Hi,
Just watched the video on Robust Stream Processing .
So when we say Window is a stateful operator , does it mean that even if
the task manager doing the window operation fails, will it pick up from
the state left earlier when it comes up ? (Have not read more on state for
now)
Also in one of our project when we deploy on cluster and check the Job
Graph , everything is shown in one box , why this happens ? Is it because
of chaining of streams ?
So the box here represent the function flow, right ?
Regards,
Vinay Patil
On Thu, Jun 30, 2016 at 7:29 PM, Vinay Patil <vinay18.pa...@gmail.com>
wrote:
> Hi Aljoscha,
>
> Just wanted to check if it works with it.
> Anyways to solve the problem what we have thought of is to push heartbeat
> message to Kafka after certain interval, so that we get continuous stream
> always and that edge case will never occur, right ?
>
> One more question I have regarding the failover case :
> Lets say I have a window of 10 secs , and in that there are e0 to en
> elements , what if during this time node goes down ?
> When the node comes up will it resume from the same state or will it
> resume from the last checkpointed state ?
>
> Can we explicitly checkpoint inside the window , may be at the start of
> the window or before we are applying window ?
>
>
> Regards,
> Vinay Patil
>
> On Thu, Jun 30, 2016 at 2:11 PM, Aljoscha Krettek <aljos...@apache.org>
> wrote:
>
>> 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.pa...@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 <aljos...@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.pa...@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 <
>> aljos...@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.pa...@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 <
>> > > aljos...@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.pa...@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 <
>> > > > > 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
>> > > > > > > > > > > > >>> > > > >> >>>
>> > > > > > > > > > > > >>> > > > >> >>
>> > > > > > > > > > > > >>> > > > >> >>
>> > > > > > > > > > > > >>> > > > >>
>> > > > > > > > > > > > >>> > > > >>
>> > > > > > > > > > > > >>> > > > >
>> > > > > > > > > > > > >>> > > >
>> > > > > > > > > > > > >>> > >
>> > > > > > > > > > > > >>> >
>> > > > > > > > > > > > >>>
>> > > > > > > > > > > > >>
>> > > > > > > > > > > > >>
>> > > > > > > > > > > > >
>> > > > > > > > > > > >
>> > > > > > > > > > >
>> > > > > > > > > >
>> > > > > > > > >
>> > > > > > > >
>> > > > > > >
>> > > > > >
>> > > > >
>> > > >
>> > >
>> >
>>
>
>
