Hi, Thanks for the response.
1) regarding the JIRA issue related to the .global and .forward functions – I believe it is a good idea to be removed as they are confusing. Actually, they are totally missing from the documentation webpage https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#windows which makes things even more confusing regarding what is their role/capabilities. 2) regarding the ".timeWindowAll()", it’s behavior is not as one / I would expect. I am not sure if this behavior is intentional or there is an error. I would expect as mentioned in my initial email that even if on the previous operators I have a parallelism of N, using this function I can get a parallelism of 1 in which I can aggregate the data from the previous operators. However, it is not really the case. More specifically, it is not the case when you execute this function in a cluster with more machine (on the other hand it works ok for the local case!). It turns out that the parallelism degree is kept when being run in the cluster (and it is to guess I would say the function is executed round-robin over the executors). So if you use this function to aggregate all data in one place you will end up aggregating it over multiple parallel instances. I am attaching bellow a dummy piece of code to exemplify. The function reads events from a network socket and multiplies these events based on the parallelism degree. The stream is partitioned based on a key. This is followed by the “main computation” that is run in parallel and finally by an aggregation part. For this aggregation part I use as suggested “.timeWindowAll()”. Assume that this aggregation function counts the events processed in the system across all instances and prints/logs this data. For example if you run this with a parallelism degree of 10 – you end up with outputs from the timeWindowAll() across all instances in the cluster. A sample output is shown below. This shows that despite that the function should be executed with parallelism 1, actually it is not – so it cannot aggregate the data into one place… Is this actually the intended behavior (case in which it would be interested to understand what is the target scenario) or is there an error? Machine1: (values in the file) /tmp/testoutput/1 (10) /tmp/testoutput/2 (20) Machine2: /tmp/testoutput/6 (10) /tmp/testoutput/4 /tmp/testoutput/7 (40) Machine3: /tmp/testoutput/5 /tmp/testoutput/3 …………… public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment .getExecutionEnvironment(); final int parallelism = 10; env.setParallelism(parallelism); DataStream<Tuple2<Integer, String>> inputStream = env.socketTextStream( hostIP, port2use, '\n').flatMap( new FlatMapFunction<String, Tuple2<Integer, String>>() { @Override public void flatMap(String arg0, Collector<Tuple2<Integer, String>> arg1) throws Exception { for (int i = 0; i < parallelism; i++) arg1.collect(new Tuple2(i, arg0)); } }); DataStream<Tuple2<Integer, Integer>> result = inputStream .keyBy(0) .timeWindow(Time.of(2, TimeUnit.SECONDS)) .apply(new WindowFunction<Tuple2<Integer, String>, Tuple2<Integer, Integer>, Tuple, TimeWindow>() { public void apply( Tuple arg0, TimeWindow arg1, java.lang.Iterable<org.apache.flink.api.java.tuple.Tuple2<Integer, String>> arg2, org.apache.flink.util.Collector<org.apache.flink.api.java.tuple.Tuple2<Integer, Integer>> arg3) throws Exception { // Compuatation .... int count = 0; for (Tuple2<Integer, String> value : arg2) { count++; arg3.collect(new Tuple2<Integer, Integer>(value.f0, value.f1.length())); } //System.out.println("Count per hash is " + count); }; }); result.timeWindowAll(Time.of(2, TimeUnit.SECONDS)) .apply(new AllWindowFunction<Tuple2<Integer, Integer>, Tuple1<Integer>, TimeWindow>() { @Override public void apply(TimeWindow arg0, Iterable<Tuple2<Integer, Integer>> arg1, Collector<Tuple1<Integer>> arg2) throws Exception { // Compuatation .... int count = 0; for (Tuple2<Integer, Integer> value : arg1) { count++; } //System.out.println("Count aggregated metrics is " // + count + " at " + System.currentTimeMillis()); arg2.collect(new Tuple1(count)); } }).setParallelism(1) .writeAsText("/tmp/testoutput", WriteMode.OVERWRITE); env.execute("main stream application"); } Regards, Dr. Radu Tudoran Research Engineer - Big Data Expert IT R&D Division [cid:image007.jpg@01CD52EB.AD060EE0] HUAWEI TECHNOLOGIES Duesseldorf GmbH European Research Center Riesstrasse 25, 80992 München E-mail: radu.tudo...@huawei.com Mobile: +49 15209084330 Telephone: +49 891588344173 HUAWEI TECHNOLOGIES Duesseldorf GmbH Hansaallee 205, 40549 Düsseldorf, Germany, www.huawei.com<http://www.huawei.com/> Registered Office: Düsseldorf, Register Court Düsseldorf, HRB 56063, Managing Director: Bo PENG, Wanzhou MENG, Lifang CHEN Sitz der Gesellschaft: Düsseldorf, Amtsgericht Düsseldorf, HRB 56063, Geschäftsführer: Bo PENG, Wanzhou MENG, Lifang CHEN This e-mail and its attachments contain confidential information from HUAWEI, which is intended only for the person or entity whose address is listed above. Any use of the information contained herein in any way (including, but not limited to, total or partial disclosure, reproduction, or dissemination) by persons other than the intended recipient(s) is prohibited. If you receive this e-mail in error, please notify the sender by phone or email immediately and delete it! From: Robert Metzger [mailto:rmetz...@apache.org] Sent: Friday, January 15, 2016 10:18 AM To: user@flink.apache.org Subject: Re: global function over partitions Hi Radu, I'm sorry for the delayed response. I'm not sure what the purpose of DataStream.global() actually is. I've opened a JIRA to document or remove it: https://issues.apache.org/jira/browse/FLINK-3240. For getting the final metrics, you can just call ".timeWindowAll()", without a ".global()" call before. The timeWindowAll() will run with a parallelism of one, hence it will receive the data from all partitions. Regards, Robert On Tue, Jan 12, 2016 at 6:59 PM, Radu Tudoran <radu.tudo...@huawei.com<mailto:radu.tudo...@huawei.com>> wrote: Hi, I am trying to compute some final statistics over a stream topology. For this I would like to gather all data from all windows and parallel partitions into a single/global window. Could you suggest a solution for this. I saw that the map function has a ".global()" but I end up with the same number of partitions as I have in the main computation. Bellow you can find a schema for the program: DataStream stream = env.Read... end.setParallelism(10); //Compute phase DataStream<Tuple...> result = stream.keyBy(_).window(_).apply(); //end compute phase //get the metrics result.map(//extract some of the Tuple fields).global().timeWindowAll(Time.of(5, TimeUnit.SECONDS),Time.of(1, TimeUnit.SECONDS)) .trigger(EventTimeTrigger.create()).apply ().writeAsText(); For this last function - I would expect that even if I had parallel computation during the compute phase, I can select part of the events from all partitions and gather all these into one unique window. However, I do not seem to be successful in this. I also tried by applying a keyBy() to the result stream in which I assigned the same hash to any event, but the result remains the same. result.map((//extract some of the Tuple fields).keyBy( new KeySelector<Tuple2<Long,Long>, Integer>() { @Override public Integer getKey(Tuple2<Long, Long> arg0) throws Exception { return 1; } @Override public int hashCode() { return 1; } }). timeWindowAll().apply() Thanks for the help/ideas
