Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

Thanks a lot. That's really help a lot !

Have a great vacation

Cheers

On Fri, Aug 21, 2015 at 7:47 PM, Gyula Fóra  wrote:

> Hi
>
> You are right, if all operators need continuous updates than the most
> straightforward way is to push all the updates to the operators like you
> described.
>
> If the cached data is the same for all operators and is small enough you
> can centralize the updates in a dedicated operator and push the updated
> data to the operators every once in a while.
>
> Cheers
> Gyula
>
>
>
> On Thu, Aug 20, 2015 at 4:31 PM Welly Tambunan  wrote:
>
>> Hi Gyula,
>>
>> I have a couple of operator on the pipeline. Filter, mapper, flatMap, and
>> each of these operator contains some cache data.
>>
>> So i think that means for every other operator on the pipeline, i will
>> need to add a new stream to update each cache data.
>>
>>
>> Cheers
>>
>> On Thu, Aug 20, 2015 at 5:33 PM, Gyula Fóra  wrote:
>>
>>> Hi,
>>>
>>> I don't think I fully understand your question, could you please try to
>>> be a little more specific?
>>>
>>> I assume by caching you mean that you keep the current model as an
>>> operator state. Why would you need to add new streams in this case?
>>>
>>> I might be slow to answer as I am currently on vacation without stable
>>> internet connection.
>>>
>>> Cheers,
>>> Gyula
>>>
>>> On Thu, Aug 20, 2015 at 5:36 AM Welly Tambunan 
>>> wrote:
>>>
 Hi Gyula,

 I have another question. So if i cache something on the operator, to
 keep it up to date,  i will always need to add and connect another stream
 of changes to the operator ?

 Is this right for every case ?

 Cheers

 On Wed, Aug 19, 2015 at 3:21 PM, Welly Tambunan 
 wrote:

> Hi Gyula,
>
> That's really helpful. The docs is improving so much since the last
> time (0.9).
>
> Thanks a lot !
>
> Cheers
>
> On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra 
> wrote:
>
>> Hey,
>>
>> If it is always better to check the events against a more up-to-date
>> model (even if the events we are checking arrived before the update) then
>> it is fine to keep the model outside of the system.
>>
>> In this case we need to make sure that we can push the updates to the
>> external system consistently. If you are using the PersistentKafkaSource
>> for instance it can happen that some messages are replayed in case of
>> failure. In this case you need to make sure that you remove duplicate
>> updates or have idempotent updates.
>>
>> You can read about the checkpoint mechanism in the Flink website:
>> https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html
>>
>> Cheers,
>> Gyula
>>
>> On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan 
>> wrote:
>>
>>> Thanks Gyula,
>>>
>>> Another question i have..
>>>
>>> > ... while external model updates would be *tricky *to keep
>>> consistent.
>>> Is that still the case if the Operator treat the external model as
>>> read-only ? We create another stream that will update the external model
>>> separately.
>>>
>>> Could you please elaborate more about this one ?
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra 
>>> wrote:
>>>
 In that case I would apply a map to wrap in some common type, like
 a n Either before the union.

 And then in the coflatmap you can unwrap it.
 On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
 wrote:

> Hi Gyula,
>
> Thanks.
>
> However update1 and update2 have a different type. Based on my
> understanding, i don't think we can use union. How can we handle this 
> one ?
>
> We like to create our event strongly type to get the domain
> language captured.
>
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
> wrote:
>
>> Hey,
>>
>> One input of your co-flatmap would be model updates and the other
>> input would be events to check against the model if I understand 
>> correctly.
>>
>> This means that if your model updates come from more than one
>> stream you need to union them into a single stream before connecting 
>> them
>> with the event stream and applying the coatmap.
>>
>> DataStream updates1 = 
>> DataStream updates2 = 
>> DataStream events = 
>>
>> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>>
>> Does this answer your question?
>>
>> Gyula
>>
>>
>> On Wednesday, August 19, 2015, Welly Tambunan 
>> wrote:
>>
>>> Hi Gyula,
>>>
>>

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

Hi

You are right, if all operators need continuous updates than the most
straightforward way is to push all the updates to the operators like you
described.

If the cached data is the same for all operators and is small enough you
can centralize the updates in a dedicated operator and push the updated
data to the operators every once in a while.

Cheers
Gyula


On Thu, Aug 20, 2015 at 4:31 PM Welly Tambunan  wrote:

> Hi Gyula,
>
> I have a couple of operator on the pipeline. Filter, mapper, flatMap, and
> each of these operator contains some cache data.
>
> So i think that means for every other operator on the pipeline, i will
> need to add a new stream to update each cache data.
>
>
> Cheers
>
> On Thu, Aug 20, 2015 at 5:33 PM, Gyula Fóra  wrote:
>
>> Hi,
>>
>> I don't think I fully understand your question, could you please try to
>> be a little more specific?
>>
>> I assume by caching you mean that you keep the current model as an
>> operator state. Why would you need to add new streams in this case?
>>
>> I might be slow to answer as I am currently on vacation without stable
>> internet connection.
>>
>> Cheers,
>> Gyula
>>
>> On Thu, Aug 20, 2015 at 5:36 AM Welly Tambunan  wrote:
>>
>>> Hi Gyula,
>>>
>>> I have another question. So if i cache something on the operator, to
>>> keep it up to date,  i will always need to add and connect another stream
>>> of changes to the operator ?
>>>
>>> Is this right for every case ?
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 3:21 PM, Welly Tambunan 
>>> wrote:
>>>
 Hi Gyula,

 That's really helpful. The docs is improving so much since the last
 time (0.9).

