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From rss rss <rssde...@gmail.com>
Subject Re: finite subset of an infinite data stream
Date Wed, 11 Nov 2015 13:51:25 GMT
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documentation is obsolete.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


[image: Встроенное изображение 2]

  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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