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Date: Fri, 27 Nov 2015 13:03:43 +0100
Message-ID: 
 <CANC1h_vb0XBz_oTdCmhUZKcPxd34LUHD1BZfxz==j6SsWLxcEw@mail.gmail.com>
Subject: Re: finite subset of an infinite data stream
From: Stephan Ewen <sewen@apache.org>
To: user@flink.apache.org
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Hi Roman!

Is it possible to do the following:

You have a queue (Kafka) with the user requests. Then you have a Flink job
that reads from that queue and does a map() over the query stream. In the
map() function, you do the call to the database, like this:

val queryStream : DataStream[Query] =3D readQueriesFromKafka();

val resultStream : DataStream[Result] =3D queryStream.map(new
RichMapFunction[Query, Result]() {

  var connection: DbConnection =3D _
  val queryStatement: PreparedStatement =3D _

  def open(cfg: Configuration) : Unit =3D {
    connection =3D // connect to data base
    query =3D // prepare query statement
  }

  def map(query: Query) : Result =3D {
    connection.runQuery(queryStatement, query.param1, query.param2, ...);
  }
}

Since the queries have quite a bit of latency, you could try and run them
with a very parallelism, or use a threadpool executor or so...

Greetings,
Stephan


On Thu, Nov 26, 2015 at 11:06 AM, rss rss <rssdev10@gmail.com> wrote:

> Hello Robert,
>
>   thank you for the answer.
>
>   I understand the idea of stream usage but I don't understand how to use
> it in my task. Aljoscha wrote an example of data parts extraction by
> external queries. It is very useful but not enough.
>
>   I have conditional queries. Simple example 'get data from the specified
> period' by a specified client (or sensor, or something other)'. Flink
> streaming API allows to configure a stream to access to a persistent
> storage. E.g. some DBMS with SQL interface. In this case I have to
> configure the stream with SQL query in a constructor like 'select * from
> data where timestamp > now() - 1day AND clientId =3D=3D id'. But '1 day' =
and
> 'id' are parameters from the input query. I don't able to configure all
> possible steams to produce all possible data combinations. Therefore I
> decided to use batch part for running a transformation process for each
> input query with a list of conditions. If it possible I will glad to use
> streaming API only. But there are no ideas other than batch API usage.
>
> Best regards,
> Roman
>
>
> 2015-11-25 21:51 GMT+04:00 Robert Metzger <rmetzger@apache.org>:
>
>> Hi Roman,
>>
>> I've looked through your code here:
>> https://github.com/rssdev10/flink-experiments/blob/init_by_kafka_sink_ka=
fka/src/main/java/proto/flink/batch/Consumer.java
>>
>> The implementation is not very efficient. Flink is not like spark where
>> the job's driver is constantly interacting with the program running in t=
he
>> cluster.
>> Flink will generate a plan from your code and submit it to the cluster
>> [1]. Your code is submitting a new plan for every message in Kafka into =
the
>> cluster.
>> It will be faster to process the data locally.
>>
>> I still think that you can use the DataStream API of Flink. Maybe use
>> this example as a starting point:
>>
>> Properties properties =3D new Properties();properties.setProperty("boots=
trap.servers", "localhost:9092");properties.setProperty("zookeeper.connect"=
, "localhost:2181");properties.setProperty("group.id", "test");DataStream<S=
tring> stream =3D env
>> 	.addSource(new FlinkKafkaConsumer082<>("topic", new SimpleStringSchema(=
), properties))
>>
>>
>> then, use the "stream" object to perform your transformations.
>>
>> The output format looks good!
>>
>>
>> [1]
>> https://ci.apache.org/projects/flink/flink-docs-master/internals/general=
_arch.html
>>
>>
>> On Sun, Nov 22, 2015 at 8:52 PM, rss rss <rssdev10@gmail.com> wrote:
>>
>>> Hello,
>>>
>>>   I have prepared a prototype of the batch subsystem that described in
>>> the previous mail.
>>> https://github.com/rssdev10/flink-experiments/tree/init_by_kafka_sink_k=
afka
>>> It does not contain correct Kafka's serialization/deserialization becau=
se I
>>> didn't see how to do it yet. But it contains a code for running Flink b=
atch
>>> processing by a message from Kafka queue -
>>> https://github.com/rssdev10/flink-experiments/blob/init_by_kafka_sink_k=
afka/src/main/java/proto/flink/batch/Consumer.java
>>> . This code is based on Kafka=E2=80=99s examples.
>>>
>>>   The question is following. Is it correct implementation of the Flink
>>> batch API activation? See method Consumer::run.