 Thanks a lot !

 Cheers

 On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra 
 wrote:

> Hey,
>
> If it is always better to check the events against a more up-to-date
> model (even if the events we are checking arrived before the update) then
> it is fine to keep the model outside of the system.
>
> In this case we need to make sure that we can push the updates to the
> external system consistently. If you are using the PersistentKafkaSource
> for instance it can happen that some messages are replayed in case of
> failure. In this case you need to make sure that you remove duplicate
> updates or have idempotent updates.
>
> You can read about the checkpoint mechanism in the Flink website:
> https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html
>
> Cheers,
> Gyula
>
> On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan 
> wrote:
>
>> Thanks Gyula,
>>
>> Another question i have..
>>
>> > ... while external model updates would be *tricky *to keep
>> consistent.
>> Is that still the case if the Operator treat the external model as
>> read-only ? We create another stream that will update the external model
>> separately.
>>
>> Could you please elaborate more about this one ?
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra 
>> wrote:
>>
>>> In that case I would apply a map to wrap in some common type, like a
>>> n Either before the union.
>>>
>>> And then in the coflatmap you can unwrap it.
>>> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
>>> wrote:
>>>
 Hi Gyula,

 Thanks.

 However update1 and update2 have a different type. Based on my
 understanding, i don't think we can use union. How can we handle this 
 one ?

 We like to create our event strongly type to get the domain
 language captured.


 Cheers

 On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
 wrote:

> Hey,
>
> One input of your co-flatmap would be model updates and the other
> input would be events to check against the model if I understand 
> correctly.
>
> This means that if your model updates come from more than one
> stream you need to union them into a single stream before connecting 
> them
> with the event stream and applying the coatmap.
>
> DataStream updates1 = 
> DataStream updates2 = 
> DataStream events = 
>
> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>
> Does this answer your question?
>
> Gyula
>
>
> On Wednesday, August 19, 2015, Welly Tambunan 
> wrote:
>
>> Hi Gyula,
>>
>> Thanks for your response.
>>
>> However the model can received multiple event for update. How can
>> we do that with co-flatmap as i can see the connect API only received
>> single datastream ?
>>
>>
>> > ... while external model updates would be tricky t

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

I have a couple of operator on the pipeline. Filter, mapper, flatMap, and
each of these operator contains some cache data.

So i think that means for every other operator on the pipeline, i will need
to add a new stream to update each cache data.


Cheers

On Thu, Aug 20, 2015 at 5:33 PM, Gyula Fóra  wrote:

> Hi,
>
> I don't think I fully understand your question, could you please try to be
> a little more specific?
>
> I assume by caching you mean that you keep the current model as an
> operator state. Why would you need to add new streams in this case?
>
> I might be slow to answer as I am currently on vacation without stable
> internet connection.
>
> Cheers,
> Gyula
>
> On Thu, Aug 20, 2015 at 5:36 AM Welly Tambunan  wrote:
>
>> Hi Gyula,
>>
>> I have another question. So if i cache something on the operator, to keep
>> it up to date,  i will always need to add and connect another stream of
>> changes to the operator ?
>>
>> Is this right for every case ?
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 3:21 PM, Welly Tambunan 
>> wrote:
>>
>>> Hi Gyula,
>>>
>>> That's really helpful. The docs is improving so much since the last time
>>> (0.9).
>>>
>>> Thanks a lot !
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra 
>>> wrote:
>>>
 Hey,

 If it is always better to check the events against a more up-to-date
 model (even if the events we are checking arrived before the update) then
 it is fine to keep the model outside of the system.

 In this case we need to make sure that we can push the updates to the
 external system consistently. If you are using the PersistentKafkaSource
 for instance it can happen that some messages are replayed in case of
 failure. In this case you need to make sure that you remove duplicate
 updates or have idempotent updates.

 You can read about the checkpoint mechanism in the Flink website:
 https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html

 Cheers,
 Gyula

 On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan 
 wrote:

> Thanks Gyula,
>
> Another question i have..
>
> > ... while external model updates would be *tricky *to keep
> consistent.
> Is that still the case if the Operator treat the external model as
> read-only ? We create another stream that will update the external model
> separately.
>
> Could you please elaborate more about this one ?
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra 
> wrote:
>
>> In that case I would apply a map to wrap in some common type, like a
>> n Either before the union.
>>
>> And then in the coflatmap you can unwrap it.
>> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
>> wrote:
>>
>>> Hi Gyula,
>>>
>>> Thanks.
>>>
>>> However update1 and update2 have a different type. Based on my
>>> understanding, i don't think we can use union. How can we handle this 
>>> one ?
>>>
>>> We like to create our event strongly type to get the domain language
>>> captured.
>>>
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
>>> wrote:
>>>
 Hey,

 One input of your co-flatmap would be model updates and the other
 input would be events to check against the model if I understand 
 correctly.

 This means that if your model updates come from more than one
 stream you need to union them into a single stream before connecting 
 them
 with the event stream and applying the coatmap.

 DataStream updates1 = 
 DataStream updates2 = 
 DataStream events = 

 events.connect(updates1.union(updates2).broadcast()).flatMap(...)

 Does this answer your question?

