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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: [Discussion] Query regarding Join and Windows
Date Thu, 30 Jun 2016 08:41:31 GMT
Hi,
I think the problem is that the DeltaFunction needs to have this signature:

DeltaFunction<CoGroupedStreams.TaggedUnion<Tuple2<String,DTO>,
Tuple2<String,DTO>>>

because the Trigger will see elements from both input streams which are
represented as a TaggedUnion that can contain an element from either side.

May I ask why you want to use the DeltaTrigger?

Cheers,
Aljoscha

On Wed, 29 Jun 2016 at 19:06 Vinay Patil <vinay18.patil@gmail.com> wrote:

> Hi,
>
> Yes , now I am getting clear with the concepts here.
> One last thing I want to try before going for custom trigger, I want to try
> Delta Trigger but I am not able to get the syntax right , this is how I am
> trying it :
>
> TypeInformation<Tuple2<String, DTO>> typeInfo = TypeInformation.of(new
> TypeHint<Tuple2<String, DTO>>() {
> });
> // source and destStream : Tuple2<String,DTO>
> sourceStream.coGroup(destStream).where(new ElementSelector()).equalTo(new
> ElementSelector())
> .window(TumblingTimeEventWindows.of(Time.seconds(10)))
> .trigger(DeltaTrigger.of(triggerMeters,
> new DeltaFunction<Tuple2<String,DTO>>() {
> private static final long serialVersionUID = 1L;
>
> @Override
> public double getDelta(
> Tuple2<String,DTO> oldDataPoint,
> Tuple2<String,DTO> newDataPoint) {
> return <some_val>;
> }
> }, typeInfo.createSerializer(env.getConfig()).apply(new JoinStreams());
>
> I am getting error cannot convert from DeltaTrigger to Trigger<? super
> CoGroupedStreams...
> What am I doing wrong here, I have referred the sample example.
>
> Regards,
> Vinay Patil
>
> On Wed, Jun 29, 2016 at 7:15 PM, Aljoscha Krettek <aljoscha@apache.org>
> wrote:
>
> > Hi,
> > you can use ingestion time if you don't care about the timestamps in your
> > events, yes. If elements from the two streams happen to arrive at such
> > times that they are not put into the same window then you won't get a
> > match, correct.
> >
> > Regarding ingestion time and out-of-order events. I think this section
> just
> > reiterates that when using ingestion time the inherent timestamps in your
> > events will not be considered and their order will not be respected.
> >
> > Regarding late data: right now, Flink always processes late data and it
> is
> > up to the Trigger to decide what to do with late data. You can implement
> > your custom trigger based on EventTimeTrigger that would immediately
> purge
> > a window when an element arrives that is later than an allowed amount of
> > lateness. In Flink 1.1 we will introduce a setting for windows that
> allows
> > to specify an allowed lateness. With this, late elements will be dropped
> > automatically. This feature is already available in the master, by the
> way.
> >
> > Cheers,
> > Aljoscha
> >
> > On Wed, 29 Jun 2016 at 14:14 Vinay Patil <vinay18.patil@gmail.com>
> wrote:
> >
> > > Hi,
> > >
> > > Ok.
> > > Inside the checkAndGetNextWatermark(lastElement, extractedTimestamp)
> > method
> > > both these parameters are coming same (timestamp value) , I was
> expecting
> > > last element timestamp value in the 1st param when I extract it.
> > >
> > > Lets say I decide to use IngestionTime (since I am getting accurate
> > results
> > > here for now), if the joining element of both streams are assigned to
> > > different windows , then it that case I will not get the match , right
> ?
> > >
> > > However in case of event time this guarantees to be in the same window
> > > since we are assigning the timestamp, correct me here.
> > >
> > >  According to documentation :
> > > * Ingestion Time programs cannot handle any out-of-order events or late
> > > data*
> > >
> > > In this context What do we mean by out-of-order events How does it know
> > > that the events are out of order, I mean on which parameter does it
> > decide
> > > that the events are out-of-order  ? As in case of event time we can say
> > the
> > > timestamps received are out of order.
> > >
> > > Late Data : does it have a threshold after which it does not accept
> late
> > > data ?
> > >
> > >
> > > Regards,
> > > Vinay Patil
> > >
> > > On Wed, Jun 29, 2016 at 5:15 PM, Aljoscha Krettek <aljoscha@apache.org
> >
> > > wrote:
> > >
> > > > Hi,
> > > > the element will be kept around indefinitely if no new watermark
> > arrives.
> > > >
> > > > I think the same problem will persist for
> > > AssignerWithPunctuatedWatermarks
> > > > since there you also might not get the required "last watermark" to
> > > trigger