>>>
>>>   Also I added a sink for Kafka -
>>> https://github.com/rssdev10/flink-experiments/blob/init_by_kafka_sink_k=
afka/src/main/java/proto/flink/batch/KafkaOutputFormat.java
>>> Is it correct? Unfortunately the Flink's documentation does not contain
>>> examples of a custom sink implementation
>>> https://ci.apache.org/projects/flink/flink-docs-master/apis/programming=
_guide.html#data-sinks
>>> .
>>>
>>>   The program works at least in the mode of one queue looped by self
>>> consumer-producer and activated by the only message from Kafka's consol=
e.
>>>
>>> Best regards,
>>> Roman
>>>
>>> 2015-11-20 16:05 GMT+03:00 rss rss <rssdev10@gmail.com>:
>>>
>>>> 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 DataStr=
eam
>>>> to a persistent storage with specified conditions. It may be SQL or si=
mple
>>>> map(...).filter("something"). But main obstacle is how to configure ne=
w
>>>> 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: =D0=92=D1=81=D1=82=D1=80=D0=BE=D0=B5=D0=BD=D0=BD=D0=BE=D0=B5 =
=D0=B8=D0=B7=D0=BE=D0=B1=D1=80=D0=B0=D0=B6=D0=B5=D0=BD=D0=B8=D0=B5 1]
>>>>
>>>>   And note, this schema describes an idea about how to emulate a real
>>>> time buffer by means of Kafka. Windowed stream infinitely produces dat=
a
>>>> sequences and sinks ones into an external queue with limited storing t=
ime
>>>> 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=E2=80=99m 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 content=
s
>>>>> 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) th=
at 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 fro=
m
>>>>> 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 a=
ble to
>>>>> make analytical queries later. And there is a stream of user's analyt=
ical
>>>>> queries. But the stream of input data is big and time of saving in th=
e
>>>>> persistent storage is big too. And we have not a very fast bigdata OL=
TP
>>>>> storage. That is the data extracted from the persistent storage by th=
e
>>>>> user's requests probably will not contain actual data. We have to hav=
e 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 Fli=
nk's
>>>>> streaming API but found obstacles. This schema is not first variant. =
It
>>>>> contains separated driver program to configure new jobs by user's que=
ries.
>>>>> 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 th=
e
>>>>> 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 exter=
nal
>>>>> 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 subsequen=
ce 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, a=
t
>>>>> which point the remainder of the transformations run, and the batch j=
ob
>>>>> finishes.
>>>>> > > You can already run new transformation chains after each call to
>>>>> "env.execute()", once the execution finished, to implement the sequen=
ce 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 h=
appens
>>>>> 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 =3D
>>>>> env.socketTextStream("localhost", 2015, '\n');
>>>>> > >         DataStream<Tuple2<String, Integer>> wordCounts =3D
>>>>> > >                 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 =3D 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 thi=
s 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 c=
ore 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 window=
s
>>>>> across the entire stream, inside which you can work more in a batch-s=
tyle,
>>>>> 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 strea=
m with
>>>>> data accumulated for a 1minute before (as example). But I not found a=
ny
>>>>> existing technique to do it.
>>>>> > >
>>>>> > >
>>>>> > > As a possible ways how to do something near to a stream=E2=80=99s=
 subset I
>>>>> see following cases:
>>>>> > >
>>>>> > > -          some transformation operation like =E2=80=98take_while=
=E2=80=99 that
>>>>> produces new stream but able to switch one to FINISHED state. Unfortu=
nately
>>>>> I not found how to switch the state of a stream from a user code of
>>>>> transformation functions;
>>>>> > >
>>>>> > > -          new DataStream or StreamSource constructors which allo=
w
>>>>> 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 pu=
re
>>>>> buffer mode.
>>>>> > >
>>>>> > > 2)      If the answer to first question is negative, is there
>>>>> something like take_while transformation or should I think about cust=
om
>>>>> implementation of it? Is it possible to implement it without modifica=
tion
>>>>> of the core of Flink?
