Re: Looping timer blog

[Reza Rokni <re...@google.com>]

Raised PR for edit to the blog sample :
https://github.com/apache/beam/pull/9010

Jan, will try and get some time later this month to have a look at
the @RequireTimeSortedInput.

On Thu, 27 Jun 2019 at 15:29, Jan Lukavský <je...@seznam.cz> wrote:

> Hi Reza,
>
> cool, I have put together a PR [1], which is still not completely ready.
> There are least still missing some tests - probably @ValidatesRunner and
> then fixing runners that won't pass that. It also misses few features
> described in the design doc, but that could be probably fixed later
> (support for allowedLateness and user-supplied sorting criterion). Would
> you like to test this on some of your code? It might suffice to put
> @RequiresTimeSortedInput on @ProcessElement method and input should start
> being sorted (should work at least for DirectRunner, FlinkRunner (stream
> and batch) and SparkRunner (batch)).
>
> [1] https://github.com/apache/beam/pull/8774
> On 6/27/19 6:16 AM, Reza Rokni wrote:
>
>
>
> On Tue, 25 Jun 2019 at 21:20, Jan Lukavský <je...@seznam.cz> wrote:
>
>>
>> On 6/25/19 1:43 PM, Reza Rokni wrote:
>>
>>
>>
>> On Tue, 25 Jun 2019 at 18:12, Jan Lukavský <je...@seznam.cz> wrote:
>>
>>> > The TTL check would be in the same Timer rather than a separate
>>> Timer.  The max value processed in each OnTimer call would be stored in
>>> valuestate and used as base to know how long it has been seen the pipeline
>>> has seen an external value for that key.
>>>
>>> OK, that seems to work, if you store maximum timestamp in a value state
>>> (that is, basically you generate per-key watermark).
>>>
>>> > You can store it in ValueState rather than BagState, but yes you store
>>> that value in State ready for the next OnTimer() fire.
>>>
>>> In my understanding of the problem, I'd say that this approach seems a
>>> little suboptimal. Consider the following, when trying to generate the OHLC
>>> data (open, high, low, close, that is move last closing price to next
>>> window opening price)
>>>
>>>  - suppose we have three times T1 < T2 < T3 < T4, where times T2 and T4
>>> denote "end of windows" (closing times)
>>>
>>>  - first (in processing time), we receive value for time T3, we cache it
>>> in ValueState, we set timer for T4
>>>
>>>  - next, we receive value for T1 - but we cannot overwrite the value
>>> already written for T3, right? What to do then? And will we reset timer to
>>> time T3?
>>>
>>>  - basically, because we received *two* values, both of which are needed
>>> and no timer could have been fired in between, we need both values stored
>>> to know which value to emit when timer fires. And consider that on batch,
>>> the timer fires only after we see all data (without any ordering).
>>>
>> I assume you are referring to late data rather than out of order data (
>> the later being taken care of with the in-memory sort). As discussed in the
>> talk late data is a sharp edge, one solution for late data is to branch it
>> away before GlobalWindow + State DoFn. This can then be output from the
>> pipeline into a sink with a marker to initiate a manual procedure for
>> correction. Essentially a manual redaction.
>>
>> Which in-memory sort do you refer to? I'm pretty sure there must be
>> sorting involved for this to work, but I'm not quite sure where exactly it
>> is in your implementation. You said that you can put data in ValueState
>> rather than BagState, so do you have a List as a value in the ValueState?
>> Or do you wrap the stateful par do with some other sorting logic? And if
>> so, how does this work on batch? I suppose that it has to all fit to
>> memory. I think this all goes around the @RequiresTimeSortedInput
>> annotation, that I propose. Maybe we can cooperate on that? :)\
>>
> Hummmm... nice this chat made me notice a bug in the looping timer example
> code we missed thanx :-) , the ValueState<Boolean> timerRunning, should
