeTimers are IMHO
>>>>>> > > isomorphic, and can be implemented in terms of each other (at least
>>>>>> > > in
>>>>>> > > one direction, and likely the other). Both are an indication that I
>>>>>> > > can't act on something yet due to external constraints (e.g. not all
>>>>>> > > the data has been published, or I lack sufficient capacity/quota to
>>>>>> > > push things downstream) but I expect to be able to (or at least would
>>>>>> > > like to check again) at some time in the processing-time future. I
>>>>>> > > can't think of a batch or streaming scenario where it would be
>>>>>> > > correct
>>>>>> > > to not wait at least that long (even in batch inputs, e.g. suppose
>>>>>> > > I'm
>>>>>> > > tailing logs and was eagerly started before they were fully written,
>>>>>> > > or waiting for some kind of (non-data-dependent) quiessence or other
>>>>>> > > operation to finish).
>>>>>> > >
>>>>>> > >
>>>>>> > > On Fri, Feb 23, 2024 at 12:36 AM Jan Lukavský <je...@seznam.cz>
>>>>>> > > wrote:
>>>>>> > > >
>>>>>> > > > For me it always helps to seek analogy in our physical reality.
>>>>>> > > > Stream
>>>>>> > > > processing actually has quite a good analogy for both event-time
>>>>>> > > > and
>>>>>> > > > processing-time - the simplest model for this being relativity
>>>>>> > > > theory.
>>>>>> > > > Event-time is the time at which events occur _at distant
>>>>>> > > > locations_. Due
>>>>>> > > > to finite and invariant speed of light (which is actually really
>>>>>> > > > involved in the explanation why any stream processing is inevitably
>>>>>> > > > unordered) these events are observed (processed) at different times
>>>>>> > > > (processing time, different for different observers). It is
>>>>>> > > > perfectly
>>>>>> > > > possible for an observer to observe events at a rate that is
>>>>>> > > > higher than
>>>>>> > > > one second per second. This also happens in reality for observers
>>>>>> > > > that
>>>>>> > > > travel at relativistic speeds (which might be an analogy for fast -
>>>>>> > > > batch - (re)processing). Besides the invariant speed, there is also
>>>>>> > > > another invariant - local clock (wall time) always ticks exactly
>>>>>> > > > at the
>>>>>> > > > rate of one second per second, no matter what. It is not possible
>>>>>> > > > to
>>>>>> > > > "move faster or slower" through (local) time.
>>>>>> > > >
>>>>>> > > > In my understanding the reason why we do not put any guarantees or
>>>>>> > > > bounds on the delay of firing processing time timers is purely
>>>>>> > > > technical
>>>>>> > > > - the processing is (per key) single-threaded, thus any timer has
>>>>>> > > > to
>>>>>> > > > wait before any element processing finishes. This is only
>>>>>> > > > consequence of
>>>>>> > > > a technical solution, not something fundamental.
>>>>>> > > >
>>>>>> > > > Having said that, my point is that according to the above analogy,
>>>>>> > > > it
>>>>>> > > > should be perfectly fine to fire processing time timers in batch
>>>>>> > > > based
>>>>>> > > > on (local wall) time only. There should be no way of manipulating
>>>>>> > > > this
>>>>>> > > > local time (excluding tests). Watermarks should be affected the
>>>>>> > > > same way
>>>>>> > > > as any buffering in a state that would happen in a stateful DoFn
>>>>>> > > > (i.e.
>>>>>> > > > set timer holds output watermark). We should probably pay
>>>>>> > > > attention to
>>>>>> > > > looping timers, but it seems possible to define a valid stopping
>>>>>> > > > condition (input watermark at infinity).
>>>>>> > > >
>>>>>> > > > Jan
>>>>>> > > >
>>>>>> > > > On 2/22/24 19:50, Kenneth Knowles wrote:
>>>>>> > > > > Forking this thread.
>>>>>> > > > >
>>>>>> > > > > The state of processing time timers in this mode of processing
>>>>>> > > > > is not
>>>>>> > > > > satisfactory and is discussed a lot but we should make everything
>>>>>> > > > > explicit.
>>>>>> > > > >
>>>>>> > > > > Currently, a state and timer DoFn has a number of logical
>>>>>> > > > > watermarks:
>>>>>> > > > > (apologies for fixed width not coming through in email lists).
>>>>>> > > > > Treat
>>>>>> > > > > timers as a back edge.