> > > > processing of the last window.
> > > >
> > > > Cheers,
> > > > Aljoscha
> > > >
> > > > On Wed, 29 Jun 2016 at 13:18 Vinay Patil <vinay18.patil@gmail.com>
> > > wrote:
> > > >
> > > > > Hi Aljoscha,
> > > > >
> > > > > This clears a lot of doubts now.
> > > > > So now lets say the stream paused for a while or it stops
> completely
> > on
> > > > > Friday , let us assume that the last message did not get processed
> > and
> > > is
> > > > > kept in the internal buffers.
> > > > >
> > > > > So when the stream starts again on Monday , will it consider the
> last
> > > > > element that is in the internal buffer for processing ?
> > > > >  How much time the internal buffer can hold the data or will it
> flush
> > > the
> > > > > data after a threshold ?
> > > > >
> > > > > I have tried using AssignerWithPunctuatedWatermarks and generated
> the
> > > > > watermark for each event, still getting one record less.
> > > > >
> > > > >
> > > > > Regards,
> > > > > Vinay Patil
> > > > >
> > > > > On Wed, Jun 29, 2016 at 2:21 PM, Aljoscha Krettek <
> > aljoscha@apache.org
> > > >
> > > > > wrote:
> > > > >
> > > > > > Hi,
> > > > > > the reason why the last element might never be emitted is the way
> > the
> > > > > > ascending timestamp extractor works. I'll try and explain with an
> > > > > example.
> > > > > >
> > > > > > Let's say we have a window size of 2 milliseconds, elements
> arrive
> > > > > starting
> > > > > > with timestamp 0, window begin timestamp is inclusive, end
> > timestamp
> > > is
> > > > > > exclusive:
> > > > > >
> > > > > > Element 0, Timestamp 0 (at this point the watermark is -1)
> > > > > > Element 1, Timestamp 1 (at this point the watermark is 0)
> > > > > > Element 2, Timestamp 1 (at this point the watermark is still 0)
> > > > > > Element 3, Timestamp 2 (at this point the watermark is 1)
> > > > > >
> > > > > > now we can process the window (0, 2) because we know from the
> > > watermark
> > > > > > that no elements can arrive for that window anymore. The window
> > > > contains
> > > > > > elements 0,1,2
> > > > > >
> > > > > > Element 4, Timestamp 3 (at this point the watermark is 2)
> > > > > > Element 5, Timestamp 4 (at this point the watermark is 3)
> > > > > >
> > > > > > now we can process window (2, 4). The window contains elements
> 3,4.
> > > > > >
> > > > > > At this point, we have Element 5 sitting in internal buffers for
> > > window
> > > > > (4,
> > > > > > 6) but if we don't receive further elements the watermark will
> > never
> > > > > > advance and we will never process that window.
> > > > > >
> > > > > > If, however, we get new elements at some point the watermark
> > advances
> > > > and
> > > > > > we don't have a problem. That's what I meant when I said that you
> > > > > shouldn't
> > > > > > have a problem if data keeps continuously arriving.
> > > > > >
> > > > > > Cheers,
> > > > > > Aljoscha
> > > > > >
> > > > > >
> > > > > > On Tue, 28 Jun 2016 at 17:14 Vinay Patil <
> vinay18.patil@gmail.com>
> > > > > wrote:
> > > > > >
> > > > > > > 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 <
> > > > aljoscha@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.patil@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 <
> > > > > > aljoscha@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.patil@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.patil@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.patil@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 <
> > > > > > > > > > aljoscha@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.patil@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 <
> > > > > > > > > fhueske@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.patil@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.patil@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.wc@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.patil@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 <
> > > > > > > > > > > >>> > mjsax@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
> > > > > > > > > > > >>> > > > >> >>>
> > > > > > > > > > > >>> > > > >> >>
> > > > > > > > > > > >>> > > > >> >>
> > > > > > > > > > > >>> > > > >>
> > > > > > > > > > > >>> > > > >>
> > > > > > > > > > > >>> > > > >
> > > > > > > > > > > >>> > > >
> > > > > > > > > > > >>> > >
> > > > > > > > > > > >>> >
> > > > > > > > > > > >>>
> > > > > > > > > > > >>
> > > > > > > > > > > >>
> > > > > > > > > > > >
> > > > > > > > > > >
> > > > > > > > > >
> > > > > > > > >
> > > > > > > >
> > > > > > >
> > > > > >
> > > > >
> > > >
> > >
> >
>

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