 Gyula


 On Wednesday, August 19, 2015, Welly Tambunan 
 wrote:

> Hi Gyula,
>
> Thanks for your response.
>
> However the model can received multiple event for update. How can
> we do that with co-flatmap as i can see the connect API only received
> single datastream ?
>
>
> > ... while external model updates would be tricky to keep
> consistent.
> Is that still the case if the Operator treat the external model as
> read-only ? We create another stream that will update the external 
> model
> separately.
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra 
> wrote:
>
>> Hey!
>>
>> I think it is safe to say that the best approach in this case is
>> creating a co-flatmap that will receive updates on one input. The 
>> events
>> should probably be broadcast

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

Hi,

I don't think I fully understand your question, could you please try to be
a little more specific?

I assume by caching you mean that you keep the current model as an operator
state. Why would you need to add new streams in this case?

I might be slow to answer as I am currently on vacation without stable
internet connection.

Cheers,
Gyula
On Thu, Aug 20, 2015 at 5:36 AM Welly Tambunan  wrote:

> Hi Gyula,
>
> I have another question. So if i cache something on the operator, to keep
> it up to date,  i will always need to add and connect another stream of
> changes to the operator ?
>
> Is this right for every case ?
>
> Cheers
>
> On Wed, Aug 19, 2015 at 3:21 PM, Welly Tambunan  wrote:
>
>> Hi Gyula,
>>
>> That's really helpful. The docs is improving so much since the last time
>> (0.9).
>>
>> Thanks a lot !
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra  wrote:
>>
>>> Hey,
>>>
>>> If it is always better to check the events against a more up-to-date
>>> model (even if the events we are checking arrived before the update) then
>>> it is fine to keep the model outside of the system.
>>>
>>> In this case we need to make sure that we can push the updates to the
>>> external system consistently. If you are using the PersistentKafkaSource
>>> for instance it can happen that some messages are replayed in case of
>>> failure. In this case you need to make sure that you remove duplicate
>>> updates or have idempotent updates.
>>>
>>> You can read about the checkpoint mechanism in the Flink website:
>>> https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html
>>>
>>> Cheers,
>>> Gyula
>>>
>>> On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan 
>>> wrote:
>>>
 Thanks Gyula,

 Another question i have..

 > ... while external model updates would be *tricky *to keep
 consistent.
 Is that still the case if the Operator treat the external model as
 read-only ? We create another stream that will update the external model
 separately.

 Could you please elaborate more about this one ?

 Cheers

 On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra 
 wrote:

> In that case I would apply a map to wrap in some common type, like a n
> Either before the union.
>
> And then in the coflatmap you can unwrap it.
> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
> wrote:
>
>> Hi Gyula,
>>
>> Thanks.
>>
>> However update1 and update2 have a different type. Based on my
>> understanding, i don't think we can use union. How can we handle this 
>> one ?
>>
>> We like to create our event strongly type to get the domain language
>> captured.
>>
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
>> wrote:
>>
>>> Hey,
>>>
>>> One input of your co-flatmap would be model updates and the other
>>> input would be events to check against the model if I understand 
>>> correctly.
>>>
>>> This means that if your model updates come from more than one stream
>>> you need to union them into a single stream before connecting them with 
>>> the
>>> event stream and applying the coatmap.
>>>
>>> DataStream updates1 = 
>>> DataStream updates2 = 
>>> DataStream events = 
>>>
>>> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>>>
>>> Does this answer your question?
>>>
>>> Gyula
>>>
>>>
>>> On Wednesday, August 19, 2015, Welly Tambunan 
>>> wrote:
>>>
 Hi Gyula,

 Thanks for your response.

 However the model can received multiple event for update. How can
 we do that with co-flatmap as i can see the connect API only received
 single datastream ?


 > ... while external model updates would be tricky to keep
 consistent.
 Is that still the case if the Operator treat the external model as
 read-only ? We create another stream that will update the external 
 model
 separately.

 Cheers

 On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra 
 wrote:

> Hey!
>
> I think it is safe to say that the best approach in this case is
> creating a co-flatmap that will receive updates on one input. The 
> events
> should probably be broadcasted in this case so you can check in 
> parallel.
>
> This approach can be used effectively with Flink's checkpoint
> mechanism, while external model updates would be tricky to keep 
> consistent.
>
> Cheers,
> Gyula
>
>
>
>
> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
> wrote:
>
>> Hi All,
>>
>> We have a streaming computation that required 

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

I have another question. So if i cache something on the operator, to keep
it up to date,  i will always need to add and connect another stream of
changes to the operator ?

Is this right for every case ?

Cheers

On Wed, Aug 19, 2015 at 3:21 PM, Welly Tambunan  wrote:

> Hi Gyula,
>
> That's really helpful. The docs is improving so much since the last time
> (0.9).
>
> Thanks a lot !
>
> Cheers
>
> On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra  wrote:
>
>> Hey,
>>
>> If it is always better to check the events against a more up-to-date
>> model (even if the events we are checking arrived before the update) then
>> it is fine to keep the model outside of the system.
>>
>> In this case we need to make sure that we can push the updates to the
>> external system consistently. If you are using the PersistentKafkaSource
>> for instance it can happen that some messages are replayed in case of
>> failure. In this case you need to make sure that you remove duplicate
>> updates or have idempotent updates.
>>
>> You can read about the checkpoint mechanism in the Flink website:
>> https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html
>>
>> Cheers,
>> Gyula
>>
>> On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan  wrote:
>>
>>> Thanks Gyula,
>>>
>>> Another question i have..
>>>
>>> > ... while external model updates would be *tricky *to keep consistent.
>>>
>>> Is that still the case if the Operator treat the external model as
>>> read-only ? We create another stream that will update the external model
>>> separately.
>>>
>>> Could you please elaborate more about this one ?
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra 
>>> wrote:
>>>
 In that case I would apply a map to wrap in some common type, like a n
 Either before the union.

