Hi Martin, I think with those requirements this is very hard (or maybe impossible) to do efficiently in a distributed setting. It might be that I'm misunderstanding things but let's look at an example. Assume that initially, we don't have any lambdas, so data can be sent to any machine because it doesn't matter where they go. Now, we get a new lambda f2 [A, C]. Say this gets routed to machine "0", now this means that messages with key A and C also need to be router to machine "0". Now, we get a new lambda f1 [D, E], say this gets routed to machine "2", meaning that messages with key D and E are also routed to machine "2".
Then, we get a new lambda f3 [C, D]. Do we now re-route all previous lambdas and inputs to different machines? They all have to go to the same machine, but which one? I'm currently thinking that there would need to be some component that does the routing, but this has to be global, so it's hard to do in a distributed setting. What do you think? Best, Aljoscha > On 1. Sep 2017, at 07:17, Martin Eden <martineden...@gmail.com> wrote: > > This might be a way forward but since side inputs are not there I will try > and key the control stream by the keys in the first co flat map. > > I'll see how it goes. > > Thanks guys, > M > > On Thu, Aug 31, 2017 at 5:16 PM, Tony Wei <tony19920...@gmail.com > <mailto:tony19920...@gmail.com>> wrote: > Hi Martin, > > Yes, that is exactly what I thought. > But the first step also needs to be fulfilled by SideInput. I'm not sure how > to achieve this in the current release. > > Best, > Tony Wei > > Martin Eden <martineden...@gmail.com <mailto:martineden...@gmail.com>>於 > 2017年8月31日 週四，下午11:32寫道： > Hi Aljoscha, Tony, > > Aljoscha: > Yes it's the first option you mentioned. > Yes, the stream has multiple values in flight for A, B, C. f1 needs to be > applied each time a new value for either A, B or C comes in. So we need to > use state to cache the latest values. So using the example data stream in my > first msg the emitted stream should be: > > 1. Data Stream: > KEY VALUE TIME > . > . > . > C V6 6 > B V6 6 > A V5 5 > A V4 4 > C V3 3 > A V3 3 > B V3 3 > B V2 2 > A V1 1 > > 2. Control Stream: > Lambda ArgumentKeys TIME > . > . > . > f2 [A, C] 4 > f1 [A, B, C] 1 > > 3. Expected emitted stream: > TIME VALUE > . > . > . > 6 f1(V5, V6, V3) > f1(V5, V6, V6) > f2(V5, V6) > 5 f1(V5, V3, V3) > f2(V5, V3) > 4 f1(V4, V3, V3) > f2(V4, V3) > 3 f1(V3, V3, V3) > 2 - > 1 - > > So essentially as soon as the argument list fills up then we apply the > function/lambda at each new arriving message in the data stream for either > argument key. > > Tony: > Yes we need to group by and pass to the lambda. > Ok, so what you are proposing might work. So your solution assumes that we > have to connect with the control stream twice? Once for the tagging and > another time re-connect-ing the control stream with the tagged stream for the > actual application of the function/lambda? > > Thanks, > Alex > > > > On Thu, Aug 31, 2017 at 2:57 PM, Aljoscha Krettek <aljos...@apache.org > <mailto:aljos...@apache.org>> wrote: > Hi Martin, > > In your original example, what does this syntax mean exactly: > > f1 [A, B, C] 1 > > Does it mean that f1 needs one A, one B and one C from the main stream? If > yes, which ones, because there are multiple As and Bs and so on. Or does it > mean that f1 can apply to an A or a B or a C? If it's the first, then I think > it's quite hard to find a partitioning such that both f1, f2, and all A, B, > and C go to the same machine. > > Best, > Aljoscha > >> On 31. Aug 2017, at 15:53, Tony Wei <tony19920...@gmail.com >> <mailto:tony19920...@gmail.com>> wrote: >> >> Hi Martin, >> >> So the problem is that you want to group those arguments in Data Stream and >> pass them to the lambda function from Control Stream at the same time. Am I >> right? >> >> If right, then you could give each lambda function an id as well. Use these >> ids to tag those arguments to which they belong. >> After that, keyBy function could be used to group those arguments belonging >> to the same lambda function. Joining this stream with Control Stream by >> function id could make arguments and function be in the same instance. >> >> What do you think? Could this solution solve your problem? >> >> Best, >> Tony Wei >> >> 2017-08-31 20:43 GMT+08:00 Martin Eden <martineden...@gmail.com >> <mailto:martineden...