>>>>> > >
>>>>> > >
>>>>> > > Regards,
>>>>> > >
>>>>> > > Roman
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> > >
>>>>> >
>>>>> >
>>>>>
>>>>>
>>>>
>>>
>>
>

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<div dir=3D"ltr">Hi Roman!<div><br></div><div>Is it possible to do the foll=
owing:</div><div><br></div><div>You have a queue (Kafka) with the user requ=
ests. Then you have a Flink job that reads from that queue and does a map()=
 over the query stream. In the map() function, you do the call to the datab=
ase, like this:</div><div><br></div><div>val queryStream : DataStream[Query=
] =3D readQueriesFromKafka();</div><div><br></div><div>val resultStream : D=
ataStream[Result] =3D queryStream.map(new RichMapFunction[Query, Result]() =
{</div><div><br></div><div>=C2=A0 var connection: DbConnection =3D _</div><=
div>=C2=A0 val queryStatement: PreparedStatement =3D _</div><div><br></div>=
<div>=C2=A0 def open(cfg: Configuration) : Unit =3D {</div><div>=C2=A0 =C2=
=A0 connection =3D // connect to data base</div><div>=C2=A0 =C2=A0 query =
=3D // prepare query statement</div><div>=C2=A0 }</div><div><br></div><div>=
=C2=A0 def map(query: Query) : Result =3D {</div><div>=C2=A0 =C2=A0 connect=
ion.runQuery(queryStatement, query.param1, query.param2, ...);</div><div>=
=C2=A0 }</div><div>}</div><div><br></div><div>Since the queries have quite =
a bit of latency, you could try and run them with a very parallelism, or us=
e a threadpool executor or so...</div><div><br></div><div>Greetings,</div><=
div>Stephan</div><div><br></div></div><div class=3D"gmail_extra"><br><div c=
lass=3D"gmail_quote">On Thu, Nov 26, 2015 at 11:06 AM, rss rss <span dir=3D=
"ltr">&lt;<a href=3D"mailto:rssdev10@gmail.com" target=3D"_blank">rssdev10@=
gmail.com</a>&gt;</span> wrote:<br><blockquote class=3D"gmail_quote" style=
=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir=
=3D"ltr">Hello Robert,<div><br></div><div>=C2=A0 thank you for the answer.=
=C2=A0</div><div><br></div><div>=C2=A0 I understand the idea of stream usag=
e but I don&#39;t understand how to use it=C2=A0in my task. Aljoscha wrote =
an example of data parts extraction by external queries. It is very useful =
but not enough.</div><div><br></div><div>=C2=A0 I have conditional queries.=
 Simple example &#39;get data from the specified period&#39; by a specified=
 client (or sensor, or something other)&#39;. Flink streaming API allows to=
 configure a stream to access to a persistent storage. E.g. some DBMS with =
SQL interface. In this case I have to configure the stream with SQL query i=
n a constructor like &#39;select * from data where timestamp &gt; now() - 1=
day AND clientId =3D=3D id&#39;. But &#39;1 day&#39; and &#39;id&#39; are p=
arameters from the input query. I don&#39;t able to configure all possible =
steams to produce all possible data combinations. Therefore I decided to us=
e batch part for running a transformation process for each input query with=
 a list of conditions. If it possible I will glad to use streaming API only=
. But there are no ideas other than batch API usage.</div><div><br></div><d=
iv>Best regards,</div><div>Roman</div><div><div class=3D"h5"><div><br></div=
><div class=3D"gmail_extra"><br><div class=3D"gmail_quote">2015-11-25 21:51=
 GMT+04:00 Robert Metzger <span dir=3D"ltr">&lt;<a href=3D"mailto:rmetzger@=
apache.org" target=3D"_blank">rmetzger@apache.org</a>&gt;</span>:<br><block=
quote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;border-left-w=
idth:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding=
-left:1ex"><div dir=3D"ltr">Hi Roman,<div><br></div><div>I&#39;ve looked th=
rough your code here:=C2=A0<a href=3D"https://github.com/rssdev10/flink-exp=
eriments/blob/init_by_kafka_sink_kafka/src/main/java/proto/flink/batch/Cons=
umer.java" target=3D"_blank">https://github.com/rssdev10/flink-experiments/=
blob/init_by_kafka_sink_kafka/src/main/java/proto/flink/batch/Consumer.java=
</a></div><div><br></div><div>The implementation is not very efficient. Fli=
nk is not like spark where the job&#39;s driver is constantly interacting w=
ith the program running in the cluster.</div><div>Flink will generate a pla=
n from your code and submit it to the cluster [1]. Your code is submitting =
a new plan for every message in Kafka into the cluster.</div><div>It will b=
e faster to process the data locally.</div><div><br></div><div>I still thin=