 And then in the coflatmap you can unwrap it.
 On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
 wrote:

> Hi Gyula,
>
> Thanks.
>
> However update1 and update2 have a different type. Based on my
> understanding, i don't think we can use union. How can we handle this one 
> ?
>
> We like to create our event strongly type to get the domain language
> captured.
>
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
> wrote:
>
>> Hey,
>>
>> One input of your co-flatmap would be model updates and the other
>> input would be events to check against the model if I understand 
>> correctly.
>>
>> This means that if your model updates come from more than one stream
>> you need to union them into a single stream before connecting them with 
>> the
>> event stream and applying the coatmap.
>>
>> DataStream updates1 = 
>> DataStream updates2 = 
>> DataStream events = 
>>
>> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>>
>> Does this answer your question?
>>
>> Gyula
>>
>>
>> On Wednesday, August 19, 2015, Welly Tambunan 
>> wrote:
>>
>>> Hi Gyula,
>>>
>>> Thanks for your response.
>>>
>>> However the model can received multiple event for update. How can we
>>> do that with co-flatmap as i can see the connect API only received 
>>> single
>>> datastream ?
>>>
>>>
>>> > ... while external model updates would be tricky to keep
>>> consistent.
>>> Is that still the case if the Operator treat the external model as
>>> read-only ? We create another stream that will update the external model
>>> separately.
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra 
>>> wrote:
>>>
 Hey!

 I think it is safe to say that the best approach in this case is
 creating a co-flatmap that will receive updates on one input. The 
 events
 should probably be broadcasted in this case so you can check in 
 parallel.

 This approach can be used effectively with Flink's checkpoint
 mechanism, while external model updates would be tricky to keep 
 consistent.

 Cheers,
 Gyula




 On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
 wrote:

> Hi All,
>
> We have a streaming computation that required to validate the data
> stream against the model provided by the user.
>
> Right now what I have done is to load the model into flink
> operator and then validate against it. However the model can be 
> updated and
> changed frequently. Fortunately we always publish this event to 
> RabbitMQ.
>
> I think we can
>
>
>1. Create RabbitMq listener for model changed event from
>inside the operator, then update the model if event arrived.
>
>  

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

That's really helpful. The docs is improving so much since the last time
(0.9).

Thanks a lot !

Cheers

On Wed, Aug 19, 2015 at 3:07 PM, Gyula Fóra  wrote:

> Hey,
>
> If it is always better to check the events against a more up-to-date model
> (even if the events we are checking arrived before the update) then it is
> fine to keep the model outside of the system.
>
> In this case we need to make sure that we can push the updates to the
> external system consistently. If you are using the PersistentKafkaSource
> for instance it can happen that some messages are replayed in case of
> failure. In this case you need to make sure that you remove duplicate
> updates or have idempotent updates.
>
> You can read about the checkpoint mechanism in the Flink website:
> https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html
>
> Cheers,
> Gyula
>
> On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan  wrote:
>
>> Thanks Gyula,
>>
>> Another question i have..
>>
>> > ... while external model updates would be *tricky *to keep consistent.
>> Is that still the case if the Operator treat the external model as
>> read-only ? We create another stream that will update the external model
>> separately.
>>
>> Could you please elaborate more about this one ?
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra  wrote:
>>
>>> In that case I would apply a map to wrap in some common type, like a n
>>> Either before the union.
>>>
>>> And then in the coflatmap you can unwrap it.
>>> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan 
>>> wrote:
>>>
 Hi Gyula,

 Thanks.

 However update1 and update2 have a different type. Based on my
 understanding, i don't think we can use union. How can we handle this one ?

 We like to create our event strongly type to get the domain language
 captured.


 Cheers

 On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
 wrote:

> Hey,
>
> One input of your co-flatmap would be model updates and the other
> input would be events to check against the model if I understand 
> correctly.
>
> This means that if your model updates come from more than one stream
> you need to union them into a single stream before connecting them with 
> the
> event stream and applying the coatmap.
>
> DataStream updates1 = 
> DataStream updates2 = 
> DataStream events = 
>
> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>
> Does this answer your question?
>
> Gyula
>
>
> On Wednesday, August 19, 2015, Welly Tambunan 
> wrote:
>
>> Hi Gyula,
>>
>> Thanks for your response.
>>
>> However the model can received multiple event for update. How can we
>> do that with co-flatmap as i can see the connect API only received single
>> datastream ?
>>
>>
>> > ... while external model updates would be tricky to keep
>> consistent.
>> Is that still the case if the Operator treat the external model as
>> read-only ? We create another stream that will update the external model
>> separately.
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra 
>> wrote:
>>
>>> Hey!
>>>
>>> I think it is safe to say that the best approach in this case is
>>> creating a co-flatmap that will receive updates on one input. The events
>>> should probably be broadcasted in this case so you can check in 
>>> parallel.
>>>
>>> This approach can be used effectively with Flink's checkpoint
>>> mechanism, while external model updates would be tricky to keep 
>>> consistent.
>>>
>>> Cheers,
>>> Gyula
>>>
>>>
>>>
>>>
>>> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
>>> wrote:
>>>
 Hi All,

 We have a streaming computation that required to validate the data
 stream against the model provided by the user.

