Hi Aljoscha, Thanks a lot for your inputs.
I still did not get you when you say you will not face this issue in case of continuous stream, lets consider the following example : Assume that the stream runs continuously from Monday to Friday, and on Friday it stops after 5.00 PM , will I still face this issue ? I am actually not able to understand how it will differ in real time streams. Regards, Vinay Patil On Tue, Jun 28, 2016 at 5:07 PM, Aljoscha Krettek <aljos...@apache.org> wrote: > Hi, > ingestion time can only be used if you don't care about the timestamp in > the elements. So if you have those you should probably use event time. > > If your timestamps really are strictly increasing then the ascending > extractor is good. And if you have a continuous stream of incoming elements > you will not see the behavior of not getting the last elements. > > By the way, when using Kafka you can also embed the timestamp extractor > directly in the Kafka consumer. This is described here: > > https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/connectors/kafka.html#kafka-consumers-and-timestamp-extractionwatermark-emission > > Cheers, > Aljoscha > > On Tue, 28 Jun 2016 at 11:44 Vinay Patil <vinay18.pa...@gmail.com> wrote: > > > Hi Aljoscha, > > > > Thank you for your response. > > So do you suggest to use different approach for extracting timestamp (as > > given in document) instead of AscendingTimeStamp Extractor ? > > Is that the reason I am seeing this unexpected behaviour ? in case of > > continuous stream I would not see any data loss ? > > > > Also assuming that the records are always going to be in order , which is > > the best approach : Ingestion Time or Event Time ? > > > > > > > > Regards, > > Vinay Patil > > > > On Tue, Jun 28, 2016 at 2:41 PM, Aljoscha Krettek <aljos...@apache.org> > > wrote: > > > > > Hi, > > > first regarding tumbling windows: even if you have 5 minute windows it > > can > > > happen that elements that are only seconds apart go into different > > windows. > > > Consider the following case: > > > > > > | x | x | > > > > > > These are two 5-mintue windows and the two elements are only seconds > > apart > > > but go into different windows because windows are aligned to epoch. > > > > > > Now, for the ascending timestamp extractor. The reason this can behave > in > > > unexpected ways is that it emits a watermark that is "last timestamp - > > 1", > > > i.e. if it has seen timestamp t it can only emit watermark t-1 because > > > there might be other elements with timestamp t arriving. If you have a > > > continuous stream of elements you wouldn't notice this. Only in this > > > constructed example does it become visible. > > > > > > Cheers, > > > Aljoscha > > > > > > On Tue, 28 Jun 2016 at 06:04 Vinay Patil <vinay18.pa...@gmail.com> > > wrote: > > > > > > > Hi, > > > > > > > > Following is the timestamp I am getting from DTO, here is the > timestamp > > > > difference between the two records : > > > > 1466115892162154279 > > > > 1466116026233613409 > > > > > > > > So the time difference is roughly 3 min, even if I apply the window > of > > > 5min > > > > , I am not getting the last record (last timestamp value above), > > > > using ascending timestamp extractor for generating the timestamp > > > (assuming > > > > that the timestamp are always in order) > > > > > > > > I was at-least expecting data to reach the co-group function. > > > > What could be the reason for the data loss ? The data we are getting > is > > > > critical, hence we cannot afford to loose any data > > > > > > > > > > > > Regards, > > > > Vinay Patil > > > > > > > > On Mon, Jun 27, 2016 at 11:31 PM, Vinay Patil < > vinay18.pa...@gmail.com > > > > > > > wrote: > > > > > > > > > Just an update, when I keep IngestionTime and remove the timestamp > I > > am > > > > > generating, I am getting all the records, but for Event Time I am > > > getting > > > > > one less record, I checked the Time Difference between two records, > > it > > > > is 3 > > > > > min, I tried keeping the window time to 5 mins, but that even did > not > > > > work. > > > > > > > > > > Even when I try assigning timestamp for IngestionTime, I get one > > record > > > > > less, so should I safely use Ingestion Time or is it always > advisable > > > to > > > > > use EventTime ? > > > > > > > > > > Regards, > > > > > Vinay Patil > > > > > > > > > > On Mon, Jun 27, 2016 at 8:16 PM, Vinay Patil < > > vinay18.pa...