k that you can use the DataStream API of Flink. Maybe use this example as a=
 starting point:</div><div><br></div><div><pre style=3D"overflow:auto;font-=
family:Menlo,Monaco,Consolas,&#39;Courier New&#39;,monospace;font-size:11.9=
px;padding:9.5px;margin-top:0px;margin-bottom:10px;line-height:1.42857;colo=
r:rgb(51,51,51);word-wrap:break-word;border:1px solid rgb(204,204,204);bord=
er-radius:4px;background-color:rgb(245,245,245)"><code style=3D"font-family=
:Menlo,&#39;Lucida Console&#39;,monospace;font-size:inherit;padding:0px;col=
or:inherit;border-radius:0px;white-space:pre-wrap;background:transparent"><=
span>Properties</span> <span>properties</span> <span style=3D"font-weight:b=
old">=3D</span> <span style=3D"font-weight:bold">new</span> <span style=3D"=
color:rgb(153,0,0);font-weight:bold">Properties</span><span style=3D"font-w=
eight:bold">();</span>
<span>properties</span><span style=3D"font-weight:bold">.</span><span style=
=3D"color:rgb(0,128,128)">setProperty</span><span style=3D"font-weight:bold=
">(</span><span style=3D"color:rgb(221,17,68)">&quot;bootstrap.servers&quot=
;</span><span style=3D"font-weight:bold">,</span> <span style=3D"color:rgb(=
221,17,68)">&quot;localhost:9092&quot;</span><span style=3D"font-weight:bol=
d">);</span>
<span>properties</span><span style=3D"font-weight:bold">.</span><span style=
=3D"color:rgb(0,128,128)">setProperty</span><span style=3D"font-weight:bold=
">(</span><span style=3D"color:rgb(221,17,68)">&quot;zookeeper.connect&quot=
;</span><span style=3D"font-weight:bold">,</span> <span style=3D"color:rgb(=
221,17,68)">&quot;localhost:2181&quot;</span><span style=3D"font-weight:bol=
d">);</span>
<span>properties</span><span style=3D"font-weight:bold">.</span><span style=
=3D"color:rgb(0,128,128)">setProperty</span><span style=3D"font-weight:bold=
">(</span><span style=3D"color:rgb(221,17,68)">&quot;<a href=3D"http://grou=
p.id" target=3D"_blank">group.id</a>&quot;</span><span style=3D"font-weight=
:bold">,</span> <span style=3D"color:rgb(221,17,68)">&quot;test&quot;</span=
><span style=3D"font-weight:bold">);</span>
<span>DataStream</span><span style=3D"font-weight:bold">&lt;</span><span>St=
ring</span><span style=3D"font-weight:bold">&gt;</span> <span>stream</span>=
 <span style=3D"font-weight:bold">=3D</span> <span>env</span>
	<span style=3D"font-weight:bold">.</span><span style=3D"color:rgb(0,128,12=
8)">addSource</span><span style=3D"font-weight:bold">(</span><span style=3D=
"font-weight:bold">new</span> <span>FlinkKafkaConsumer082</span><span style=
=3D"font-weight:bold">&lt;&gt;(</span><span style=3D"color:rgb(221,17,68)">=
&quot;topic&quot;</span><span style=3D"font-weight:bold">,</span> <span sty=
le=3D"font-weight:bold">new</span> <span style=3D"color:rgb(153,0,0);font-w=
eight:bold">SimpleStringSchema</span><span style=3D"font-weight:bold">(),</=
span> <span>properties</span><span style=3D"font-weight:bold">))</span><br>=
</code></pre></div><div><br></div><div>then, use the &quot;stream&quot; obj=
ect to perform your transformations.=C2=A0</div><div><br></div><div>The out=
put format looks good!</div><div><br></div><div><br></div><div>[1]=C2=A0<a =
href=3D"https://ci.apache.org/projects/flink/flink-docs-master/internals/ge=
neral_arch.html" target=3D"_blank">https://ci.apache.org/projects/flink/fli=
nk-docs-master/internals/general_arch.html</a></div><div><div><div><br></di=
v><div class=3D"gmail_extra"><br><div class=3D"gmail_quote">On Sun, Nov 22,=
 2015 at 8:52 PM, rss rss <span dir=3D"ltr">&lt;<a href=3D"mailto:rssdev10@=
gmail.com" target=3D"_blank">rssdev10@gmail.com</a>&gt;</span> wrote:<br><b=
lockquote class=3D"gmail_quote" style=3D"margin:0px 0px 0px 0.8ex;border-le=
ft-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;pad=
ding-left:1ex"><div dir=3D"ltr"><div><div><div><div>Hello,<br><br></div>=C2=
=A0 I have prepared a prototype of the batch subsystem that described in th=
e previous mail.=20
      <a href=3D"https://github.com/rssdev10/flink-experiments/tree/init_by=
_kafka_sink_kafka" target=3D"_blank">https://github.com/rssdev10/flink-expe=
riments/tree/init_by_kafka_sink_kafka</a> It does not contain correct Kafka=
&#39;s serialization/deserialization
      because I didn&#39;t see how to do it yet. But it contains a code for=
 running Flink batch processing by a
      message from Kafka queue -
