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From Yunus Olgun <yunol...@gmail.com>
Subject Re: CustomPartitioner that simulates ForwardPartitioner and watermarks
Date Wed, 27 Sep 2017 16:32:20 GMT
Hi Kostas,

Yes, you have summarized well. I want to only forward the data to the next local operator,
but broadcast the watermark through the cluster.

- I can’t set parallelism of taskB to 1. The stream is too big for that. Also, the data
is ordered at each partition. I don’t want to change that order.

- I don’t need KeyedStream. Also taskA and taskB will always have the same parallelism with
each other. But this parallelism can be increased in the future.

The use case is: The source is Kafka. At our peak hours or when we want to run the streaming
job with old data from Kafka, always the same thing happens. Even at trivial jobs. Some consumers
consumes faster than others. They produce too much data to downstream but watermark advances
slowly at the speed of the slowest consumer. This extra data gets piled up at downstream operators.
When the downstream operator is an aggregation, it is ok. But when it is a in-Flink join;
state size gets too big, checkpoints take much longer and overall the job becomes slower or
fails. Also it effects other jobs at the cluster.

So, basically I want to implement a throttler. It compares timestamp of a record and the global
watermark. If the difference is larger than a constant threshold it starts sleeping 1 ms for
each incoming record. This way, fast operators wait for the slowest one.

The only problem is that, this solution came at the cost of one network shuffle and data serialization/deserialization.
Since the stream is large I want to avoid the network shuffle at the least. 

I thought operator instances within a taskmanager would get the same indexId, but apparently
this is not the case.

Thanks,

> On 27. Sep 2017, at 17:16, Kostas Kloudas <k.kloudas@data-artisans.com> wrote:
> 
> Hi Yunus,
> 
> I am not sure if I understand correctly the question.
> 
> Am I correct to assume that you want the following?
> 
> 				———————————> time
> 
> 		ProcessA						ProcessB
> 
> Task1: W(3) E(1) E(2) E(5)			W(3) W(7) E(1) E(2) E(5)
> 
> Task2: W(7) E(3) E(10) E(6)			W(3) W(7) E(3) E(10) E(6)
> 
> 
> In the above, elements flow from left to right and W() stands for watermark and E() stands
for element.
> In other words, between Process(TaksA) and Process(TaskB) you want to only forward the
elements, but broadcast the watermarks, right?
> 
> If this is the case, a trivial solution would be to set the parallelism of TaskB to 1,
so that all elements go through the same node.
> 
> One other solution is what you did, BUT by using a custom partitioner you cannot use
keyed state in your process function B because the 
> stream is no longer keyed.
> 
> A similar approach to what you did but without the limitation above, is that in the first
processFunction (TaskA) you can append the 
> taskId to the elements themselves and then do a keyBy(taskId) between the first and the
second process function.
> 
> These are the solutions that I can come up with, assuming that you want to do what I
described.
> 
> But in general, could you please describe a bit more what is your use case? 
> This way we may figure out another approach to achieve your goal. 
> In fact, I am not sure if you earn anything by broadcasting the watermark, other than

> re-implementing (to some extent) Flink’s windowing mechanism.
> 
> Thanks,
> Kostas
> 
>> On Sep 27, 2017, at 4:35 PM, Yunus Olgun <yunolgun@gmail.com <mailto:yunolgun@gmail.com>>
wrote:
>> 
>> Hi,
>> 
>> I have a simple streaming job such as:
>> 
>> source.process(taskA)
>>           .process(taskB)
>> 
>> I want taskB to access minimum watermark of all parallel taskA instances, but the
data is ordered and should not be shuffled. ForwardPartitioner uses watermark of only one
predecessor. So, I have used a customPartitioner.
>> 
>> source.process(taskA)
>>           .map(AssignPartitionID)
>>           .partitionCustom(IdPartitioner)
>>           .map(StripPartitionID)
>>           .process(taskB)
>> 
>> At AssignPartitionID function, I attach getRuntimeContext().getIndexOfThisSubtask()
as a partitionId to the object. At IdPartitioner, I return this partitionId.
>> 
>> This solved the main requirement but I have another concern now,
>> 
>> Network shuffle: I don’t need a network shuffle. I thought within a taskmanager,
indexId of taskA subtasks would be same as indexId of taskB subtasks. Unfortunately, they
are not. Is there a way to make partitionCustom distribute data like ForwardPartitioner, to
the next local operator? 
>> 
>> As I know, this still requires object serialization/deserialization since operators
can’t be chained anymore. Is there a way to get minimum watermark from upstream operators
without network shuffle and object serilization/deserialization?
>> 
>> Regards,
> 


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