@gmail.com> > > > > > wrote: > > > > > > > > > >> Hi , > > > > >> > > > > >> Actually I am only publishing 5 messages each to two different > kafka > > > > >> topics (using Junit), even if I keep the window to 500 seconds the > > > > result > > > > >> is same. > > > > >> > > > > >> I am not understanding why it is not sending the 5th element to > > > co-group > > > > >> operator even when the keys are same. > > > > >> > > > > >> I actually cannot share the actual client code. > > > > >> But this is what the streams look like : > > > > >> sourceStream.coGroup(destStream) > > > > >> here the sourceStream and destStream is actually > Tuple2<String,DTO> > > , > > > > and > > > > >> the ElementSelector returns tuple.f0 which is the key. > > > > >> > > > > >> I am generating the timestamp based on a field from the DTO which > is > > > > >> guaranteed to be in order. > > > > >> > > > > >> Will using the triggers help here ? > > > > >> > > > > >> > > > > >> Regards, > > > > >> Vinay Patil > > > > >> > > > > >> *+91-800-728-4749* > > > > >> > > > > >> On Mon, Jun 27, 2016 at 7:05 PM, Aljoscha Krettek < > > > aljos...@apache.org> > > > > >> wrote: > > > > >> > > > > >>> Hi, > > > > >>> what timestamps are you assigning? Is it guaranteed that all of > > them > > > > >>> would > > > > >>> fall into the same 30 second window? > > > > >>> > > > > >>> The issue with duplicate printing in the ElementSelector is > > strange? > > > > >>> Could > > > > >>> you post a more complete code example so that I can reproduce the > > > > >>> problem? > > > > >>> > > > > >>> Cheers, > > > > >>> Aljoscha > > > > >>> > > > > >>> On Mon, 27 Jun 2016 at 13:21 Vinay Patil < > vinay18.pa...@gmail.com> > > > > >>> wrote: > > > > >>> > > > > >>> > Hi , > > > > >>> > > > > > >>> > I am able to get the matching and non-matching elements. > > > > >>> > > > > > >>> > However when I am unit testing the code , I am getting one > record > > > > less > > > > >>> > inside the overriden cogroup function. > > > > >>> > Testing the following way : > > > > >>> > > > > > >>> > 1) Insert 5 messages into local kafka topic (test1) > > > > >>> > 2) Insert different 5 messages into local kafka topic (test2) > > > > >>> > 3) Consume 1) and 2) and I have two different kafka streams > > > > >>> > 4) Generate ascending timestamp(using Event Time) for both > > streams > > > > and > > > > >>> > create key(String) > > > > >>> > > > > > >>> > Now till 4) I am able to get all the records (checked by > printing > > > the > > > > >>> > stream in text file) > > > > >>> > > > > > >>> > However when I send the stream to co-group operator, I am > > receiving > > > > one > > > > >>> > less record, using the following syntax: > > > > >>> > > > > > >>> > sourceStream.coGroup(destStream) > > > > >>> > .where(new ElementSelector()) > > > > >>> > .equalTo(new ElementSelector()) > > > > >>> > .window(TumblingEventTimeWindows.of(Time.seconds(30))) > > > > >>> > .apply(new JoinStreams); > > > > >>> > > > > > >>> > Also in the Element Selector I have inserted a sysout, I am > > getting > > > > 20 > > > > >>> > sysouts instead of 10 (10 sysouts for source and 10 for dest > > > stream) > > > > >>> > > > > > >>> > Unable to understand why one record is coming less to co-group > > > > >>> > > > > > >>> > > > > > >>> > > > > > >>> > Regards, > > > > >>> > Vinay Patil > > > > >>> > > > > > >>> > On Wed, Jun 15, 2016 at 1:39 PM, Fabian Hueske < > > fhue...@gmail.com> > > > > >>> wrote: > > > > >>> > > > > > >>> > > Can you add a flag to each element emitted by the > > CoGroupFunction > > > > >>> that > > > > >>> > > indicates whether it was joined or not? > > > > >>> > > Then you can use split to distinguish between both cases and > > > handle > > > > >>> both > > > > >>> > > streams differently. > > > > >>> > > > > > > >>> > > Best, Fabian > > > > >>> > > > > > > >>> > > 2016-06-15 6:45 GMT+02:00 Vinay Patil < > vinay18.pa...