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 <rssde...@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 <se...@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 <rssde...@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 >>> >> >> >