>>>>>> > > > >
>>>>>> > > > > input --(A)----(C)--> ParDo(DoFn) ----(D)---> output
>>>>>> > > > > ^ |
>>>>>> > > > > |--(B)-----------------|
>>>>>> > > > > timers
>>>>>> > > > >
>>>>>> > > > > (A) Input Element watermark: this is the watermark that promises
>>>>>> > > > > there
>>>>>> > > > > is no incoming element with a timestamp earlier than it. Each
>>>>>> > > > > input
>>>>>> > > > > element's timestamp holds this watermark. Note that *event time
>>>>>> > > > > timers
>>>>>> > > > > firing is according to this watermark*. But a runner commits
>>>>>> > > > > changes
>>>>>> > > > > to this watermark *whenever it wants*, in a way that can be
>>>>>> > > > > consistent. So the runner can absolute process *all* the elements
>>>>>> > > > > before advancing the watermark (A), and only afterwards start
>>>>>> > > > > firing
>>>>>> > > > > timers.
>>>>>> > > > >
>>>>>> > > > > (B) Timer watermark: this is a watermark that promises no timer
>>>>>> > > > > is set
>>>>>> > > > > with an output timestamp earlier than it. Each timer that has an
>>>>>> > > > > output timestamp holds this watermark. Note that timers can set
>>>>>> > > > > new
>>>>>> > > > > timers, indefinitely, so this may never reach infinity even in a
>>>>>> > > > > drain
>>>>>> > > > > scenario.
>>>>>> > > > >
>>>>>> > > > > (C) (derived) total input watermark: this is a watermark that is
>>>>>> > > > > the
>>>>>> > > > > minimum of the two above, and ensures that all state for the
>>>>>> > > > > DoFn for
>>>>>> > > > > expired windows can be GCd after calling @OnWindowExpiration.
>>>>>> > > > >
>>>>>> > > > > (D) output watermark: this is a promise that the DoFn will not
>>>>>> > > > > output
>>>>>> > > > > earlier than the watermark. It is held by the total input
>>>>>> > > > > watermark.
>>>>>> > > > >
>>>>>> > > > > So a any timer, processing or not, holds the total input
>>>>>> > > > > watermark
>>>>>> > > > > which prevents window GC, hence the timer must be fired. You can
>>>>>> > > > > set
>>>>>> > > > > timers without a timestamp and they will not hold (B) hence not
>>>>>> > > > > hold
>>>>>> > > > > the total input / GC watermark (C). Then if a timer fires for an
>>>>>> > > > > expired window, it is ignored. But in general a timer that sets
>>>>>> > > > > an
>>>>>> > > > > output timestamp is saying that it may produce output, so it
>>>>>> > > > > *must* be
>>>>>> > > > > fired, even in batch, for data integrity. There was a time before
>>>>>> > > > > timers had output timestamps that we said that you *always* have
>>>>>> > > > > to
>>>>>> > > > > have an @OnWindowExpiration callback for data integrity, and
>>>>>> > > > > processing time timers could not hold the watermark. That is
>>>>>> > > > > changed now.
>>>>>> > > > >
>>>>>> > > > > One main purpose of processing time timers in streaming is to be
>>>>>> > > > > a
>>>>>> > > > > "timeout" for data buffered in state, to eventually flush. In
>>>>>> > > > > this
>>>>>> > > > > case the output timestamp should be the minimum of the elements
>>>>>> > > > > in
>>>>>> > > > > state (or equivalent). In batch, of course, this kind of timer
>>>>>> > > > > is not
>>>>>> > > > > relevant and we should definitely not wait for it, because the
>>>>>> > > > > goal is
>>>>>> > > > > to just get through all the data. We can justify this by saying
>>>>>> > > > > that
>>>>>> > > > > the worker really has no business having any idea what time it
>>>>>> > > > > really
>>>>>> > > > > is, and the runner can just run the clock at whatever speed it
>>>>>> > > > > wants.
>>>>>> > > > >
>>>>>> > > > > Another purpose, brought up on the Throttle thread, is to wait or
>>>>>> > > > > backoff. In this case it would be desired for the timer to
>>>>>> > > > > actually
>>>>>> > > > > cause batch processing to pause and wait. This kind of behavior
>>>>>> > > > > has
>>>>>> > > > > not been explored much. Notably the runner can absolutely
>>>>>> > > > > process all
>>>>>> > > > > elements first, then start to fire any enqueued processing time
>>>>>> > > > > timers. In the same way that state in batch can just be in
>>>>>> > > > > memory,
>>>>>> > > > > this *could* just be a call to sleep(). It all seems a bit
>>>>>> > > > > sketchy so
>>>>>> > > > > I'd love clearer opinions.
>>>>>> > > > >
>>>>>> > > > > These two are both operational effects - as you would expect for
>>>>>> > > > > processing time timers - and they seem to be in conflict. Maybe
>>>>>> > > > > they
>>>>>> > > > > just need different features?
>>>>>> > > > >
>>>>>> > > > > I'd love to hear some more uses of processing time timers from
>>>>>> > > > > the
>>>>>> > > > > community.
>>>>>> > > > >
>>>>>> > > > > Kenn
>>>>>> > >