@gmail.com > > >: > > > > >>> > > > > > > >>> > > > Hi Jark, > > > > >>> > > > > > > > >>> > > > I am able to get the non-matching elements in a stream :, > > > > >>> > > > > > > > >>> > > > Of-course we can collect the matching elements in the same > > > stream > > > > >>> as > > > > >>> > > well, > > > > >>> > > > however I want to perform additional operations on the > joined > > > > >>> stream > > > > >>> > > before > > > > >>> > > > writing it to S3, so I would have to include a separate > join > > > > >>> operator > > > > >>> > for > > > > >>> > > > the same two streams, right ? > > > > >>> > > > Correct me if I am wrong. > > > > >>> > > > > > > > >>> > > > I have pasted the dummy code which collects the > non-matching > > > > >>> records (i > > > > >>> > > > have to perform this on the actual data, correct me if I am > > > dong > > > > >>> > wrong). > > > > >>> > > > > > > > >>> > > > sourceStream.coGroup(destStream).where(new > > > > >>> > ElementSelector()).equalTo(new > > > > >>> > > > ElementSelector()) > > > > >>> > > > .window(TumblingEventTimeWindows.of(Time.seconds(30))) > > > > >>> > > > .apply(new CoGroupFunction<Integer, Integer, Integer>() { > > > > >>> > > > > > > > >>> > > > private static final long serialVersionUID = > > > > 6408179761497497475L; > > > > >>> > > > > > > > >>> > > > @Override > > > > >>> > > > public void coGroup(Iterable<Integer> paramIterable, > > > > >>> Iterable<Integer> > > > > >>> > > > paramIterable1, > > > > >>> > > > Collector<Integer> paramCollector) throws Exception { > > > > >>> > > > long exactSizeIfKnown = > > > > >>> > > paramIterable.spliterator().getExactSizeIfKnown(); > > > > >>> > > > long exactSizeIfKnown2 = > > > > >>> > > > paramIterable1.spliterator().getExactSizeIfKnown(); > > > > >>> > > > if(exactSizeIfKnown == 0 ) { > > > > >>> > > > paramCollector.collect(paramIterable1.iterator().next()); > > > > >>> > > > } else if (exactSizeIfKnown2 == 0) { > > > > >>> > > > paramCollector.collect(paramIterable.iterator().next()); > > > > >>> > > > } > > > > >>> > > > } > > > > >>> > > > }).print(); > > > > >>> > > > > > > > >>> > > > Regards, > > > > >>> > > > Vinay Patil > > > > >>> > > > > > > > >>> > > > > > > > >>> > > > On Tue, Jun 14, 2016 at 1:37 PM, Vinay Patil < > > > > >>> vinay18.pa...@gmail.com> > > > > >>> > > > wrote: > > > > >>> > > > > > > > >>> > > > > You are right, debugged it for all elements , I can do > that > > > > now. > > > > >>> > > > > Thanks a lot. > > > > >>> > > > > > > > > >>> > > > > Regards, > > > > >>> > > > > Vinay Patil > > > > >>> > > > > > > > > >>> > > > > On Tue, Jun 14, 2016 at 11:56 AM, Jark Wu < > > > > >>> > wuchong...@alibaba-inc.com> > > > > >>> > > > > wrote: > > > > >>> > > > > > > > > >>> > > > >> In `coGroup(Iterable<Integer> iter1, Iterable<Integer> > > > iter2, > > > > >>> > > > >> Collector<Integer> out)` , when both iter1 and iter2 > are > > > not > > > > >>> > empty, > > > > >>> > > it > > > > >>> > > > >> means they are matched elements from both stream. > > > > >>> > > > >> When one of iter1 and iter2 is empty , it means that > they > > > are > > > > >>> > > unmatched. > > > > >>> > > > >> > > > > >>> > > > >> > > > > >>> > > > >> - Jark Wu (wuchong) > > > > >>> > > > >> > > > > >>> > > > >> > 在 2016年6月14日，下午12:46，Vinay Patil < > > vinay18.pa...@gmail.com > > > > > > > > >>> 写道： > > > > >>> > > > >> > > > > > >>> > > > >> > Hi Matthias , > > > > >>> > > > >> > > > > > >>> > > > >> > I did not get you, even if we use Co-Group we have to > > > apply > > > > >>> it on > > > > >>> > a > > > > >>> > > > key > > > > >>> > > > >> > > > > > >>> > > > >> > sourceStream.coGroup(destStream) > > > > >>> > > > >> > .where(new ElementSelector()) > > > > >>> > > > >> > .equalTo(new ElementSelector()) > > > > >>> > > > >> > .window(TumblingEventTimeWindows.of(Time.seconds(30))) > > > > >>> > > > >> > .apply(new CoGroupFunction<Integer, Integer, > Integer>() > > { > > > > >>> > > > >> > private static final long serialVersionUID = > > > > >>> 6408179761497497475L; > > > > >>> > > > >> > > > > > >>> > > > >> > @Override > > > > >>> > > > >> > public void coGroup(Iterable<Integer> paramIterable, > > > > >>> > > Iterable<Integer> > > > > >>> > > > >> > paramIterable1, > > > > >>> > > > >> > Collector<Integer> paramCollector) throws Exception { > > > > >>> > > > >> > Iterator<Integer> iterator = paramIterable.iterator(); > > > > >>> > > > >> > while(iterator.hasNext()) { > > > > >>> > > > >> > } > > > > >>> > > > >> > } > > > > >>> > > > >> > }); > > > > >>> > > > >> > > > > > >>> > > > >> > when I debug this ,only the matched element from both > > > stream > > > > >>> will > > > > >>> > > come > > > > >>> > > > >> in > > > > >>> > > > >> > the coGroup function. > > > > >>> > > > >> > > > > > >>> > > > >> > What I want is how do I check for unmatched elements > > from > > > > both > > > > >>> > > streams > > > > >>> > > > >> and > > > > >>> > > > >> > write it to sink. > > > > >>> > > > >> > > > > > >>> > > > >> > Regards, > > > > >>> > > > >> > Vinay Patil > > > > >>> > > > >> > > > > > >>> > > > >> > *+91-800-728-4749* > > > > >>> > > > >> > > > > > >>> > > > >> > On Tue, Jun 14, 2016 at 2:07 AM, Matthias J. Sax < > > > > >>> > mj...@apache.org> > > > > >>> > > > >> wrote: > > > > >>> > > > >> > > > > > >>> > > > >> >> You need to do an outer-join. However, there is no > > > build-in > > > > >>> > support > > > > >>> > > > for > > > > >>> > > > >> >> outer-joins yet. > > > > >>> > > > >> >> > > > > >>> > > > >> >> You can use Window-CoGroup to implement the > outer-join > > as > > > > an > > > > >>> own > > > > >>> > > > >> operator. > > > > >>> > > > >> >> > > > > >>> > > > >> >> > > > > >>> > > > >> >> -Matthias > > > > >>> > > > >> >> > > > > >>> > > > >> >> On 06/13/2016 06:53 PM, Vinay Patil wrote: > > > > >>> > > > >> >>> Hi, > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> I have a question regarding the join operation, > > consider > > > > the > > > > >>> > > > following > > > > >>> > > > >> >>> dummy example: > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> StreamExecutionEnvironment env = > > > > >>> > > > >> >>> > StreamExecutionEnvironment.getExecutionEnvironment(); > > > > >>> > > > >> >>> > > > > >>> > > > env.setStreamTimeCharacteristic(TimeCharacteristic.IngestionTime); > > > > >>> > > > >> >>> DataStreamSource<Integer> sourceStream = > > > > >>> > > > >> >>> env.fromElements(10,20,23,25,30,33,102,18); > > > > >>> > > > >> >>> DataStreamSource<Integer> destStream = > > > > >>> > > > >> >> env.fromElements(20,30,40,50,60,10); > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> sourceStream.join(destStream) > > > > >>> > > > >> >>> .where(new ElementSelector()) > > > > >>> > > > >> >>> .equalTo(new ElementSelector()) > > > > >>> > > > >> >>> > > > > .window(TumblingEventTimeWindows.of(Time.milliseconds(10))) > > > > >>> > > > >> >>> .apply(new JoinFunction<Integer, Integer, > Integer>() { > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> private static final long serialVersionUID = 1L; > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> @Override > > > > >>> > > > >> >>> public Integer join(Integer paramIN1, Integer > > paramIN2) > > > > >>> throws > > > > >>> > > > >> Exception > > > > >>> > > > >> >> { > > > > >>> > > > >> >>> return paramIN1; > > > > >>> > > > >> >>> } > > > > >>> > > > >> >>> }).print(); > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> I perfectly get the elements that are matching in > both > > > the > > > > >>> > > streams, > > > > >>> > > > >> >> however > > > > >>> > > > >> >>> my requirement is to write these matched elements > and > > > also > > > > >>> the > > > > >>> > > > >> unmatched > > > > >>> > > > >> >>> elements to sink(S3) > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> How do I get the unmatched elements from each > stream ? > > > > >>> > > > >> >>> > > > > >>> > > > >> >>> Regards, > > > > >>> > > > >> >>> Vinay Patil > > > > >>> > > > >> >>> > > > > >>> > > > >> >> > > > > >>> > > > >> >> > > > > >>> > > > >> > > > > >>> > > > >> > > > > >>> > > > > > > > > >>> > > > > > > > >>> > > > > > > >>> > > > > > >>> > > > > >> > > > > >> > > > > > > > > > > > > > > >