 Right now what I have done is to load the model into flink operator
 and then validate against it. However the model can be updated and 
 changed
 frequently. Fortunately we always publish this event to RabbitMQ.

 I think we can


1. Create RabbitMq listener for model changed event from inside
the operator, then update the model if event arrived.

But i think this will create race condition if not handle
correctly and it seems odd to keep this

2. We can move the model into external in external memory cache
storage and keep the model up to date using flink. So the operator 
 will
retrieve that from memory cache

3. Create two stream and using co operator for managing the
shared state.


 What is your s

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

Hey,

If it is always better to check the events against a more up-to-date model
(even if the events we are checking arrived before the update) then it is
fine to keep the model outside of the system.

In this case we need to make sure that we can push the updates to the
external system consistently. If you are using the PersistentKafkaSource
for instance it can happen that some messages are replayed in case of
failure. In this case you need to make sure that you remove duplicate
updates or have idempotent updates.

You can read about the checkpoint mechanism in the Flink website:
https://ci.apache.org/projects/flink/flink-docs-master/internals/stream_checkpointing.html

Cheers,
Gyula
On Wed, Aug 19, 2015 at 9:56 AM Welly Tambunan  wrote:

> Thanks Gyula,
>
> Another question i have..
>
> > ... while external model updates would be *tricky *to keep consistent.
> Is that still the case if the Operator treat the external model as
> read-only ? We create another stream that will update the external model
> separately.
>
> Could you please elaborate more about this one ?
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra  wrote:
>
>> In that case I would apply a map to wrap in some common type, like a n
>> Either before the union.
>>
>> And then in the coflatmap you can unwrap it.
>> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan  wrote:
>>
>>> Hi Gyula,
>>>
>>> Thanks.
>>>
>>> However update1 and update2 have a different type. Based on my
>>> understanding, i don't think we can use union. How can we handle this one ?
>>>
>>> We like to create our event strongly type to get the domain language
>>> captured.
>>>
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra 
>>> wrote:
>>>
 Hey,

 One input of your co-flatmap would be model updates and the other input
 would be events to check against the model if I understand correctly.

 This means that if your model updates come from more than one stream
 you need to union them into a single stream before connecting them with the
 event stream and applying the coatmap.

 DataStream updates1 = 
 DataStream updates2 = 
 DataStream events = 

 events.connect(updates1.union(updates2).broadcast()).flatMap(...)

 Does this answer your question?

 Gyula


 On Wednesday, August 19, 2015, Welly Tambunan 
 wrote:

> Hi Gyula,
>
> Thanks for your response.
>
> However the model can received multiple event for update. How can we
> do that with co-flatmap as i can see the connect API only received single
> datastream ?
>
>
> > ... while external model updates would be tricky to keep consistent.
>
> Is that still the case if the Operator treat the external model as
> read-only ? We create another stream that will update the external model
> separately.
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  wrote:
>
>> Hey!
>>
>> I think it is safe to say that the best approach in this case is
>> creating a co-flatmap that will receive updates on one input. The events
>> should probably be broadcasted in this case so you can check in parallel.
>>
>> This approach can be used effectively with Flink's checkpoint
>> mechanism, while external model updates would be tricky to keep 
>> consistent.
>>
>> Cheers,
>> Gyula
>>
>>
>>
>>
>> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
>> wrote:
>>
>>> Hi All,
>>>
>>> We have a streaming computation that required to validate the data
>>> stream against the model provided by the user.
>>>
>>> Right now what I have done is to load the model into flink operator
>>> and then validate against it. However the model can be updated and 
>>> changed
>>> frequently. Fortunately we always publish this event to RabbitMQ.
>>>
>>> I think we can
>>>
>>>
>>>1. Create RabbitMq listener for model changed event from inside
>>>the operator, then update the model if event arrived.
>>>
>>>But i think this will create race condition if not handle
>>>correctly and it seems odd to keep this
>>>
>>>2. We can move the model into external in external memory cache
>>>storage and keep the model up to date using flink. So the operator 
>>> will
>>>retrieve that from memory cache
>>>
>>>3. Create two stream and using co operator for managing the
>>>shared state.
>>>
>>>
>>> What is your suggestion on keeping the state up to date from
>>> external event ? Is there some kind of best practice for maintaining 
>>> model
>>> up to date on streaming operator ?
>>>
>>> Thanks a lot
>>>
>>>
>>> Cheers
>>>
>>>
>>> --
>>> Welly Tambunan
>>> Triplelands
>>>
>>> http://weltam.wordpress.com
>>> h

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Thanks Gyula,

Another question i have..

> ... while external model updates would be *tricky *to keep consistent.
Is that still the case if the Operator treat the external model as
read-only ? We create another stream that will update the external model
separately.

Could you please elaborate more about this one ?