      <a href=3D"https://github.com/rssdev10/flink-experiments/blob/init_by=
_kafka_sink_kafka/src/main/java/proto/flink/batch/Consumer.java" target=3D"=
_blank">https://github.com/rssdev10/flink-experiments/blob/init_by_kafka_si=
nk_kafka/src/main/java/proto/flink/batch/Consumer.java</a>
      . This code is based on Kafka=E2=80=99s examples.<br><br></div>=C2=A0=
 The question is following. Is it correct implementation of the Flink batch=
 API activation? See method Consumer::run.<br><br></div>=C2=A0 Also I added=
 a sink for Kafka - <a href=3D"https://github.com/rssdev10/flink-experiment=
s/blob/init_by_kafka_sink_kafka/src/main/java/proto/flink/batch/KafkaOutput=
Format.java" target=3D"_blank">https://github.com/rssdev10/flink-experiment=
s/blob/init_by_kafka_sink_kafka/src/main/java/proto/flink/batch/KafkaOutput=
Format.java</a> <br>Is it correct? Unfortunately the Flink&#39;s documentat=
ion does not contain examples of a custom sink implementation <a href=3D"ht=
tps://ci.apache.org/projects/flink/flink-docs-master/apis/programming_guide=
.html#data-sinks" target=3D"_blank">https://ci.apache.org/projects/flink/fl=
ink-docs-master/apis/programming_guide.html#data-sinks</a> . <br><br></div>=
=C2=A0 The program works at least in the mode of one queue looped by self c=
onsumer-producer and activated by the only message from Kafka&#39;s console=
.<br><div><div><div><div><br></div><div>Best regards,<br></div><div>Roman<b=
r></div></div></div></div></div><div><div><div class=3D"gmail_extra"><br><d=
iv class=3D"gmail_quote">2015-11-20 16:05 GMT+03:00 rss rss <span dir=3D"lt=
r">&lt;<a href=3D"mailto:rssdev10@gmail.com" target=3D"_blank">rssdev10@gma=
il.com</a>&gt;</span>:<br><blockquote class=3D"gmail_quote" style=3D"margin=
:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204)=
;border-left-style:solid;padding-left:1ex"><div dir=3D"ltr">Hello=C2=A0<spa=
n style=3D"font-size:12.8px">Aljoscha</span>,<div><br></div><div>=C2=A0 Tha=
nks, 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.</div><div><b=
r></div><div>=C2=A0 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 do=
n&#39;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 DataSe=
t or DataStream to a persistent storage with specified conditions. It may b=
e SQL or simple map(...).filter(&quot;something&quot;). But main obstacle i=
s how to configure new data processing schema been inside the current strea=
m transformation. E.g. being inside the connected query stream map function=
.</div><div><br></div><div>=C2=A0 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&#39;t =
see other way to resolve the task than separate queries to persistent stora=
ge in batch part.</div><div><br></div><div><img src=3D"cid:ii_15124efd793ad=
866" alt=3D"=D0=92=D1=81=D1=82=D1=80=D0=BE=D0=B5=D0=BD=D0=BD=D0=BE=D0=B5 =
=D0=B8=D0=B7=D0=BE=D0=B1=D1=80=D0=B0=D0=B6=D0=B5=D0=BD=D0=B8=D0=B5 1" heigh=
t=3D"203" width=3D"507"><br></div><div><br></div><div>=C2=A0 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 who=
le. Any=C2=A0consumers=C2=A0connected to the queue are received an actual d=
ata. I don&#39;t like this idea because it is excess network communication =
but it looks workable.</div><div><br></div><div>=C2=A0 BTW: it is something=
 like lambda/kappa architecture implementation. I don&#39;t like these term=
s but actually it is.</div><div><br></div><div>Best regards,</div><div>Roma=
n</div><div>=C2=A0=C2=A0</div></div><div><div><div class=3D"gmail_extra"><b=
r><div class=3D"gmail_quote">2015-11-20 13:26 GMT+04:00 Aljoscha Krettek <s=
pan dir=3D"ltr">&lt;<a href=3D"mailto:aljoscha@apache.org" target=3D"_blank=
">aljoscha@apache.org</a>&gt;</span>:<br><blockquote class=3D"gmail_quote" =
style=3D"margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:r=
gb(204,204,204);border-left-style:solid;padding-left:1ex">Hi,<br>
I=E2=80=99m very sorry, yes you would need my custom branch: <a href=3D"htt=
ps://github.com/aljoscha/flink/commits/state-enhance" rel=3D"noreferrer" ta=
rget=3D"_blank">https://github.com/aljoscha/flink/commits/state-enhance</a>=
<br>
<br>
Cheers,<br>
Aljoscha<br>
<div><div>&gt; On 20 Nov 2015, at 10:13, rss rss &lt;<a href=3D"mailto:rssd=
ev10@gmail.com" target=3D"_blank">rssdev10@gmail.com</a>&gt; wrote:<br>
&gt;<br>
&gt; Hello Aljoscha,<br>
&gt;<br>
&gt;=C2=A0 =C2=A0very thanks. I tried to build your example but have an obs=
