Re: Keep Model in Operator instance up to date
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 if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com wrote: In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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
Re: Keep Model in Operator instance up to date
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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com wrote: In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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
Re: Keep Model in Operator instance up to date
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 if05...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com wrote: In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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 if05...@gmail.com 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
Keep Model in Operator instance up to date
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 http://www.triplelands.com/blog/
Re: Keep Model in Operator instance up to date
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 if05...@gmail.com 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 http://www.triplelands.com/blog/
Re: Keep Model in Operator instance up to date
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 gyf...@apache.org 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 if05...@gmail.com 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 http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/
Re: Keep Model in Operator instance up to date
In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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 if05...@gmail.com 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 http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/
Re: Keep Model in Operator instance up to date
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 if05...@gmail.com 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 gyula.f...@gmail.com wrote: In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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 if05...@gmail.com 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 http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/
Re: Keep Model in Operator instance up to date
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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyula.f...@gmail.com wrote: In that case I would apply a map to wrap in some common type, like a n Eithert1,t2 before the union. And then in the coflatmap you can unwrap it. On Wed, Aug 19, 2015 at 9:50 AM Welly Tambunan if05...@gmail.com 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 gyula.f...@gmail.com 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 if05...@gmail.com 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 gyf...@apache.org 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 if05...@gmail.com 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 http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/ -- Welly Tambunan Triplelands http://weltam.wordpress.com http://www.triplelands.com http://www.triplelands.com/blog/ -- Welly Tambunan