> actually be a ValueState<Long> minTimestamp and the check to set the timer
> needs to be if (NULL or  element.Timestamp is < then timer.Timestamp).
> Which also requires the use of timer read pattern as we don't have
> timer.read()
> https://stackoverflow.com/questions/55912522/setting-a-timer-to-the-minimum-timestamp-seen/55912542#55912542.
> I will fix and put a PR to correct the blog.
>
> For the hold and propagate pattern (for those following the original
> thread the pattern is not covered in the blog example code, but discussed
> at the summit):
> OnProcess()
> - You can drop the accumulators into BagState.
> - A timer is set at the minimum time interval.
> OnTimer()
> - The list is sorted in memory, for a lot of timeseries use cases (for
> example ohlc) the memory issues are heavily mitigated as we can use a Fixed
> Windows partial aggregations before the GlobalWindow stage. (Partial
> because they dont have the correct Open value set until they flow into the
> Global Window). Of course how big the window is dictates the compression
> you would get.
> - The current timer is set again to fire in the next interval window.
>
> @RequiresTimeSortedInput looks super interesting, happy to help out.
> Although its a harder problem then the targeted timeseries use cases where
> FixedWindows aggregations can be used before the final step.
>
>>
>> Or? Am I missing something?
>>>
>>> Jan
>>> On 6/25/19 6:00 AM, Reza Rokni wrote:
>>>
>>>
>>>
>>> On Fri, 21 Jun 2019 at 18:02, Jan Lukavský <je...@seznam.cz> wrote:
>>>
>>>> Hi Reza,
>>>>
>>>> great prezentation on the Beam Summit. I have had a few posts here in
>>>> the list during last few weeks, some of which might actually be related to
>>>> both looping timers and validity windows. But maybe you will be able to see
>>>> a different approach, than I do, so questions:
>>>>
>>>>  a) because of [1] timers might not be exactly ordered (the JIRA talks
>>>> about DirectRunner, but I suppose the issue is present on all runners that
>>>> use immutable bundles of size > 1, so might be related to Dataflow as
>>>> well). This might cause issues when you try to introduce TTL for looping
>>>> timers, because the TTL timer might get fired before regular looping timer,
>>>> which might cause incorrect results (state cleared before have been
>>>> flushed).
>>>>
>>> The TTL check would be in the same Timer rather than a separate Timer.
>>> The max value processed in each OnTimer call would be stored in valuestate
>>> and used as base to know how long it has been seen the pipeline has seen an
>>> external value for that key.
>>>
>>>>  b) because stateful pardo doesn't sort by timestamp, that implies,
>>>> that you have to store last values in BagState (as opposed to the blog,
>>>> where you just emit identity value of sum operation), right?
>>>>
>>> You can store it in ValueState rather than BagState, but yes you store
>>> that value in State ready for the next OnTimer() fire.
>>>
>>>>  c) because of how stateful pardo currently works on batch, does that
>>>> imply that all values (per key) would have to be stored in memory? would
>>>> that scale?
>>>>
>>> This is one of the sharp edges and the answer is ... it depends :-) I
>>> would recommend always using a  FixedWindow+Combiner before this step, this
>>> will compress the values into something much smaller. For example in case
>>> of building 'candles' this will compress down to low/hi/first/last values
>>> per FixedWindow length. If the window length is very small there maybe no
>>> compression, but in most cases I have seen this is a ok compromise.
>>>
>>>> There is a discussion about problem a) in [2], but maybe there is some
>>>> different approach possible. For problem b) and c) there is a proposal [3].
>>>> When the input is sorted, it starts to work both in batch and with
>>>> ValueState, because the last value is the *valid* value.
>>>>
>>> There was also a discussion on dev@ around a sorted Map state, which