tacle with org.apache.flink.runtime.state.AbstractStateBackend class. Where=
 to get it? I guess it stored in your local branch only. Would you please t=
o send me patches for public branch or share the branch with me?<br>
&gt;<br>
&gt; Best regards,<br>
&gt; Roman<br>
&gt;<br>
&gt;<br>
&gt; 2015-11-18 17:24 GMT+04:00 Aljoscha Krettek &lt;<a href=3D"mailto:aljo=
scha@apache.org" target=3D"_blank">aljoscha@apache.org</a>&gt;:<br>
&gt; Hi,<br>
&gt; I wrote a little example that could be what you are looking for: <a hr=
ef=3D"https://github.com/dataArtisans/query-window-example" rel=3D"noreferr=
er" target=3D"_blank">https://github.com/dataArtisans/query-window-example<=
/a><br>
&gt;<br>
&gt; It basically implements a window operator with a modifiable window siz=
e that also allows querying the current accumulated window contents using a=
 second input stream.<br>
&gt;<br>
&gt; There is a README file in the github repository, but please let me kno=
w if you need further explanations.<br>
&gt;<br>
&gt; Cheers,<br>
&gt; Aljoscha<br>
&gt;<br>
&gt; &gt; On 18 Nov 2015, at 12:02, Robert Metzger &lt;<a href=3D"mailto:rm=
etzger@apache.org" target=3D"_blank">rmetzger@apache.org</a>&gt; wrote:<br>
&gt; &gt;<br>
&gt; &gt; Hi Roman,<br>
&gt; &gt;<br>
&gt; &gt; I&#39;ve updated the documentation. It seems that it got out of s=
ync. Thank you for notifying us about this.<br>
&gt; &gt;<br>
&gt; &gt; My colleague Aljoscha has some experimental code that is probably=
 doing what you are looking for: A standing window (your RT-buffer) that yo=
u can query using a secondary stream (your user&#39;s queries).<br>
&gt; &gt; He&#39;ll post the code soon to this email thread.<br>
&gt; &gt;<br>
&gt; &gt; Regards,<br>
&gt; &gt; Robert<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; On Wed, Nov 11, 2015 at 2:51 PM, rss rss &lt;<a href=3D"mailto:rs=
sdev10@gmail.com" target=3D"_blank">rssdev10@gmail.com</a>&gt; wrote:<br>
&gt; &gt; Hello,<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0thanks, Stephan, but triggers are not that I searched=
. And BTW, the documentation is obsolete. There is no Count class now. I fo=
und CountTrigger only.<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0Thanks Robert, your example may be useful for me but =
in some other point. I mentioned &quot;union&quot; as an ordinary union of =
similar data. It is the same as &quot;union&quot; in the datastream documen=
tation.<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0The task is very simple. We have an infinite stream o=
f 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 a=
ble to make analytical queries later. And there is a stream of user&#39;s a=
nalytical queries. But the stream of input data is big and time of saving i=
n the persistent storage is big too. And we have not a very fast bigdata OL=
TP storage. That is the data extracted from the persistent storage by the u=
ser&#39;s requests probably will not contain actual data. We have to have s=
ome 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&#=
39;t speak about data deduplication and ordering now.). And of course the u=
ser&#39;s query are unpredictable regarding data filtering conditions.<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0The attached schema is attempt to understand how it m=
ay be implemented with Flink. I tried to imagine how to implement it by Fli=
nk&#39;s streaming API but found obstacles. This schema is not first varian=
t. It contains separated driver program to configure new jobs by user&#39;s=
 queries. The reason I not found a way how to link the stream of user&#39;s=
 queries with further data processing. But it is some near to <a href=3D"ht=
tps://gist.github.com/fhueske/4ea5422edb5820915fa4" rel=3D"noreferrer" targ=
et=3D"_blank">https://gist.github.com/fhueske/4ea5422edb5820915fa4</a><br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; &lt;flink_streams.png&gt;<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0The main question is how to process each user&#39;s q=
uery combining it with actual data from the real time buffer and batch requ=
est to the persistent storage. Unfortunately I not found a decision in Stre=
aming API only.<br>
&gt; &gt;<br>
&gt; &gt; Regards,<br>
&gt; &gt; Roman<br>
&gt; &gt;<br>
&gt; &gt; 2015-11-11 15:45 GMT+04:00 Robert Metzger &lt;<a href=3D"mailto:r=
metzger@apache.org" target=3D"_blank">rmetzger@apache.org</a>&gt;:<br>
