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From rss rss <rssde...@gmail.com>
Subject Re: finite subset of an infinite data stream
Date Fri, 20 Nov 2015 13:05:53 GMT
Hello Aljoscha,

  Thanks, I looked your code. I think, It is really useful for getting real
time data from some sensors. And as a simple example it may be considered
in a modern Internet of Thing context. E.g. there are some temperature
sensor or sensor of water flow; and I want to build simple application when
the data flow from the sensors is saved to persistent storage but a small
real time buffer I want to use for visualizing on a display by a query.

  But at the same time my question have a second part. I need to link the
real time data with data from persistent storage. And I don't see how your
example may help in this. Let the input query contains some data fetch
condition. In this case we have to build a separate DataSet or DataStream
to a persistent storage with specified conditions. It may be SQL or simple
map(...).filter("something"). But main obstacle is how to configure new
data processing schema been inside the current stream transformation. E.g.
being inside the connected query stream map function.

  Week ago I have prepared other schema of my task solving with separation
of streaming and batch subsystems. See the attached image. It may be
changed accordingly your example but I don't see other way to resolve the
task than separate queries to persistent storage in batch part.

[image: Встроенное изображение 1]

  And note, this schema describes an idea about how to emulate a real time
buffer by means of Kafka. Windowed stream infinitely produces data
sequences and sinks ones into an external queue with limited storing time
or without storing in whole. Any consumers connected to the queue are
received an actual data. I don't like this idea because it is excess
network communication but it looks workable.

  BTW: it is something like lambda/kappa architecture implementation. I
don't like these terms but actually it is.

Best regards,
Roman


2015-11-20 13:26 GMT+04:00 Aljoscha Krettek <aljoscha@apache.org>:

