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From Stephan Ewen <se...@apache.org>
Subject Re: Watermarks as "process completion" flags
Date Mon, 30 Nov 2015 10:07:10 GMT
Hi Anton!

That you can do!

You can look at the interfaces "Checkpointed" and "checkpointNotifier".
There you will get a call at every checkpoint (and can look at what records
are before that checkpoint). You also get a call once the checkpoint is
complete, which corresponds to the point when everything has flown through
the DAG.

I think it is nice to implement it like that, because it works
non-blocking: The stream continues while the the records-you-wait-for flow
through the DAG, and you get an asynchronous notification once they have
flown all the way through.

Greetings,
Stephan


On Mon, Nov 30, 2015 at 11:03 AM, Anton Polyakov <polyakov.anton@gmail.com>
wrote:

> I think I can turn my problem into a simpler one.
>
> Effectively what I need - I need way to checkpoint certain events in input
> stream and once this checkpoint reaches end of DAG take some action. So I
> need a signal at the sink which can tell "all events in source before
> checkpointed event are now processed".
>
> As far as I understand flagged record don't quite work since DAG doesn't
> propagate source events one-to-one. Some transformations might create 3
> child events out of 1 source. If I want to make sure I fully processed
> source event, I need to wait till all childs are processed.
>
>
>
> On Sun, Nov 29, 2015 at 4:12 PM, Anton Polyakov <polyakov.anton@gmail.com>
> wrote:
>
>> Hi Fabian
>>
>> Defining a special flag for record seems like a checkpoint barrier. I
>> think I will end up re-implementing checkpointing myself. I found the
>> discussion in flink-dev:
>> mail-archives.apache.org/mod_mbox/flink-dev/201511.mbox/…
>> <http://mail-archives.apache.org/mod_mbox/flink-dev/201511.mbox/%3CCA+faj9xDFAUG_zi==E2H8s-8R4cn8ZBDON_hf+1Rud5pJqvZ4A@mail.gmail.com%3E>
which
>> seems to solve my task. Essentially they want to have a mechanism which
>> will mark record produced by job as “last” and then wait until it’s fully
>> propagated through DAG. Similarly to what I need. Essentially my job which
>> produces trades can also thought as being finished once it produced all
>> trades, then I just need to wait till latest trade produced by this job is
>> processed.
>>
>> So although windows can probably also be applied, I think propagating
>> barrier through DAG and checkpointing at final job is what I need.
>>
>> Can I possibly utilize internal Flink’s checkpoint barriers (i.e. like
>> triggering a custom checkoint or finishing streaming job)?
>>
>> On 24 Nov 2015, at 21:53, Fabian Hueske <fhueske@gmail.com> wrote:
>>
>> Hi Anton,
>>
>> If I got your requirements right, you are looking for a solution that
>> continuously produces updated partial aggregates in a streaming fashion.
>> When a  special event (no more trades) is received, you would like to store
>> the last update as a final result. Is that correct?
>>
>> You can compute continuous updates using a reduce() or fold() function.
>> These will produce a new update for each incoming event.
>> For example:
>>
>> val s: DataStream[(Int, Long)] = ...
>> s.keyBy(_._1)
>>   .reduce( (x,y) => (x._1, y._2 + y._2) )
>>
>> would continuously compute a sum for every key (_._1) and produce an
>> update for each incoming record.
>>
>> You could add a flag to the record and implement a ReduceFunction that
>> marks a record as final when the no-more-trades event is received.
>> With a filter and a data sink you could emit such final records to a
>> persistent data store.
>>
>> Btw.: You can also define custom trigger policies for windows. A custom
>> trigger is called for each element that is added to a window and when
>> certain timers expire. For example with a custom trigger, you can evaluate
>> a window for every second element that is added. You can also define
>> whether the elements in the window should be retained or removed after the
>> evaluation.
>>
>> Best, Fabian
>>
>>
>>
>> 2015-11-24 21:32 GMT+01:00 Anton Polyakov <polyakov.anton@gmail.com>:
>>
>>> Hi Max
>>>
>>> thanks for reply. From what I understand window works in a way that it
>>> buffers records while window is open, then apply transformation once window
>>> close is triggered and pass transformed result.
>>> In my case then window will be open for few hours, then the whole amount
>>> of trades will be processed once window close is triggered. Actually I want
>>> to process events as they are produced without buffering them. It is more
>>> like a stream with some special mark versus windowing seems more like a
>>> batch (if I understand it correctly).
>>>
>>> In other words - buffering and waiting for window to close, then
>>> processing will be equal to simply doing one-off processing when all events
>>> are produced. I am looking for a solution when I am processing events as
>>> they are produced and when source signals "done" my processing is also
>>> nearly done.
>>>
>>>
>>> On Tue, Nov 24, 2015 at 2:41 PM, Maximilian Michels <mxm@apache.org>
>>> wrote:
>>>
>>>> Hi Anton,
>>>>
>>>> You should be able to model your problem using the Flink Streaming
>>>> API. The actions you want to perform on the streamed records
>>>> correspond to transformations on Windows. You can indeed use
>>>> Watermarks to signal the window that a threshold for an action has
>>>> been reached. Otherwise an eviction policy should also do it.
>>>>
>>>> Without more details about what you want to do I can only refer you to
>>>> the streaming API documentation:
>>>> Please see
>>>> https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/streaming_guide.html
>>>>
>>>> Thanks,
>>>> Max
>>>>
>>>> On Sun, Nov 22, 2015 at 8:53 PM, Anton Polyakov
>>>> <polyakov.anton@gmail.com> wrote:
>>>> > Hi
>>>> >
>>>> > I am very new to Flink and in fact never used it. My task (which I
>>>> currently solve using home grown Redis-based solution) is quite simple -
I
>>>> have a system which produces some events (trades, it is a financial system)
>>>> and computational chain which computes some measure accumulatively over
>>>> these events. Those events form a long but finite stream, they are produced
>>>> as a result of end of day flow. Computational logic forms a processing DAG
>>>> which computes some measure over these events (VaR). Each trade is
>>>> processed through DAG and at different stages might produce different set
>>>> of subsequent events (like return vectors), eventually they all arrive into
>>>> some aggregator which computes accumulated measure (reducer).
>>>> >
>>>> > Ideally I would like to process trades as they appear (i.e. stream
>>>> them) and once producer reaches end of portfolio (there will be no more
>>>> trades), I need to write final resulting measure and mark it as “end of
day
>>>> record”. Of course I also could use a classical batch - i.e. wait until
all
>>>> trades are produced and then batch process them, but this will be too
>>>> inefficient.
>>>> >
>>>> > If I use Flink, I will need a sort of watermark saying - “done, no
>>>> more trades” and once this watermark reaches end of DAG, final measure
can
>>>> be saved. More generally would be cool to have an indication at the end of
>>>> DAG telling to which input stream position current measure corresponds.
>>>> >
>>>> > I feel my problem is very typical yet I can’t find any solution. All
>>>> examples operate either on infinite streams where nobody cares about
>>>> completion or classical batch examples which rely on fact all input data
is
>>>> ready.
>>>> >
>>>> > Can you please hint me.
>>>> >
>>>> > Thank you vm
>>>> > Anton
>>>>
>>>
>>>
>>
>>
>

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