Cheers

On Wed, Aug 19, 2015 at 2:52 PM, Gyula Fóra  wrote:

> In that case I would apply a map to wrap in some common type, like a n
> Either before the union.
>
> And then in the coflatmap you can unwrap it.
> On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan  wrote:
>
>> Hi Gyula,
>>
>> Thanks.
>>
>> However update1 and update2 have a different type. Based on my
>> understanding, i don't think we can use union. How can we handle this one ?
>>
>> We like to create our event strongly type to get the domain language
>> captured.
>>
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra  wrote:
>>
>>> Hey,
>>>
>>> One input of your co-flatmap would be model updates and the other input
>>> would be events to check against the model if I understand correctly.
>>>
>>> This means that if your model updates come from more than one stream you
>>> need to union them into a single stream before connecting them with the
>>> event stream and applying the coatmap.
>>>
>>> DataStream updates1 = 
>>> DataStream updates2 = 
>>> DataStream events = 
>>>
>>> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>>>
>>> Does this answer your question?
>>>
>>> Gyula
>>>
>>>
>>> On Wednesday, August 19, 2015, Welly Tambunan  wrote:
>>>
 Hi Gyula,

 Thanks for your response.

 However the model can received multiple event for update. How can we do
 that with co-flatmap as i can see the connect API only received single
 datastream ?


 > ... while external model updates would be tricky to keep consistent.
 Is that still the case if the Operator treat the external model as
 read-only ? We create another stream that will update the external model
 separately.

 Cheers

 On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  wrote:

> Hey!
>
> I think it is safe to say that the best approach in this case is
> creating a co-flatmap that will receive updates on one input. The events
> should probably be broadcasted in this case so you can check in parallel.
>
> This approach can be used effectively with Flink's checkpoint
> mechanism, while external model updates would be tricky to keep 
> consistent.
>
> Cheers,
> Gyula
>
>
>
>
> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
> wrote:
>
>> Hi All,
>>
>> We have a streaming computation that required to validate the data
>> stream against the model provided by the user.
>>
>> Right now what I have done is to load the model into flink operator
>> and then validate against it. However the model can be updated and 
>> changed
>> frequently. Fortunately we always publish this event to RabbitMQ.
>>
>> I think we can
>>
>>
>>1. Create RabbitMq listener for model changed event from inside
>>the operator, then update the model if event arrived.
>>
>>But i think this will create race condition if not handle
>>correctly and it seems odd to keep this
>>
>>2. We can move the model into external in external memory cache
>>storage and keep the model up to date using flink. So the operator 
>> will
>>retrieve that from memory cache
>>
>>3. Create two stream and using co operator for managing the
>>shared state.
>>
>>
>> What is your suggestion on keeping the state up to date from external
>> event ? Is there some kind of best practice for maintaining model up to
>> date on streaming operator ?
>>
>> Thanks a lot
>>
>>
>> Cheers
>>
>>
>> --
>> Welly Tambunan
>> Triplelands
>>
>> http://weltam.wordpress.com
>> http://www.triplelands.com 
>>
>


 --
 Welly Tambunan
 Triplelands

 http://weltam.wordpress.com
 http://www.triplelands.com 

>>>
>>
>>
>> --
>> Welly Tambunan
>> Triplelands
>>
>> http://weltam.wordpress.com
>> http://www.triplelands.com 
>>
>


-- 
Welly Tambunan
Triplelands

http://weltam.wordpress.com
http://www.triplelands.com 

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

In that case I would apply a map to wrap in some common type, like a n
Either before the union.

And then in the coflatmap you can unwrap it.
On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan  wrote:

> Hi Gyula,
>
> Thanks.
>
> However update1 and update2 have a different type. Based on my
> understanding, i don't think we can use union. How can we handle this one ?
>
> We like to create our event strongly type to get the domain language
> captured.
>
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra  wrote:
>
>> Hey,
>>
>> One input of your co-flatmap would be model updates and the other input
>> would be events to check against the model if I understand correctly.
>>
>> This means that if your model updates come from more than one stream you
>> need to union them into a single stream before connecting them with the
>> event stream and applying the coatmap.
>>
>> DataStream updates1 = 
>> DataStream updates2 = 
>> DataStream events = 
>>
>> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>>
>> Does this answer your question?
>>
>> Gyula
>>
>>
>> On Wednesday, August 19, 2015, Welly Tambunan  wrote:
>>
>>> Hi Gyula,
>>>
>>> Thanks for your response.
>>>
>>> However the model can received multiple event for update. How can we do
>>> that with co-flatmap as i can see the connect API only received single
>>> datastream ?
>>>
>>>
>>> > ... while external model updates would be tricky to keep consistent.
>>> Is that still the case if the Operator treat the external model as
>>> read-only ? We create another stream that will update the external model
>>> separately.
>>>
>>> Cheers
>>>
>>> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  wrote:
>>>
 Hey!

 I think it is safe to say that the best approach in this case is
 creating a co-flatmap that will receive updates on one input. The events
 should probably be broadcasted in this case so you can check in parallel.

 This approach can be used effectively with Flink's checkpoint
 mechanism, while external model updates would be tricky to keep consistent.