@gmail.com>>: >> Thanks for your reply Tony, >> >> Yes we are in the latter case, where the functions/lambdas come in the >> control stream. Think of them as strings containing the logic of the >> function. The values for each of the arguments to the function come from the >> data stream. That is why we need to co-locate the data stream messages for >> the corresponding keys with the control message that has the function to be >> applied. >> >> We have a way of interpreting the logic described in the string and >> executing it on the incoming values from the data stream. This is kicked off >> from within the Flink runtime (synchronous to a flatMap of the >> RichCoFlatMapFunction) but is not using Flink predefined operators or >> functions. >> >> So yeah I see your point about mapping the arguments but the problem is not >> really that, the problem is making sure that the values in the control >> stream are in the same instance of the task/ keyed managed state as a the >> actual control stream message. Once they are we can pass them in. >> >> Any other thoughts? >> >> M >> >> >> >> >> >> >> >> On Thu, Aug 31, 2017 at 12:06 PM, Tony Wei <tony19920...@gmail.com >> <mailto:tony19920...@gmail.com>> wrote: >> Hi Martin, >> >> About problem 2. How were those lambda functions created? Pre-defined >> functions / operators or automatically generated based on the message from >> Control Stream? >> >> For the former, you could give each function one id and user flapMap to >> duplicate data with multiple ids. Then, you could use filter function and >> send them to the corresponding operators. >> >> For the general case like the latter, because you had broadcasted the >> messages to all tasks, it could always build a mapping table from argument >> keys to lambda functions in each sub-task and use the map to process the >> data. But I was wondering if it is possible to generate a completely new >> function in the runtime. >> >> Best, >> Tony Wei >> >> 2017-08-31 18:33 GMT+08:00 Martin Eden <martineden...@gmail.com >> <mailto:martineden...@gmail.com>>: >> Thanks for your reply Tony. >> >> So there are actually 2 problems to solve: >> >> 1. All control stream msgs need to be broadcasted to all tasks. >> >> 2. The data stream messages with the same keys as those specified in the >> control message need to go to the same task as well, so that all the values >> required for the lambda (i.e. functions f1, f2 ...) are there. >> >> In my understanding side inputs (which are actually not available in the >> current release) would address problem 1. >> >> To address problem 1 I also tried >> dataStream.keyBy(key).connect(controlStream.broadcast).flatMap(new >> RichCoFlatMapFunction) but I get a runtime exception telling me I still need >> to do a keyBy before the flatMap. So are the upcoming side inputs the only >> way to broadcast a control stream to all tasks of a coFlatMap? Or is there >> another way? >> >> As for problem 2, I am still pending a reply. Would appreciate if anyone has >> some suggestions. >> >> Thanks, >> M >> >> >> >> >> On Thu, Aug 31, 2017 at 9:59 AM, Tony Wei <tony19920...@gmail.com >> <mailto:tony19920...@gmail.com>> wrote: >> Hi Martin, >> >> Let me understand your question first. >> You have two Stream: Data Stream and Control Stream and you want to select >> data in Data Stream based on the key set got from Control Stream. >> >> If I were not misunderstanding your question, I think SideInput is what you >> want. >> https://cwiki.apache.org/confluence/display/FLINK/FLIP-17+Side+Inputs+for+DataStream+API#FLIP-17SideInputsforDataStreamAPI-StoringSide-InputData >> >> <https://cwiki.apache.org/confluence/display/FLINK/FLIP-17+Side+Inputs+for+DataStream+API#FLIP-17SideInputsforDataStreamAPI-StoringSide-InputData> >> It lets you to define one stream as a SideInput and can be assigned to the >> other stream, then the data in SideInput stream will be broadcasted. >> >> So far, I have no idea if there is any solution to solve this without >> SideInput. >> >> Best, >> Tony Wei >> >> 2017-08-31 16:10 GMT+08:00 Martin Eden <martineden...@gmail.com >> <mailto:martineden...@gmail.com>>: >> Hi all, >> >> I am trying to implement the following using Flink: >> >> I have 2 input message streams: >> >> 1. Data Stream: >> KEY VALUE TIME >> . >> . >> . >> C V6 6 >> B V6 6 >> A V5 5 >> A V4 4 >> C V3 3 >> A V3 3 >> B V3 3 >> B V2 2 >> A V1 1 >> >> 2. Control Stream: >> Lambda ArgumentKeys TIME >> . >> . >> . >> f2 [A, C] 4 >> f1 [A, B, C] 1 >> >> I want to apply the lambdas coming in the control stream to the selection of >> keys that are coming in the data stream. >> >> Since we have 2 streams I naturally thought of connecting them using >> .connect. For this I need to key both of them by a certain criteria. And >> here lies the problem, how can I make sure the messages with keys A,B,C >> specified in the control stream end up in the same task as well as the >> control message (f1, [A, B, C]) itself. Basically I don't know how to key by >> to achieve this. >> >> I suspect a custom partitioner is required that partitions the data stream >> based on the messages in the control stream? Is this even possible? >> >> Any suggestions welcomed! >> >> Thanks, >> M >> >> >> >> >> >> >> >> >> >> >> > > >