&gt; &gt; I think what you call &quot;union&quot; is a &quot;connected stre=
am&quot; in Flink. Have a look at this example: <a href=3D"https://gist.git=
hub.com/fhueske/4ea5422edb5820915fa4" rel=3D"noreferrer" target=3D"_blank">=
https://gist.github.com/fhueske/4ea5422edb5820915fa4</a><br>
&gt; &gt; It shows how to dynamically update a list of filters by external =
requests.<br>
&gt; &gt; Maybe that&#39;s what you are looking for?<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; On Wed, Nov 11, 2015 at 12:15 PM, Stephan Ewen &lt;<a href=3D"mai=
lto:sewen@apache.org" target=3D"_blank">sewen@apache.org</a>&gt; wrote:<br>
&gt; &gt; Hi!<br>
&gt; &gt;<br>
&gt; &gt; I don not really understand what exactly you want to do, especial=
ly the &quot;union an infinite real time data stream with filtered persiste=
nt data where the condition of filtering is provided by external requests&q=
uot;.<br>
&gt; &gt;<br>
&gt; &gt; If you want to work on substreams in general, there are two optio=
ns:<br>
&gt; &gt;<br>
&gt; &gt; 1) Create the substream in a streaming window. You can &quot;cut&=
quot; the stream based on special records/events that signal that the subse=
quence is done. Have a look at the &quot;Trigger&quot; class for windows, i=
t can react to elements and their contents:<br>
&gt; &gt;<br>
&gt; &gt; <a href=3D"https://ci.apache.org/projects/flink/flink-docs-master=
/apis/streaming_guide.html#windows-on-keyed-data-streams" rel=3D"noreferrer=
" target=3D"_blank">https://ci.apache.org/projects/flink/flink-docs-master/=
apis/streaming_guide.html#windows-on-keyed-data-streams</a> (secion on Adva=
nced Windowing).<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; 2) You can trigger sequences of batch jobs. The batch job data so=
urce (input format) can decide when to stop consuming the stream, at which =
point the remainder of the transformations run, and the batch job finishes.=
<br>
&gt; &gt; You can already run new transformation chains after each call to =
&quot;env.execute()&quot;, once the execution finished, to implement the se=
quence of batch jobs.<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; I would try and go for the windowing solution if that works, beca=
use that will give you better fault tolerance / high availability. In the r=
epeated batch jobs case, you need to worry yourself about what happens when=
 the driver program (that calls env.execute()) fails.<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; Hope that helps...<br>
&gt; &gt;<br>
&gt; &gt; Greetings,<br>
&gt; &gt; Stephan<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; On Mon, Nov 9, 2015 at 1:24 PM, rss rss &lt;<a href=3D"mailto:rss=
dev10@gmail.com" target=3D"_blank">rssdev10@gmail.com</a>&gt; wrote:<br>
&gt; &gt; Hello,<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0thanks for the answer but windows produce periodical =
results. I used your example but the data source is changed to TCP stream:<=
br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0DataStream&lt;String&gt; text =
=3D env.socketTextStream(&quot;localhost&quot;, 2015, &#39;\n&#39;);<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0DataStream&lt;Tuple2&lt;String, =
Integer&gt;&gt; wordCounts =3D<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0text=
<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0.fla=
tMap(new LineSplitter())<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0.key=
By(0)<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0.tim=
eWindow(Time.of(5, TimeUnit.SECONDS))<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0.sum=
(1);<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0wordCounts.print();<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0env.execute(&quot;WordCount Exam=
ple&quot;);<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 I see an infinite results printing instead of the only list=
.<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 The data source is following script:<br>
&gt; &gt; -----------------------------------------------------<br>
&gt; &gt; #!/usr/bin/env ruby<br>
&gt; &gt;<br>
&gt; &gt; require &#39;socket&#39;<br>
&gt; &gt;<br>
&gt; &gt; server =3D TCPServer.new 2015<br>
&gt; &gt; loop do<br>
&gt; &gt;=C2=A0 =C2=A0Thread.start(server.accept) do |client|<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0puts Time.now.to_s + &#39;: New client!&#39;<b=
