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From Tony Wei <tony19920...@gmail.com>
Subject Re: dynamically partitioned stream
Date Thu, 31 Aug 2017 11:06:53 GMT
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 <martineden131@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 <tony19920430@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-17SideInputsforDataStrea
>> mAPI-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 <martineden131@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
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>

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