>>> would be very cool for this usecase.
>>>
>>>> This has even connection with the mentioned validity windows, as if you
>>>> sort by timestamp, the _last_ value is the _valid_ value, so is essentially
>>>> boils down to keep single value per key (and again, starts to work in both
>>>> batch and stream).
>>>>
>>> one for Tyler :-)
>>>
>>>> I even have a suspicion, that sorting by timestamp has close relation
>>>> to retractions, because when you are using sorted streams, retractions
>>>> actually became only diff between last emitted pane, and current pane. That
>>>> might even help implement that in general, but I might be missing
>>>> something. This just popped in my head today, as I was thinking why there
>>>> was actually no (or little) need for retractions in Euphoria model (very
>>>> similar to Beam, actually differs by the sorting thing :)), and why it the
>>>> need pops out so often in Beam.
>>>>
>>> Retractions will be possible with this, but it does mean that we would
>>> need to keep old versions around, something built in would be very cool
>>> rather than building it with this pattern.
>>>
>>>> I'd be very happy to hear what you think about all of this.
>>>>
>>>> Cheers,
>>>>
>>>> Jan
>>>>
>>>> [1] https://issues.apache.org/jira/browse/BEAM-7520
>>>>
>>>> [2]
>>>> https://lists.apache.org/thread.html/1a3a0dd9da682e159f78f131d335782fd92b047895001455ff659613@%3Cdev.beam.apache.org%3E
>>>>
>>>> [3]
>>>> https://docs.google.com/document/d/1ObLVUFsf1NcG8ZuIZE4aVy2RYKx2FfyMhkZYWPnI9-c/edit?usp=sharing
>>>> On 6/21/19 8:12 AM, Reza Rokni wrote:
>>>>
>>>> Great question, one thing that we did not cover in the blog and I think
>>>> we should have is the use case where you would want to bootstrap the
>>>> pipeline.
>>>>
>>>> One option would be on startup to have an extra bounded source that is
>>>> read and flattened into the main pipeline, the source will need to contain
>>>> values in  Timestamped<V> format which would correspond to the first window
>>>> that you would like to kickstart the process from.  Will see if I can try
>>>> and find some time to code up an example and add that and the looping timer
>>>> code into the Beam patterns.
>>>>
>>>> https://beam.apache.org/documentation/patterns/overview/
>>>>
>>>> Cheers
>>>> Reza
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Fri, 21 Jun 2019 at 07:59, Manu Zhang <ow...@gmail.com>
>>>> wrote:
>>>>
>>>>> Indeed interesting pattern.
>>>>>
>>>>> One minor question. It seems the timer is triggered by the first
>>>>> element so what if there is no data in the "first interval" ?
>>>>>
>>>>> Thanks for the write-up.
>>>>> Manu
>>>>>
>>>>> On Wed, Jun 19, 2019 at 12:15 PM Reza Rokni <re...@google.com> wrote:
>>>>>
>>>>>> Hi folks,
>>>>>>
>>>>>> Just wanted to drop a note here on a new pattern that folks may find
>>>>>> interesting, called  Looping Timers. It allows for default values to be
>>>>>> created in interval windows in the absence of any external data coming into
>>>>>> the pipeline. The details are in this blog below:
>>>>>>
>>>>>> https://beam.apache.org/blog/2019/06/11/looping-timers.html
>>>>>>
>>>>>> Its main utility is when dealing with time series data. There are
>>>>>> still rough edges, like dealing with TTL and it would be great to hear
>>>>>> feedback on ways it can be improved.
>>>>>>
>>>>>> The next pattern to publish in this domain will assist will hold and
>>>>>> propagation of values from one interval window to the next, which coupled
>>>>>> to looping timers starts to solve some interesting problems.
>>>>>>
>>>>>> Cheers
>>>>>>
>>>>>> Reza
>>>>>>
>>>>>>
>>>>>>
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The above terms reflect a potential business arrangement, are provided
solely as a basis for further discussion, and are not intended to be and do
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will be created, implied, or inferred until an agreement in final form is
executed in writing by all parties involved.