r>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0loop do<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0client.puts &quot;#{Time.now} #{[*(&#39=
;A&#39;..&#39;Z&#39;)].sample(3).join}&quot;<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0 =C2=A0sleep rand(1000)/1000.0<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0end<br>
&gt; &gt;=C2=A0 =C2=A0 =C2=A0client.close<br>
&gt; &gt;=C2=A0 =C2=A0end<br>
&gt; &gt; end<br>
&gt; &gt; -----------------------------------------------------<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0My purpose is to union an infinite real time data str=
eam with filtered persistent data where the condition of filtering is provi=
ded by external requests. And the only result of union is interested. In th=
is case I guess I need a way to terminate the stream. May be I wrong.<br>
&gt; &gt;<br>
&gt; &gt;=C2=A0 =C2=A0Moreover it should be possible to link the streams by=
 next request with other filtering criteria. That is create new data transf=
ormation chain after running of env.execute(&quot;WordCount Example&quot;).=
 Is it possible now? If not, is it possible with minimal changes of the cor=
e of Flink?<br>
&gt; &gt;<br>
&gt; &gt; Regards,<br>
&gt; &gt; Roman<br>
&gt; &gt;<br>
&gt; &gt; 2015-11-09 12:34 GMT+04:00 Stephan Ewen &lt;<a href=3D"mailto:sew=
en@apache.org" target=3D"_blank">sewen@apache.org</a>&gt;:<br>
&gt; &gt; Hi!<br>
&gt; &gt;<br>
&gt; &gt; If you want to work on subsets of streams, the answer is usually =
to use windows, &quot;stream.keyBy(...).timeWindow(Time.of(1, MINUTE))&quot=
;.<br>
&gt; &gt;<br>
&gt; &gt; The transformations that you want to make, do they fit into a win=
dow function?<br>
&gt; &gt;<br>
&gt; &gt; There are thoughts to introduce something like global time window=
s across the entire stream, inside which you can work more in a batch-style=
, but that is quite an extensive change to the core.<br>
&gt; &gt;<br>
&gt; &gt; Greetings,<br>
&gt; &gt; Stephan<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; On Sun, Nov 8, 2015 at 5:15 PM, rss rss &lt;<a href=3D"mailto:rss=
dev10@gmail.com" target=3D"_blank">rssdev10@gmail.com</a>&gt; wrote:<br>
&gt; &gt; Hello,<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; I need to extract a finite subset like a data buffer from an infi=
nite data stream. The best way for me is to obtain a finite stream with dat=
a accumulated for a 1minute before (as example). But I not found any existi=
ng technique to do it.<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; As a possible ways how to do something near to a stream=E2=80=99s=
 subset I see following cases:<br>
&gt; &gt;<br>
&gt; &gt; -=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 some transformation operation=
 like =E2=80=98take_while=E2=80=99 that produces new stream but able to swi=
tch one to FINISHED state. Unfortunately I not found how to switch the stat=
e of a stream from a user code of transformation functions;<br>
&gt; &gt;<br>
&gt; &gt; -=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 new DataStream or StreamSourc=
e constructors which allow to connect a data processing chain to the source=
 stream. It may be something like mentioned take_while transform function o=
r modified StreamSource.run method with data from the source stream.<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; That is I have two questions.<br>
&gt; &gt;<br>
&gt; &gt; 1)=C2=A0 =C2=A0 =C2=A0 Is there any technique to extract accumula=
ted data from a stream as a stream (to union it with another stream)? This =
is like pure buffer mode.<br>
&gt; &gt;<br>
&gt; &gt; 2)=C2=A0 =C2=A0 =C2=A0 If the answer to first question is negativ=
e, is there something like take_while transformation or should I think abou=
t custom implementation of it? Is it possible to implement it without modif=
ication of the core of Flink?<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt; Regards,<br>
&gt; &gt;<br>
&gt; &gt; Roman<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt; &gt;<br>
&gt;<br>
&gt;<br>
<br>
</div></div></blockquote></div><br></div>
</div></div></blockquote></div><br></div>
</div></div></blockquote></div><br></div></div></div></div>
</blockquote></div><br></div></div></div></div>
</blockquote></div><br></div>

--001a114a889ca777a20525847b3e--

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