 Cheers,
 Gyula




 On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
 wrote:

> Hi All,
>
> We have a streaming computation that required to validate the data
> stream against the model provided by the user.
>
> Right now what I have done is to load the model into flink operator
> and then validate against it. However the model can be updated and changed
> frequently. Fortunately we always publish this event to RabbitMQ.
>
> I think we can
>
>
>1. Create RabbitMq listener for model changed event from inside
>the operator, then update the model if event arrived.
>
>But i think this will create race condition if not handle
>correctly and it seems odd to keep this
>
>2. We can move the model into external in external memory cache
>storage and keep the model up to date using flink. So the operator will
>retrieve that from memory cache
>
>3. Create two stream and using co operator for managing the shared
>state.
>
>
> What is your suggestion on keeping the state up to date from external
> event ? Is there some kind of best practice for maintaining model up to
> date on streaming operator ?
>
> Thanks a lot
>
>
> Cheers
>
>
> --
> Welly Tambunan
> Triplelands
>
> http://weltam.wordpress.com
> http://www.triplelands.com 
>

>>>
>>>
>>> --
>>> Welly Tambunan
>>> Triplelands
>>>
>>> http://weltam.wordpress.com
>>> http://www.triplelands.com 
>>>
>>
>
>
> --
> Welly Tambunan
> Triplelands
>
> http://weltam.wordpress.com
> http://www.triplelands.com 
>

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

Thanks.

However update1 and update2 have a different type. Based on my
understanding, i don't think we can use union. How can we handle this one ?

We like to create our event strongly type to get the domain language
captured.


Cheers

On Wed, Aug 19, 2015 at 2:37 PM, Gyula Fóra  wrote:

> Hey,
>
> One input of your co-flatmap would be model updates and the other input
> would be events to check against the model if I understand correctly.
>
> This means that if your model updates come from more than one stream you
> need to union them into a single stream before connecting them with the
> event stream and applying the coatmap.
>
> DataStream updates1 = 
> DataStream updates2 = 
> DataStream events = 
>
> events.connect(updates1.union(updates2).broadcast()).flatMap(...)
>
> Does this answer your question?
>
> Gyula
>
>
> On Wednesday, August 19, 2015, Welly Tambunan  wrote:
>
>> Hi Gyula,
>>
>> Thanks for your response.
>>
>> However the model can received multiple event for update. How can we do
>> that with co-flatmap as i can see the connect API only received single
>> datastream ?
>>
>>
>> > ... while external model updates would be tricky to keep consistent.
>> Is that still the case if the Operator treat the external model as
>> read-only ? We create another stream that will update the external model
>> separately.
>>
>> Cheers
>>
>> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  wrote:
>>
>>> Hey!
>>>
>>> I think it is safe to say that the best approach in this case is
>>> creating a co-flatmap that will receive updates on one input. The events
>>> should probably be broadcasted in this case so you can check in parallel.
>>>
>>> This approach can be used effectively with Flink's checkpoint mechanism,
>>> while external model updates would be tricky to keep consistent.
>>>
>>> Cheers,
>>> Gyula
>>>
>>>
>>>
>>>
>>> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan 
>>> wrote:
>>>
 Hi All,

 We have a streaming computation that required to validate the data
 stream against the model provided by the user.

 Right now what I have done is to load the model into flink operator and
 then validate against it. However the model can be updated and changed
 frequently. Fortunately we always publish this event to RabbitMQ.

 I think we can


1. Create RabbitMq listener for model changed event from inside the
operator, then update the model if event arrived.

But i think this will create race condition if not handle correctly
and it seems odd to keep this

2. We can move the model into external in external memory cache
storage and keep the model up to date using flink. So the operator will
retrieve that from memory cache

3. Create two stream and using co operator for managing the shared
state.


 What is your suggestion on keeping the state up to date from external
 event ? Is there some kind of best practice for maintaining model up to
 date on streaming operator ?

 Thanks a lot


 Cheers


 --
 Welly Tambunan
 Triplelands

 http://weltam.wordpress.com
 http://www.triplelands.com 

>>>
>>
>>
>> --
>> Welly Tambunan
>> Triplelands
>>
>> http://weltam.wordpress.com
>> http://www.triplelands.com 
>>
>


-- 
Welly Tambunan
Triplelands

http://weltam.wordpress.com
http://www.triplelands.com 

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

Hey,

One input of your co-flatmap would be model updates and the other input
would be events to check against the model if I understand correctly.

This means that if your model updates come from more than one stream you
need to union them into a single stream before connecting them with the
event stream and applying the coatmap.

DataStream updates1 = 
DataStream updates2 = 
DataStream events = 

events.connect(updates1.union(updates2).broadcast()).flatMap(...)

Does this answer your question?

Gyula


On Wednesday, August 19, 2015, Welly Tambunan  wrote:

> Hi Gyula,
>
> Thanks for your response.
>
> However the model can received multiple event for update. How can we do
> that with co-flatmap as i can see the connect API only received single
> datastream ?
>
>
> > ... while external model updates would be tricky to keep consistent.
> Is that still the case if the Operator treat the external model as
> read-only ? We create another stream that will update the external model
> separately.
>
> Cheers
>
> On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  > wrote:
>
>> Hey!
>>
>> I think it is safe to say that the best approach in this case is creating
>> a co-flatmap that will receive updates on one input. The events should
>> probably be broadcasted in this case so you can check in parallel.
>>
>> This approach can be used effectively with Flink's checkpoint mechanism,
>> while external model updates would be tricky to keep consistent.
>>
>> Cheers,
>> Gyula
>>
>>
>>
>>
>> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan > > wrote:
>>
>>> Hi All,
>>>
>>> We have a streaming computation that required to validate the data
>>> stream against the model provided by the user.
>>>
>>> Right now what I have done is to load the model into flink operator and
>>> then validate against it. However the model can be updated and changed
>>> frequently. Fortunately we always publish this event to RabbitMQ.
>>>
>>> I think we can
>>>
>>>
>>>1. Create RabbitMq listener for model changed event from inside the
>>>operator, then update the model if event arrived.
>>>
>>>But i think this will create race condition if not handle correctly
>>>and it seems odd to keep this
>>>
>>>2. We can move the model into external in external memory cache
>>>storage and keep the model up to date using flink. So the operator will
>>>retrieve that from memory cache
>>>
>>>3. Create two stream and using co operator for managing the shared
>>>state.
>>>
>>>
>>> What is your suggestion on keeping the state up to date from external
>>> event ? Is there some kind of best practice for maintaining model up to
>>> date on streaming operator ?
>>>
>>> Thanks a lot
>>>
>>>
>>> Cheers
>>>
>>>
>>> --
>>> Welly Tambunan
>>> Triplelands
>>>
>>> http://weltam.wordpress.com
>>> http://www.triplelands.com 
>>>
>>
>
>
> --
> Welly Tambunan
> Triplelands
>
> http://weltam.wordpress.com
> http://www.triplelands.com 
>