> Hi,
> I’m very sorry, yes you would need my custom branch:
> https://github.com/aljoscha/flink/commits/state-enhance
>
> Cheers,
> Aljoscha
> > On 20 Nov 2015, at 10:13, rss rss <rssdev10@gmail.com> wrote:
> >
> > Hello Aljoscha,
> >
> >   very thanks. I tried to build your example but have an obstacle with
> org.apache.flink.runtime.state.AbstractStateBackend class. Where to get it?
> I guess it stored in your local branch only. Would you please to send me
> patches for public branch or share the branch with me?
> >
> > Best regards,
> > Roman
> >
> >
> > 2015-11-18 17:24 GMT+04:00 Aljoscha Krettek <aljoscha@apache.org>:
> > Hi,
> > I wrote a little example that could be what you are looking for:
> https://github.com/dataArtisans/query-window-example
> >
> > It basically implements a window operator with a modifiable window size
> that also allows querying the current accumulated window contents using a
> second input stream.
> >
> > There is a README file in the github repository, but please let me know
> if you need further explanations.
> >
> > Cheers,
> > Aljoscha
> >
> > > On 18 Nov 2015, at 12:02, Robert Metzger <rmetzger@apache.org> wrote:
> > >
> > > Hi Roman,
> > >
> > > I've updated the documentation. It seems that it got out of sync.
> Thank you for notifying us about this.
> > >
> > > My colleague Aljoscha has some experimental code that is probably
> doing what you are looking for: A standing window (your RT-buffer) that you
> can query using a secondary stream (your user's queries).
> > > He'll post the code soon to this email thread.
> > >
> > > Regards,
> > > Robert
> > >
> > >
> > > On Wed, Nov 11, 2015 at 2:51 PM, rss rss <rssdev10@gmail.com> wrote:
> > > Hello,
> > >
> > >   thanks, Stephan, but triggers are not that I searched. And BTW, the
> documentation is obsolete. There is no Count class now. I found
> CountTrigger only.
> > >
> > >   Thanks Robert, your example may be useful for me but in some other
> point. I mentioned "union" as an ordinary union of similar data. It is the
> same as "union" in the datastream documentation.
> > >
> > >   The task is very simple. We have an infinite stream of data from
> sensors, billing system etc. There is no matter what it is but it is
> infinite. We have to store the data in any persistent storage to be able to
> make analytical queries later. And there is a stream of user's analytical
> queries. But the stream of input data is big and time of saving in the
> persistent storage is big too. And we have not a very fast bigdata OLTP
> storage. That is the data extracted from the persistent storage by the
> user's requests probably will not contain actual data. We have to have some
> real time buffer (RT-Buffer in the schema) with actual data and have to
> union it with the data processing results from persistent storage (I don't
> speak about data deduplication and ordering now.). And of course the user's
> query are unpredictable regarding data filtering conditions.
> > >
> > >   The attached schema is attempt to understand how it may be
> implemented with Flink. I tried to imagine how to implement it by Flink's
> streaming API but found obstacles. This schema is not first variant. It
> contains separated driver program to configure new jobs by user's queries.
> The reason I not found a way how to link the stream of user's queries with
> further data processing. But it is some near to
> https://gist.github.com/fhueske/4ea5422edb5820915fa4
> > >
> > >
> > > <flink_streams.png>
> > >
> > >   The main question is how to process each user's query combining it
> with actual data from the real time buffer and batch request to the
> persistent storage. Unfortunately I not found a decision in Streaming API
> only.
> > >
> > > Regards,
> > > Roman
> > >
> > > 2015-11-11 15:45 GMT+04:00 Robert Metzger <rmetzger@apache.org>:
> > > I think what you call "union" is a "connected stream" in Flink. Have a
> look at this example: https://gist.github.com/fhueske/4ea5422edb5820915fa4
> > > It shows how to dynamically update a list of filters by external
> requests.
> > > Maybe that's what you are looking for?
> > >
> > >
> > >
> > > On Wed, Nov 11, 2015 at 12:15 PM, Stephan Ewen <sewen@apache.org>
> wrote:
> > > Hi!
> > >
> > > I don not really understand what exactly you want to do, especially
> the "union an infinite real time data stream with filtered persistent data
> where the condition of filtering is provided by external requests".
> > >
> > > If you want to work on substreams in general, there are two options:
> > >
> > > 1) Create the substream in a streaming window. You can "cut" the
> stream based on special records/events that signal that the subsequence is
> done. Have a look at the "Trigger" class for windows, it can react to
> elements and their contents:
> > >
> > >
> https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#windows-on-keyed-data-streams
> (secion on Advanced Windowing).
> > >
> > >
> > > 2) You can trigger sequences of batch jobs. The batch job data source
> (input format) can decide when to stop consuming the stream, at which point
> the remainder of the transformations run, and the batch job finishes.
> > > You can already run new transformation chains after each call to
> "env.execute()", once the execution finished, to implement the sequence of
> batch jobs.
> > >
> > >
> > > I would try and go for the windowing solution if that works, because
> that will give you better fault tolerance / high availability. In the
> repeated batch jobs case, you need to worry yourself about what happens
> when the driver program (that calls env.execute()) fails.
> > >
> > >
> > > Hope that helps...
> > >
> > > Greetings,
> > > Stephan
> > >
> > >
> > >
> > > On Mon, Nov 9, 2015 at 1:24 PM, rss rss <rssdev10@gmail.com> wrote:
> > > Hello,
> > >
> > >   thanks for the answer but windows produce periodical results. I used
> your example but the data source is changed to TCP stream:
> > >
> > >         DataStream<String> text = env.socketTextStream("localhost",
> 2015, '\n');
> > >         DataStream<Tuple2<String, Integer>> wordCounts =
> > >                 text
> > >                 .flatMap(new LineSplitter())
> > >                 .keyBy(0)
> > >                 .timeWindow(Time.of(5, TimeUnit.SECONDS))
> > >                 .sum(1);
> > >
> > >         wordCounts.print();
> > >         env.execute("WordCount Example");
> > >
> > >  I see an infinite results printing instead of the only list.
> > >
> > >  The data source is following script:
> > > -----------------------------------------------------
> > > #!/usr/bin/env ruby
> > >
> > > require 'socket'
> > >
> > > server = TCPServer.new 2015
> > > loop do
> > >   Thread.start(server.accept) do |client|
> > >     puts Time.now.to_s + ': New client!'
> > >     loop do
> > >       client.puts "#{Time.now} #{[*('A'..'Z')].sample(3).join}"
> > >       sleep rand(1000)/1000.0
> > >     end
> > >     client.close
> > >   end
> > > end
> > > -----------------------------------------------------
> > >
> > >   My purpose is to union an infinite real time data stream with
> filtered persistent data where the condition of filtering is provided by
> external requests. And the only result of union is interested. In this case
> I guess I need a way to terminate the stream. May be I wrong.
> > >
> > >   Moreover it should be possible to link the streams by next request
> with other filtering criteria. That is create new data transformation chain
> after running of env.execute("WordCount Example"). Is it possible now? If
> not, is it possible with minimal changes of the core of Flink?
> > >
> > > Regards,
> > > Roman
> > >
> > > 2015-11-09 12:34 GMT+04:00 Stephan Ewen <sewen@apache.org>:
> > > Hi!
> > >
> > > If you want to work on subsets of streams, the answer is usually to
> use windows, "stream.keyBy(...).timeWindow(Time.of(1, MINUTE))".
> > >
> > > The transformations that you want to make, do they fit into a window
> function?
> > >
> > > There are thoughts to introduce something like global time windows
> across the entire stream, inside which you can work more in a batch-style,
> but that is quite an extensive change to the core.
> > >
> > > Greetings,
> > > Stephan
> > >
> > >
> > > On Sun, Nov 8, 2015 at 5:15 PM, rss rss <rssdev10@gmail.com> wrote:
> > > Hello,
> > >
> > >
> > > I need to extract a finite subset like a data buffer from an infinite
> data stream. The best way for me is to obtain a finite stream with data
> accumulated for a 1minute before (as example). But I not found any existing
> technique to do it.
> > >
> > >
> > > As a possible ways how to do something near to a stream’s subset I see
> following cases:
> > >
> > > -          some transformation operation like ‘take_while’ that
> produces new stream but able to switch one to FINISHED state. Unfortunately
> I not found how to switch the state of a stream from a user code of
> transformation functions;
> > >
> > > -          new DataStream or StreamSource constructors which allow to
> connect a data processing chain to the source stream. It may be something
> like mentioned take_while transform function or modified StreamSource.run
> method with data from the source stream.
> > >
> > >
> > > That is I have two questions.
> > >
> > > 1)      Is there any technique to extract accumulated data from a
> stream as a stream (to union it with another stream)? This is like pure
> buffer mode.
> > >
> > > 2)      If the answer to first question is negative, is there
> something like take_while transformation or should I think about custom
> implementation of it? Is it possible to implement it without modification
> of the core of Flink?
> > >
> > >
> > > Regards,
> > >
> > > Roman
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> >
> >
>
>

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