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From Martin Eden <martineden...@gmail.com>
Subject Re: dynamically partitioned stream
Date Wed, 06 Sep 2017 14:41:32 GMT
Hi Aljoscha, Tony,

We actually do not need all the keys to be on all nodes where lambdas are.
We just need the keys that represent the data for the lambda arguments to
be routed to the same node as the lambda, whichever one it might be.

Essentially in the solution we emit the data multiple times and by doing
that we roughly multiply the input rate by the average number of lambdas a
key is a part of (X). In terms of memory this is O(X * N) where N is the
number of keys int the data. N is the large bit. If X ~ N then we have O
(N^2) complexity for the Flink state. And in that case yes I see your point
about performance Aljoscha. But if X << N, as is our case, then we have
O(N) which should be manageable by Flink's distributed state mechanism
right? Do you see any gotchas in this new light? Are my assumptions correct?

Thanks,
M





On Sat, Sep 2, 2017 at 3:38 AM, Tony Wei <tony19920430@gmail.com> wrote:

> Hi Martin, Aljoscha
>
> I think Aljoscha is right. My origin thought was to keep the state only
> after a lambda function coming.
>
> Use Aljoscha's scenario as example, initially, all data will be discarded
> because there is no any lambdas. When lambda f1 [D, E] and f2 [A, C]
> comes, A, C begin to be routed to machine "0" and D, E begin to be routed
> to machine "1". Then, when we get a new lambda f3 [C, D], we can
> duplicate C, D and route these copies to machine "2".
>
> However, after reading your example again, I found what you want is a
> whole picture for all variables' state in a global view, so that no matter
> what time a new lambda comes it can always get its variables' state
> immediately. In that case, I have the same opinion as Aljoscha.
>
> Best,
> Tony Wei
>
> 2017-09-01 23:59 GMT+08:00 Aljoscha Krettek <aljoscha@apache.org>:
>
>> 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 <martineden131@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 <tony19920430@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 <martineden131@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 <aljoscha@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 <tony19920430@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 <martineden131@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 <tony19920430@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 <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+Si
>>>>>>>>> de+Inputs+for+DataStream+API#FLIP-17SideInputsforDataStream
>>>>>>>>> API-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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