Re: Keep Model in Operator instance up to date

[Welly Tambunan]

Hi Gyula,

Thanks for your response.

However the model can received multiple event for update. How can we do
that with co-flatmap as i can see the connect API only received single
datastream ?


> ... while external model updates would be tricky to keep consistent.
Is that still the case if the Operator treat the external model as
read-only ? We create another stream that will update the external model
separately.

Cheers

On Wed, Aug 19, 2015 at 2:05 PM, Gyula Fóra  wrote:

> Hey!
>
> I think it is safe to say that the best approach in this case is creating
> a co-flatmap that will receive updates on one input. The events should
> probably be broadcasted in this case so you can check in parallel.
>
> This approach can be used effectively with Flink's checkpoint mechanism,
> while external model updates would be tricky to keep consistent.
>
> Cheers,
> Gyula
>
>
>
>
> On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan  wrote:
>
>> Hi All,
>>
>> We have a streaming computation that required to validate the data stream
>> against the model provided by the user.
>>
>> Right now what I have done is to load the model into flink operator and
>> then validate against it. However the model can be updated and changed
>> frequently. Fortunately we always publish this event to RabbitMQ.
>>
>> I think we can
>>
>>
>>1. Create RabbitMq listener for model changed event from inside the
>>operator, then update the model if event arrived.
>>
>>But i think this will create race condition if not handle correctly
>>and it seems odd to keep this
>>
>>2. We can move the model into external in external memory cache
>>storage and keep the model up to date using flink. So the operator will
>>retrieve that from memory cache
>>
>>3. Create two stream and using co operator for managing the shared
>>state.
>>
>>
>> What is your suggestion on keeping the state up to date from external
>> event ? Is there some kind of best practice for maintaining model up to
>> date on streaming operator ?
>>
>> Thanks a lot
>>
>>
>> Cheers
>>
>>
>> --
>> Welly Tambunan
>> Triplelands
>>
>> http://weltam.wordpress.com
>> http://www.triplelands.com 
>>
>


-- 
Welly Tambunan
Triplelands

http://weltam.wordpress.com
http://www.triplelands.com 

Re: Keep Model in Operator instance up to date

[Gyula Fóra]

Hey!

I think it is safe to say that the best approach in this case is creating a
co-flatmap that will receive updates on one input. The events should
probably be broadcasted in this case so you can check in parallel.

This approach can be used effectively with Flink's checkpoint mechanism,
while external model updates would be tricky to keep consistent.

Cheers,
Gyula




On Wed, Aug 19, 2015 at 8:44 AM Welly Tambunan  wrote:

> Hi All,
>
> We have a streaming computation that required to validate the data stream
> against the model provided by the user.
>
> Right now what I have done is to load the model into flink operator and
> then validate against it. However the model can be updated and changed
> frequently. Fortunately we always publish this event to RabbitMQ.
>
> I think we can
>
>
>1. Create RabbitMq listener for model changed event from inside the
>operator, then update the model if event arrived.
>
>But i think this will create race condition if not handle correctly
>and it seems odd to keep this
>
>2. We can move the model into external in external memory cache
>storage and keep the model up to date using flink. So the operator will
>retrieve that from memory cache
>
>3. Create two stream and using co operator for managing the shared
>state.
>
>
> What is your suggestion on keeping the state up to date from external
> event ? Is there some kind of best practice for maintaining model up to
> date on streaming operator ?
>
> Thanks a lot
>
>
> Cheers
>
>
> --
> Welly Tambunan
> Triplelands
>
> http://weltam.wordpress.com
> http://www.triplelands.com 
>

Keep Model in Operator instance up to date

[Welly Tambunan]

Hi All,

We have a streaming computation that required to validate the data stream
against the model provided by the user.

Right now what I have done is to load the model into flink operator and
then validate against it. However the model can be updated and changed
frequently. Fortunately we always publish this event to RabbitMQ.

I think we can


   1. Create RabbitMq listener for model changed event from inside the
   operator, then update the model if event arrived.

   But i think this will create race condition if not handle correctly and
   it seems odd to keep this

   2. We can move the model into external in external memory cache storage
   and keep the model up to date using flink. So the operator will retrieve
   that from memory cache

   3. Create two stream and using co operator for managing the shared
   state.


What is your suggestion on keeping the state up to date from external event
? Is there some kind of best practice for maintaining model up to date on
streaming operator ?

Thanks a lot


Cheers

-- 
Welly Tambunan
Triplelands

http://weltam.wordpress.com
http://www.triplelands.com